diff --git a/cn/docs/_print/index.html b/cn/docs/_print/index.html index 9c7aeb17a..80be3e3de 100644 --- a/cn/docs/_print/index.html +++ b/cn/docs/_print/index.html @@ -1287,7 +1287,7 @@ hugeClient.close(); } } -

4.4 运行Example

运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start

4.5 Example示例说明

示例说明见HugeGraph-Client基本API介绍

3.6 - HugeGraph-Computer Quick Start

1 HugeGraph-Computer 概述

HugeGraph-Computer 是分布式图处理系统 (OLAP). 它是 Pregel 的一个实现. 它可以运行在 Kubernetes 上。

特性

2 开始

2.1 在本地运行 PageRank 算法

要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 JREJDK 11 或更高版本。

还需要首先部署 HugeGraph-Server 和 Etcd.

2.1 Download the compiled archive

有两种方式可以获取 HugeGraph-Computer：

下载最新版本的 HugeGraph-Computer release 包：

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
+

4.4 运行Example

运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start

4.5 Example示例说明

示例说明见HugeGraph-Client基本API介绍

3.6 - HugeGraph-Computer Quick Start

1 HugeGraph-Computer 概述

HugeGraph-Computer 是分布式图处理系统 (OLAP). 它是 Pregel 的一个实现. 它可以运行在 Kubernetes 上。

特性

2 开始

2.1 在本地运行 PageRank 算法

要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 JREJDK 11 或更高版本。

还需要首先部署 HugeGraph-Server 和 Etcd.

有两种方式可以获取 HugeGraph-Computer：

2.1 Download the compiled archive

下载最新版本的 HugeGraph-Computer release 包：

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
 tar zxvf hugegraph-computer-${version}.tar.gz

2.2 Clone source code to compile and package

克隆最新版本的 HugeGraph-Computer 源码包：

$ git clone https://github.com/apache/hugegraph-computer.git

编译生成tar包:

cd hugegraph-computer
@@ -1349,7 +1349,7 @@
 # 注意: 诊断日志仅在作业失败时存在，并且只会保存一小时。
 kubectl get event --field-selector reason=ComputerJobFailed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system

2.2.8 显示作业的成功事件

NOTE: it will only be saved for one hour

kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-

2.2.9 查询算法结果

如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。

3 内置算法文档

3.1 支持的算法列表:

中心性算法:
社区算法:
路径算法:

更多算法请看: Built-In algorithms

3.2 算法描述

TODO

4 算法开发指南

TODO

4 - Config

4.1 - HugeGraph 配置

1 概述

配置文件的目录为 hugegraph-release/conf，所有关于服务和图本身的配置都在此目录下。

主要的配置文件包括：gremlin-server.yaml、rest-server.properties 和 hugegraph.properties

HugeGraphServer 内部集成了 GremlinServer 和 RestServer，而 gremlin-server.yaml 和 rest-server.properties 就是用来配置这两个Server的。

下面对这三个配置文件逐一介绍。

2 gremlin-server.yaml

gremlin-server.yaml 文件默认的内容如下：

# host and port of gremlin server, need to be consistent with host and port in rest-server.properties
+

2.2.9 查询算法结果

如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。

3 内置算法文档

3.1 支持的算法列表:

中心性算法:
社区算法:
路径算法:

更多算法请看: Built-In algorithms

3.2 算法描述

TODO

4 算法开发指南

TODO

4 - Config

4.1 - HugeGraph 配置

1 概述

配置文件的目录为 hugegraph-release/conf，所有关于服务和图本身的配置都在此目录下。

主要的配置文件包括：gremlin-server.yaml、rest-server.properties 和 hugegraph.properties

HugeGraphServer 内部集成了 GremlinServer 和 RestServer，而 gremlin-server.yaml 和 rest-server.properties 就是用来配置这两个Server的。

下面对这三个配置文件逐一介绍。

2 gremlin-server.yaml

gremlin-server.yaml 文件默认的内容如下：

# host and port of gremlin server, need to be consistent with host and port in rest-server.properties
 #host: 127.0.0.1
 #port: 8182
 
@@ -1614,7 +1614,7 @@
 国家代码：CN
  1. 根据服务端私钥，导出服务端证书
keytool -export -alias serverkey -keystore server.keystore -file server.crt

server.crt 就是服务端的证书

客户端

keytool -import -alias serverkey -file server.crt -keystore client.truststore
-

client.truststore 是给客户端⽤的，其中保存着受信任的证书

4.5 - HugeGraph-Computer 配置

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe log4j.xml path for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.

5 - API

5.1 - HugeGraph RESTful API

HugeGraph-Server通过HugeGraph-API基于HTTP协议为Client提供操作图的接口，主要包括元数据和 +

client.truststore 是给客户端⽤的，其中保存着受信任的证书

4.5 - HugeGraph-Computer 配置

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe content of log4j.xml for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.

5 - API

5.1 - HugeGraph RESTful API

HugeGraph-Server通过HugeGraph-API基于HTTP协议为Client提供操作图的接口，主要包括元数据和 图数据的增删改查，遍历算法，变量，图操作及其他操作。

5.1.1 - Schema API

1.1 Schema

HugeGraph 提供单一接口获取某个图的全部 Schema 信息，包括：PropertyKey、VertexLabel、EdgeLabel 和 IndexLabel。

Method & Url
GET http://localhost:8080/graphs/hugegraph/schema
Response Status
200
Response Body
{
diff --git a/cn/docs/config/_print/index.html b/cn/docs/config/_print/index.html
index abfa89f42..732b2252c 100644
--- a/cn/docs/config/_print/index.html
+++ b/cn/docs/config/_print/index.html
@@ -265,7 +265,7 @@
 国家代码：CN
  1. 根据服务端私钥，导出服务端证书
keytool -export -alias serverkey -keystore server.keystore -file server.crt

server.crt 就是服务端的证书

客户端

keytool -import -alias serverkey -file server.crt -keystore client.truststore
-

client.truststore 是给客户端⽤的，其中保存着受信任的证书

5 - HugeGraph-Computer 配置

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe log4j.xml path for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.
+

client.truststore 是给客户端⽤的，其中保存着受信任的证书

5 - HugeGraph-Computer 配置

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe content of log4j.xml for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.
diff --git a/cn/docs/config/config-computer/index.html b/cn/docs/config/config-computer/index.html index ccd1251f0..eda61bfcb 100644 --- a/cn/docs/config/config-computer/index.html +++ b/cn/docs/config/config-computer/index.html @@ -11,13 +11,13 @@ algorithm.message_class org.apache.hugegraph.computer.core.config.Null -The …"> +The …">

HugeGraph-Computer 配置

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe log4j.xml path for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.

Last modified November 27, 2022: Add HugeGraph-Computer Doc (#155) (19ab2ff)
+ Print entire section

HugeGraph-Computer 配置

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe content of log4j.xml for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.

Last modified November 28, 2022: improve computer doc (#157) (862b048)
diff --git a/cn/docs/config/index.xml b/cn/docs/config/index.xml index 76d40b4fd..05d0efd00 100644 --- a/cn/docs/config/index.xml +++ b/cn/docs/config/index.xml @@ -2192,7 +2192,7 @@ auth.user_tokens=[hugegraph1:token-value-1, hugegraph2:token-value-2] <tr> <td>log4jXml</td> <td></td> -<td>The log4j.xml path for computer job.</td> +<td>The content of log4j.xml for computer job.</td> <td>false</td> </tr> <tr> diff --git a/cn/docs/index.xml b/cn/docs/index.xml index e11113784..cb4ffc092 100644 --- a/cn/docs/index.xml +++ b/cn/docs/index.xml @@ -9559,7 +9559,7 @@ batch_size_fail_threshold_in_kb: 1000 <tr> <td>log4jXml</td> <td></td> -<td>The log4j.xml path for computer job.</td> +<td>The content of log4j.xml for computer job.</td> <td>false</td> </tr> <tr> @@ -10492,7 +10492,7 @@ batch_size_fail_threshold_in_kb: 1000 <li>HugeStudio打包时，编译失败但没有报错，导致发布包无法启动（HugeGraph-1368）</li> </ul>Docs: HugeGraph-Computer Quick Start/cn/docs/quickstart/hugegraph-computer/Mon, 01 Jan 0001 00:00:00 +0000/cn/docs/quickstart/hugegraph-computer/ <h2 id="1-hugegraph-computer-概述">1 HugeGraph-Computer 概述</h2> -<p><code>HugeGraph-Computer</code> 是分布式图处理系统 (OLAP). 它是 <a href="https://kowshik.github.io/JPregel/pregel_paper.pdf">Pregel</a> 的一个实现. 它可以运行在 Kubernetes 上。</p> +<p><a href="https://github.com/apache/incubator-hugegraph-computer"><code>HugeGraph-Computer</code></a> 是分布式图处理系统 (OLAP). 它是 <a href="https://kowshik.github.io/JPregel/pregel_paper.pdf">Pregel</a> 的一个实现. 它可以运行在 Kubernetes 上。</p> <h3 id="特性">特性</h3> <ul> <li>支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。</li> @@ -10509,12 +10509,12 @@ batch_size_fail_threshold_in_kb: 1000 <p>要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 JREJDK 11 或更高版本。</p> <p>还需要首先部署 HugeGraph-Server 和 <a href="https://etcd.io/docs/v3.5/quickstart/">Etcd</a>.</p> </blockquote> -<h4 id="21-download-the-compiled-archive">2.1 Download the compiled archive</h4> <p>有两种方式可以获取 HugeGraph-Computer：</p> <ul> <li>下载已编译的压缩包</li> <li>克隆源码编译打包</li> </ul> +<h4 id="21-download-the-compiled-archive">2.1 Download the compiled archive</h4> <p>下载最新版本的 HugeGraph-Computer release 包：</p> <div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>wget https://github.com/apache/hugegraph-computer/releases/download/v<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>/hugegraph-loader-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz </span></span><span style="display:flex;"><span>tar zxvf hugegraph-computer-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz @@ -10611,8 +10611,8 @@ batch_size_fail_threshold_in_kb: 1000 <div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>kubectl get event --field-selector <span style="color:#000">reason</span><span style="color:#ce5c00;font-weight:bold">=</span>ComputerJobSucceed --field-selector involvedObject.name<span style="color:#ce5c00;font-weight:bold">=</span>pagerank-sample -n hugegraph-computer-system </span></span></code></pre></div><h4 id="229-查询算法结果">2.2.9 查询算法结果</h4> <p>如果输出到 <code>Hugegraph-Server</code> 则与 Locally 模式一致，如果输出到 <code>HDFS</code> ，请检查 <code>hugegraph-computerresults{jobId}</code>目录下的结果文件。</p> -<h3 id="3-内置算法文档">3 内置算法文档</h3> -<h4 id="31--支持的算法列表">3.1 支持的算法列表:</h4> +<h2 id="3-内置算法文档">3 内置算法文档</h2> +<h3 id="31-支持的算法列表">3.1 支持的算法列表:</h3> <h6 id="中心性算法">中心性算法:</h6> <ul> <li>PageRank</li> @@ -10634,9 +10634,9 @@ batch_size_fail_threshold_in_kb: 1000 <li>RingsDetectionWithFilter</li> </ul> <p>更多算法请看: <a href="https://github.com/apache/hugegraph-computer/tree/master/computer-algorithm/src/main/java/org/apache/hugegraph/computer/algorithm">Built-In algorithms</a></p> -<h4 id="32-算法描述">3.2 算法描述</h4> +<h3 id="32-算法描述">3.2 算法描述</h3> <p>TODO</p> -<h3 id="4-算法开发指南">4 算法开发指南</h3> +<h2 id="4-算法开发指南">4 算法开发指南</h2> <p>TODO</p>Docs: HugeGraph 0.6 Release Notes/cn/docs/changelog/hugegraph-0.6.1-release-notes/Mon, 01 Jan 0001 00:00:00 +0000/cn/docs/changelog/hugegraph-0.6.1-release-notes/ <h3 id="api--java-client">API &amp; Java Client</h3> <h4 id="功能更新">功能更新</h4> diff --git a/cn/docs/quickstart/_print/index.html b/cn/docs/quickstart/_print/index.html index 0333f9c15..1fe071636 100644 --- a/cn/docs/quickstart/_print/index.html +++ b/cn/docs/quickstart/_print/index.html @@ -1281,7 +1281,7 @@ hugeClient.close(); } } -

4.4 运行Example

运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start

4.5 Example示例说明

示例说明见HugeGraph-Client基本API介绍

6 - HugeGraph-Computer Quick Start

1 HugeGraph-Computer 概述

HugeGraph-Computer 是分布式图处理系统 (OLAP). 它是 Pregel 的一个实现. 它可以运行在 Kubernetes 上。

特性

  • 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。
  • 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。
  • 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。
  • 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。
  • 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。
  • 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。
  • 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。

2 开始

2.1 在本地运行 PageRank 算法

要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 JREJDK 11 或更高版本。

还需要首先部署 HugeGraph-Server 和 Etcd.

2.1 Download the compiled archive

有两种方式可以获取 HugeGraph-Computer：

  • 下载已编译的压缩包
  • 克隆源码编译打包

下载最新版本的 HugeGraph-Computer release 包：

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
+

4.4 运行Example

运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start

4.5 Example示例说明

示例说明见HugeGraph-Client基本API介绍

6 - HugeGraph-Computer Quick Start

1 HugeGraph-Computer 概述

HugeGraph-Computer 是分布式图处理系统 (OLAP). 它是 Pregel 的一个实现. 它可以运行在 Kubernetes 上。

特性

  • 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。
  • 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。
  • 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。
  • 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。
  • 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。
  • 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。
  • 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。

2 开始

2.1 在本地运行 PageRank 算法

要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 JREJDK 11 或更高版本。

还需要首先部署 HugeGraph-Server 和 Etcd.

有两种方式可以获取 HugeGraph-Computer：

  • 下载已编译的压缩包
  • 克隆源码编译打包

2.1 Download the compiled archive

下载最新版本的 HugeGraph-Computer release 包：

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
 tar zxvf hugegraph-computer-${version}.tar.gz

2.2 Clone source code to compile and package

克隆最新版本的 HugeGraph-Computer 源码包：

$ git clone https://github.com/apache/hugegraph-computer.git

编译生成tar包:

cd hugegraph-computer
@@ -1343,7 +1343,7 @@
 # 注意: 诊断日志仅在作业失败时存在，并且只会保存一小时。
 kubectl get event --field-selector reason=ComputerJobFailed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system

2.2.8 显示作业的成功事件

NOTE: it will only be saved for one hour

kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-

2.2.9 查询算法结果

如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。

3 内置算法文档

3.1 支持的算法列表:

中心性算法:
  • PageRank
  • BetweennessCentrality
  • ClosenessCentrality
  • DegreeCentrality
社区算法:
  • ClusteringCoefficient
  • Kcore
  • Lpa
  • TriangleCount
  • Wcc
路径算法:
  • RingsDetection
  • RingsDetectionWithFilter

更多算法请看: Built-In algorithms

3.2 算法描述

TODO

4 算法开发指南

TODO

+

2.2.9 查询算法结果

如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。

3 内置算法文档

3.1 支持的算法列表:

中心性算法:
社区算法:
路径算法:

更多算法请看: Built-In algorithms

3.2 算法描述

TODO

4 算法开发指南

TODO

diff --git a/cn/docs/quickstart/hugegraph-computer/index.html b/cn/docs/quickstart/hugegraph-computer/index.html index a4c13f2ad..4a5359d89 100644 --- a/cn/docs/quickstart/hugegraph-computer/index.html +++ b/cn/docs/quickstart/hugegraph-computer/index.html @@ -6,25 +6,25 @@ 算法基于BSP(Bulk …">

HugeGraph-Computer Quick Start

1 HugeGraph-Computer 概述

HugeGraph-Computer 是分布式图处理系统 (OLAP). 它是 Pregel 的一个实现. 它可以运行在 Kubernetes 上。

特性

  • 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。
  • 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。
  • 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。
  • 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。
  • 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。
  • 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。
  • 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。

2 开始

2.1 在本地运行 PageRank 算法

要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 JREJDK 11 或更高版本。

还需要首先部署 HugeGraph-Server 和 Etcd.

2.1 Download the compiled archive

有两种方式可以获取 HugeGraph-Computer：

  • 下载已编译的压缩包
  • 克隆源码编译打包

下载最新版本的 HugeGraph-Computer release 包：

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
+ Print entire section

HugeGraph-Computer Quick Start

1 HugeGraph-Computer 概述

HugeGraph-Computer 是分布式图处理系统 (OLAP). 它是 Pregel 的一个实现. 它可以运行在 Kubernetes 上。

特性

  • 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。
  • 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。
  • 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。
  • 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。
  • 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。
  • 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。
  • 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。

2 开始

2.1 在本地运行 PageRank 算法

要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 JREJDK 11 或更高版本。

还需要首先部署 HugeGraph-Server 和 Etcd.

有两种方式可以获取 HugeGraph-Computer：

  • 下载已编译的压缩包
  • 克隆源码编译打包

2.1 Download the compiled archive

下载最新版本的 HugeGraph-Computer release 包：

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
 tar zxvf hugegraph-computer-${version}.tar.gz

2.2 Clone source code to compile and package

克隆最新版本的 HugeGraph-Computer 源码包：

$ git clone https://github.com/apache/hugegraph-computer.git

编译生成tar包:

cd hugegraph-computer
@@ -86,7 +86,7 @@
 # 注意: 诊断日志仅在作业失败时存在，并且只会保存一小时。
 kubectl get event --field-selector reason=ComputerJobFailed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system

2.2.8 显示作业的成功事件

NOTE: it will only be saved for one hour

kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-

2.2.9 查询算法结果

如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。

3 内置算法文档

3.1 支持的算法列表:

中心性算法:
  • PageRank
  • BetweennessCentrality
  • ClosenessCentrality
  • DegreeCentrality
社区算法:
  • ClusteringCoefficient
  • Kcore
  • Lpa
  • TriangleCount
  • Wcc
路径算法:
  • RingsDetection
  • RingsDetectionWithFilter

更多算法请看: Built-In algorithms

3.2 算法描述

TODO

4 算法开发指南

TODO


Last modified November 27, 2022: Add HugeGraph-Computer Doc (#155) (19ab2ff)
+

2.2.9 查询算法结果

如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。

3 内置算法文档

3.1 支持的算法列表:

中心性算法:
社区算法:
路径算法:

更多算法请看: Built-In algorithms

3.2 算法描述

TODO

4 算法开发指南

TODO


Last modified November 28, 2022: improve computer doc (#157) (862b048)
diff --git a/cn/docs/quickstart/index.xml b/cn/docs/quickstart/index.xml index 49fcd23e9..7ed86312d 100644 --- a/cn/docs/quickstart/index.xml +++ b/cn/docs/quickstart/index.xml @@ -2560,7 +2560,7 @@ HugeGraph Toolchain 版本: toolchain-1.0.0</p> <h3 id="45-example示例说明">4.5 Example示例说明</h3> <p>示例说明见<a href="/docs/clients/hugegraph-client">HugeGraph-Client基本API介绍</a></p>Docs: HugeGraph-Computer Quick Start/cn/docs/quickstart/hugegraph-computer/Mon, 01 Jan 0001 00:00:00 +0000/cn/docs/quickstart/hugegraph-computer/ <h2 id="1-hugegraph-computer-概述">1 HugeGraph-Computer 概述</h2> -<p><code>HugeGraph-Computer</code> 是分布式图处理系统 (OLAP). 它是 <a href="https://kowshik.github.io/JPregel/pregel_paper.pdf">Pregel</a> 的一个实现. 它可以运行在 Kubernetes 上。</p> +<p><a href="https://github.com/apache/incubator-hugegraph-computer"><code>HugeGraph-Computer</code></a> 是分布式图处理系统 (OLAP). 它是 <a href="https://kowshik.github.io/JPregel/pregel_paper.pdf">Pregel</a> 的一个实现. 它可以运行在 Kubernetes 上。</p> <h3 id="特性">特性</h3> <ul> <li>支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。</li> @@ -2577,12 +2577,12 @@ HugeGraph Toolchain 版本: toolchain-1.0.0</p> <p>要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 JREJDK 11 或更高版本。</p> <p>还需要首先部署 HugeGraph-Server 和 <a href="https://etcd.io/docs/v3.5/quickstart/">Etcd</a>.</p> </blockquote> -<h4 id="21-download-the-compiled-archive">2.1 Download the compiled archive</h4> <p>有两种方式可以获取 HugeGraph-Computer：</p> <ul> <li>下载已编译的压缩包</li> <li>克隆源码编译打包</li> </ul> +<h4 id="21-download-the-compiled-archive">2.1 Download the compiled archive</h4> <p>下载最新版本的 HugeGraph-Computer release 包：</p> <div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>wget https://github.com/apache/hugegraph-computer/releases/download/v<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>/hugegraph-loader-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz </span></span><span style="display:flex;"><span>tar zxvf hugegraph-computer-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz @@ -2679,8 +2679,8 @@ HugeGraph Toolchain 版本: toolchain-1.0.0</p> <div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>kubectl get event --field-selector <span style="color:#000">reason</span><span style="color:#ce5c00;font-weight:bold">=</span>ComputerJobSucceed --field-selector involvedObject.name<span style="color:#ce5c00;font-weight:bold">=</span>pagerank-sample -n hugegraph-computer-system </span></span></code></pre></div><h4 id="229-查询算法结果">2.2.9 查询算法结果</h4> <p>如果输出到 <code>Hugegraph-Server</code> 则与 Locally 模式一致，如果输出到 <code>HDFS</code> ，请检查 <code>hugegraph-computerresults{jobId}</code>目录下的结果文件。</p> -<h3 id="3-内置算法文档">3 内置算法文档</h3> -<h4 id="31--支持的算法列表">3.1 支持的算法列表:</h4> +<h2 id="3-内置算法文档">3 内置算法文档</h2> +<h3 id="31-支持的算法列表">3.1 支持的算法列表:</h3> <h6 id="中心性算法">中心性算法:</h6> <ul> <li>PageRank</li> @@ -2702,7 +2702,7 @@ HugeGraph Toolchain 版本: toolchain-1.0.0</p> <li>RingsDetectionWithFilter</li> </ul> <p>更多算法请看: <a href="https://github.com/apache/hugegraph-computer/tree/master/computer-algorithm/src/main/java/org/apache/hugegraph/computer/algorithm">Built-In algorithms</a></p> -<h4 id="32-算法描述">3.2 算法描述</h4> +<h3 id="32-算法描述">3.2 算法描述</h3> <p>TODO</p> -<h3 id="4-算法开发指南">4 算法开发指南</h3> +<h2 id="4-算法开发指南">4 算法开发指南</h2> <p>TODO</p> \ No newline at end of file diff --git a/cn/sitemap.xml b/cn/sitemap.xml index 9e45b4426..5cd7d7873 100644 --- a/cn/sitemap.xml +++ b/cn/sitemap.xml @@ -1 +1 @@ -/cn/docs/guides/architectural/2022-11-27T21:05:55+08:00/cn/docs/config/config-guide/2022-04-17T11:36:55+08:00/cn/docs/language/hugegraph-gremlin/2022-09-15T15:16:23+08:00/cn/docs/performance/hugegraph-benchmark-0.5.6/2022-09-15T15:16:23+08:00/cn/docs/quickstart/hugegraph-server/2022-09-15T15:16:23+08:00/cn/docs/introduction/readme/2022-11-27T21:36:10+08:00/cn/docs/changelog/hugegraph-0.12.0-release-notes/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/schema/2022-04-17T11:36:55+08:00/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-rocksdb/2022-04-17T11:36:55+08:00/cn/docs/config/config-option/2022-09-15T15:16:23+08:00/cn/docs/guides/desgin-concept/2022-04-17T11:36:55+08:00/cn/docs/download/download/2022-09-15T15:16:23+08:00/cn/docs/language/hugegraph-example/2022-09-15T15:16:23+08:00/cn/docs/clients/hugegraph-client/2022-09-15T15:16:23+08:00/cn/docs/performance/api-preformance/2022-04-17T11:36:55+08:00/cn/docs/quickstart/hugegraph-loader/2022-09-15T15:16:23+08:00/cn/docs/clients/restful-api/propertykey/2022-05-12T21:24:05+08:00/cn/docs/changelog/hugegraph-0.11.2-release-notes/2022-04-17T11:36:55+08:00/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-cassandra/2022-04-17T11:36:55+08:00/cn/docs/config/config-authentication/2022-04-17T11:36:55+08:00/cn/docs/clients/gremlin-console/2022-04-17T11:36:55+08:00/cn/docs/guides/custom-plugin/2022-09-15T15:16:23+08:00/cn/docs/performance/hugegraph-loader-performance/2022-04-17T11:36:55+08:00/cn/docs/quickstart/hugegraph-tools/2022-09-15T15:16:23+08:00/cn/docs/quickstart/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.10.4-release-notes/2022-04-17T11:36:55+08:00/cn/docs/performance/api-preformance/hugegraph-api-0.4.4/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/vertexlabel/2022-04-17T11:36:55+08:00/cn/docs/guides/backup-restore/2022-04-17T11:36:55+08:00/cn/docs/config/2022-04-17T11:36:55+08:00/cn/docs/config/config-https/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/edgelabel/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.9.2-release-notes/2022-04-17T11:36:55+08:00/cn/docs/performance/api-preformance/hugegraph-api-0.2/2022-04-17T11:36:55+08:00/cn/docs/quickstart/hugegraph-hubble/2022-04-17T11:36:55+08:00/cn/docs/clients/2022-04-17T11:36:55+08:00/cn/docs/config/config-computer/2022-11-27T21:05:55+08:00/cn/docs/guides/faq/2022-09-15T15:16:23+08:00/cn/docs/clients/restful-api/indexlabel/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.8.0-release-notes/2022-04-17T11:36:55+08:00/cn/docs/quickstart/hugegraph-client/2022-04-17T11:36:55+08:00/cn/docs/guides/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/rebuild/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.7.4-release-notes/2022-04-17T11:36:55+08:00/cn/docs/quickstart/hugegraph-computer/2022-11-27T21:05:55+08:00/cn/docs/language/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.6.1-release-notes/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/vertex/2022-09-15T15:16:23+08:00/cn/docs/clients/restful-api/edge/2022-09-15T15:16:23+08:00/cn/docs/performance/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.5.6-release-notes/2022-04-17T11:36:55+08:00/cn/docs/changelog/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.4.4-release-notes/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/traverser/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/rank/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.3.3-release-notes/2022-04-17T11:36:55+08:00/cn/docs/changelog/hugegraph-0.2-release-notes/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/variable/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/graphs/2022-05-27T09:27:37+08:00/cn/docs/changelog/hugegraph-0.2.4-release-notes/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/task/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/gremlin/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/auth/2022-04-17T11:36:55+08:00/cn/docs/clients/restful-api/other/2022-04-17T11:36:55+08:00/cn/docs/2022-09-15T15:16:23+08:00/cn/blog/news/2022-04-17T11:36:55+08:00/cn/blog/releases/2022-04-17T11:36:55+08:00/cn/blog/2018/10/06/easy-documentation-with-docsy/2022-04-17T11:36:55+08:00/cn/blog/2018/10/06/the-second-blog-post/2022-04-17T11:36:55+08:00/cn/blog/2018/01/04/another-great-release/2022-04-17T11:36:55+08:00/cn/docs/cla/2022-04-17T11:36:55+08:00/cn/docs/performance/hugegraph-benchmark-0.4.4/2022-09-15T15:16:23+08:00/cn/docs/summary/2022-11-27T21:05:55+08:00/cn/about/2022-04-17T11:36:55+08:00/cn/blog/2022-04-17T11:36:55+08:00/cn/categories//cn/community/2022-04-17T11:36:55+08:00/cn/2022-05-11T21:17:34+08:00/cn/search/2022-04-17T11:36:55+08:00/cn/tags/ \ No newline at end of file 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\ No newline at end of file diff --git a/docs/_print/index.html b/docs/_print/index.html index 897311d0f..5ff96965b 100644 --- a/docs/_print/index.html +++ b/docs/_print/index.html @@ -1283,7 +1283,7 @@ hugeClient.close(); } } -

4.4 Run The Example

Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start.

4.5 More Information About Example

SeeIntroduce basic API of HugeGraph-Client.

3.6 - HugeGraph-Computer Quick Start

1 HugeGraph-Computer Overview

The HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework.

Features

2 Get Started

There are two ways to get HugeGraph-Computer:

2.1 Run PageRank algorithm locally

To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 11 or later versions.

You also need to deploy HugeGraph-Server and Etcd.

2.1 Download the compiled archive

Download the latest version of the HugeGraph-Computer release package:

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
+

4.4 Run The Example

Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start.

4.5 More Information About Example

SeeIntroduce basic API of HugeGraph-Client.

3.6 - HugeGraph-Computer Quick Start

1 HugeGraph-Computer Overview

The HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework.

Features

2 Get Started

2.1 Run PageRank algorithm locally

To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 11 or later versions.

You also need to deploy HugeGraph-Server and Etcd.

There are two ways to get HugeGraph-Computer:

2.1 Download the compiled archive

Download the latest version of the HugeGraph-Computer release package:

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
 tar zxvf hugegraph-computer-${version}.tar.gz

2.2 Clone source code to compile and package

Clone the latest version of HugeGraph-Computer source package:

$ git clone https://github.com/apache/hugegraph-computer.git

Compile and generate tar package:

cd hugegraph-computer
@@ -1345,7 +1345,7 @@
 # NOTE: diagnostic log exist only when the job fails, and it will only be saved for one hour.
 kubectl get event --field-selector reason=ComputerJobFailed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system

2.2.8 Show success event of a job

NOTE: it will only be saved for one hour

kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-

2.2.9 Query algorithm results

If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.

3 Built-In algorithms document

3.1 Supported algorithms list:

Centrality Algorithm:
Community Algorithm:
Path Algorithm:

More algorithms please see: Built-In algorithms

3.2 Algorithm describe

TODO

4 Algorithm development guide

TODO

4 - Config

4.1 - HugeGraph 配置

1 概述

配置文件的目录为 hugegraph-release/conf，所有关于服务和图本身的配置都在此目录下。

主要的配置文件包括：gremlin-server.yaml、rest-server.properties 和 hugegraph.properties

HugeGraphServer 内部集成了 GremlinServer 和 RestServer，而 gremlin-server.yaml 和 rest-server.properties 就是用来配置这两个Server的。

下面对这三个配置文件逐一介绍。

2 gremlin-server.yaml

gremlin-server.yaml 文件默认的内容如下：

# host and port of gremlin server, need to be consistent with host and port in rest-server.properties
+

2.2.9 Query algorithm results

If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.

3 Built-In algorithms document

3.1 Supported algorithms list:

Centrality Algorithm:
Community Algorithm:
Path Algorithm:

More algorithms please see: Built-In algorithms

3.2 Algorithm describe

TODO

4 Algorithm development guide

TODO

4 - Config

4.1 - HugeGraph 配置

1 概述

配置文件的目录为 hugegraph-release/conf，所有关于服务和图本身的配置都在此目录下。

主要的配置文件包括：gremlin-server.yaml、rest-server.properties 和 hugegraph.properties

HugeGraphServer 内部集成了 GremlinServer 和 RestServer，而 gremlin-server.yaml 和 rest-server.properties 就是用来配置这两个Server的。

下面对这三个配置文件逐一介绍。

2 gremlin-server.yaml

gremlin-server.yaml 文件默认的内容如下：

# host and port of gremlin server, need to be consistent with host and port in rest-server.properties
 #host: 127.0.0.1
 #port: 8182
 
@@ -1610,7 +1610,7 @@
 国家代码：CN
  1. 根据服务端私钥，导出服务端证书
keytool -export -alias serverkey -keystore server.keystore -file server.crt

server.crt 就是服务端的证书

客户端

keytool -import -alias serverkey -file server.crt -keystore client.truststore
-

client.truststore 是给客户端⽤的，其中保存着受信任的证书

4.5 - HugeGraph-Computer Config

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe log4j.xml path for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.

5 - API

5.1 - HugeGraph RESTful API

HugeGraph-Server通过HugeGraph-API基于HTTP协议为Client提供操作图的接口，主要包括元数据和 +

client.truststore 是给客户端⽤的，其中保存着受信任的证书

4.5 - HugeGraph-Computer Config

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe content of log4j.xml for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.

5 - API

5.1 - HugeGraph RESTful API

HugeGraph-Server通过HugeGraph-API基于HTTP协议为Client提供操作图的接口，主要包括元数据和 图数据的增删改查，遍历算法，变量，图操作及其他操作。

5.1.1 - Schema API

1.1 Schema

HugeGraph 提供单一接口获取某个图的全部 Schema 信息，包括：PropertyKey、VertexLabel、EdgeLabel 和 IndexLabel。

Method & Url
GET http://localhost:8080/graphs/hugegraph/schema
Response Status
200
Response Body
{
diff --git a/docs/config/_print/index.html b/docs/config/_print/index.html
index 39610354b..74f038926 100644
--- a/docs/config/_print/index.html
+++ b/docs/config/_print/index.html
@@ -265,7 +265,7 @@
 国家代码：CN
  1. 根据服务端私钥，导出服务端证书
keytool -export -alias serverkey -keystore server.keystore -file server.crt

server.crt 就是服务端的证书

客户端

keytool -import -alias serverkey -file server.crt -keystore client.truststore
-

client.truststore 是给客户端⽤的，其中保存着受信任的证书

5 - HugeGraph-Computer Config

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe log4j.xml path for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.
+

client.truststore 是给客户端⽤的，其中保存着受信任的证书

5 - HugeGraph-Computer Config

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe content of log4j.xml for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.
diff --git a/docs/config/config-computer/index.html b/docs/config/config-computer/index.html index 24e2e1f08..b2f026140 100644 --- a/docs/config/config-computer/index.html +++ b/docs/config/config-computer/index.html @@ -11,13 +11,13 @@ algorithm.message_class org.apache.hugegraph.computer.core.config.Null -The …"> +The …">

HugeGraph-Computer Config

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe log4j.xml path for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.

Last modified November 27, 2022: Add HugeGraph-Computer Doc (#155) (19ab2ff)
+ Print entire section

HugeGraph-Computer Config

Computer Config Options

config optiondefault valuedescription
algorithm.message_classorg.apache.hugegraph.computer.core.config.NullThe class of message passed when compute vertex.
algorithm.params_classorg.apache.hugegraph.computer.core.config.NullThe class used to transfer algorithms’ parameters before algorithm been run.
algorithm.result_classorg.apache.hugegraph.computer.core.config.NullThe class of vertex’s value, the instance is used to store computation result for the vertex.
allocator.max_vertices_per_thread10000Maximum number of vertices per thread processed in each memory allocator
bsp.etcd_endpointshttp://localhost:2379The end points to access etcd.
bsp.log_interval30000The log interval(in ms) to print the log while waiting bsp event.
bsp.max_super_step10The max super step of the algorithm.
bsp.register_timeout300000The max timeout to wait for master and works to register.
bsp.wait_master_timeout86400000The max timeout(in ms) to wait for master bsp event.
bsp.wait_workers_timeout86400000The max timeout to wait for workers bsp event.
hgkv.max_data_block_size65536The max byte size of hgkv-file data block.
hgkv.max_file_size2147483648The max number of bytes in each hgkv-file.
hgkv.max_merge_files10The max number of files to merge at one time.
hgkv.temp_file_dir/tmp/hgkvThis folder is used to store temporary files, temporary files will be generated during the file merging process.
hugegraph.namehugegraphThe graph name to load data and write results back.
hugegraph.urlhttp://127.0.0.1:8080The hugegraph url to load data and write results back.
input.edge_directionOUTThe data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded.
input.edge_freqMULTIPLEThe frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it.
input.filter_classorg.apache.hugegraph.computer.core.input.filter.DefaultInputFilterThe class to create input-filter object, input-filter is used to Filter vertex edges according to user needs.
input.loader_schema_pathThe schema path of loader input, only takes effect when the input.source_type=loader is enabled
input.loader_struct_pathThe struct path of loader input, only takes effect when the input.source_type=loader is enabled
input.max_edges_in_one_vertex200The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit.
input.source_typehugegraph-serverThe source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’.
input.split_fetch_timeout300The timeout in seconds to fetch input splits
input.split_max_splits10000000The maximum number of input splits
input.split_page_size500The page size for streamed load input split data
input.split_size1048576The input split size in bytes
job.idlocal_0001The job id on Yarn cluster or K8s cluster.
job.partitions_count1The partitions count for computing one graph algorithm job.
job.partitions_thread_nums4The number of threads for partition parallel compute.
job.workers_count1The workers count for computing one graph algorithm job.
master.computation_classorg.apache.hugegraph.computer.core.master.DefaultMasterComputationMaster-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master.
output.batch_size500The batch size of output
output.batch_threads1The threads number used to batch output
output.hdfs_core_site_pathThe hdfs core site path.
output.hdfs_delimiter,The delimiter of hdfs output.
output.hdfs_kerberos_enablefalseIs Kerberos authentication enabled for Hdfs.
output.hdfs_kerberos_keytabThe Hdfs’s key tab file for kerberos authentication.
output.hdfs_kerberos_principalThe Hdfs’s principal for kerberos authentication.
output.hdfs_krb5_conf/etc/krb5.confKerberos configuration file.
output.hdfs_merge_partitionstrueWhether merge output files of multiple partitions.
output.hdfs_path_prefix/hugegraph-computer/resultsThe directory of hdfs output result.
output.hdfs_replication3The replication number of hdfs.
output.hdfs_site_pathThe hdfs site path.
output.hdfs_urlhdfs://127.0.0.1:9000The hdfs url of output.
output.hdfs_userhadoopThe hdfs user of output.
output.output_classorg.apache.hugegraph.computer.core.output.LogOutputThe class to output the computation result of each vertex. Be called after iteration computation.
output.result_namevalueThe value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS.
output.result_write_typeOLAP_COMMONThe result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE].
output.retry_interval10The retry interval when output failed
output.retry_times3The retry times when output failed
output.single_threads1The threads number used to single output
output.thread_pool_shutdown_timeout60The timeout seconds of output threads pool shutdown
output.with_adjacent_edgesfalseOutput the adjacent edges of the vertex or not
output.with_edge_propertiesfalseOutput the properties of the edge or not
output.with_vertex_propertiesfalseOutput the properties of the vertex or not
sort.thread_nums4The number of threads performing internal sorting.
transport.client_connect_timeout3000The timeout(in ms) of client connect to server.
transport.client_threads4The number of transport threads for client.
transport.close_timeout10000The timeout(in ms) of close server or close client.
transport.finish_session_timeout0The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests).
transport.heartbeat_interval20000The minimum interval(in ms) between heartbeats on client side.
transport.io_modeAUTOThe network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically.
transport.max_pending_requests8The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests.
transport.max_syn_backlog511The capacity of SYN queue on server side, 0 means using system default value.
transport.max_timeout_heartbeat_count120The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side.
transport.min_ack_interval200The minimum interval(in ms) of server reply ack.
transport.min_pending_requests6The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests.
transport.network_retries3The number of retry attempts for network communication,if network unstable.
transport.provider_classorg.apache.hugegraph.computer.core.network.netty.NettyTransportProviderThe transport provider, currently only supports Netty.
transport.receive_buffer_size0The size of socket receive-buffer in bytes, 0 means using system default value.
transport.recv_file_modetrueWhether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable.
transport.send_buffer_size0The size of socket send-buffer in bytes, 0 means using system default value.
transport.server_host127.0.0.1The server hostname or ip to listen on to transfer data.
transport.server_idle_timeout360000The max timeout(in ms) of server idle.
transport.server_port0The server port to listen on to transfer data. The system will assign a random port if it’s set to 0.
transport.server_threads4The number of transport threads for server.
transport.sync_request_timeout10000The timeout(in ms) to wait response after sending sync-request.
transport.tcp_keep_alivetrueWhether enable TCP keep-alive.
transport.transport_epoll_ltfalseWhether enable EPOLL level-trigger.
transport.write_buffer_high_mark67108864The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark.
transport.write_buffer_low_mark33554432The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b
transport.write_socket_timeout3000The timeout(in ms) to write data to socket buffer.
valuefile.max_segment_size1073741824The max number of bytes in each segment of value-file.
worker.combiner_classorg.apache.hugegraph.computer.core.config.NullCombiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value.
worker.computation_classorg.apache.hugegraph.computer.core.config.NullThe class to create worker-computation object, worker-computation is used to compute each vertex in each superstep.
worker.data_dirs[jobs]The directories separated by ‘,’ that received vertices and messages can persist into.
worker.edge_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same edge into one properties at inputstep.
worker.partitionerorg.apache.hugegraph.computer.core.graph.partition.HashPartitionerThe partitioner that decides which partition a vertex should be in, and which worker a partition should be in.
worker.received_buffers_bytes_limit104857600The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file.
worker.vertex_properties_combiner_classorg.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombinerThe combiner can combine several properties of the same vertex into one properties at inputstep.
worker.wait_finish_messages_timeout86400000The max timeout(in ms) message-handler wait for finish-message of all workers.
worker.wait_sort_timeout600000The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers.
worker.write_buffer_capacity52428800The initial size of write buffer that used to store vertex or message.
worker.write_buffer_threshold52428800The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message.

K8s Operator Config Options

NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL

config optiondefault valuedescription
k8s.auto_destroy_podtrueWhether to automatically destroy all pods when the job is completed or failed.
k8s.close_reconciler_timeout120The max timeout(in ms) to close reconciler.
k8s.internal_etcd_urlhttp://127.0.0.1:2379The internal etcd url for operator system.
k8s.max_reconcile_retry3The max retry times of reconcile.
k8s.probe_backlog50The maximum backlog for serving health probes.
k8s.probe_port9892The value is the port that the controller bind to for serving health probes.
k8s.ready_check_internal1000The time interval(ms) of check ready.
k8s.ready_timeout30000The max timeout(in ms) of check ready.
k8s.reconciler_count10The max number of reconciler thread.
k8s.resync_period600000The minimum frequency at which watched resources are reconciled.
k8s.timezoneAsia/ShanghaiThe timezone of computer job and operator.
k8s.watch_namespacehugegraph-computer-systemThe value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched.

HugeGraph-Computer CRD

CRD: https://github.com/apache/hugegraph-computer/blob/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

specdefault valuedescriptionrequired
algorithmNameThe name of algorithm.true
jobIdThe job id.true
imageThe image of algorithm.true
computerConfThe map of computer config options.true
workerInstancesThe number of worker instances, it will instead the ‘job.workers_count’ option.true
pullPolicyAlwaysThe pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policyfalse
pullSecretsThe pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-podfalse
masterCpuThe cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
workerCpuThe cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpufalse
masterMemoryThe memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
workerMemoryThe memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memoryfalse
log4jXmlThe content of log4j.xml for computer job.false
jarFileThe jar path of computer algorithm.false
remoteJarUriThe remote jar uri of computer algorithm, it will overlay algorithm image.false
jvmOptionsThe java startup parameters of computer job.false
envVarsplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/false
envFromplease refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/false
masterCommandbin/start-computer.shThe run command of master, equivalent to ‘Entrypoint’ field of Docker.false
masterArgs["-r master", “-d k8s”]The run args of master, equivalent to ‘Cmd’ field of Docker.false
workerCommandbin/start-computer.shThe run command of worker, equivalent to ‘Entrypoint’ field of Docker.false
workerArgs["-r worker", “-d k8s”]The run args of worker, equivalent to ‘Cmd’ field of Docker.false
volumesPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
volumeMountsPlease refer to: https://kubernetes.io/docs/concepts/storage/volumes/false
secretPathsThe map of k8s-secret name and mount path.false
configMapPathsThe map of k8s-configmap name and mount path.false
podTemplateSpecPlease refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpecfalse
securityContextPlease refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/false

KubeDriver Config Options

config optiondefault valuedescription
k8s.build_image_bash_pathThe path of command used to build image.
k8s.enable_internal_algorithmtrueWhether enable internal algorithm.
k8s.framework_image_urlhugegraph/hugegraph-computer:latestThe image url of computer framework.
k8s.image_repository_passwordThe password for login image repository.
k8s.image_repository_registryThe address for login image repository.
k8s.image_repository_urlhugegraph/hugegraph-computerThe url of image repository.
k8s.image_repository_usernameThe username for login image repository.
k8s.internal_algorithm[pageRank]The name list of all internal algorithm.
k8s.internal_algorithm_image_urlhugegraph/hugegraph-computer:latestThe image url of internal algorithm.
k8s.jar_file_dir/cache/jars/The directory where the algorithm jar to upload location.
k8s.kube_config~/.kube/configThe path of k8s config file.
k8s.log4j_xml_pathThe log4j.xml path for computer job.
k8s.namespacehugegraph-computer-systemThe namespace of hugegraph-computer system.
k8s.pull_secret_names[]The names of pull-secret for pulling image.

Last modified November 28, 2022: improve computer doc (#157) (862b048)
diff --git a/docs/config/index.xml b/docs/config/index.xml index b68eac4a6..0c2b81b58 100644 --- a/docs/config/index.xml +++ b/docs/config/index.xml @@ -2192,7 +2192,7 @@ auth.user_tokens=[hugegraph1:token-value-1, hugegraph2:token-value-2] <tr> <td>log4jXml</td> <td></td> -<td>The log4j.xml path for computer job.</td> +<td>The content of log4j.xml for computer job.</td> <td>false</td> </tr> <tr> diff --git a/docs/index.xml b/docs/index.xml index 8e2ab24e8..47175023d 100644 --- a/docs/index.xml +++ b/docs/index.xml @@ -9285,7 +9285,7 @@ batch_size_fail_threshold_in_kb: 1000 <tr> <td>log4jXml</td> <td></td> -<td>The log4j.xml path for computer job.</td> +<td>The content of log4j.xml for computer job.</td> <td>false</td> </tr> <tr> @@ -10027,7 +10027,7 @@ batch_size_fail_threshold_in_kb: 1000 <p>You can get the asynchronous job status by <code>GET http://localhost:8080/graphs/hugegraph/tasks/${task_id}</code> (the task_id here should be 3). See More <a href="../task">AsyncJob RESTfull API</a></p> </blockquote>Docs: HugeGraph-Computer Quick Start/docs/quickstart/hugegraph-computer/Mon, 01 Jan 0001 00:00:00 +0000/docs/quickstart/hugegraph-computer/ <h2 id="1-hugegraph-computer-overview">1 HugeGraph-Computer Overview</h2> -<p>The <code>HugeGraph-Computer</code> is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of <a href="https://kowshik.github.io/JPregel/pregel_paper.pdf">Pregel</a>. It runs on Kubernetes framework.</p> +<p>The <a href="https://github.com/apache/incubator-hugegraph-computer"><code>HugeGraph-Computer</code></a> is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of <a href="https://kowshik.github.io/JPregel/pregel_paper.pdf">Pregel</a>. It runs on Kubernetes framework.</p> <h3 id="features">Features</h3> <ul> <li>Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage.</li> @@ -10039,16 +10039,16 @@ batch_size_fail_threshold_in_kb: 1000 <li>Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management.</li> </ul> <h2 id="2-get-started">2 Get Started</h2> -<p>There are two ways to get HugeGraph-Computer:</p> -<ul> -<li>Download the compiled tarball</li> -<li>Clone source code then compile and package</li> -</ul> <h3 id="21-run-pagerank-algorithm-locally">2.1 Run PageRank algorithm locally</h3> <blockquote> <p>To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 11 or later versions.</p> <p>You also need to deploy HugeGraph-Server and <a href="https://etcd.io/docs/v3.5/quickstart/">Etcd</a>.</p> </blockquote> +<p>There are two ways to get HugeGraph-Computer:</p> +<ul> +<li>Download the compiled tarball</li> +<li>Clone source code then compile and package</li> +</ul> <h4 id="21-download-the-compiled-archive">2.1 Download the compiled archive</h4> <p>Download the latest version of the HugeGraph-Computer release package:</p> <div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>wget https://github.com/apache/hugegraph-computer/releases/download/v<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>/hugegraph-loader-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz @@ -10146,8 +10146,8 @@ batch_size_fail_threshold_in_kb: 1000 <div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>kubectl get event --field-selector <span style="color:#000">reason</span><span style="color:#ce5c00;font-weight:bold">=</span>ComputerJobSucceed --field-selector involvedObject.name<span style="color:#ce5c00;font-weight:bold">=</span>pagerank-sample -n hugegraph-computer-system </span></span></code></pre></div><h4 id="229-query-algorithm-results">2.2.9 Query algorithm results</h4> <p>If the output to <code>Hugegraph-Server</code> is consistent with Locally, if output to <code>HDFS</code>, please check the result file in the directory of <code>/hugegraph-computer/results/{jobId}</code> directory.</p> -<h3 id="3-built-in-algorithms-document">3 Built-In algorithms document</h3> -<h4 id="31--supported-algorithms-list">3.1 Supported algorithms list:</h4> +<h2 id="3-built-in-algorithms-document">3 Built-In algorithms document</h2> +<h3 id="31-supported-algorithms-list">3.1 Supported algorithms list:</h3> <h6 id="centrality-algorithm">Centrality Algorithm:</h6> <ul> <li>PageRank</li> @@ -10169,9 +10169,9 @@ batch_size_fail_threshold_in_kb: 1000 <li>RingsDetectionWithFilter</li> </ul> <p>More algorithms please see: <a href="https://github.com/apache/hugegraph-computer/tree/master/computer-algorithm/src/main/java/org/apache/hugegraph/computer/algorithm">Built-In algorithms</a></p> -<h4 id="32-algorithm-describe">3.2 Algorithm describe</h4> +<h3 id="32-algorithm-describe">3.2 Algorithm describe</h3> <p>TODO</p> -<h3 id="4-algorithm-development-guide">4 Algorithm development guide</h3> +<h2 id="4-algorithm-development-guide">4 Algorithm development guide</h2> <p>TODO</p>Docs: Vertex API/docs/clients/restful-api/vertex/Mon, 01 Jan 0001 00:00:00 +0000/docs/clients/restful-api/vertex/ <h3 id="21-vertex">2.1 Vertex</h3> <p>顶点类型中的 Id 策略决定了顶点的 Id 类型，其对应关系如下：</p> diff --git a/docs/quickstart/_print/index.html b/docs/quickstart/_print/index.html index caeb25e5b..09c7cf37b 100644 --- a/docs/quickstart/_print/index.html +++ b/docs/quickstart/_print/index.html @@ -1278,7 +1278,7 @@ hugeClient.close(); } } -

4.4 Run The Example

Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start.

4.5 More Information About Example

SeeIntroduce basic API of HugeGraph-Client.

6 - HugeGraph-Computer Quick Start

1 HugeGraph-Computer Overview

The HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework.

Features

  • Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage.
  • Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep.
  • Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data.
  • The part of edges or the messages of super node can be in memory, so you will never lose it.
  • You can load the data from HDFS or HugeGraph, or any other system.
  • You can output the results to HDFS or HugeGraph, or any other system.
  • Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management.

2 Get Started

There are two ways to get HugeGraph-Computer:

  • Download the compiled tarball
  • Clone source code then compile and package

2.1 Run PageRank algorithm locally

To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 11 or later versions.

You also need to deploy HugeGraph-Server and Etcd.

2.1 Download the compiled archive

Download the latest version of the HugeGraph-Computer release package:

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
+

4.4 Run The Example

Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start.

4.5 More Information About Example

SeeIntroduce basic API of HugeGraph-Client.

6 - HugeGraph-Computer Quick Start

1 HugeGraph-Computer Overview

The HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework.

Features

  • Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage.
  • Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep.
  • Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data.
  • The part of edges or the messages of super node can be in memory, so you will never lose it.
  • You can load the data from HDFS or HugeGraph, or any other system.
  • You can output the results to HDFS or HugeGraph, or any other system.
  • Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management.

2 Get Started

2.1 Run PageRank algorithm locally

To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 11 or later versions.

You also need to deploy HugeGraph-Server and Etcd.

There are two ways to get HugeGraph-Computer:

  • Download the compiled tarball
  • Clone source code then compile and package

2.1 Download the compiled archive

Download the latest version of the HugeGraph-Computer release package:

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
 tar zxvf hugegraph-computer-${version}.tar.gz

2.2 Clone source code to compile and package

Clone the latest version of HugeGraph-Computer source package:

$ git clone https://github.com/apache/hugegraph-computer.git

Compile and generate tar package:

cd hugegraph-computer
@@ -1340,7 +1340,7 @@
 # NOTE: diagnostic log exist only when the job fails, and it will only be saved for one hour.
 kubectl get event --field-selector reason=ComputerJobFailed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system

2.2.8 Show success event of a job

NOTE: it will only be saved for one hour

kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-

2.2.9 Query algorithm results

If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.

3 Built-In algorithms document

3.1 Supported algorithms list:

Centrality Algorithm:
  • PageRank
  • BetweennessCentrality
  • ClosenessCentrality
  • DegreeCentrality
Community Algorithm:
  • ClusteringCoefficient
  • Kcore
  • Lpa
  • TriangleCount
  • Wcc
Path Algorithm:
  • RingsDetection
  • RingsDetectionWithFilter

More algorithms please see: Built-In algorithms

3.2 Algorithm describe

TODO

4 Algorithm development guide

TODO

+

2.2.9 Query algorithm results

If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.

3 Built-In algorithms document

3.1 Supported algorithms list:

Centrality Algorithm:
Community Algorithm:
Path Algorithm:

More algorithms please see: Built-In algorithms

3.2 Algorithm describe

TODO

4 Algorithm development guide

TODO

diff --git a/docs/quickstart/hugegraph-computer/index.html b/docs/quickstart/hugegraph-computer/index.html index 5c122ecab..e9facedf0 100644 --- a/docs/quickstart/hugegraph-computer/index.html +++ b/docs/quickstart/hugegraph-computer/index.html @@ -1,14 +1,14 @@ HugeGraph-Computer Quick Start | HugeGraph

HugeGraph-Computer Quick Start

1 HugeGraph-Computer Overview

The HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework.

Features

  • Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage.
  • Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep.
  • Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data.
  • The part of edges or the messages of super node can be in memory, so you will never lose it.
  • You can load the data from HDFS or HugeGraph, or any other system.
  • You can output the results to HDFS or HugeGraph, or any other system.
  • Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management.

2 Get Started

There are two ways to get HugeGraph-Computer:

  • Download the compiled tarball
  • Clone source code then compile and package

2.1 Run PageRank algorithm locally

To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 11 or later versions.

You also need to deploy HugeGraph-Server and Etcd.

2.1 Download the compiled archive

Download the latest version of the HugeGraph-Computer release package:

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
+ Print entire section

HugeGraph-Computer Quick Start

1 HugeGraph-Computer Overview

The HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework.

Features

  • Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage.
  • Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep.
  • Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data.
  • The part of edges or the messages of super node can be in memory, so you will never lose it.
  • You can load the data from HDFS or HugeGraph, or any other system.
  • You can output the results to HDFS or HugeGraph, or any other system.
  • Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management.

2 Get Started

2.1 Run PageRank algorithm locally

To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 11 or later versions.

You also need to deploy HugeGraph-Server and Etcd.

There are two ways to get HugeGraph-Computer:

  • Download the compiled tarball
  • Clone source code then compile and package

2.1 Download the compiled archive

Download the latest version of the HugeGraph-Computer release package:

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
 tar zxvf hugegraph-computer-${version}.tar.gz

2.2 Clone source code to compile and package

Clone the latest version of HugeGraph-Computer source package:

$ git clone https://github.com/apache/hugegraph-computer.git

Compile and generate tar package:

cd hugegraph-computer
@@ -70,7 +70,7 @@
 # NOTE: diagnostic log exist only when the job fails, and it will only be saved for one hour.
 kubectl get event --field-selector reason=ComputerJobFailed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system

2.2.8 Show success event of a job

NOTE: it will only be saved for one hour

kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-

2.2.9 Query algorithm results

If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.

3 Built-In algorithms document

3.1 Supported algorithms list:

Centrality Algorithm:
  • PageRank
  • BetweennessCentrality
  • ClosenessCentrality
  • DegreeCentrality
Community Algorithm:
  • ClusteringCoefficient
  • Kcore
  • Lpa
  • TriangleCount
  • Wcc
Path Algorithm:
  • RingsDetection
  • RingsDetectionWithFilter

More algorithms please see: Built-In algorithms

3.2 Algorithm describe

TODO

4 Algorithm development guide

TODO


Last modified November 27, 2022: Add HugeGraph-Computer Doc (#155) (19ab2ff)
+

2.2.9 Query algorithm results

If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.

3 Built-In algorithms document

3.1 Supported algorithms list:

Centrality Algorithm:
Community Algorithm:
Path Algorithm:

More algorithms please see: Built-In algorithms

3.2 Algorithm describe

TODO

4 Algorithm development guide

TODO


Last modified November 28, 2022: improve computer doc (#157) (862b048)
diff --git a/docs/quickstart/index.xml b/docs/quickstart/index.xml index d0709c0b4..7be3d4148 100644 --- a/docs/quickstart/index.xml +++ b/docs/quickstart/index.xml @@ -2575,7 +2575,7 @@ And there is no need to guarantee the order between the two parameters.</p> <h3 id="45-more-information-about-example">4.5 More Information About Example</h3> <p>See<a href="/docs/clients/hugegraph-client">Introduce basic API of HugeGraph-Client</a>.</p>Docs: HugeGraph-Computer Quick Start/docs/quickstart/hugegraph-computer/Mon, 01 Jan 0001 00:00:00 +0000/docs/quickstart/hugegraph-computer/ <h2 id="1-hugegraph-computer-overview">1 HugeGraph-Computer Overview</h2> -<p>The <code>HugeGraph-Computer</code> is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of <a href="https://kowshik.github.io/JPregel/pregel_paper.pdf">Pregel</a>. It runs on Kubernetes framework.</p> +<p>The <a href="https://github.com/apache/incubator-hugegraph-computer"><code>HugeGraph-Computer</code></a> is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of <a href="https://kowshik.github.io/JPregel/pregel_paper.pdf">Pregel</a>. It runs on Kubernetes framework.</p> <h3 id="features">Features</h3> <ul> <li>Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage.</li> @@ -2587,16 +2587,16 @@ And there is no need to guarantee the order between the two parameters.</p> <li>Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management.</li> </ul> <h2 id="2-get-started">2 Get Started</h2> -<p>There are two ways to get HugeGraph-Computer:</p> -<ul> -<li>Download the compiled tarball</li> -<li>Clone source code then compile and package</li> -</ul> <h3 id="21-run-pagerank-algorithm-locally">2.1 Run PageRank algorithm locally</h3> <blockquote> <p>To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 11 or later versions.</p> <p>You also need to deploy HugeGraph-Server and <a href="https://etcd.io/docs/v3.5/quickstart/">Etcd</a>.</p> </blockquote> +<p>There are two ways to get HugeGraph-Computer:</p> +<ul> +<li>Download the compiled tarball</li> +<li>Clone source code then compile and package</li> +</ul> <h4 id="21-download-the-compiled-archive">2.1 Download the compiled archive</h4> <p>Download the latest version of the HugeGraph-Computer release package:</p> <div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>wget https://github.com/apache/hugegraph-computer/releases/download/v<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>/hugegraph-loader-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz @@ -2694,8 +2694,8 @@ And there is no need to guarantee the order between the two parameters.</p> <div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>kubectl get event --field-selector <span style="color:#000">reason</span><span style="color:#ce5c00;font-weight:bold">=</span>ComputerJobSucceed --field-selector involvedObject.name<span style="color:#ce5c00;font-weight:bold">=</span>pagerank-sample -n hugegraph-computer-system </span></span></code></pre></div><h4 id="229-query-algorithm-results">2.2.9 Query algorithm results</h4> <p>If the output to <code>Hugegraph-Server</code> is consistent with Locally, if output to <code>HDFS</code>, please check the result file in the directory of <code>/hugegraph-computer/results/{jobId}</code> directory.</p> -<h3 id="3-built-in-algorithms-document">3 Built-In algorithms document</h3> -<h4 id="31--supported-algorithms-list">3.1 Supported algorithms list:</h4> +<h2 id="3-built-in-algorithms-document">3 Built-In algorithms document</h2> +<h3 id="31-supported-algorithms-list">3.1 Supported algorithms list:</h3> <h6 id="centrality-algorithm">Centrality Algorithm:</h6> <ul> <li>PageRank</li> @@ -2717,7 +2717,7 @@ And there is no need to guarantee the order between the two parameters.</p> <li>RingsDetectionWithFilter</li> </ul> <p>More algorithms please see: <a href="https://github.com/apache/hugegraph-computer/tree/master/computer-algorithm/src/main/java/org/apache/hugegraph/computer/algorithm">Built-In algorithms</a></p> -<h4 id="32-algorithm-describe">3.2 Algorithm describe</h4> +<h3 id="32-algorithm-describe">3.2 Algorithm describe</h3> <p>TODO</p> -<h3 id="4-algorithm-development-guide">4 Algorithm development guide</h3> +<h2 id="4-algorithm-development-guide">4 Algorithm development guide</h2> <p>TODO</p> \ No newline at end of file diff --git a/en/sitemap.xml b/en/sitemap.xml index 3f62bb2d3..124260219 100644 --- a/en/sitemap.xml +++ b/en/sitemap.xml @@ -1 +1 @@ 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