rally-eventdata-track

Repository containing a Rally track for simulating event-based data use-cases. The track supports bulk indexing of auto-generated events as well as simulated Kibana queries and a range of management operations to make the track self-contained.

This track can be used as-is, extended or adapted to better match your use case or simply be used as an example of how custom parameter sources and runners can be used to create more complex and realistic simulations and benchmarks.

Installation

Once Rally has been configured, modify the rally.ini file to link to the eventdata track repository:

[tracks]
default.url = https://github.com/elastic/rally-tracks
eventdata.url = https://github.com/elastic/rally-eventdata-track

The track can be run by specifying the following runtime parameters: --track=eventdata --track-repository=eventdata.

Another option is to download the repository and point to it using the --track-path command line parameter.

Track parameters supported by all challenges

Note: In general, track parameters are only defined for a subset of the challenges so please refer to the documentation of the respective challenge for a list of supported track parameters. Only the parameters documented in the table below are guaranteed to work with all challenges as intended.

Parameter	Explanation	Type	Default Value
record_raw_event_size	Adds a new field _raw_event_size to the index which contains the size of the raw logging event in bytes.	bool	False
query_index_prefix	Start of the index name(s) used in queries for this track.	str	elasticlogs_q
query_index_pattern	Index pattern used in queries for this track.	str	$query_index_prefix + "-*"
refresh_interval	Index refresh interval	str	5s
verbose	Emits additional debug logs. Enable this only when testing changes but not when running regular benchmarks as this influences performance negatively.	bool	False

Note: It is recommended to store any track parameters in a json file and pass them to Rally using --track-params=./params-file.json.

Following is an example of a valid parameters json file: params-file.json

{
  "number_of_replicas": 1,
  "number_of_shards": 3
}

You can specify what challenge you want to run with the --challenge=YOUR_CHALLENGE_NAME

Available Challenges

elasticlogs-1bn-load

This challenge indexes 1 billion events into a number of indices of 2 primary shards each, and results in around 200GB of indices being generated on disk. This can vary depending on the environment. It can be used to give an idea of how max indexing performance behaves over an extended period of time.

The table below shows the track parameters that can be adjusted along with default values:

Parameter	Explanation	Type	Default Value
number_of_replicas	Number of index replicas	int	0
number_of_shards	Number of primary shards	int	2
bulk_indexing_clients	Number of bulk indexing clients/connections	int	20
disk_type	Type of disk used. If disk_type is not ssd, a single merge scheduler thread will be specified in the index template	string	ssd
translog_sync	If value is not request, translog will be configured to use async mode	string	request
rollover_enabled	Enables the automatic rollover of indices after 100 million entries or 1 day.	bool	true

elasticlogs-querying

This challenge runs mixed Kibana queries against the index created in the elasticlogs-1bn-load track. No concurrent indexing is performed.

The table below shows the track parameters that can be adjusted along with default values:

Parameter	Explanation	Type	Default Value
query_time_period	The period to run the parallel query tasks specified in seconds	int	1800

combined-indexing-and-querying

This challenge assumes that the elasticlogs-1bn-load track has been executed as it simulates querying against these indices. It shows how indexing and querying through simulated Kibana dashboards can be combined to provide a more realistic benchmark.

In this challenge rate-limited indexing at varying levels is combined with a fixed level of querying. If metrics from the run are stored in Elasticsearch, it is possible to analyse these in Kibana in order to identify how indexing rate affects query latency and vice versa.

The table below shows the track parameters that can be adjusted along with default values:

Parameter	Explanation	Type	Default Value
number_of_replicas	Number of index replicas	int	0
number_of_shards	Number of primary shards	int	2
bulk_indexing_clients	Number of bulk indexing clients/connections	int	24
disk_type	Type of disk used. If disk_type is not ssd, a single merge scheduler thread will be specified in the index template	string	ssd
translog_sync	If value is not request, translog will be configured to use async mode	string	request
rate_limit_duration_secs	Duration in seconds for each rate limited benchmark rate_limit_step	int	1200
rate_limit_step	Number of requests per second to use as a rate_limit_step. 2 indicates rate limiting will increase in steps of 2k EPS	int	2
rate_limit_max	Maximum number of requests per second to use for rate-limiting. 32 indicates a top target indexing rate of 32k EPS	int	32

elasticlogs-continuous-index-and-query

This challenge is suitable for long term execution and runs in two phases. Both phases (p1, p2) index documents containing auto-generated events, however, p1 indexes events at the max possible speed, whereas p2 throttles indexing to a specified rate and in parallel executes four queries simulating Kibana dashboards and queries. The created index gets rolled over after the configured max size. The maximum amount of rolled over indices are also configurable.

The table below shows the track parameters that can be adjusted along with default values:

Parameter	Explanation	Type	Default Value
number_of_replicas	Number of index replicas	int	0
number_of_shards	Number of primary shards	int	2
p1_bulk_indexing_clients	Number of clients used to index during phase 1	int	40
p1_bulk_size	The build-size for the autogenerated events during phase 1	int	1000
p1_duration_secs	Duration of phase 1 execution in sec	int	7200
p2_bulk_indexing_clients	Number of clients used to index during phase 2	int	16
p2_bulk_size	The build-size for the autogenerated events during phase 2	int	1000
p2_ops	Number of bulk indexing ops/s for phase 2. A value of 10 with p2_bulk_size=10 throttles indexing to 10000 docs/s	int	10
rollover_max_size	Max index size condition for rollover API	str	30gb
rollover_max_age	Max age condition for rollover API	str	1d
p2_query1_target_interval	Frequency of execution (every N sec) of Kibana query: kibana-traffic-country-dashboard_60m	int	30
p2_query2_target_interval	Frequency of execution (every N sec) of Kibana query: kibana-discover_30m	int	30
p2_query3_target_interval	Frequency of execution (every N sec) of Kibana query: kibana-traffic-dashboard_30m	int	30
p2_query4_target_interval	Frequency of execution (every N sec) of Kibana query: kibana-content_issues-dashboard_30m"	int	30
max_rolledover_indices	Max amount of recently rolled over indices to retain	int	20
rolledover_indices_suffix_separator	Separator for extracting suffix to help determining which rolled-over indices to delete	str	-

The indices use the alias elasticlogs_q_write (assuming the default value for query_index_prefix) and start with elasticlogs_q-000001. As an example, for a cluster with rolled over indices: elasticlogs-000001, elasticlogs-000002, ... 000010 a value of max_rolledover_indices=8 results to the removal of elasticlogs-000001 and elasticlogs-000002.

A value of max_rolledover_indices=20 on a three node bare-metal cluster with the following specifications:

• CPU: Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz
• RAM: 32 GB
• SSD: 2 x Crucial MX200 in RAID-0 configuration
• OS: Linux Kernel version 4.13.0-38
• OS tuning:
  • Turbo boost disabled (/sys/devices/system/cpu/intel_pstate/no_turbo)
  • THP at default madvise (/sys/kernel/mm/transparent_hugepage/{defrag,enabled})
• JVM: Oracle JDK 1.8.0_131

ends up consuming a constant of 407GiB per node.

The following is an example of configurable parameters for this challenge.

params-file.json

{
  "number_of_replicas": 1,
  "number_of_shards": 3,
  "p1_bulk_indexing_clients": 32,
  "p1_bulk_size": 1000,
  "p1_duration_secs": 28800,
  "p2_bulk_indexing_clients": 12,
  "p2_bulk_size": 1000,
  "p2_ops": 30,
  "max_rolledover_indices": 20,
  "rollover_max_size": "30gb"
}

index-logs-fixed-daily-volume

This challenge indexes a fixed (raw) logging volume of logs per day into daily indices. This challenge will complete tasks as quickly as possible and won't take the amount of days specified in the number_of_days field. The table below shows the track parameters that can be adjusted along with default values:

Parameter	Explanation	Type	Default Value
bulk_indexing_clients	Number of bulk indexing clients/connections	int	8
bulk_size	Number of documents to send per bulk	int	1000
daily_logging_volume	The raw logging volume. Supported units are bytes (without any unit), kb, MB and GB). For the value, only integers are allowed.	str	100GB
starting_point	The first timestamp for which logs should be generated.	str	2018-05-01:00:00:00
number_of_days	The number of simulated days for which data should be generated.	int	24
number_of_shards	Number of primary shards	int	3

index-and-query-logs-fixed-daily-volume

Indexes several days of logs with a fixed (raw) logging volume per day and running queries concurrently. This challenge will complete tasks as quickly as possible and won't take the amount of days specified in the number_of_days field. The table below shows the track parameters that can be adjusted along with default values:

Parameter	Explanation	Type	Default Value
bulk_indexing_clients	Number of bulk indexing clients/connections	int	8
bulk_size	Number of documents to send per bulk	int	1000
daily_logging_volume	The raw logging volume. Supported units are bytes (without any unit), kb, MB and GB). For the value, only integers are allowed.	str	100GB
starting_point	The first timestamp for which logs should be generated.	str	2018-05-25 00:00:00
number_of_days	The number of simulated days for which data should be generated.	int	24
number_of_shards	Number of primary shards	int	3

Custom parameter sources

elasticlogs_bulk_source

This parameter source generates bulk indexing requests filled with auto-generated data. This data is generated based on statistics from a subset of real traffic to the elastic.co website. Data has been anonymised and post-processed and is modelled on the format used by the Filebeat Nginx Module.

The generator allows data to be generated in real-time or against a set date/tine interval. A sample event will contain the following fields:

{
  "@timestamp": "2017-06-01T00:01:08.866644Z",
  "offset": 7631775,
  "user_name": "-",
  "source": "/usr/local/var/log/nginx/access.log",
  "fileset": {
    "module": "nginx",
    "name": "access"
  },
  "input": {
    "type": "log"
  },
  "beat": {
    "version": "6.3.0",
    "hostname": "web-EU-1.elastic.co",
    "name": "web-EU-1.elastic.co"
  },
  "prospector": {
    "type": "log"
  },
  "nginx": {
    "access": {
      "user_agent": {
        "major": "44",
        "os": "Mac OS X",
        "os_major": "10",
        "name": "Firefox",
        "os_name": "Mac OS X",
        "device": "Other"
      },
      "remote_ip": "5.134.208.0",
      "remote_ip_list": [
        "5.134.208.0"
      ],
      "geoip": {
        "continent_name": "Europe",
        "city_name": "Grupa",
        "country_name": "Poland",
        "country_iso_code": "PL",
        "location": {
          "lat": 53.5076,
          "lon": 18.6358
        }
      },
      "referrer": "https://www.elastic.co/guide/en/marvel/current/getting-started.html",
      "url": "/guide/en/kibana/current/images/autorefresh-pause.png",
      "body_sent": {
        "bytes": 2122
      },
      "method": "GET",
      "response_code": "200",
      "http_version": "1.1"
    }
  }
}

elasticlogs_kibana_source

This parameter source supports simulating three different types of dashboards. One of the following needs to be selected by specifying the mandatory parameter dashboard:

traffic - This dashboard contains 7 visualisations and presents different types of traffic statistics. In structure it is similar to the Nginx Overview dashboard that comes with the Filebeat Nginx Module. It does aggregate across all records in the index and is therefore a 'heavy' dashboard.

content_issues - This dashboard contains 5 visualisations and is designed to be used for analysis of records with a 404 response code, e.g. to find links that are no longer leading anywhere. This only aggregates across a small subset of the records in an index and is therefore a 'light' dashboard.

discover - This simulates querying data through the Discover application in Kibana.

Extending and adapting

This track can be used as it is, but was designed so that it would be easy to extend or modify it. There are two directories named operations and challenges, containing files with the standard components of this track that can be used as an example. The main track.json file will automatically load all files with a .json suffix from these directories. This makes it simple to add new operations and challenges without having to update or modify any of the original files.

Elasticsearch Compatibility

This track requires Elasticsearch 6.x. Earlier versions are not supported.

Versioning Scheme

From time to time, setting and mapping formats change in Elasticsearch. As we want to be able to support multiple versions of Elasticsearch, we also need to version track specifications. Therefore, this repository contains multiple branches. The following examples should give you an idea how the versioning scheme works:

• master: tracks on this branch are compatible with the latest development version of Elasticsearch
• 6: compatible with all Elasticsearch 6.x releases.
• 2: compatible with all Elasticsearch releases with the major release number 2 (e.g. 2.1, 2.2, 2.2.1)
• 1.7: compatible with all Elasticsearch releases with the major release number 1 and minor release number 7 (e.g. 1.7.0, 1.7.1, 1.7.2)

As you can see, branches can match exact release numbers but Rally is also lenient in case settings mapping formats did not change for a few releases. Rally will try to match in the following order:

1. major.minor.patch-extension_label (e.g. 6.0.0-alpha2)
2. major.minor.patch (e.g. 6.2.3)
3. major.minor (e.g. 6.2)
4. major (e.g. 6)

Apart from that, the master branch is always considered to be compatible with the Elasticsearch master branch.

To specify the version to check against, add --distribution-version when running Rally. If the version is not specified, Rally assumes that you want to benchmark against the Elasticsearch master version.

Example: If you want to benchmark Elasticsearch 6.2.4, run the following command:

esrally --distribution-version=6.2.4

How to Contribute

If you want to contribute to this track, please ensure that it works against the master version of Elasticsearch (i.e. submit PRs against the master branch). We can then check whether it's feasible to backport the track to earlier Elasticsearch versions.

See all details in the contributor guidelines.

Running tests

This track contains associated unit tests. In order to run them, please issue the following commands

# only required once for the initial setup
make prereq
make install
# to run the tests
make test

It also contains an integration test suite that can be run with make it.

License

This software is licensed under the Apache License, version 2 ("ALv2"), quoted below.

Copyright 2015-2019 Elasticsearch https://www.elastic.co

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.