Nebula-Up

News: If you would like to try NebulaGraph(not all toolings) from your desktop env, it's recommended to try with NebulaGraph Docker Extension on Docker Desktop for macOS and Windows, all you need is one-click.

Update: the All-in-one mode is introduced! Check here and try it!

Nebula-Up is a PoC utility to enable developer to bootstrap an nebula-graph cluster with nebula-graph-studio(Web UI) + nebula-graph-console(Command UI) ready out of box in an oneliner run. All required packages will be handled with nebula-up as well, including Docker on Linux(Ubuntu/CentOS), Docker Desktop on macOS(including both Intel and M1 chip-based), and Docker Desktop Windows.

Also, it's optimized to leverage China Repo Mirrors(docker, brew, gitee, etc...) in case needed to enable a smooth deployment for both Mainland China users and others.

macOS and Linux with Shell:

curl -fsSL nebula-up.siwei.io/install.sh | bash

Note: you could specify the version of NebulaGraph like:

curl -fsSL nebula-up.siwei.io/install.sh | bash -s -- v3

All-in-one mode

With all-in-one mode, you could play with many Nebula Tools in one command, too:

Roadmap:

• Nebula Dashboard
• NebulaGraph Studio
• NebulaGraph Console
• Nebula BR(backup & restore)
• NebulaGraph Spark utils
  • NebulaGraph Spark Connector/PySpark REPL
  • NebulaGraph Algorithm
  • NebulaGraph Algorithm in Jupyter Notebook and PySpark
  • NebulaGraph Exchange
• NebulaGraph Importer
• NebulaGraph Fulltext Search
• Nebula Bench
• Nebula Client REPL
  • Try Python SDK in iPython
  • Try Java SDK in REPL
• Nebula Build, Debug and Dev Env
• Nebula Flink Connector Playground
• NebulaGraph + DGL(Deep Graph Library)
• NebulaGraph AI Suite(ngai) Playground
  • Read, WRite, Algo on Spark engine
  • Call ngai algo from ngai_graphd with UDF

Install all in one

# Install Nebula Core with all-in-one mode
curl -fsSL nebula-up.siwei.io/all-in-one.sh | bash

Install Nebula Core and One of the coponent:

# Install Core with Backup and Restore with MinIO
curl -fsSL nebula-up.siwei.io/all-in-one.sh | bash -s -- v3 br
# Install Core with Spark Connector, Nebula Algorithm, Nebula Exchange
curl -fsSL nebula-up.siwei.io/all-in-one.sh | bash -s -- v3 spark
# Install Core with Dashboard
curl -fsSL nebula-up.siwei.io/all-in-one.sh | bash -s -- v3 dashboard

How to play with all-in-one mode:

Console and Basketballplayer Dataset Loading

Then you could call Nebula Console like:

# Connect to nebula with console
~/.nebula-up/console.sh
# Execute queryies like
~/.nebula-up/console.sh -e "SHOW HOSTS"
# Load the sample dataset
~/.nebula-up/load-basketballplayer-dataset.sh
# Make a Graph Query the sample dataset
~/.nebula-up/console.sh -e 'USE basketballplayer; FIND ALL PATH FROM "player100" TO "team204" OVER * WHERE follow.degree is EMPTY or follow.degree >=0 YIELD path AS p;'

Monitor the whole cluster with Nebula Dashboard

Visit http://127.0.0.1:7003 with user: root, password: nebula.

Note, thanks to the sponsorship of Microsoft, we have a demo site of Nebula-UP on Azure: you could visit the dashboard here: http://nebula-demo.siwei.io:7003 .

Access NebulaGraph Studio

Visit http://127.0.0.1:7001 with user: root, password: nebula, host: graphd:9669(for non-all-in-one case, this should be <host-ip>:9669).

Note, thanks to the sponsorship of Microsoft, we have a demo site of Nebula-UP on Azure: you could visit the studio here: http://nebula-demo.siwei.io:7001 .

Query Data with Nebula Spark Connector in PySpark Shell

Or play in PySpark like:

~/.nebula-up/nebula-pyspark.sh

# call Nebula Spark Connector Reader
df = spark.read.format(
  "com.vesoft.nebula.connector.NebulaDataSource").option(
    "type", "vertex").option(
    "spaceName", "basketballplayer").option(
    "label", "player").option(
    "returnCols", "name,age").option(
    "metaAddress", "metad0:9559").option(
    "partitionNumber", 1).load()

# show the dataframe with limit 2
df.show(n=2)

The output may look like:

      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.4.5
      /_/

Using Python version 2.7.16 (default, Jan 14 2020 07:22:06)
SparkSession available as 'spark'.
>>> df = spark.read.format(
...   "com.vesoft.nebula.connector.NebulaDataSource").option(
...     "type", "vertex").option(
...     "spaceName", "basketballplayer").option(
...     "label", "player").option(
...     "returnCols", "name,age").option(
...     "metaAddress", "metad0:9559").option(
...     "partitionNumber", 1).load()
>>> df.show(n=2)
+---------+--------------+---+
|_vertexId|          name|age|
+---------+--------------+---+
|player105|   Danny Green| 31|
|player109|Tiago Splitter| 34|
+---------+--------------+---+
only showing top 2 rows

Run Nebula Exchange

Or run an example Nebula Exchange job to import data from CSV file:

~/.nebula-up/nebula-exchange-example.sh

You could check the example configuration file in ~/.nebula-up/nebula-up/spark/exchange.conf

Run NebulaGraph Algorithm

Reference: https://github.com/wey-gu/nebula-livejournal

Load LiveJournal dataset with Nebula Importer:

~/.nebula-up/load-LiveJournal-dataset.sh

Run Nebula Algorithm like:

~/.nebula-up/nebula-algo-pagerank-example.sh

Run NebulaGraph Algorithm from Jupyter Notebook with PySpark

Visit Jupyter Notebook with http://127.0.0.1:8888 with token: nebula.

Refer to the example notebook in ~/.nebula-up/nebula-up/spark/notebook/

Try Backup and Restore with MinIO as Storage

# Create a full backup to MinIO(Be sure to run load-basketballplayer-dataset.sh before doing so)
~/.nebula-up/nebula-br-backup-full.sh
# Show all backups
~/.nebula-up/nebula-br-show.sh
# Restore to a backup named BACKUP_2022_05_08_11_38_08
~/.nebula-up/nebula-br-restore-full.sh BACKUP_2022_05_08_11_38_08

Note, you could also browser files in MinIO with from http://127.0.0.1:9001 with user: minioadmin, password: minioadmin.

Note, thanks to the sponsorship of Microsoft, we have a demo site of Nebula-UP on Azure: you could visit the MinIO site here: http://nebula-demo.siwei.io:9001 .

Limitation: BR in Nebula-UP is not fully-functional for now. Please expect to restore failure in the final phases.

Play with NebulaGraph and Deep Graph Library(DGL)

Please refer to NebulaGraph-DGL Playground.

---

Support matrix

Please, help issue or PR to add your new verified cases.

❓ stands for not verified/supported yet.

Support matrix per function

Function/Platform	x86_Linux	x86_macOS	x86_win	arm_Linux	arm_macOS
NebulaGraph	✅	✅(via Docker Desktop)	✅(via Docker Desktop)	✅	✅(via Docker Desktop)
Studio	✅	✅(via Docker Desktop)	✅(via Docker Desktop)	✅(via Docker-emulated x86 Desktop)	✅(via Docker Desktop-emulated x86)
Console	✅	✅	✅	✅	✅
Dashboard	✅	✅(via Docker Desktop)	✅(via Docker Desktop)	✅(via tonistiigi/binfmt)	✅(via Docker Desktop-emulated x86)
BR	✅	❓	❓	❓	❓
Spark	✅	❓	❓	❓	❓
Importer	✅	❓	❓	❓	❓

Support matrix per command

Command /Platform	x86_Linux	x86_macOS	x86_win	arm_Linux	arm_macOS
curl -fsSL nebula-up.siwei.io/install.sh | bash	✅	✅	✅	✅	✅
curl -fsSL nebula-up.siwei.io/all-in-one.sh | bash	✅	❓	❓	❓	❓
curl -fsSL nebula-up.siwei.io/all-in-one.sh | bash -s -- v3 dashboard	✅	✅	✅	✅	✅
curl -fsSL nebula-up.siwei.io/all-in-one.sh | bash -s -- v3 br	✅	❓	❓	❓	❓
curl -fsSL nebula-up.siwei.io/all-in-one.sh | bash -s -- v3 br	✅	❓	❓	❓	❓

---

TBD:

• Finished Windows(Docker Desktop instead of the WSL 1&2 in initial phase) part, leveraging chocolatey package manager as homebrew was used in macOS
• Fully optimized for CN users, for now, git/apt/yum repo were not optimised, newly installed docker repo, brew repo were automatically optimised for CN internet access
• Packaging similar content into homebrew/chocolatey?
• CI/UT