Nebula Spark Connector 2.0

中文版

Introduction

Nebula Spark Connector 2.0 only supports Nebula Graph 2.x. If you are using Nebula Graph v1.x, please use Nebula Spark Connector v1.0 .

How to Compile

1. Package Nebula Spark Connector 2.0.

   $ git clone https://github.com/vesoft-inc/nebula-spark-connector.git
   $ cd nebula-spark-connector/nebula-spark-connector
   $ mvn clean package -Dmaven.test.skip=true -Dgpg.skip -Dmaven.javadoc.skip=true

   After the packaging, you can see the newly generated nebula-spark-connector-2.5-SNAPSHOT.jar under the nebula-spark-connector/nebula-spark-connector/target/ directory.

New Features (Compared to Nebula Spark Connector 1.0)

• Supports more connection configurations, such as timeout, connectionRetry, and executionRetry.
• Supports more data configurations, such as whether vertexId can be written as vertex's property, whether srcId, dstId and rank can be written as edge's properties.
• Spark Reader Supports non-property, all-property, and specific-properties read.
• Spark Reader Supports reading data from Nebula Graph to Graphx as VertexRD and EdgeRDD, it also supports String type vertexId.
• Nebula Spark Connector 2.0 uniformly uses SparkSQL's DataSourceV2 for data source expansion.
• Nebula Spark Connector 2.1.0 support UPDATE write mode to NebulaGraph, see Update Vertex .
• Nebula Spark Connector 2.5.0 support DELETE write mode to NebulaGraph, see Delete Vertex

How to Use

If you use Maven to manage your project, add the following dependency to your pom.xml:

<dependency>
   <groupId>com.vesoft</groupId>
   <artifactId>nebula-spark-connector</artifactId>
   <version>2.5-SNAPSHOT</version>
</dependency>

Write DataFrame INSERT into Nebula Graph as Vertices:

  val config = NebulaConnectionConfig
    .builder()
    .withMetaAddress("127.0.0.1:9559")
    .withGraphAddress("127.0.0.1:9669")
    .build()
  val nebulaWriteVertexConfig: WriteNebulaVertexConfig = WriteNebulaVertexConfig
    .builder()
    .withSpace("test")
    .withTag("person")
    .withVidField("id")
    .withVidAsProp(true)
    .withBatch(1000)
    .build()
  df.write.nebula(config, nebulaWriteVertexConfig).writeVertices()

Write DataFrame UPDATE into Nebula Graph as Vertices:

  val config = NebulaConnectionConfig
    .builder()
    .withMetaAddress("127.0.0.1:9559")
    .withGraphAddress("127.0.0.1:9669")
    .build()
  val nebulaWriteVertexConfig: WriteNebulaVertexConfig = WriteNebulaVertexConfig
    .builder()
    .withSpace("test")
    .withTag("person")
    .withVidField("id")
    .withVidAsProp(true)
    .withBatch(1000)
    .withWriteMode(WriteMode.UPDATE)
    .build()
  df.write.nebula(config, nebulaWriteVertexConfig).writeVertices()

Write DataFrame DELETE into Nebula Graph as Vertices:

  val config = NebulaConnectionConfig
    .builder()
    .withMetaAddress("127.0.0.1:9559")
    .withGraphAddress("127.0.0.1:9669")
    .build()
  val nebulaWriteVertexConfig: WriteNebulaVertexConfig = WriteNebulaVertexConfig
    .builder()
    .withSpace("test")
    .withTag("person")
    .withVidField("id")
    .withBatch(1000)
    .withWriteMode(WriteMode.DELETE)
    .build()
  df.write.nebula(config, nebulaWriteVertexConfig).writeVertices()

Read vertices from Nebula Graph:

  val config = NebulaConnectionConfig
    .builder()
    .withMetaAddress("127.0.0.1:9559")
    .withConenctionRetry(2)
    .build()
  val nebulaReadVertexConfig: ReadNebulaConfig = ReadNebulaConfig
    .builder()
    .withSpace("exchange")
    .withLabel("person")
    .withNoColumn(false)
    .withReturnCols(List("birthday"))
    .withLimit(10)
    .withPartitionNum(10)
    .build()
  val vertex = spark.read.nebula(config, nebulaReadVertexConfig).loadVerticesToDF()

Read vertices and edges from Nebula Graph to construct Graphx's graph:

  val config = NebulaConnectionConfig
    .builder()
    .withMetaAddress("127.0.0.1:9559")
    .build()
  val nebulaReadVertexConfig = ReadNebulaConfig
    .builder()
    .withSpace("exchange")
    .withLabel("person")
    .withNoColumn(false)
    .withReturnCols(List("birthday"))
    .withLimit(10)
    .withPartitionNum(10)
    .build()
  val nebulaReadEdgeConfig = ReadNebulaConfig
    .builder()
    .withSpace("exchange")
    .withLabel("knows1")
    .withNoColumn(false)
    .withReturnCols(List("timep"))
    .withLimit(10)
    .withPartitionNum(10)
    .build()

  val vertex = spark.read.nebula(config, nebulaReadVertexConfig).loadVerticesToGraphx()
  val edgeRDD = spark.read.nebula(config, nebulaReadEdgeConfig).loadEdgesToGraphx()
  val graph = Graph(vertexRDD, edgeRDD)

After getting Graphx's Graph, you can develop graph algorithms in Graphx like Nebula-Algorithm.

For more information on usage, please refer to Example.

Version match

There are the version correspondence between Nebula Spark Connector and Nebula:

Nebula Spark Connector Version	Nebula Version
2.0.0	2.0.0, 2.0.1
2.0.1	2.0.0, 2.0.1
2.1.0	2.0.0, 2.0.1
2.5.0	2.5.0, 2.5.1
2.5.1	2.5.0, 2.5.1
2.6.0	2.6.0
2.5-SNAPSHOT	nightly

Performance

We use LDBC dataset to test nebula-spark-connector's performance, here's the result.

• For reader

We choose tag Comment and edge REPLY_OF for space sf30 and sf100 to test the connector reader. And the application's resources are: standalone mode with three workers, 2G driver-memory, 3 num-executors, 30G executor-memory and 20 executor-cores. The ReadNebulaConfig has 2000 limit and 100 partitionNum, the same partition number with nebula space parts.

data type	ldbc 6.712million with No Property	ldbc 22 million with No Property	ldbc 6.712million with All Property	ldbc 22million with All Property
vertex	9.405s	64.611s	13.897s	57.417s
edge	10.798s	71.499s	10.244s	67.43s

• For writer

We choose ldbc comment.csv to write into tag Comment, choose comment_replyOf_post.csv and comment_replyOf_comment.csv to write into edge REPLY_OF. And the application's resources are: standalone mode with three workers, 2G driver-memory, 3 num-executors, 30G executor-memory and 20 executor-cores. The writeConfig has 2000 batch sizes, and the DataFrame has 60 partitions.

data type	ldbc 6.712million with All Property	ldbc 22 million with All Property
vertex	66.578s	112.829s
edge	39.138s	51.198s

Note: ldbc edge for REPLY_OF has no property.

How to Contribute

Nebula Spark Connector 2.0 is a completely opensource project, opensource enthusiasts are welcome to participate in the following ways:

• Go to Nebula Graph Forum to discuss with other users. You can raise your own questions, help others' problems, share your thoughts.
• Write or improve documents.
• Submit code to add new features or fix bugs.