Import data from Kafka

This topic provides a simple guide to importing Data stored on Kafka into NebulaGraph using Exchange.

Environment

This example is done on MacOS. Here is the environment configuration information:

• Hardware specifications:
  • CPU: 1.7 GHz Quad-Core Intel Core i7
  • Memory: 16 GB

• Spark: 2.4.7, stand-alone

• NebulaGraph: 3.2.1. Deploy NebulaGraph with Docker Compose.

Prerequisites

Before importing data, you need to confirm the following information:

• NebulaGraph has been installed and deployed with the following information:

  • IP addresses and ports of Graph and Meta services.
  • The user name and password with write permission to NebulaGraph.

• Exchange has been compiled, or download the compiled .jar file directly.

• Spark has been installed.

• Learn about the Schema created in NebulaGraph, including names and properties of Tags and Edge types, and more.

• The Kafka service has been installed and started.

Steps

Step 1: Create the Schema in NebulaGraph

Analyze the data to create a Schema in NebulaGraph by following these steps:

1. Identify the Schema elements. The Schema elements in the NebulaGraph are shown in the following table.

   Element	Name	Property
   Tag	player	name string, age int
   Tag	team	name string
   Edge Type	follow	degree int
   Edge Type	serve	start_year int, end_year int

2. Create a graph space basketballplayer in the NebulaGraph and create a Schema as shown below.

   ## Create a graph space.
   nebula> CREATE SPACE basketballplayer \
           (partition_num = 10, \
           replica_factor = 1, \
           vid_type = FIXED_STRING(30));
   
   ## Use the graph space basketballplayer.
   nebula> USE basketballplayer;
   
   ## Create the Tag player.
   nebula> CREATE TAG player(name string, age int);
   
   ## Create the Tag team.
   nebula> CREATE TAG team(name string);
   
   ## Create the Edge type follow.
   nebula> CREATE EDGE follow(degree int);
   
   ## Create the Edge type serve.
   nebula> CREATE EDGE serve(start_year int, end_year int);
   

For more information, see Quick start workflow.

Step 2: Modify configuration files

Note

If some data is stored in Kafka's value field, you need to modify the source code, get the value from Kafka, parse the value through the from_JSON function, and return it as a Dataframe.

After Exchange is compiled, copy the conf file target/classes/application.conf to set Kafka data source configuration. In this example, the copied file is called kafka_application.conf. For details on each configuration item, see Parameters in the configuration file.

{
  # Spark configuration
  spark: {
    app: {
      name: Nebula Exchange 3.0.0
    }
    driver: {
      cores: 1
      maxResultSize: 1G
    }
    cores: {
      max: 16
    }
  }


  # NebulaGraph configuration
  nebula: {
    address:{
      # Specify the IP addresses and ports for Graph and all Meta services.
      # If there are multiple addresses, the format is "ip1:port","ip2:port","ip3:port".
      # Addresses are separated by commas.
      graph:["127.0.0.1:9669"]
      meta:["127.0.0.1:9559"]
    }
    # The account entered must have write permission for the NebulaGraph space.
    user: root
    pswd: nebula
    # Fill in the name of the graph space you want to write data to in the NebulaGraph.
    space: basketballplayer
    connection: {
      timeout: 3000
      retry: 3
    }
    execution: {
      retry: 3
    }
    error: {
      max: 32
      output: /tmp/errors
    }
    rate: {
      limit: 1024
      timeout: 1000
    }
  }
  # Processing vertexes
  tags: [
    # Set the information about the Tag player.
    {

      # The corresponding Tag name in NebulaGraph.
      name: player
      type: {
        # Specify the data source file format to Kafka.
        source: kafka
        # Specify how to import the data into NebulaGraph: Client or SST.
        sink: client
      }
      # Kafka server address.
      service: "127.0.0.1:9092"
      # Message category.
      topic: "topic_name1"

      # Kafka data has a fixed domain name: key, value, topic, partition, offset, timestamp, timestampType.
      # If multiple fields need to be specified after Spark reads as DataFrame, separate them with commas.
      # Specify the field name in fields. For example, use key for name in NebulaGraph and value for age in Nebula, as shown in the following.
      fields: [key,value]
      nebula.fields: [name,age]

      # Specify a column of data in the table as the source of vertex VID in the NebulaGraph.
      # The key is the same as the value above, indicating that key is used as both VID and property name.
      vertex:{
          field:key
      }


      # The number of data written to NebulaGraph in a single batch.
      batch: 10

      # The number of Spark partitions.
      partition: 10
      # The interval for message reading. Unit: second.
      interval.seconds: 10
    }
    # Set the information about the Tag Team.
    {
      name: team
      type: {
        source: kafka
        sink: client
      }
      service: "127.0.0.1:9092"
      topic: "topic_name2"
      fields: [key]
      nebula.fields: [name]
      vertex:{
          field:key
      }
      batch: 10
      partition: 10
      interval.seconds: 10
    }

  ]

  # Processing edges
  edges: [
    # Set the information about the Edge Type follow.
    {
      # The corresponding Edge Type name in NebulaGraph.
      name: follow

      type: {
        # Specify the data source file format to Kafka.
        source: kafka

        # Specify how to import the Edge type data into NebulaGraph.
        # Specify how to import the data into NebulaGraph: Client or SST.
        sink: client
      }

      # Kafka server address.
      service: "127.0.0.1:9092"
      # Message category.
      topic: "topic_name3"

      # Kafka data has a fixed domain name: key, value, topic, partition, offset, timestamp, timestampType.
      # If multiple fields need to be specified after Spark reads as DataFrame, separate them with commas.
      # Specify the field name in fields. For example, use key for degree in Nebula, as shown in the following.
      fields: [key]
      nebula.fields: [degree]

      # In source, use a column in the topic as the source of the edge's source vertex.
      # In target, use a column in the topic as the source of the edge's destination vertex.
      source:{
          field:timestamp
      }


      target:{
          field:offset
      }

      # (Optional) Specify a column as the source of the rank.
      #ranking: rank

      # The number of data written to NebulaGraph in a single batch.
      batch: 10

      # The number of Spark partitions.
      partition: 10

      # The interval for message reading. Unit: second.
      interval.seconds: 10
    }

    # Set the information about the Edge Type serve.
    {
      name: serve
      type: {
        source: kafka
        sink: client
      }
      service: "127.0.0.1:9092"
      topic: "topic_name4"

      fields: [timestamp,offset]
      nebula.fields: [start_year,end_year]
      source:{
          field:key
      }

      target:{
          field:value
      }

      # (Optional) Specify a column as the source of the rank.
      #ranking: rank

      batch: 10
      partition: 10
      interval.seconds: 10
    }
  ]
}

Step 3: Import data into NebulaGraph

Run the following command to import Kafka data into NebulaGraph. For a description of the parameters, see Options for import.

${SPARK_HOME}/bin/spark-submit --master "local" --class com.vesoft.nebula.exchange.Exchange <nebula-exchange-3.0.0.jar_path> -c <kafka_application.conf_path>

Note

JAR packages are available in two ways: compiled them yourself, or download the compiled .jar file directly.

For example:

${SPARK_HOME}/bin/spark-submit  --master "local" --class com.vesoft.nebula.exchange.Exchange  /root/nebula-exchange/nebula-exchange/target/nebula-exchange-3.0.0.jar  -c /root/nebula-exchange/nebula-exchange/target/classes/kafka_application.conf

You can search for batchSuccess.<tag_name/edge_name> in the command output to check the number of successes. For example, batchSuccess.follow: 300.

Step 4: (optional) Validate data

Users can verify that data has been imported by executing a query in the NebulaGraph client (for example, NebulaGraph Studio). For example:

GO FROM "player100" OVER follow;

Users can also run the SHOW STATS command to view statistics.

Step 5: (optional) Rebuild indexes in NebulaGraph

With the data imported, users can recreate and rebuild indexes in NebulaGraph. For details, see Index overview.

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Last update: February 1, 2023