Import data from general JDBC

JDBC data refers to the data of various databases accessed through the JDBC interface. This topic provides an example of how to use Exchange to export MySQL data and import to NebulaGraph.

Data set

This topic takes the basketballplayer dataset as an example.

In this example, the data set has been stored in MySQL. All vertexes and edges are stored in the player, team, follow, and serve tables. The following are some of the data for each table.

mysql> desc player;
+----------+-------------+------+-----+---------+-------+
| Field    | Type        | Null | Key | Default | Extra |
+----------+-------------+------+-----+---------+-------+
| playerid | int         | YES  |     | NULL    |       |
| age      | int         | YES  |     | NULL    |       |
| name     | varchar(30) | YES  |     | NULL    |       |
+----------+-------------+------+-----+---------+-------+

mysql> desc team;
+--------+-------------+------+-----+---------+-------+
| Field  | Type        | Null | Key | Default | Extra |
+--------+-------------+------+-----+---------+-------+
| teamid | int         | YES  |     | NULL    |       |
| name   | varchar(30) | YES  |     | NULL    |       |
+--------+-------------+------+-----+---------+-------+

mysql> desc follow;
+------------+-------------+------+-----+---------+-------+
| Field      | Type        | Null | Key | Default | Extra |
+------------+-------------+------+-----+---------+-------+
| src_player | int         | YES  |     | NULL    |       |
| dst_player | int         | YES  |     | NULL    |       |
| degree     | int         | YES  |     | NULL    |       |
+------------+-------------+------+-----+---------+-------+

mysql> desc serve;
+------------+-------------+------+-----+---------+-------+
| Field      | Type        | Null | Key | Default | Extra |
+------------+-------------+------+-----+---------+-------+
| playerid   | int         | YES  |     | NULL    |       |
| teamid     | int         | YES  |     | NULL    |       |
| start_year | int         | YES  |     | NULL    |       |
| end_year   | int         | YES  |     | NULL    |       |
+------------+-------------+------+-----+---------+-------+

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

• Hadoop: 2.9.2, pseudo-distributed deployment

• MySQL: 8.0.23

• NebulaGraph: 3.4.0. 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 Hadoop 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

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

{
  # Spark configuration
  spark: {
    app: {
      name: NebulaGraph Exchange 3.4.0
    }
    driver: {
      cores: 1
      maxResultSize: 1G
    }
    cores: {
      max: 16
    }
  }

  # NebulaGraph configuration
  nebula: {
    address:{
      # Specify the IP addresses and ports for Graph and 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"]
      # the address of any of the meta services.
      # if your NebulaGraph server is in virtual network like k8s, please config the leader address of meta.
      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 Tag name in NebulaGraph.
      name: player
      type: {
        # Specify the data source file format to JDBC.
        source: jdbc
        # Specify how to import the data into NebulaGraph: Client or SST.
        sink: client
      }

      # URL of the JDBC data source. The example is MySql database.
      url:"jdbc:mysql://127.0.0.1:3306/basketball?useUnicode=true&characterEncoding=utf-8"

      # JDBC driver 
      driver:"com.mysql.cj.jdbc.Driver"

      # Database user name and password
      user:root
      password:"12345"

      table:player
      sentence:"select playerid, age, name from player order by playerid"

      # (optional)Multiple connections read parameters. See https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html
      partitionColumn:playerid    # optional. Must be a numeric, date, or timestamp column from the table in question.
      lowerBound:1                # optional
      upperBound:5                # optional
      numPartitions:5             # optional


      fetchSize:2           # The JDBC fetch size, which determines how many rows to fetch per round trip.

      # Specify the column names in the player table in fields, and their corresponding values are specified as properties in the NebulaGraph.
      # The sequence of fields and nebula.fields must correspond to each other.
      # If multiple column names need to be specified, separate them by commas.
      fields: [age,name]
      nebula.fields: [age,name]

      # Specify a column of data in the table as the source of VIDs in the NebulaGraph.
      vertex: {
        field:playerid
      }

      # The number of data written to NebulaGraph in a single batch.
      batch: 256

      # The number of Spark partitions.
      partition: 32
    }
    # Set the information about the Tag Team.
    {
      name: team
      type: {
        source: jdbc
        sink: client
      }

      url:"jdbc:mysql://127.0.0.1:3306/basketball?useUnicode=true&characterEncoding=utf-8"
      driver:"com.mysql.cj.jdbc.Driver"
      user:root
      password:"12345"
      table:team
      sentence:"select teamid, name from team order by teamid"
      partitionColumn:teamid    
      lowerBound:1                
      upperBound:5                
      numPartitions:5             
      fetchSize:2  

      fields: [name]
      nebula.fields: [name]
      vertex: {
        field: teamid
      }
      batch: 256
      partition: 32
    }

  ]

  # 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 JDBC.
        source: jdbc

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

      url:"jdbc:mysql://127.0.0.1:3306/basketball?useUnicode=true&characterEncoding=utf-8"
      driver:"com.mysql.cj.jdbc.Driver"
      user:root
      password:"12345"
      table:follow
      sentence:"select src_player,dst_player,degree from follow order by src_player"
      partitionColumn:src_player    
      lowerBound:1                
      upperBound:5                
      numPartitions:5             
      fetchSize:2  

      # Specify the column names in the follow table in fields, and their corresponding values are specified as properties in the NebulaGraph.
      # The sequence of fields and nebula.fields must correspond to each other.
      # If multiple column names need to be specified, separate them by commas.
      fields: [degree]
      nebula.fields: [degree]

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

      target: {
        field: dst_player
      }

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

      # The number of data written to NebulaGraph in a single batch.
      batch: 256

      # The number of Spark partitions.
      partition: 32
    }

    # Set the information about the Edge Type serve.
    {
      name: serve
      type: {
        source: jdbc
        sink: client
      }

      url:"jdbc:mysql://127.0.0.1:3306/basketball?useUnicode=true&characterEncoding=utf-8"
      driver:"com.mysql.cj.jdbc.Driver"
      user:root
      password:"12345"
      table:serve
      sentence:"select playerid,teamid,start_year,end_year from serve order by playerid"
      partitionColumn:playerid    
      lowerBound:1                
      upperBound:5                
      numPartitions:5             
      fetchSize:2

      fields: [start_year,end_year]
      nebula.fields: [start_year,end_year]
      source: {
        field: playerid
      }
      target: {
        field: teamid
      }
      batch: 256
      partition: 32
    }
  ]
}

Step 3: Import data into NebulaGraph

Run the following command to import general JDBC 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-3.4.0.jar_path> -c <jdbc_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-3.4.0.jar  -c /root/nebula-exchange/nebula-exchange/target/classes/jdbc_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:

LOOKUP ON player YIELD id(vertex);

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 19, 2024