Skip to content

Import data from Hive

This topic provides an example of how to use Exchange to import Nebula Graph data stored in Hive.

Data set

This topic takes the basketballplayer dataset as an example.

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

scala> spark.sql("describe basketball.player").show
+--------+---------+-------+
|col_name|data_type|comment|
+--------+---------+-------+
|playerid|   string|   null|
|     age|   bigint|   null|
|    name|   string|   null|
+--------+---------+-------+

scala> spark.sql("describe basketball.team").show
+----------+---------+-------+
|  col_name|data_type|comment|
+----------+---------+-------+
|    teamid|   string|   null|
|      name|   string|   null|
+----------+---------+-------+

scala> spark.sql("describe basketball.follow").show
+----------+---------+-------+
|  col_name|data_type|comment|
+----------+---------+-------+
|src_player|   string|   null|
|dst_player|   string|   null|
|    degree|   bigint|   null|
+----------+---------+-------+

scala> spark.sql("describe basketball.serve").show
+----------+---------+-------+
|  col_name|data_type|comment|
+----------+---------+-------+
|  playerid|   string|   null|
|    teamid|   string|   null|
|start_year|   bigint|   null|
|  end_year|   bigint|   null|
+----------+---------+-------+

Note

The Hive data type bigint corresponds to the Nebula Graph int.

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
  • Hive: 2.3.7, Hive Metastore database is MySQL 8.0.22

Prerequisites

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

  • Nebula Graph 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 Nebula Graph.
  • Spark has been installed.
  • Learn about the Schema created in Nebula Graph, including names and properties of Tags and Edge types, and more.
  • Hadoop has been installed and started, and the Hive Metastore database (MySQL in this example) has been started.

Steps

Step 1: Create the Schema in Nebula Graph

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

  1. Identify the Schema elements. The Schema elements in the Nebula Graph 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 Nebula Graph 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: Use Spark SQL to confirm Hive SQL statements

After the Spark-shell environment is started, run the following statements to ensure that Spark can read data in Hive.

scala> sql("select playerid, age, name from basketball.player").show
scala> sql("select teamid, name from basketball.team").show
scala> sql("select src_player, dst_player, degree from basketball.follow").show
scala> sql("select playerid, teamid, start_year, end_year from basketball.serve").show

The following is the result read from the table basketball.player.

+---------+----+-----------------+
| playerid| age|             name|
+---------+----+-----------------+
|player100|  42|       Tim Duncan|
|player101|  36|      Tony Parker|
|player102|  33|LaMarcus Aldridge|
|player103|  32|         Rudy Gay|
|player104|  32|  Marco Belinelli|
+---------+----+-----------------+
...

Step 3: Modify configuration file

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

{
  # Spark configuration
  spark: {
    app: {
      name: Nebula Exchange 2.6.1
    }
    driver: {
      cores: 1
      maxResultSize: 1G
    }
    cores {
      max: 16
    }
  }

  # If Spark and Hive are deployed in different clusters, you need to configure the parameters for connecting to Hive. Otherwise, skip these configurations.
  #hive: {
  #  waredir: "hdfs://NAMENODE_IP:9000/apps/svr/hive-xxx/warehouse/"
  #  connectionURL: "jdbc:mysql://your_ip:3306/hive_spark?characterEncoding=UTF-8"
  #  connectionDriverName: "com.mysql.jdbc.Driver"
  #  connectionUserName: "user"
  #  connectionPassword: "password"
  #}

  # Nebula Graph 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 Nebula Graph space.
    user: root
    pswd: nebula
    # Fill in the name of the graph space you want to write data to in the Nebula Graph.
    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 Nebula Graph.
      name: player
      type: {
        # Specify the data source file format to Hive.
        source: hive
        # Specify how to import the data into Nebula Graph: Client or SST.
        sink: client
      }

      # Set the SQL statement to read the data of player table in basketball database.
      exec: "select playerid, age, name from basketball.player"

      # Specify the column names in the player table in fields, and their corresponding values are specified as properties in the Nebula Graph.
      # 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 vertex VID in the Nebula Graph.
      vertex:{
        field:playerid
      }

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

      # The number of Spark partitions.
      partition: 32
    }
    # Set the information about the Tag Team.
    {
      name: team
      type: {
        source: hive
        sink: client
      }
      exec: "select teamid, name from basketball.team"
      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 Nebula Graph.
      name: follow

      type: {
        # Specify the data source file format to Hive.
        source: hive

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

      # Set the SQL statement to read the data of follow table in the basketball database.
      exec: "select src_player, dst_player, degree from basketball.follow"

      # Specify the column names in the follow table in Fields, and their corresponding values are specified as properties in the Nebula Graph.
      # 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 starting 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
      }

      # The number of data written to Nebula Graph 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: hive
        sink: client
      }
      exec: "select playerid, teamid, start_year, end_year from basketball.serve"
      fields: [start_year,end_year]
      nebula.fields: [start_year,end_year]
      source: {
        field: playerid
      }
      target: {
        field: teamid
      }
      batch: 256
      partition: 32
    }
  ]
}

Step 4: Import data into Nebula Graph

Run the following command to import Hive data into Nebula Graph. 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-2.6.1.jar_path> -c <hive_application.conf_path> -h

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-2.6.1.jar  -c /root/nebula-exchange/nebula-exchange/target/classes/hive_application.conf -h

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 5: (optional) Validate data

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

GO FROM "player100" OVER follow;

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

Step 6: (optional) Rebuild indexes in Nebula Graph

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


Last update: November 1, 2021
Back to top