Import data from Hive

This topic provides an example of how to use Exchange to import NebulaGraph 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 NebulaGraph 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

• NebulaGraph: 3.4.1. 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.

• 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 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: 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: NebulaGraph Exchange 3.4.0
    }
    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"
  #}

  # 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"]
      # 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 Hive.
        source: hive
        # Specify how to import the data into NebulaGraph: 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 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 vertex VID 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: 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 NebulaGraph.
      name: follow

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

        # Specify how to import the Edge type data into NebulaGraph.
        # Specify how to import the data into NebulaGraph: 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 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 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
      }

      # (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: 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
      }

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

      batch: 256
      partition: 32
    }
  ]
}

Step 4: Import data into NebulaGraph

Run the following command to import Hive 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 <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-3.4.0.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 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 6: (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