版本：Next

Paimon

Paimon 数据连接器

描述

Apache Paimon数据连接器。支持cdc写以及自动建表。

支持的数据源信息

数据源	依赖	Maven
Paimon	hive-exec	Download
Paimon	libfb303	Download

数据源依赖

为了兼容不同版本的Hadoop和Hive，在项目pom文件中Hive -exec的作用域为provided，所以如果您使用Flink引擎，首先可能需要将以下Jar包添加到<FLINK_HOME>/lib目录下，如果您使用Spark引擎并与Hadoop集成，则不需要添加以下Jar包。

hive-exec-xxx.jar
libfb303-xxx.jar

有些版本的hive-exec包没有libfb303-xxx.jar，所以您还需要手动导入Jar包。

主要特性

exactly-once

连接器选项

名称	类型	是否必须	默认值	描述
warehouse	字符串	是	-	Paimon warehouse路径
catalog_type	字符串	否	filesystem	Paimon的catalog类型，目前支持filesystem和hive
catalog_uri	字符串	否	-	Paimon catalog的uri，仅当catalog_type为hive时需要配置
database	字符串	是	-	数据库名称
table	字符串	是	-	表名
hdfs_site_path	字符串	否	-	hdfs-site.xml文件路径
schema_save_mode	枚举	否	CREATE_SCHEMA_WHEN_NOT_EXIST	Schema保存模式
data_save_mode	枚举	否	APPEND_DATA	数据保存模式
paimon.table.primary-keys	字符串	否	-	主键字段列表，联合主键使用逗号分隔(注意：分区字段需要包含在主键字段中)
paimon.table.partition-keys	字符串	否	-	分区字段列表，多字段使用逗号分隔
paimon.table.write-props	Map	否	-	Paimon表初始化指定的属性, 参考
paimon.hadoop.conf	Map	否	-	Hadoop配置文件属性信息
paimon.hadoop.conf-path	字符串	否	-	Hadoop配置文件目录，用于加载'core-site.xml', 'hdfs-site.xml', 'hive-site.xml'文件配置

更新日志

你必须配置changelog-producer=input来启用paimon表的changelog产生模式。如果你使用了paimon sink的自动建表功能，你可以在paimon.table.write-props中指定这个属性。

Paimon表的changelog产生模式有四种，分别是none、input、lookup 和 full-compaction。

目前支持全部changelog-producer模式。默认是none模式。

none
input
lookup
full-compaction
注意：当你使用流模式去读paimon表的数据时，不同模式将会产生不同的结果。

文件系统

Paimon连接器支持向多文件系统写入数据。目前支持的文件系统有hdfs和s3。如果您使用s3文件系统。您可以配置fs.s3a.access-key ， fs.s3a.secret-key， fs.s3a.endpoint， fs.s3a.path.style.access， fs.s3a.aws.credentials。在paimon.hadoop.conf选项中设置提供程序的属性。除此之外，warehouse应该以s3a://开头。

示例

单表

env {
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}

source {
  Mysql-CDC {
    base-url = "jdbc:mysql://127.0.0.1:3306/seatunnel"
    username = "root"
    password = "******"
    table-names = ["seatunnel.role"]
  }
}

transform {
}

sink {
  Paimon {
    catalog_name="seatunnel_test"
    warehouse="file:///tmp/seatunnel/paimon/hadoop-sink/"
    database="seatunnel"
    table="role"
  }
}

单表(基于S3文件系统)

env {
  execution.parallelism = 1
  job.mode = "BATCH"
}

source {
  FakeSource {
    schema = {
      fields {
        c_map = "map<string, string>"
        c_array = "array<int>"
        c_string = string
        c_boolean = boolean
        c_tinyint = tinyint
        c_smallint = smallint
        c_int = int
        c_bigint = bigint
        c_float = float
        c_double = double
        c_bytes = bytes
        c_date = date
        c_decimal = "decimal(38, 18)"
        c_timestamp = timestamp
      }
    }
  }
}

sink {
  Paimon {
    warehouse = "s3a://test/"
    database = "seatunnel_namespace11"
    table = "st_test"
    paimon.hadoop.conf = {
        fs.s3a.access-key=G52pnxg67819khOZ9ezX
        fs.s3a.secret-key=SHJuAQqHsLrgZWikvMa3lJf5T0NfM5LMFliJh9HF
        fs.s3a.endpoint="http://minio4:9000"
        fs.s3a.path.style.access=true
        fs.s3a.aws.credentials.provider=org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider
    }
  }
}

单表(指定hadoop HA配置和kerberos配置)

env {
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}

source {
  Mysql-CDC {
    base-url = "jdbc:mysql://127.0.0.1:3306/seatunnel"
    username = "root"
    password = "******"
    table-names = ["seatunnel.role"]
  }
}

transform {
}

sink {
  Paimon {
    catalog_name="seatunnel_test"
    warehouse="hdfs:///tmp/seatunnel/paimon/hadoop-sink/"
    database="seatunnel"
    table="role"
    paimon.hadoop.conf = {
      fs.defaultFS = "hdfs://nameservice1"
      dfs.nameservices = "nameservice1"
      dfs.ha.namenodes.nameservice1 = "nn1,nn2"
      dfs.namenode.rpc-address.nameservice1.nn1 = "hadoop03:8020"
      dfs.namenode.rpc-address.nameservice1.nn2 = "hadoop04:8020"
      dfs.client.failover.proxy.provider.nameservice1 = "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider"
      dfs.client.use.datanode.hostname = "true"
      security.kerberos.login.principal = "your-kerberos-principal"
      security.kerberos.login.keytab = "your-kerberos-keytab-path"
    }
  }
}

单表(使用Hive catalog)

env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  FakeSource {
    schema = {
      fields {
        pk_id = bigint
        name = string
        score = int
      }
      primaryKey {
        name = "pk_id"
        columnNames = [pk_id]
      }
    }
    rows = [
      {
        kind = INSERT
        fields = [1, "A", 100]
      },
      {
        kind = INSERT
        fields = [2, "B", 100]
      },
      {
        kind = INSERT
        fields = [3, "C", 100]
      },
      {
        kind = INSERT
        fields = [3, "C", 100]
      },
      {
        kind = INSERT
        fields = [3, "C", 100]
      },
      {
        kind = INSERT
        fields = [3, "C", 100]
      }
      {
        kind = UPDATE_BEFORE
        fields = [1, "A", 100]
      },
      {
        kind = UPDATE_AFTER
        fields = [1, "A_1", 100]
      },
      {
        kind = DELETE
        fields = [2, "B", 100]
      }
    ]
  }
}

sink {
  Paimon {
    schema_save_mode = "RECREATE_SCHEMA"
    catalog_name="seatunnel_test"
    catalog_type="hive"
    catalog_uri="thrift://hadoop04:9083"
    warehouse="hdfs:///tmp/seatunnel"
    database="seatunnel_test"
    table="st_test3"
    paimon.hadoop.conf = {
      fs.defaultFS = "hdfs://nameservice1"
      dfs.nameservices = "nameservice1"
      dfs.ha.namenodes.nameservice1 = "nn1,nn2"
      dfs.namenode.rpc-address.nameservice1.nn1 = "hadoop03:8020"
      dfs.namenode.rpc-address.nameservice1.nn2 = "hadoop04:8020"
      dfs.client.failover.proxy.provider.nameservice1 = "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider"
      dfs.client.use.datanode.hostname = "true"
    }
  }
}

指定paimon的写属性的单表

env {
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}

source {
  Mysql-CDC {
    base-url = "jdbc:mysql://127.0.0.1:3306/seatunnel"
    username = "root"
    password = "******"
    table-names = ["seatunnel.role"]
  }
}

sink {
  Paimon {
    catalog_name="seatunnel_test"
    warehouse="file:///tmp/seatunnel/paimon/hadoop-sink/"
    database="seatunnel"
    table="role"
    paimon.table.write-props = {
        bucket = 2
        file.format = "parquet"
    }
    paimon.table.partition-keys = "dt"
    paimon.table.primary-keys = "pk_id,dt"
  }
}

使用`changelog-producer`属性写入

env {
 parallelism = 1
 job.mode = "STREAMING"
 checkpoint.interval = 5000
}

source {
 Mysql-CDC {
  base-url = "jdbc:mysql://127.0.0.1:3306/seatunnel"
  username = "root"
  password = "******"
  table-names = ["seatunnel.role"]
 }
}

sink {
 Paimon {
  catalog_name = "seatunnel_test"
  warehouse = "file:///tmp/seatunnel/paimon/hadoop-sink/"
  database = "seatunnel"
  table = "role"
  paimon.table.write-props = {
   changelog-producer = full-compaction
   changelog-tmp-path = /tmp/paimon/changelog
  }
 }
}

动态分桶paimon单表

只有在主键表并指定bucket = -1时才会生效

核心参数：参考官网

名称	类型	是否必须	默认值	描述
dynamic-bucket.target-row-num	long	是	2000000L	控制一个bucket的写入的行数
dynamic-bucket.initial-buckets	int	否		控制初始化桶的数量

env {
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}

source {
  Mysql-CDC {
    base-url = "jdbc:mysql://127.0.0.1:3306/seatunnel"
    username = "root"
    password = "******"
    table-names = ["seatunnel.role"]
  }
}

sink {
  Paimon {
    catalog_name="seatunnel_test"
    warehouse="file:///tmp/seatunnel/paimon/hadoop-sink/"
    database="seatunnel"
    table="role"
    paimon.table.write-props = {
        bucket = -1
        dynamic-bucket.target-row-num = 50000
    }
    paimon.table.partition-keys = "dt"
    paimon.table.primary-keys = "pk_id,dt"
  }
}

多表

示例1

env {
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}

source {
  Mysql-CDC {
    base-url = "jdbc:mysql://127.0.0.1:3306/seatunnel"
    username = "root"
    password = "******"
    table-names = ["seatunnel.role","seatunnel.user","galileo.Bucket"]
  }
}

transform {
}

sink {
  Paimon {
    catalog_name="seatunnel_test"
    warehouse="file:///tmp/seatunnel/paimon/hadoop-sink/"
    database="${database_name}"
    table="${table_name}"
  }
}

示例2

env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  Jdbc {
    driver = oracle.jdbc.driver.OracleDriver
    url = "jdbc:oracle:thin:@localhost:1521/XE"
    user = testUser
    password = testPassword

    table_list = [
      {
        table_path = "TESTSCHEMA.TABLE_1"
      },
      {
        table_path = "TESTSCHEMA.TABLE_2"
      }
    ]
  }
}

transform {
}

sink {
  Paimon {
    catalog_name="seatunnel_test"
    warehouse="file:///tmp/seatunnel/paimon/hadoop-sink/"
    database="${schema_name}_test"
    table="${table_name}_test"
  }
}

Paimon

描述​

支持的数据源信息​

数据源依赖​

主要特性​

连接器选项​

更新日志​

文件系统​

示例​

单表​

单表(基于S3文件系统)​

单表(指定hadoop HA配置和kerberos配置)​

单表(使用Hive catalog)​

指定paimon的写属性的单表​

使用changelog-producer属性写入​

动态分桶paimon单表​

核心参数：参考官网​

多表​

示例1​

示例2​

描述