JDBC PostgreSQL Source Connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Using Dependency

For Spark/Flink Engine

1. You need to ensure that the jdbc driver jar package has been placed in directory ${SEATUNNEL_HOME}/plugins/.

For SeaTunnel Zeta Engine

1. You need to ensure that the jdbc driver jar package has been placed in directory ${SEATUNNEL_HOME}/lib/.

Key Features

• batch
• stream
• exactly-once
• column projection
• parallelism
• support user-defined split

supports query SQL and can achieve projection effect.

Description

Read external data source data through JDBC.

Supported DataSource Info

Database Dependency

Please download the support list corresponding to ‘Maven’ and copy it to the ‘$SEATNUNNEL_HOME/plugins/jdbc/lib/‘ working directory
For example PostgreSQL datasource: cp postgresql-xxx.jar $SEATNUNNEL_HOME/plugins/jdbc/lib/
If you want to manipulate the GEOMETRY type in PostgreSQL, add postgresql-xxx.jar and postgis-jdbc-xxx.jar to $SEATNUNNEL_HOME/plugins/jdbc/lib/

Data Type Mapping

Options

Parallel Reader

The JDBC Source connector supports parallel reading of data from tables. SeaTunnel will use certain rules to split the data in the table, which will be handed over to readers for reading. The number of readers is determined by the parallelism option.

Split Key Rules:

1. If partition_column is not null, It will be used to calculate split. The column must in Supported split data type.
2. If partition_column is null, seatunnel will read the schema from table and get the Primary Key and Unique Index. If there are more than one column in Primary Key and Unique Index, The first column which in the supported split data type will be used to split data. For example, the table have Primary Key(nn guid, name varchar), because guid id not in supported split data type, so the column name will be used to split data.

Supported split data type:

• String
• Number(int, bigint, decimal, …)
• Date

Options Related To Split

split.size

How many rows in one split, captured tables are split into multiple splits when read of table.

split.even-distribution.factor.lower-bound

Not recommended for use

The lower bound of the chunk key distribution factor. This factor is used to determine whether the table data is evenly distributed. If the distribution factor is calculated to be greater than or equal to this lower bound (i.e., (MAX(id) - MIN(id) + 1) / row count), the table chunks would be optimized for even distribution. Otherwise, if the distribution factor is less, the table will be considered as unevenly distributed and the sampling-based sharding strategy will be used if the estimated shard count exceeds the value specified by sample-sharding.threshold. The default value is 0.05.

split.even-distribution.factor.upper-bound

Not recommended for use

The upper bound of the chunk key distribution factor. This factor is used to determine whether the table data is evenly distributed. If the distribution factor is calculated to be less than or equal to this upper bound (i.e., (MAX(id) - MIN(id) + 1) / row count), the table chunks would be optimized for even distribution. Otherwise, if the distribution factor is greater, the table will be considered as unevenly distributed and the sampling-based sharding strategy will be used if the estimated shard count exceeds the value specified by sample-sharding.threshold. The default value is 100.0.

split.sample-sharding.threshold

This configuration specifies the threshold of estimated shard count to trigger the sample sharding strategy. When the distribution factor is outside the bounds specified by chunk-key.even-distribution.factor.upper-bound and chunk-key.even-distribution.factor.lower-bound, and the estimated shard count (calculated as approximate row count / chunk size) exceeds this threshold, the sample sharding strategy will be used. This can help to handle large datasets more efficiently. The default value is 1000 shards.

split.inverse-sampling.rate

The inverse of the sampling rate used in the sample sharding strategy. For example, if this value is set to 1000, it means a 1/1000 sampling rate is applied during the sampling process. This option provides flexibility in controlling the granularity of the sampling, thus affecting the final number of shards. It’s especially useful when dealing with very large datasets where a lower sampling rate is preferred. The default value is 1000.

partition_column [string]

The column name for split data.

partition_upper_bound [BigDecimal]

The partition_column max value for scan, if not set SeaTunnel will query database get max value.

partition_lower_bound [BigDecimal]

The partition_column min value for scan, if not set SeaTunnel will query database get min value.

partition_num [int]

Not recommended for use, The correct approach is to control the number of split through split.size

How many splits do we need to split into, only support positive integer. default value is job parallelism.

tips

If the table can not be split(for example, table have no Primary Key or Unique Index, and partition_column is not set), it will run in single concurrency.

Use table_path to replace query for single table reading. If you need to read multiple tables, use table_list.

Task Example

Simple:

This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console.

# Defining the runtime environment
env {
  parallelism = 4
  job.mode = "BATCH"
}
source{
    Jdbc {
        url = "jdbc:postgresql://localhost:5432/test"
        driver = "org.postgresql.Driver"
        user = "root"
        password = "test"
        query = "select * from source limit 16"
    }
}
transform {
    # please go to https://seatunnel.apache.org/docs/transform-v2/sql
}
sink {
    Console {}
}

parallel by partition_column

Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table

env {
  parallelism = 4
  job.mode = "BATCH"
}
source{
    jdbc{
        url = "jdbc:postgresql://localhost:5432/test"
        driver = "org.postgresql.Driver"
        user = "root"
        password = "test"
        query = "select * from source"
        partition_column= "id"
        partition_num = 5
    }
}
sink {
  Console {}
}

parallel by Primary Key or Unique Index

Configuring table_path will turn on auto split, you can configure split.* to adjust the split strategy

env {
  parallelism = 4
  job.mode = "BATCH"
}
source {
    Jdbc {
        url = "jdbc:postgresql://localhost:5432/test"
        driver = "org.postgresql.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        table_path = "test.public.AllDataType_1"
        query = "select * from public.AllDataType_1"
        split.size = 10000
    }
}
sink {
  Console {}
}

Parallel Boundary:

It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured

source{
    jdbc{
        url = "jdbc:postgresql://localhost:5432/test"
        driver = "org.postgresql.Driver"
        user = "root"
        password = "test"
        query = "select * from source"
        partition_column= "id"
        # The name of the table returned
        result_table_name = "jdbc"
        partition_lower_bound = 1
        partition_upper_bound = 50
        partition_num = 5
    }
}

Multiple table read:

Configuring table_list will turn on auto split, you can configure `split.` to adjust the split strategy*

env {
  job.mode = "BATCH"
  parallelism = 4
}
source {
  Jdbc {
    url="jdbc:postgresql://datasource01:5432/demo"
    user="iDm82k6Q0Tq+wUprWnPsLQ=="
    driver="org.postgresql.Driver"
    password="iDm82k6Q0Tq+wUprWnPsLQ=="
    "table_list"=[
        {
            "table_path"="demo.public.AllDataType_1"
        },
        {
            "table_path"="demo.public.alldatatype"
        }
    ]
    #where_condition= "where id > 100"
    split.size = 10000
    #split.even-distribution.factor.upper-bound = 100
    #split.even-distribution.factor.lower-bound = 0.05
    #split.sample-sharding.threshold = 1000
    #split.inverse-sampling.rate = 1000
  }
}
sink {
  Console {}
}