zero323 zero323 - 1 month ago 13x
Python Question

How to use JDBC source to write and read data in (Py)Spark?

The goal of this question is to document:

  • steps required to read and write data using JDBC connections in PySpark

  • possible issues with JDBC sources and know solutions

With small changes these methods should work with other supported languages including Scala and R.


Writing data

  1. Include applicable JDBC driver when you submit the application or start shell. You can use for example --packages:

    bin/pyspark --packages group:name:version  

    or combining driver-class-path and jars

    bin/pyspark --driver-class-path $PATH_TO_DRIVER_JAR --jars $PATH_TO_DRIVER_JAR

    These properties can be also set using PYSPARK_SUBMIT_ARGS environment variable before JVM instance has been started or using conf/spark-defaults.conf to set spark.jars.packages or spark.jars / spark.driver.extraClassPath.

  2. Choose desired mode. Spark JDBC writer supports following modes:

    • append: Append contents of this :class:DataFrame to existing data.
    • overwrite: Overwrite existing data.
    • ignore: Silently ignore this operation if data already exists.
    • error (default case): Throw an exception if data already exists.

    Upserts or other fine-grained modifications are not supported

    mode = ...
  3. Prepare JDBC URI, for example:

    # You can encode credentials in URI or pass
    # separately using properties argument
    # of jdbc method or options
    url = "jdbc:postgresql://localhost/foobar"
  4. (Optional) Create a dictionary of JDBC arguments.

    properties = {
        "user": "foo",
        "password": "bar"
  5. Use DataFrame.write.jdbc

    df.write.jdbc(url=url, table="baz", mode=mode, properties=properties)

    to save the data (see pyspark.sql.DataFrameWriter for details).

Known issues:

  • Suitable driver cannot be found when driver has been included using --packages (java.sql.SQLException: No suitable driver found for jdbc: ...)

    Assuming there is no driver version mismatch to solve this you can add driver class to the properties. For example:

    properties = {
        "driver": "org.postgresql.Driver"
  • using df.write.format("jdbc").options(...).save() may result in:

    java.lang.RuntimeException: org.apache.spark.sql.execution.datasources.jdbc.DefaultSource does not allow create table as select.

    Solution unknown.

  • in Pyspark 1.3 you can try calling Java method directly:

    df._jdf.insertIntoJDBC(url, "baz", True)

Reading data

  1. Follow steps 1-4 from Writing data
  2. Use, table="baz", properties=properties)


        .options(url=url, dbtable="baz", **properties)

Known issues and gotchas:

Where to find suitable drivers: