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JDBCRelation is a BaseRelation with support for column pruning with filter pushdown and inserting or overwriting data.

JDBCRelation in web UI (Details for Query)


JDBCRelation is an PrunedFilteredScan and supports supports column pruning with filter pushdown.


JDBCRelation is an InsertableRelation and supports inserting or overwriting data.

Creating Instance

JDBCRelation takes the following to be created:

JDBCRelation is created (possibly using apply) when:

Creating JDBCRelation

  parts: Array[Partition],
  jdbcOptions: JDBCOptions)(
  sparkSession: SparkSession): JDBCRelation

apply gets the schema (establishing a connection to the database system directly) and creates a JDBCRelation.


  resolver: Resolver,
  jdbcOptions: JDBCOptions): StructType

getSchema resolves the table (from the given JDBCOptions).

With the customSchema option specified, getSchema gets the custom schema (based on the table schema from the database system). Otherwise, getSchema returns the table schema from the database system.

getSchema is used when:


  schema: StructType,
  resolver: Resolver,
  timeZoneId: String,
  jdbcOptions: JDBCOptions): Array[Partition]


In the end, columnPartition prints out the following INFO message to the logs:

Number of partitions: [numPartitions], WHERE clauses of these partitions:

columnPartition is used when:


Enable ALL logging level for org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation logger to see what happens inside.

Add the following line to conf/

Refer to Logging.

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[[toString]] When requested for a human-friendly text representation, JDBCRelation requests the <> for the name of the table and the <> (if defined).

JDBCRelation([table]) [numPartitions=[number]]
scala> df.explain
== Physical Plan ==
*Scan JDBCRelation(projects) [numPartitions=1] [id#0,name#1,website#2] ReadSchema: struct<id:int,name:string,website:string>

[[needConversion]] JDBCRelation turns the needConversion flag off (to announce that <> returns an RDD[InternalRow] already and DataSourceStrategy execution planning strategy does not have to do the RDD conversion).

=== [[unhandledFilters]] Finding Unhandled Filter Predicates -- unhandledFilters Method

[source, scala]

unhandledFilters(filters: Array[Filter]): Array[Filter]

unhandledFilters is part of BaseRelation abstraction.

unhandledFilters returns the Filter predicates in the input filters that could not be converted to a SQL expression (and are therefore unhandled by the JDBC data source natively).

=== [[schema]] Schema of Tuples (Data) -- schema Property

[source, scala]

schema: StructType

schema uses JDBCRDD to resolveTable given the JDBCOptions (that simply returns the schema of the table, also known as the default table schema).

If customSchema JDBC option was defined, schema uses JdbcUtils to replace the data types in the default table schema.

schema is part of BaseRelation abstraction.

=== [[insert]] Inserting or Overwriting Data to JDBC Table -- insert Method

[source, scala]

insert(data: DataFrame, overwrite: Boolean): Unit

insert is part of the InsertableRelation abstraction.

insert simply requests the input DataFrame for a <> that in turn is requested to save the data to a table using the JDBC data source (itself!) with the url, table and all options.

insert also requests the DataFrameWriter to set the save mode as Overwrite or Append per the input overwrite flag.


insert uses a "trick" to reuse a code that is responsible for saving data to a JDBC table.

=== [[buildScan]] Building Distributed Data Scan with Column Pruning and Filter Pushdown -- buildScan Method

[source, scala]

buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row]

buildScan is part of the PrunedFilteredScan abstraction.

buildScan uses the JDBCRDD object to create a RDD[Row] for a distributed data scan.