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org.apache.spark.sql

SparkSessionExtensions

class SparkSessionExtensions extends AnyRef

ExperimentalDeveloper API

Holder for injection points to the SparkSession. We make NO guarantee about the stability regarding binary compatibility and source compatibility of methods here.

This current provides the following extension points:

  • Analyzer Rules.
  • Check Analysis Rules.
  • Optimizer Rules.
  • Planning Strategies.
  • Customized Parser.
  • (External) Catalog listeners.

The extensions can be used by calling withExtension on the SparkSession.Builder, for example:

SparkSession.builder()
  .master("...")
  .conf("...", true)
  .withExtensions { extensions =>
    extensions.injectResolutionRule { session =>
      ...
    }
    extensions.injectParser { (session, parser) =>
      ...
    }
  }
  .getOrCreate()

Note that none of the injected builders should assume that the SparkSession is fully initialized and should not touch the session's internals (e.g. the SessionState).

Annotations
@DeveloperApi() @Experimental() @Unstable()
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Instance Constructors

  1. new SparkSessionExtensions()

Type Members

  1. type CheckRuleBuilder = (SparkSession) ⇒ (LogicalPlan) ⇒ Unit
  2. type FunctionDescription = (FunctionIdentifier, ExpressionInfo, FunctionBuilder)
  3. type ParserBuilder = (SparkSession, ParserInterface) ⇒ ParserInterface
  4. type RuleBuilder = (SparkSession) ⇒ Rule[LogicalPlan]
  5. type StrategyBuilder = (SparkSession) ⇒ Strategy

Value Members

  1. def injectCheckRule(builder: CheckRuleBuilder): Unit

    Inject an check analysis Rule builder into the SparkSession.

    Inject an check analysis Rule builder into the SparkSession. The injected rules will be executed after the analysis phase. A check analysis rule is used to detect problems with a LogicalPlan and should throw an exception when a problem is found.

  2. def injectFunction(functionDescription: FunctionDescription): Unit

    Injects a custom function into the org.apache.spark.sql.catalyst.analysis.FunctionRegistry at runtime for all sessions.

  3. def injectOptimizerRule(builder: RuleBuilder): Unit

    Inject an optimizer Rule builder into the SparkSession.

    Inject an optimizer Rule builder into the SparkSession. The injected rules will be executed during the operator optimization batch. An optimizer rule is used to improve the quality of an analyzed logical plan; these rules should never modify the result of the LogicalPlan.

  4. def injectParser(builder: ParserBuilder): Unit

    Inject a custom parser into the SparkSession.

    Inject a custom parser into the SparkSession. Note that the builder is passed a session and an initial parser. The latter allows for a user to create a partial parser and to delegate to the underlying parser for completeness. If a user injects more parsers, then the parsers are stacked on top of each other.

  5. def injectPlannerStrategy(builder: StrategyBuilder): Unit

    Inject a planner Strategy builder into the SparkSession.

    Inject a planner Strategy builder into the SparkSession. The injected strategy will be used to convert a LogicalPlan into a executable org.apache.spark.sql.execution.SparkPlan.

  6. def injectPostHocResolutionRule(builder: RuleBuilder): Unit

    Inject an analyzer Rule builder into the SparkSession.

    Inject an analyzer Rule builder into the SparkSession. These analyzer rules will be executed after resolution.

  7. def injectResolutionRule(builder: RuleBuilder): Unit

    Inject an analyzer resolution Rule builder into the SparkSession.

    Inject an analyzer resolution Rule builder into the SparkSession. These analyzer rules will be executed as part of the resolution phase of analysis.