org.apache.spark.sql.execution

CacheManager

class CacheManager extends Logging

Provides support in a SQLContext for caching query results and automatically using these cached results when subsequent queries are executed. Data is cached using byte buffers stored in an InMemoryRelation. This relation is automatically substituted query plans that return the sameResult as the originally cached query.

Internal to Spark SQL.

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Instance Constructors

  1. new CacheManager()

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def cacheQuery(query: Dataset[_], tableName: Option[String] = None, storageLevel: StorageLevel = MEMORY_AND_DISK): Unit

    Caches the data produced by the logical representation of the given Dataset.

    Caches the data produced by the logical representation of the given Dataset. Unlike RDD.cache(), the default storage level is set to be MEMORY_AND_DISK because recomputing the in-memory columnar representation of the underlying table is expensive.

  8. def clearCache(): Unit

    Clears all cached tables.

  9. def clone(): AnyRef

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  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes
    protected
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    Logging

  16. def isEmpty: Boolean

    Checks if the cache is empty.

  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  18. def isTraceEnabled(): Boolean

    Attributes
    protected
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    Logging

  19. def log: Logger

    Attributes
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    Definition Classes
    Logging

  20. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logDebug(msg: ⇒ String): Unit

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  22. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logError(msg: ⇒ String): Unit

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  24. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  25. def logInfo(msg: ⇒ String): Unit

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  26. def logName: String

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  27. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  28. def logTrace(msg: ⇒ String): Unit

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  29. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  30. def logWarning(msg: ⇒ String): Unit

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  31. def lookupCachedData(plan: LogicalPlan): Option[CachedData]

    Optionally returns cached data for the given LogicalPlan.

  32. def lookupCachedData(query: Dataset[_]): Option[CachedData]

    Optionally returns cached data for the given Dataset

  33. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  34. final def notify(): Unit

    Definition Classes
    AnyRef

  35. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  36. def recacheByPath(spark: SparkSession, resourcePath: String): Unit

    Tries to re-cache all the cache entries that contain resourcePath in one or more HadoopFsRelation node(s) as part of its logical plan.

  37. def recacheByPlan(spark: SparkSession, plan: LogicalPlan): Unit

    Tries to re-cache all the cache entries that refer to the given plan.

  38. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
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  39. def toString(): String

    Definition Classes
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  40. def uncacheQuery(spark: SparkSession, plan: LogicalPlan, blocking: Boolean): Unit

    Un-cache all the cache entries that refer to the given plan.

  41. def uncacheQuery(query: Dataset[_], blocking: Boolean = true): Unit

    Un-cache all the cache entries that refer to the given plan.

  42. def useCachedData(plan: LogicalPlan): LogicalPlan

    Replaces segments of the given logical plan with cached versions where possible.

  43. final def wait(): Unit

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  44. final def wait(arg0: Long, arg1: Int): Unit

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  45. final def wait(arg0: Long): Unit

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Inherited from Logging

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