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c

org.apache.spark.sql.columnar

SimpleMetricsCachedBatchSerializer

abstract class SimpleMetricsCachedBatchSerializer extends CachedBatchSerializer with Logging

Provides basic filtering for CachedBatchSerializer implementations. The requirement to extend this is that all of the batches produced by your serializer are instances of SimpleMetricsCachedBatch. This does not calculate the metrics needed to be stored in the batches. That is up to each implementation. The metrics required are really just min and max values and those are optional especially for complex types. Because those metrics are simple and it is likely that compression will also be done on the data we thought it best to let each implementation decide on the most efficient way to calculate the metrics, possibly combining them with compression passes that might also be done across the data.

Annotations
@DeveloperApi() @Since("3.1.0")
Source
CachedBatchSerializer.scala
Linear Supertypes
Logging, CachedBatchSerializer, Serializable, AnyRef, Any
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  1. SimpleMetricsCachedBatchSerializer
  2. Logging
  3. CachedBatchSerializer
  4. Serializable
  5. AnyRef
  6. Any
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Instance Constructors

  1. new SimpleMetricsCachedBatchSerializer()

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Abstract Value Members

  1. abstract def convertCachedBatchToColumnarBatch(input: RDD[CachedBatch], cacheAttributes: Seq[Attribute], selectedAttributes: Seq[Attribute], conf: SQLConf): RDD[ColumnarBatch]

    Convert the cached data into a ColumnarBatch.

    Convert the cached data into a ColumnarBatch. This currently is only used if supportsColumnarOutput() returns true for the associated schema, but there are other checks that can force row based output. One of the main advantages of doing columnar output over row based output is that the code generation is more standard and can be combined with code generation for downstream operations.

    input

    the cached batches that should be converted.

    cacheAttributes

    the attributes of the data in the batch.

    selectedAttributes

    the fields that should be loaded from the data and the order they should appear in the output batch.

    conf

    the configuration for the job.

    returns

    an RDD of the input cached batches transformed into the ColumnarBatch format.

    Definition Classes
    CachedBatchSerializer
  2. abstract def convertCachedBatchToInternalRow(input: RDD[CachedBatch], cacheAttributes: Seq[Attribute], selectedAttributes: Seq[Attribute], conf: SQLConf): RDD[InternalRow]

    Convert the cached batch into InternalRows.

    Convert the cached batch into InternalRows. If you want this to be performant, code generation is advised.

    input

    the cached batches that should be converted.

    cacheAttributes

    the attributes of the data in the batch.

    selectedAttributes

    the field that should be loaded from the data and the order they should appear in the output rows.

    conf

    the configuration for the job.

    returns

    RDD of the rows that were stored in the cached batches.

    Definition Classes
    CachedBatchSerializer
  3. abstract def convertColumnarBatchToCachedBatch(input: RDD[ColumnarBatch], schema: Seq[Attribute], storageLevel: StorageLevel, conf: SQLConf): RDD[CachedBatch]

    Convert an RDD[ColumnarBatch] into an RDD[CachedBatch] in preparation for caching the data.

    Convert an RDD[ColumnarBatch] into an RDD[CachedBatch] in preparation for caching the data. This will only be called if supportsColumnarInput() returned true for the given schema and the plan up to this point would could produce columnar output without modifying it.

    input

    the input RDD to be converted.

    schema

    the schema of the data being stored.

    storageLevel

    where the data will be stored.

    conf

    the config for the query.

    returns

    The data converted into a format more suitable for caching.

    Definition Classes
    CachedBatchSerializer
  4. abstract def convertInternalRowToCachedBatch(input: RDD[InternalRow], schema: Seq[Attribute], storageLevel: StorageLevel, conf: SQLConf): RDD[CachedBatch]

    Convert an RDD[InternalRow] into an RDD[CachedBatch] in preparation for caching the data.

    Convert an RDD[InternalRow] into an RDD[CachedBatch] in preparation for caching the data.

    input

    the input RDD to be converted.

    schema

    the schema of the data being stored.

    storageLevel

    where the data will be stored.

    conf

    the config for the query.

    returns

    The data converted into a format more suitable for caching.

    Definition Classes
    CachedBatchSerializer
  5. abstract def supportsColumnarInput(schema: Seq[Attribute]): Boolean

    Can convertColumnarBatchToCachedBatch() be called instead of convertInternalRowToCachedBatch() for this given schema? True if it can and false if it cannot.

    Can convertColumnarBatchToCachedBatch() be called instead of convertInternalRowToCachedBatch() for this given schema? True if it can and false if it cannot. Columnar input is only supported if the plan could produce columnar output. Currently this is mostly supported by input formats like parquet and orc, but more operations are likely to be supported soon.

    schema

    the schema of the data being stored.

    returns

    True if columnar input can be supported, else false.

    Definition Classes
    CachedBatchSerializer
  6. abstract def supportsColumnarOutput(schema: StructType): Boolean

    Can convertCachedBatchToColumnarBatch() be called instead of convertCachedBatchToInternalRow() for this given schema? True if it can and false if it cannot.

    Can convertCachedBatchToColumnarBatch() be called instead of convertCachedBatchToInternalRow() for this given schema? True if it can and false if it cannot. Columnar output is typically preferred because it is more efficient. Note that convertCachedBatchToInternalRow() must always be supported as there are other checks that can force row based output.

    schema

    the schema of the data being checked.

    returns

    true if columnar output should be used for this schema, else false.

    Definition Classes
    CachedBatchSerializer

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def buildFilter(predicates: Seq[Expression], cachedAttributes: Seq[Attribute]): (Int, Iterator[CachedBatch]) => Iterator[CachedBatch]

    Builds a function that can be used to filter batches prior to being decompressed.

    Builds a function that can be used to filter batches prior to being decompressed. In most cases extending SimpleMetricsCachedBatchSerializer will provide the filter logic necessary. You will need to provide metrics for this to work. SimpleMetricsCachedBatch provides the APIs to hold those metrics and explains the metrics used, really just min and max. Note that this is intended to skip batches that are not needed, and the actual filtering of individual rows is handled later.

    predicates

    the set of expressions to use for filtering.

    cachedAttributes

    the schema/attributes of the data that is cached. This can be helpful if you don't store it with the data.

    returns

    a function that takes the partition id and the iterator of batches in the partition. It returns an iterator of batches that should be decompressed.

    Definition Classes
    SimpleMetricsCachedBatchSerializer → CachedBatchSerializer
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
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    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: AnyRef): Boolean
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  9. final def getClass(): Class[_ <: AnyRef]
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    @IntrinsicCandidate() @native()
  10. def hashCode(): Int
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  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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    protected
    Definition Classes
    Logging
  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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    protected
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  13. final def isInstanceOf[T0]: Boolean
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  14. def isTraceEnabled(): Boolean
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  15. def log: Logger
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  16. def logDebug(msg: => String, throwable: Throwable): Unit
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  17. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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  18. def logDebug(entry: LogEntry): Unit
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  19. def logDebug(msg: => String): Unit
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  20. def logError(msg: => String, throwable: Throwable): Unit
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  21. def logError(entry: LogEntry, throwable: Throwable): Unit
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  22. def logError(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
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  26. def logInfo(entry: LogEntry): Unit
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  27. def logInfo(msg: => String): Unit
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  28. def logName: String
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  29. def logTrace(msg: => String, throwable: Throwable): Unit
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  30. def logTrace(entry: LogEntry, throwable: Throwable): Unit
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  31. def logTrace(entry: LogEntry): Unit
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  32. def logTrace(msg: => String): Unit
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  33. def logWarning(msg: => String, throwable: Throwable): Unit
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  34. def logWarning(entry: LogEntry, throwable: Throwable): Unit
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  35. def logWarning(entry: LogEntry): Unit
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  36. def logWarning(msg: => String): Unit
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  37. final def ne(arg0: AnyRef): Boolean
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  38. final def notify(): Unit
    Definition Classes
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    @IntrinsicCandidate() @native()
  39. final def notifyAll(): Unit
    Definition Classes
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    @IntrinsicCandidate() @native()
  40. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
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  41. def toString(): String
    Definition Classes
    AnyRef → Any
  42. def vectorTypes(attributes: Seq[Attribute], conf: SQLConf): Option[Seq[String]]

    The exact java types of the columns that are output in columnar processing mode.

    The exact java types of the columns that are output in columnar processing mode. This is a performance optimization for code generation and is optional.

    attributes

    the attributes to be output.

    conf

    the config for the query that will read the data.

    Definition Classes
    CachedBatchSerializer
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  44. final def wait(arg0: Long): Unit
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    @throws(classOf[java.lang.InterruptedException]) @native()
  45. final def wait(): Unit
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  46. def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
    Attributes
    protected
    Definition Classes
    Logging

Deprecated Value Members

  1. def finalize(): Unit
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    protected[lang]
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Logging

Inherited from CachedBatchSerializer

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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