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t

org.apache.spark.sql.columnar

CachedBatchSerializer

trait CachedBatchSerializer extends Serializable

Provides APIs that handle transformations of SQL data associated with the cache/persist APIs.

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@DeveloperApi() @Since( "3.1.0" )
Source
CachedBatchSerializer.scala
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Abstract Value Members

  1. abstract 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

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  16. 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.

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