revans2 commented on a change in pull request #29067:
URL: https://github.com/apache/spark/pull/29067#discussion_r455803871
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryRelation.scala
##########
@@ -19,84 +19,301 @@ package org.apache.spark.sql.execution.columnar
import org.apache.commons.lang3.StringUtils
+import org.apache.spark.TaskContext
+import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.internal.Logging
import org.apache.spark.network.util.JavaUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
+import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.QueryPlan
-import org.apache.spark.sql.catalyst.plans.logical
+import org.apache.spark.sql.catalyst.plans.{logical, QueryPlan}
import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, LogicalPlan,
Statistics}
import org.apache.spark.sql.catalyst.util.truncatedString
import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.vectorized.{OffHeapColumnVector,
OnHeapColumnVector, WritableColumnVector}
+import org.apache.spark.sql.internal.{SQLConf, StaticSQLConf}
+import org.apache.spark.sql.types.{AtomicType, BinaryType, StructType,
UserDefinedType}
+import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector}
import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.LongAccumulator
+import org.apache.spark.util.{LongAccumulator, Utils}
+/**
+ * Basic interface that all cached batches of data must support. This is
primarily to allow
+ * for metrics to be handled outside of the encoding and decoding steps in a
standard way.
+ */
+@DeveloperApi
+@Since("3.1.0")
+trait CachedBatch {
+ def numRows: Int
+ def sizeInBytes: Long
+}
/**
- * CachedBatch is a cached batch of rows.
- *
- * @param numRows The total number of rows in this batch
- * @param buffers The buffers for serialized columns
- * @param stats The stat of columns
+ * Provides APIs for compressing, filtering, and decompressing SQL data that
will be
+ * persisted/cached.
*/
-private[columnar]
-case class CachedBatch(numRows: Int, buffers: Array[Array[Byte]], stats:
InternalRow)
+@DeveloperApi
+@Since("3.1.0")
+trait CachedBatchSerializer extends Serializable {
+ /**
+ * Run the given plan and convert its output to a implementation of
[[CachedBatch]].
+ * @param cachedPlan the plan to run.
+ * @return the RDD containing the batches of data to cache.
+ */
+ def convertForCache(cachedPlan: SparkPlan): RDD[CachedBatch]
+
+ /**
+ * Builds a function that can be used to filter which batches are loaded.
+ * In most cases extending [[SimpleMetricsCachedBatchSerializer]] will
provide what
+ * you need with the added expense of calculating the min and max value for
some
+ * data columns, depending on their data type. Note that this is intended to
skip batches
+ * that are not needed, and the actual filtering of individual rows is
handled later.
+ * @param predicates the set of expressions to use for filtering.
+ * @param cachedAttributes the schema/attributes of the data that is cached.
This can be helpful
+ * if you don't store it with the data.
+ * @return a function that takes the partition id and the iterator of
batches in the partition.
+ * It returns an iterator of batches that should be loaded.
+ */
+ def buildFilter(predicates: Seq[Expression],
+ cachedAttributes: Seq[Attribute]): (Int, Iterator[CachedBatch]) =>
Iterator[CachedBatch]
+
+ /**
+ * Decompress the cached data into a ColumnarBatch. This currently is only
used for basic types
+ * BooleanType | ByteType | ShortType | IntegerType | LongType | FloatType |
DoubleType
+ * That may change in the future.
+ * @param input the cached batches that should be decompressed.
+ * @param cacheAttributes the attributes of the data in the batch.
+ * @param selectedAttributes the field that should be loaded from the data,
and the order they
+ * should appear in the output batch.
+ * @param conf the configuration for the job.
+ * @return an RDD of the input cached batches transformed into the
ColumnarBatch format.
+ */
+ def decompressColumnar(
+ input: RDD[CachedBatch],
+ cacheAttributes: Seq[Attribute],
+ selectedAttributes: Seq[Attribute],
+ conf: SQLConf): RDD[ColumnarBatch]
+
+ /**
+ * Decompress the cached batch into [[InternalRow]]. If you want this to be
performant, code
+ * generation is advised.
+ * @param input the cached batches that should be decompressed.
+ * @param cacheAttributes the attributes of the data in the batch.
+ * @param selectedAttributes the field that should be loaded from the data,
and the order they
+ * should appear in the output batch.
+ * @param conf the configuration for the job.
+ * @return RDD of the rows that were stored in the cached batches.
+ */
+ def decompressToRows(
+ input: RDD[CachedBatch],
+ cacheAttributes: Seq[Attribute],
+ selectedAttributes: Seq[Attribute],
+ conf: SQLConf): RDD[InternalRow]
+}
-case class CachedRDDBuilder(
- useCompression: Boolean,
- batchSize: Int,
- storageLevel: StorageLevel,
- @transient cachedPlan: SparkPlan,
- tableName: Option[String]) {
+/**
+ * A [[CachedBatch]] that stores some simple metrics that can be used for
filtering of batches with
+ * the [[SimpleMetricsCachedBatchSerializer]].
+ */
+@DeveloperApi
+@Since("3.1.0")
+trait SimpleMetricsCachedBatch extends CachedBatch {
Review comment:
`DefaultCachedBatch` is a `case class`, and I didn't think it was
acceptable to have a `case class` inherent from a regular `class`. If I am
wrong on that just let me know and I'll make it an abstract class.
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