anishshri-db commented on code in PR #48401:
URL: https://github.com/apache/spark/pull/48401#discussion_r1837372697
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala:
##########
@@ -259,6 +259,19 @@ class IncrementalExecution(
}
}
+ object StateStoreColumnFamilySchemasRule extends SparkPlanPartialRule {
Review Comment:
lets add a comment to explain what the rule does ?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MapStateImplWithTTL.scala:
##########
@@ -36,21 +36,27 @@ import org.apache.spark.util.NextIterator
* @param ttlConfig - the ttl configuration (time to live duration etc.)
* @param batchTimestampMs - current batch processing timestamp.
* @param metrics - metrics to be updated as part of stateful processing
+ * @param avroEnc - optional Avro serializer and deserializer for this state
variable that
+ * is used by the StateStore to encode state in Avro format
+ * @param secondaryIndexAvroEnc - optional Avro serializer and deserializer
for TTL state that
+ * is used by the StateStore to encode state in Avro format
* @tparam K - type of key for map state variable
* @tparam V - type of value for map state variable
* @return - instance of MapState of type [K,V] that can be used to store
state persistently
*/
class MapStateImplWithTTL[K, V](
- store: StateStore,
- stateName: String,
- keyExprEnc: ExpressionEncoder[Any],
- userKeyEnc: ExpressionEncoder[Any],
- valEncoder: ExpressionEncoder[Any],
- ttlConfig: TTLConfig,
- batchTimestampMs: Long,
- metrics: Map[String, SQLMetric] = Map.empty)
+ store: StateStore,
Review Comment:
indent is off ?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala:
##########
@@ -580,8 +587,9 @@ case class TransformWithStateExec(
new SerializableConfiguration(session.sessionState.newHadoopConf()))
child.execute().mapPartitionsWithIndex[InternalRow](
(i: Int, iter: Iterator[InternalRow]) => {
- initNewStateStoreAndProcessData(i, hadoopConfBroadcast) { store =>
- processData(store, iter)
+ initNewStateStoreAndProcessData(
+ i, hadoopConfBroadcast) { store =>
Review Comment:
nit: lets revert to older style ?
##########
sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/state/RocksDBStateStoreSuite.scala:
##########
@@ -339,6 +339,82 @@ class RocksDBStateStoreSuite extends
StateStoreSuiteBase[RocksDBStateStoreProvid
}
}
+ test("rocksdb range scan - fixed size non-ordering columns with Avro
encoding") {
+
Review Comment:
nit: extra newlines ?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MapStateImplWithTTL.scala:
##########
@@ -36,21 +36,27 @@ import org.apache.spark.util.NextIterator
* @param ttlConfig - the ttl configuration (time to live duration etc.)
* @param batchTimestampMs - current batch processing timestamp.
* @param metrics - metrics to be updated as part of stateful processing
+ * @param avroEnc - optional Avro serializer and deserializer for this state
variable that
+ * is used by the StateStore to encode state in Avro format
Review Comment:
nit: lets confirm the expected style for these multi-line arg comments
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StateStoreColumnFamilySchemaUtils.scala:
##########
@@ -16,36 +16,132 @@
*/
package org.apache.spark.sql.execution.streaming
+import org.apache.spark.internal.Logging
import org.apache.spark.sql.Encoder
+import org.apache.spark.sql.avro.{AvroDeserializer, AvroOptions,
AvroSerializer, SchemaConverters}
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import
org.apache.spark.sql.execution.streaming.TransformWithStateKeyValueRowSchemaUtils._
-import
org.apache.spark.sql.execution.streaming.state.{NoPrefixKeyStateEncoderSpec,
PrefixKeyScanStateEncoderSpec, StateStoreColFamilySchema}
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.execution.streaming.state.{AvroEncoder,
NoPrefixKeyStateEncoderSpec, PrefixKeyScanStateEncoderSpec,
RangeKeyScanStateEncoderSpec, StateStoreColFamilySchema}
+import org.apache.spark.sql.types.{BinaryType, BooleanType, ByteType,
DataType, DoubleType, FloatType, IntegerType, LongType, NullType, ShortType,
StructField, StructType}
Review Comment:
maybe just `types._` ?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala:
##########
@@ -718,6 +732,22 @@ object TransformWithStateExec {
stateStoreCkptIds = None
)
+ val stateStoreEncoding = child.session.sessionState.conf.getConf(
+ SQLConf.STREAMING_STATE_STORE_ENCODING_FORMAT
+ )
+
+ def getDriverProcessorHandle(): DriverStatefulProcessorHandleImpl = {
+ val driverProcessorHandle = new DriverStatefulProcessorHandleImpl(
+ timeMode, keyEncoder, initializeAvroEnc =
+ stateStoreEncoding == StateStoreEncoding.Avro.toString)
Review Comment:
same here
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala:
##########
@@ -104,7 +106,10 @@ case class TransformWithStateExec(
* @return a new instance of the driver processor handle
*/
private def getDriverProcessorHandle(): DriverStatefulProcessorHandleImpl = {
- val driverProcessorHandle = new
DriverStatefulProcessorHandleImpl(timeMode, keyEncoder)
+
+ val driverProcessorHandle = new DriverStatefulProcessorHandleImpl(
+ timeMode, keyEncoder, initializeAvroEnc =
+ stateStoreEncoding == StateStoreEncoding.Avro.toString)
Review Comment:
lets move to separate function ?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StateStoreColumnFamilySchemaUtils.scala:
##########
@@ -16,36 +16,132 @@
*/
package org.apache.spark.sql.execution.streaming
+import org.apache.spark.internal.Logging
import org.apache.spark.sql.Encoder
+import org.apache.spark.sql.avro.{AvroDeserializer, AvroOptions,
AvroSerializer, SchemaConverters}
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import
org.apache.spark.sql.execution.streaming.TransformWithStateKeyValueRowSchemaUtils._
-import
org.apache.spark.sql.execution.streaming.state.{NoPrefixKeyStateEncoderSpec,
PrefixKeyScanStateEncoderSpec, StateStoreColFamilySchema}
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.execution.streaming.state.{AvroEncoder,
NoPrefixKeyStateEncoderSpec, PrefixKeyScanStateEncoderSpec,
RangeKeyScanStateEncoderSpec, StateStoreColFamilySchema}
+import org.apache.spark.sql.types.{BinaryType, BooleanType, ByteType,
DataType, DoubleType, FloatType, IntegerType, LongType, NullType, ShortType,
StructField, StructType}
-object StateStoreColumnFamilySchemaUtils {
+object StateStoreColumnFamilySchemaUtils extends Serializable {
+
+ def apply(initializeAvroSerde: Boolean): StateStoreColumnFamilySchemaUtils =
+ new StateStoreColumnFamilySchemaUtils(initializeAvroSerde)
+
+ /**
+ * Avro uses zig-zag encoding for some fixed-length types, like Longs and
Ints. For range scans
+ * we want to use big-endian encoding, so we need to convert the source
schema to replace these
+ * types with BinaryType.
+ *
+ * @param schema The schema to convert
+ * @param ordinals If non-empty, only convert fields at these ordinals.
+ * If empty, convert all fields.
+ */
+ def convertForRangeScan(schema: StructType, ordinals: Seq[Int] = Seq.empty):
StructType = {
+ val ordinalSet = ordinals.toSet
+
+ StructType(schema.fields.zipWithIndex.flatMap { case (field, idx) =>
+ if ((ordinals.isEmpty || ordinalSet.contains(idx)) &&
isFixedSize(field.dataType)) {
+ // For each numeric field, create two fields:
+ // 1. A boolean for sign (positive = true, negative = false)
+ // 2. The original numeric value in big-endian format
+ Seq(
+ StructField(s"${field.name}_marker", ByteType, nullable = false),
+ field.copy(name = s"${field.name}_value", BinaryType)
+ )
+ } else {
+ Seq(field)
+ }
+ })
+ }
+
+ private def isFixedSize(dataType: DataType): Boolean = dataType match {
+ case _: ByteType | _: BooleanType | _: ShortType | _: IntegerType | _:
LongType |
+ _: FloatType | _: DoubleType => true
+ case _ => false
+ }
+
+ def getTtlColFamilyName(stateName: String): String = {
+ "$ttl_" + stateName
+ }
+}
+
+/**
+ *
+ * @param initializeAvroSerde Whether or not to create the Avro serializers
and deserializers
+ * for this state type. This class is used to
create the
+ * StateStoreColumnFamilySchema for each state
variable from the driver
+ */
+class StateStoreColumnFamilySchemaUtils(initializeAvroSerde: Boolean)
+ extends Logging with Serializable {
+ private def getAvroSerializer(schema: StructType): AvroSerializer = {
+ val avroType = SchemaConverters.toAvroType(schema)
+ new AvroSerializer(schema, avroType, nullable = false)
+ }
+
+ private def getAvroDeserializer(schema: StructType): AvroDeserializer = {
+ val avroType = SchemaConverters.toAvroType(schema)
+ val avroOptions = AvroOptions(Map.empty)
+ new AvroDeserializer(avroType, schema,
+ avroOptions.datetimeRebaseModeInRead,
avroOptions.useStableIdForUnionType,
+ avroOptions.stableIdPrefixForUnionType,
avroOptions.recursiveFieldMaxDepth)
+ }
+
+ /**
+ * If initializeAvroSerde is true, this method will create an Avro
Serializer and Deserializer
+ * for a particular key and value schema.
+ */
+ private[sql] def getAvroSerde(
+ keySchema: StructType,
+ valSchema: StructType,
+ suffixKeySchema: Option[StructType] = None
+ ): Option[AvroEncoder] = {
+ if (initializeAvroSerde) {
+
Review Comment:
nit: extra newline ?
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