ericm-db commented on code in PR #45991: URL: https://github.com/apache/spark/pull/45991#discussion_r1575071946
########## sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MapStateImplWithTTL.scala: ########## @@ -0,0 +1,280 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.sql.execution.streaming + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.Encoder +import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder +import org.apache.spark.sql.execution.streaming.TransformWithStateKeyValueRowSchema.{COMPOSITE_KEY_ROW_SCHEMA, VALUE_ROW_SCHEMA_WITH_TTL} +import org.apache.spark.sql.execution.streaming.state.{PrefixKeyScanStateEncoderSpec, StateStore, StateStoreErrors} +import org.apache.spark.sql.streaming.{MapState, TTLConfig} +import org.apache.spark.util.NextIterator + +/** + * Class that provides a concrete implementation for map state associated with state + * variables (with ttl expiration support) used in the streaming transformWithState operator. + * @param store - reference to the StateStore instance to be used for storing state + * @param stateName - name of the state variable + * @param keyExprEnc - Spark SQL encoder for key + * @param userKeyEnc - Spark SQL encoder for the map key + * @param valEncoder - SQL encoder for state variable + * @param ttlConfig - the ttl configuration (time to live duration etc.) + * @param batchTimestampMs - current batch processing timestamp. + * @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: Encoder[K], + valEncoder: Encoder[V], + ttlConfig: TTLConfig, + batchTimestampMs: Long) extends CompositeKeyTTLStateImpl(stateName, store, batchTimestampMs) + with MapState[K, V] with Logging { + + private val keySerializer = keyExprEnc.createSerializer() + private val stateTypesEncoder = new CompositeKeyStateEncoder( + keySerializer, userKeyEnc, valEncoder, COMPOSITE_KEY_ROW_SCHEMA, stateName, hasTtl = true) + + private val ttlExpirationMs = + StateTTL.calculateExpirationTimeForDuration(ttlConfig.ttlDuration, batchTimestampMs) + + createColumnFamily() + + private def createColumnFamily(): Unit = { + store.createColFamilyIfAbsent(stateName, COMPOSITE_KEY_ROW_SCHEMA, VALUE_ROW_SCHEMA_WITH_TTL, + PrefixKeyScanStateEncoderSpec(COMPOSITE_KEY_ROW_SCHEMA, 1)) + } + + /** Whether state exists or not. */ + override def exists(): Boolean = { + iterator().nonEmpty + } + + /** Get the state value if it exists */ + override def getValue(key: K): V = { + StateStoreErrors.requireNonNullStateValue(key, stateName) + val encodedCompositeKey = stateTypesEncoder.encodeCompositeKey(key) + val retRow = store.get(encodedCompositeKey, stateName) + + if (retRow != null) { + val resState = stateTypesEncoder.decodeValue(retRow) + + if (!stateTypesEncoder.isExpired(retRow, batchTimestampMs)) { + resState + } else { + null.asInstanceOf[V] + } + } else { + null.asInstanceOf[V] + } + } + + /** Check if the user key is contained in the map */ + override def containsKey(key: K): Boolean = { + StateStoreErrors.requireNonNullStateValue(key, stateName) + getValue(key) != null + } + + /** Update value for given user key */ + override def updateValue(key: K, value: V): Unit = { + StateStoreErrors.requireNonNullStateValue(key, stateName) + StateStoreErrors.requireNonNullStateValue(value, stateName) + + val encodedValue = stateTypesEncoder.encodeValue(value, ttlExpirationMs) + val encodedCompositeKey = stateTypesEncoder.encodeCompositeKey(key) + store.put(encodedCompositeKey, encodedValue, stateName) + + val serializedGroupingKey = stateTypesEncoder.serializeGroupingKey() Review Comment: There's an existing method for this, will call this method instead. ########## sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MapStateImplWithTTL.scala: ########## @@ -0,0 +1,280 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.sql.execution.streaming + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.Encoder +import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder +import org.apache.spark.sql.execution.streaming.TransformWithStateKeyValueRowSchema.{COMPOSITE_KEY_ROW_SCHEMA, VALUE_ROW_SCHEMA_WITH_TTL} +import org.apache.spark.sql.execution.streaming.state.{PrefixKeyScanStateEncoderSpec, StateStore, StateStoreErrors} +import org.apache.spark.sql.streaming.{MapState, TTLConfig} +import org.apache.spark.util.NextIterator + +/** + * Class that provides a concrete implementation for map state associated with state + * variables (with ttl expiration support) used in the streaming transformWithState operator. + * @param store - reference to the StateStore instance to be used for storing state + * @param stateName - name of the state variable + * @param keyExprEnc - Spark SQL encoder for key + * @param userKeyEnc - Spark SQL encoder for the map key + * @param valEncoder - SQL encoder for state variable + * @param ttlConfig - the ttl configuration (time to live duration etc.) + * @param batchTimestampMs - current batch processing timestamp. + * @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: Encoder[K], + valEncoder: Encoder[V], + ttlConfig: TTLConfig, + batchTimestampMs: Long) extends CompositeKeyTTLStateImpl(stateName, store, batchTimestampMs) + with MapState[K, V] with Logging { + + private val keySerializer = keyExprEnc.createSerializer() + private val stateTypesEncoder = new CompositeKeyStateEncoder( + keySerializer, userKeyEnc, valEncoder, COMPOSITE_KEY_ROW_SCHEMA, stateName, hasTtl = true) + + private val ttlExpirationMs = + StateTTL.calculateExpirationTimeForDuration(ttlConfig.ttlDuration, batchTimestampMs) + + createColumnFamily() + + private def createColumnFamily(): Unit = { + store.createColFamilyIfAbsent(stateName, COMPOSITE_KEY_ROW_SCHEMA, VALUE_ROW_SCHEMA_WITH_TTL, + PrefixKeyScanStateEncoderSpec(COMPOSITE_KEY_ROW_SCHEMA, 1)) + } + + /** Whether state exists or not. */ + override def exists(): Boolean = { + iterator().nonEmpty + } + + /** Get the state value if it exists */ + override def getValue(key: K): V = { + StateStoreErrors.requireNonNullStateValue(key, stateName) + val encodedCompositeKey = stateTypesEncoder.encodeCompositeKey(key) + val retRow = store.get(encodedCompositeKey, stateName) + + if (retRow != null) { + val resState = stateTypesEncoder.decodeValue(retRow) + + if (!stateTypesEncoder.isExpired(retRow, batchTimestampMs)) { + resState + } else { + null.asInstanceOf[V] + } + } else { + null.asInstanceOf[V] + } + } + + /** Check if the user key is contained in the map */ + override def containsKey(key: K): Boolean = { + StateStoreErrors.requireNonNullStateValue(key, stateName) + getValue(key) != null + } + + /** Update value for given user key */ + override def updateValue(key: K, value: V): Unit = { + StateStoreErrors.requireNonNullStateValue(key, stateName) + StateStoreErrors.requireNonNullStateValue(value, stateName) + + val encodedValue = stateTypesEncoder.encodeValue(value, ttlExpirationMs) + val encodedCompositeKey = stateTypesEncoder.encodeCompositeKey(key) + store.put(encodedCompositeKey, encodedValue, stateName) + + val serializedGroupingKey = stateTypesEncoder.serializeGroupingKey() + val serializedUserKey = stateTypesEncoder.serializeUserKey(key) + upsertTTLForStateKey(ttlExpirationMs, serializedGroupingKey, serializedUserKey) + } + + /** Get the map associated with grouping key */ + override def iterator(): Iterator[(K, V)] = { + val encodedGroupingKey = stateTypesEncoder.encodeGroupingKey() + val unsafeRowPairIterator = store.prefixScan(encodedGroupingKey, stateName) + new NextIterator[(K, V)] { + override protected def getNext(): (K, V) = { + val iter = unsafeRowPairIterator.dropWhile { rowPair => + stateTypesEncoder.isExpired(rowPair.value, batchTimestampMs) + } + if (iter.hasNext) { + val currentRowPair = iter.next() + val key = stateTypesEncoder.decodeCompositeKey(currentRowPair.key) + val value = stateTypesEncoder.decodeValue(currentRowPair.value) + (key, value) + } else { + finished = true + null.asInstanceOf[(K, V)] + } + } + + override protected def close(): Unit = {} + } + } + + /** Get the list of keys present in map associated with grouping key */ + override def keys(): Iterator[K] = { + iterator().map(_._1) + } + + /** Get the list of values present in map associated with grouping key */ + override def values(): Iterator[V] = { + iterator().map(_._2) + } + + /** Remove user key from map state */ + override def removeKey(key: K): Unit = { + StateStoreErrors.requireNonNullStateValue(key, stateName) + val compositeKey = stateTypesEncoder.encodeCompositeKey(key) + store.remove(compositeKey, stateName) + } + + /** Remove this state. */ + override def clear(): Unit = { + store.removeColFamilyIfExists(stateName) + createColumnFamily() + clearTTLState() + } + + /** + * Clears the user state associated with this grouping key + * if it has expired. This function is called by Spark to perform + * cleanup at the end of transformWithState processing. + * + * Spark uses a secondary index to determine if the user state for + * this grouping key has expired. However, its possible that the user + * has updated the TTL and secondary index is out of date. Implementations + * must validate that the user State has actually expired before cleanup based + * on their own State data. + * + * @param groupingKey grouping key for which cleanup should be performed. + * @param userKey user key for which cleanup should be performed. + */ + override def clearIfExpired(groupingKey: Array[Byte], userKey: Array[Byte]): Long = { + val encodedCompositeKey = stateTypesEncoder.encodeCompositeKey(groupingKey, userKey) + val retRow = store.get(encodedCompositeKey, stateName) + var numRemovedElements = 0L + if (retRow != null) { + if (stateTypesEncoder.isExpired(retRow, batchTimestampMs)) { + store.remove(encodedCompositeKey, stateName) + numRemovedElements += 1 + } + } + numRemovedElements + } + + /* + * Internal methods to probe state for testing. The below methods exist for unit tests + * to read the state ttl values, and ensure that values are persisted correctly in + * the underlying state store. + */ + + /** + * Retrieves the value from State even if its expired. This method is used + * in tests to read the state store value, and ensure if its cleaned up at the + * end of the micro-batch. + */ + private[sql] def getWithoutEnforcingTTL(userKey: K): Option[V] = { + val encodedCompositeKey = stateTypesEncoder.encodeCompositeKey(userKey) + val retRow = store.get(encodedCompositeKey, stateName) + + if (retRow != null) { + val resState = stateTypesEncoder.decodeValue(retRow) + Some(resState) + } else { + None + } + } + + /** + * Read the ttl value associated with the grouping and user key. + */ + private[sql] def getTTLValue(userKey: K): Option[(V, Long)] = { + val encodedCompositeKey = stateTypesEncoder.encodeCompositeKey(userKey) + val retRow = store.get(encodedCompositeKey, stateName) + + // if the returned row is not null, we want to return the value associated with the + // ttlExpiration + Option(retRow).flatMap { row => + val ttlExpiration = stateTypesEncoder.decodeTtlExpirationMs(row) + ttlExpiration.map(expiration => (stateTypesEncoder.decodeValue(row), expiration)) + } + } + + /** + * Get all ttl values stored in ttl state for current implicit + * grouping key. + */ + private[sql] def getValuesInTTLState(): Iterator[Long] = { Review Comment: Good point, removed this method. -- This is an automated message from the Apache Git Service. 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