sahnib commented on code in PR #45991: URL: https://github.com/apache/spark/pull/45991#discussion_r1567707784
########## sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MapStateImplWithTTL.scala: ########## @@ -0,0 +1,265 @@ +/* + * 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 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) + + initialize() + + private def initialize(): 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 = { + keys().foreach { itr => + removeKey(itr) + } + } + + /** + * 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) Review Comment: Should we just store the composite key in `TTLState`? This can prevent us from having to re-encode it again here in `clearIfExpired` function. This probably means, we might be able to use `SingleKeyStateTTL` in all places. ########## sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MapStateImplWithTTL.scala: ########## @@ -0,0 +1,265 @@ +/* + * 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 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) + + initialize() + + private def initialize(): 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 = { + keys().foreach { itr => + removeKey(itr) + } Review Comment: I wonder if its faster to drop the column family and recreate it here, rather than deleting all keys? Also, I should have noticed in previous PRs, this is an opportunity to also clear the ttlState associated with this state variable. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
