WweiL commented on code in PR #41558: URL: https://github.com/apache/spark/pull/41558#discussion_r1227301618
########## connector/connect/common/src/main/scala/org/apache/spark/sql/streaming/GroupState.scala: ########## @@ -0,0 +1,336 @@ +/* + * 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.streaming + +import org.apache.spark.annotation.{Evolving, Experimental} +import org.apache.spark.sql.catalyst.plans.logical.LogicalGroupState + +/** + * :: Experimental :: + * + * Wrapper class for interacting with per-group state data in `mapGroupsWithState` and + * `flatMapGroupsWithState` operations on `KeyValueGroupedDataset`. + * + * Detail description on `[map/flatMap]GroupsWithState` operation + * -------------------------------------------------------------- Both, `mapGroupsWithState` and + * `flatMapGroupsWithState` in `KeyValueGroupedDataset` will invoke the user-given function on + * each group (defined by the grouping function in `Dataset.groupByKey()`) while maintaining a + * user-defined per-group state between invocations. For a static batch Dataset, the function will + * be invoked once per group. For a streaming Dataset, the function will be invoked for each group + * repeatedly in every trigger. That is, in every batch of the `StreamingQuery`, the function will + * be invoked once for each group that has data in the trigger. Furthermore, if timeout is set, + * then the function will be invoked on timed-out groups (more detail below). + * + * The function is invoked with the following parameters. + * - The key of the group. + * - An iterator containing all the values for this group. + * - A user-defined state object set by previous invocations of the given function. + * + * In case of a batch Dataset, there is only one invocation and the state object will be empty as + * there is no prior state. Essentially, for batch Datasets, `[map/flatMap]GroupsWithState` is + * equivalent to `[map/flatMap]Groups` and any updates to the state and/or timeouts have no + * effect. + * + * The major difference between `mapGroupsWithState` and `flatMapGroupsWithState` is that the + * former allows the function to return one and only one record, whereas the latter allows the + * function to return any number of records (including no records). Furthermore, the + * `flatMapGroupsWithState` is associated with an operation output mode, which can be either + * `Append` or `Update`. Semantically, this defines whether the output records of one trigger is + * effectively replacing the previously output records (from previous triggers) or is appending to + * the list of previously output records. Essentially, this defines how the Result Table (refer to + * the semantics in the programming guide) is updated, and allows us to reason about the semantics + * of later operations. + * + * Important points to note about the function (both mapGroupsWithState and + * flatMapGroupsWithState). + * - In a trigger, the function will be called only the groups present in the batch. So do not + * assume that the function will be called in every trigger for every group that has state. + * - There is no guaranteed ordering of values in the iterator in the function, neither with + * batch, nor with streaming Datasets. + * - All the data will be shuffled before applying the function. + * - If timeout is set, then the function will also be called with no values. See more details + * on `GroupStateTimeout` below. + * + * Important points to note about using `GroupState`. + * - The value of the state cannot be null. So updating state with null will throw + * `IllegalArgumentException`. + * - Operations on `GroupState` are not thread-safe. This is to avoid memory barriers. + * - If `remove()` is called, then `exists()` will return `false`, `get()` will throw + * `NoSuchElementException` and `getOption()` will return `None` + * - After that, if `update(newState)` is called, then `exists()` will again return `true`, + * `get()` and `getOption()`will return the updated value. + * + * Important points to note about using `GroupStateTimeout`. + * - The timeout type is a global param across all the groups (set as `timeout` param in + * `[map|flatMap]GroupsWithState`, but the exact timeout duration/timestamp is configurable + * per group by calling `setTimeout...()` in `GroupState`. + * - Timeouts can be either based on processing time (i.e. + * `GroupStateTimeout.ProcessingTimeTimeout`) or event time (i.e. + * `GroupStateTimeout.EventTimeTimeout`). + * - With `ProcessingTimeTimeout`, the timeout duration can be set by calling + * `GroupState.setTimeoutDuration`. The timeout will occur when the clock has advanced by the + * set duration. Guarantees provided by this timeout with a duration of D ms are as follows: + * - Timeout will never occur before the clock time has advanced by D ms + * - Timeout will occur eventually when there is a trigger in the query (i.e. after D ms). So + * there is no strict upper bound on when the timeout would occur. For example, the trigger + * interval of the query will affect when the timeout actually occurs. If there is no data + * in the stream (for any group) for a while, then there will not be any trigger and timeout + * function call will not occur until there is data. + * - Since the processing time timeout is based on the clock time, it is affected by the + * variations in the system clock (i.e. time zone changes, clock skew, etc.). + * - With `EventTimeTimeout`, the user also has to specify the event time watermark in the query + * using `Dataset.withWatermark()`. With this setting, data that is older than the watermark + * is filtered out. The timeout can be set for a group by setting a timeout timestamp + * using`GroupState.setTimeoutTimestamp()`, and the timeout would occur when the watermark + * advances beyond the set timestamp. You can control the timeout delay by two parameters - + * (i) watermark delay and an additional duration beyond the timestamp in the event (which is + * guaranteed to be newer than watermark due to the filtering). Guarantees provided by this + * timeout are as follows: + * - Timeout will never occur before the watermark has exceeded the set timeout. + * - Similar to processing time timeouts, there is no strict upper bound on the delay when the + * timeout actually occurs. The watermark can advance only when there is data in the stream + * and the event time of the data has actually advanced. + * - When the timeout occurs for a group, the function is called for that group with no values, + * and `GroupState.hasTimedOut()` set to true. + * - The timeout is reset every time the function is called on a group, that is, when the group + * has new data, or the group has timed out. So the user has to set the timeout duration every + * time the function is called, otherwise, there will not be any timeout set. + * + * `[map/flatMap]GroupsWithState` can take a user defined initial state as an additional argument. + * This state will be applied when the first batch of the streaming query is processed. If there + * are no matching rows in the data for the keys present in the initial state, the state is still + * applied and the function will be invoked with the values being an empty iterator. + * + * Scala example of using GroupState in `mapGroupsWithState`: + * {{{ + * // A mapping function that maintains an integer state for string keys and returns a string. + * // Additionally, it sets a timeout to remove the state if it has not received data for an hour. + * def mappingFunction(key: String, value: Iterator[Int], state: GroupState[Int]): String = { + * + * if (state.hasTimedOut) { // If called when timing out, remove the state + * state.remove() + * + * } else if (state.exists) { // If state exists, use it for processing + * val existingState = state.get // Get the existing state + * val shouldRemove = ... // Decide whether to remove the state + * if (shouldRemove) { + * state.remove() // Remove the state + * + * } else { + * val newState = ... + * state.update(newState) // Set the new state + * state.setTimeoutDuration("1 hour") // Set the timeout + * } + * + * } else { + * val initialState = ... + * state.update(initialState) // Set the initial state + * state.setTimeoutDuration("1 hour") // Set the timeout + * } + * ... + * // return something + * } + * + * dataset + * .groupByKey(...) + * .mapGroupsWithState(GroupStateTimeout.ProcessingTimeTimeout)(mappingFunction) + * }}} + * + * Java example of using `GroupState`: + * {{{ + * // A mapping function that maintains an integer state for string keys and returns a string. + * // Additionally, it sets a timeout to remove the state if it has not received data for an hour. + * MapGroupsWithStateFunction<String, Integer, Integer, String> mappingFunction = + * new MapGroupsWithStateFunction<String, Integer, Integer, String>() { + * + * @Override + * public String call(String key, Iterator<Integer> value, GroupState<Integer> state) { + * if (state.hasTimedOut()) { // If called when timing out, remove the state + * state.remove(); + * + * } else if (state.exists()) { // If state exists, use it for processing + * int existingState = state.get(); // Get the existing state + * boolean shouldRemove = ...; // Decide whether to remove the state + * if (shouldRemove) { + * state.remove(); // Remove the state + * + * } else { + * int newState = ...; + * state.update(newState); // Set the new state + * state.setTimeoutDuration("1 hour"); // Set the timeout + * } + * + * } else { + * int initialState = ...; // Set the initial state + * state.update(initialState); + * state.setTimeoutDuration("1 hour"); // Set the timeout + * } + * ... + * // return something + * } + * }; + * + * dataset + * .groupByKey(...) + * .mapGroupsWithState( + * mappingFunction, Encoders.INT, Encoders.STRING, GroupStateTimeout.ProcessingTimeTimeout); + * }}} + * + * @tparam S + * User-defined type of the state to be stored for each group. Must be encodable into Spark SQL + * types (see `Encoder` for more details). + * @since 2.2.0 Review Comment: nit version -- 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: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
