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[SPARK-43129] Scala core API for streaming Spark Connect
### What changes were proposed in this pull request? Implements core streaming API in Scala for running streaming queries over Spark Connect. This is functionally equivalent to Python side PR apache#40586 There are no server side changes here since it was done earlier in Python PR. We can run most streaming queries. Notably, queries using `foreachBatch()` are not yet supported. ### Why are the changes needed? This adds structured streaming support in Scala for Spark connect. ### Does this PR introduce _any_ user-facing change? Adds more streaming API to Scala Spark Connect client. ### How was this patch tested? - Unit test - Manual testing Closes apache#40783 from rangadi/scala-m1. Authored-by: Raghu Angadi <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]>
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connector/connect/client/jvm/src/main/java/org/apache/spark/sql/streaming/Trigger.java
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.streaming; | ||
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| import java.util.concurrent.TimeUnit; | ||
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| import scala.concurrent.duration.Duration; | ||
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| import org.apache.spark.annotation.Evolving; | ||
| import org.apache.spark.sql.execution.streaming.AvailableNowTrigger$; | ||
| import org.apache.spark.sql.execution.streaming.ContinuousTrigger; | ||
| import org.apache.spark.sql.execution.streaming.OneTimeTrigger$; | ||
| import org.apache.spark.sql.execution.streaming.ProcessingTimeTrigger; | ||
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| /** | ||
| * Policy used to indicate how often results should be produced by a [[StreamingQuery]]. | ||
| * | ||
| * @since 3.5.0 | ||
| */ | ||
| @Evolving | ||
| public class Trigger { | ||
| // This is a copy of the same class in sql/core/.../streaming/Trigger.java | ||
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| /** | ||
| * A trigger policy that runs a query periodically based on an interval in processing time. | ||
| * If `interval` is 0, the query will run as fast as possible. | ||
| * | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger ProcessingTime(long intervalMs) { | ||
| return ProcessingTimeTrigger.create(intervalMs, TimeUnit.MILLISECONDS); | ||
| } | ||
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| /** | ||
| * (Java-friendly) | ||
| * A trigger policy that runs a query periodically based on an interval in processing time. | ||
| * If `interval` is 0, the query will run as fast as possible. | ||
| * | ||
| * {{{ | ||
| * import java.util.concurrent.TimeUnit | ||
| * df.writeStream().trigger(Trigger.ProcessingTime(10, TimeUnit.SECONDS)) | ||
| * }}} | ||
| * | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger ProcessingTime(long interval, TimeUnit timeUnit) { | ||
| return ProcessingTimeTrigger.create(interval, timeUnit); | ||
| } | ||
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| /** | ||
| * (Scala-friendly) | ||
| * A trigger policy that runs a query periodically based on an interval in processing time. | ||
| * If `duration` is 0, the query will run as fast as possible. | ||
| * | ||
| * {{{ | ||
| * import scala.concurrent.duration._ | ||
| * df.writeStream.trigger(Trigger.ProcessingTime(10.seconds)) | ||
| * }}} | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger ProcessingTime(Duration interval) { | ||
| return ProcessingTimeTrigger.apply(interval); | ||
| } | ||
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| /** | ||
| * A trigger policy that runs a query periodically based on an interval in processing time. | ||
| * If `interval` is effectively 0, the query will run as fast as possible. | ||
| * | ||
| * {{{ | ||
| * df.writeStream.trigger(Trigger.ProcessingTime("10 seconds")) | ||
| * }}} | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger ProcessingTime(String interval) { | ||
| return ProcessingTimeTrigger.apply(interval); | ||
| } | ||
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| /** | ||
| * A trigger that processes all available data in a single batch then terminates the query. | ||
| * | ||
| * @since 3.5.0 | ||
| * @deprecated This is deprecated as of Spark 3.4.0. Use {@link #AvailableNow()} to leverage | ||
| * better guarantee of processing, fine-grained scale of batches, and better gradual | ||
| * processing of watermark advancement including no-data batch. | ||
| * See the NOTES in {@link #AvailableNow()} for details. | ||
| */ | ||
| @Deprecated | ||
| public static Trigger Once() { | ||
| return OneTimeTrigger$.MODULE$; | ||
| } | ||
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| /** | ||
| * A trigger that processes all available data at the start of the query in one or multiple | ||
| * batches, then terminates the query. | ||
| * | ||
| * Users are encouraged to set the source options to control the size of the batch as similar as | ||
| * controlling the size of the batch in {@link #ProcessingTime(long)} trigger. | ||
| * | ||
| * NOTES: | ||
| * - This trigger provides a strong guarantee of processing: regardless of how many batches were | ||
| * left over in previous run, it ensures all available data at the time of execution gets | ||
| * processed before termination. All uncommitted batches will be processed first. | ||
| * - Watermark gets advanced per each batch, and no-data batch gets executed before termination | ||
| * if the last batch advances the watermark. This helps to maintain smaller and predictable | ||
| * state size and smaller latency on the output of stateful operators. | ||
| * | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger AvailableNow() { | ||
| return AvailableNowTrigger$.MODULE$; | ||
| } | ||
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| /** | ||
| * A trigger that continuously processes streaming data, asynchronously checkpointing at | ||
| * the specified interval. | ||
| * | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger Continuous(long intervalMs) { | ||
| return ContinuousTrigger.apply(intervalMs); | ||
| } | ||
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| /** | ||
| * A trigger that continuously processes streaming data, asynchronously checkpointing at | ||
| * the specified interval. | ||
| * | ||
| * {{{ | ||
| * import java.util.concurrent.TimeUnit | ||
| * df.writeStream.trigger(Trigger.Continuous(10, TimeUnit.SECONDS)) | ||
| * }}} | ||
| * | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger Continuous(long interval, TimeUnit timeUnit) { | ||
| return ContinuousTrigger.create(interval, timeUnit); | ||
| } | ||
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| /** | ||
| * (Scala-friendly) | ||
| * A trigger that continuously processes streaming data, asynchronously checkpointing at | ||
| * the specified interval. | ||
| * | ||
| * {{{ | ||
| * import scala.concurrent.duration._ | ||
| * df.writeStream.trigger(Trigger.Continuous(10.seconds)) | ||
| * }}} | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger Continuous(Duration interval) { | ||
| return ContinuousTrigger.apply(interval); | ||
| } | ||
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| /** | ||
| * A trigger that continuously processes streaming data, asynchronously checkpointing at | ||
| * the specified interval. | ||
| * | ||
| * {{{ | ||
| * df.writeStream.trigger(Trigger.Continuous("10 seconds")) | ||
| * }}} | ||
| * @since 3.5.0 | ||
| */ | ||
| public static Trigger Continuous(String interval) { | ||
| return ContinuousTrigger.apply(interval); | ||
| } | ||
| } |
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