LuciferYang commented on code in PR #40783: URL: https://github.com/apache/spark/pull/40783#discussion_r1166248261
########## connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala: ########## @@ -0,0 +1,299 @@ +/* + * 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 java.util.Locale +import java.util.concurrent.TimeoutException + +import scala.collection.JavaConverters._ + +import org.apache.spark.annotation.Evolving +import org.apache.spark.api.java.function.VoidFunction2 +import org.apache.spark.connect.proto.Command +import org.apache.spark.connect.proto.WriteStreamOperationStart +import org.apache.spark.sql.Dataset +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 + +/** + * Interface used to write a streaming `Dataset` to external storage systems (e.g. file systems, + * key-value stores, etc). Use `Dataset.writeStream` to access this. + * + * @since 3.5.0 + */ +@Evolving +final class DataStreamWriter[T] private[sql](ds: Dataset[T]) { + + /** + * Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. + * <ul> + * <li> `OutputMode.Append()`: only the new rows in the streaming DataFrame/Dataset will be + * written to the sink.</li> + * <li> `OutputMode.Complete()`: all the rows in the streaming DataFrame/Dataset will be written + * to the sink every time there are some updates.</li> + * <li> `OutputMode.Update()`: only the rows that were updated in the streaming + * DataFrame/Dataset will be written to the sink every time there are some updates. + * If the query doesn't contain aggregations, it will be equivalent to + * `OutputMode.Append()` mode.</li> + * </ul> + * + * @since 3.5.0 + */ + def outputMode(outputMode: OutputMode): DataStreamWriter[T] = { + sinkBuilder.setOutputMode(outputMode.toString.toLowerCase(Locale.ROOT)) + this + } + + /** + * Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. + * <ul> + * <li> `append`: only the new rows in the streaming DataFrame/Dataset will be written to + * the sink.</li> + * <li> `complete`: all the rows in the streaming DataFrame/Dataset will be written to the sink + * every time there are some updates.</li> + * <li> `update`: only the rows that were updated in the streaming DataFrame/Dataset will + * be written to the sink every time there are some updates. If the query doesn't + * contain aggregations, it will be equivalent to `append` mode.</li> + * </ul> + * + * @since 3.5.0 + */ + def outputMode(outputMode: String): DataStreamWriter[T] = { + sinkBuilder.setOutputMode(outputMode) + this + } + + /** + * Set the trigger for the stream query. The default value is `ProcessingTime(0)` and it will run + * the query as fast as possible. + * + * Scala Example: + * {{{ + * df.writeStream.trigger(ProcessingTime("10 seconds")) + * + * import scala.concurrent.duration._ + * df.writeStream.trigger(ProcessingTime(10.seconds)) + * }}} + * + * Java Example: + * {{{ + * df.writeStream().trigger(ProcessingTime.create("10 seconds")) + * + * import java.util.concurrent.TimeUnit + * df.writeStream().trigger(ProcessingTime.create(10, TimeUnit.SECONDS)) + * }}} + * + * @since 3.5.0 + */ + def trigger(trigger: Trigger): DataStreamWriter[T] = { + trigger match { + case ProcessingTimeTrigger(intervalMs) => + sinkBuilder.setProcessingTimeInterval(s"$intervalMs milliseconds") + case AvailableNowTrigger => + sinkBuilder.setAvailableNow(true) + case OneTimeTrigger => + sinkBuilder.setOnce(true) + case ContinuousTrigger(intervalMs) => + sinkBuilder.setContinuousCheckpointInterval(s"$intervalMs milliseconds") + } + this + } + + /** + * Specifies the name of the [[StreamingQuery]] that can be started with `start()`. + * This name must be unique among all the currently active queries in the associated SQLContext. + * + * @since 3.5.0 + */ + def queryName(queryName: String): DataStreamWriter[T] = { + sinkBuilder.setQueryName(queryName) + this + } + + /** + * Specifies the underlying output data source. + * + * @since 3.5.0 + */ + def format(source: String): DataStreamWriter[T] = { + sinkBuilder.setFormat(source) + this + } + + /** + * Partitions the output by the given columns on the file system. If specified, the output is + * laid out on the file system similar to Hive's partitioning scheme. As an example, when we + * partition a dataset by year and then month, the directory layout would look like: + * + * <ul> + * <li> year=2016/month=01/</li> + * <li> year=2016/month=02/</li> + * </ul> + * + * Partitioning is one of the most widely used techniques to optimize physical data layout. + * It provides a coarse-grained index for skipping unnecessary data reads when queries have + * predicates on the partitioned columns. In order for partitioning to work well, the number + * of distinct values in each column should typically be less than tens of thousands. + * + * @since 3.5.0 + */ + @scala.annotation.varargs + def partitionBy(colNames: String*): DataStreamWriter[T] = { + sinkBuilder.clearPartitioningColumnNames() + sinkBuilder.addAllPartitioningColumnNames(colNames.asJava) + this + } + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: String): DataStreamWriter[T] = { + sinkBuilder.putOptions(key, value) + this + } + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Boolean): DataStreamWriter[T] = option(key, value.toString) + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Long): DataStreamWriter[T] = option(key, value.toString) + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Double): DataStreamWriter[T] = option(key, value.toString) + + /** + * (Scala-specific) Adds output options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: scala.collection.Map[String, String]): DataStreamWriter[T] = { + this.options(options.asJava) + this + } + + /** + * Adds output options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: java.util.Map[String, String]): DataStreamWriter[T] = { + this.options(options.asScala) + this + } + + /** + * Starts the execution of the streaming query, which will continually output results to the given + * path as new data arrives. The returned [[StreamingQuery]] object can be used to interact with + * the stream. + * + * @since 3.5.0 + */ + def start(path: String): StreamingQuery = { + sinkBuilder.setPath(path) + start() + } + + /** + * Starts the execution of the streaming query, which will continually output results to the given + * path as new data arrives. The returned [[StreamingQuery]] object can be used to interact with + * the stream. Throws a `TimeoutException` if the following conditions are met: + * - Another run of the same streaming query, that is a streaming query + * sharing the same checkpoint location, is already active on the same + * Spark Driver + * - The SQL configuration `spark.sql.streaming.stopActiveRunOnRestart` + * is enabled + * - The active run cannot be stopped within the timeout controlled by + * the SQL configuration `spark.sql.streaming.stopTimeout` + * + * @since 3.5.0 + */ + @throws[TimeoutException] + def start(): StreamingQuery = { + val startCmd = Command + .newBuilder() + .setWriteStreamOperationStart(sinkBuilder.build()) + .build() + + val resp = ds.sparkSession.execute(startCmd).head + RemoteStreamingQuery.fromStartCommandResponse(ds.sparkSession, resp) + } + + /** + * TODO(SPARK-43133) Add foreach() API in Scala. + */ + // def foreach(writer: ForeachWriter[T]): DataStreamWriter[T] = { + + + /** + * :: Experimental :: + * + * (Scala-specific) Sets the output of the streaming query to be processed using the provided + * function. This is supported only in the micro-batch execution modes (that is, when the + * trigger is not continuous). In every micro-batch, the provided function will be called in + * every micro-batch with (i) the output rows as a Dataset and (ii) the batch identifier. + * The batchId can be used to deduplicate and transactionally write the output + * (that is, the provided Dataset) to external systems. The output Dataset is guaranteed + * to be exactly the same for the same batchId (assuming all operations are deterministic + * in the query). + * + * @since 3.5.0 + */ + @Evolving + def foreachBatch(function: (Dataset[T], Long) => Unit): DataStreamWriter[T] = { Review Comment: For unsupported Apis, I suggest removing the interface declaration first and adding the corresponding ProblemFilters rule in `CheckConnectJvmClientCompatibility` ########## connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala: ########## @@ -0,0 +1,299 @@ +/* + * 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 java.util.Locale +import java.util.concurrent.TimeoutException + +import scala.collection.JavaConverters._ + +import org.apache.spark.annotation.Evolving +import org.apache.spark.api.java.function.VoidFunction2 +import org.apache.spark.connect.proto.Command +import org.apache.spark.connect.proto.WriteStreamOperationStart +import org.apache.spark.sql.Dataset +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 + +/** + * Interface used to write a streaming `Dataset` to external storage systems (e.g. file systems, + * key-value stores, etc). Use `Dataset.writeStream` to access this. + * + * @since 3.5.0 + */ +@Evolving +final class DataStreamWriter[T] private[sql](ds: Dataset[T]) { + + /** + * Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. + * <ul> + * <li> `OutputMode.Append()`: only the new rows in the streaming DataFrame/Dataset will be + * written to the sink.</li> + * <li> `OutputMode.Complete()`: all the rows in the streaming DataFrame/Dataset will be written + * to the sink every time there are some updates.</li> + * <li> `OutputMode.Update()`: only the rows that were updated in the streaming + * DataFrame/Dataset will be written to the sink every time there are some updates. + * If the query doesn't contain aggregations, it will be equivalent to + * `OutputMode.Append()` mode.</li> + * </ul> + * + * @since 3.5.0 + */ + def outputMode(outputMode: OutputMode): DataStreamWriter[T] = { + sinkBuilder.setOutputMode(outputMode.toString.toLowerCase(Locale.ROOT)) + this + } + + /** + * Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. + * <ul> + * <li> `append`: only the new rows in the streaming DataFrame/Dataset will be written to + * the sink.</li> + * <li> `complete`: all the rows in the streaming DataFrame/Dataset will be written to the sink + * every time there are some updates.</li> + * <li> `update`: only the rows that were updated in the streaming DataFrame/Dataset will + * be written to the sink every time there are some updates. If the query doesn't + * contain aggregations, it will be equivalent to `append` mode.</li> + * </ul> + * + * @since 3.5.0 + */ + def outputMode(outputMode: String): DataStreamWriter[T] = { + sinkBuilder.setOutputMode(outputMode) + this + } + + /** + * Set the trigger for the stream query. The default value is `ProcessingTime(0)` and it will run + * the query as fast as possible. + * + * Scala Example: + * {{{ + * df.writeStream.trigger(ProcessingTime("10 seconds")) + * + * import scala.concurrent.duration._ + * df.writeStream.trigger(ProcessingTime(10.seconds)) + * }}} + * + * Java Example: + * {{{ + * df.writeStream().trigger(ProcessingTime.create("10 seconds")) + * + * import java.util.concurrent.TimeUnit + * df.writeStream().trigger(ProcessingTime.create(10, TimeUnit.SECONDS)) + * }}} + * + * @since 3.5.0 + */ + def trigger(trigger: Trigger): DataStreamWriter[T] = { + trigger match { + case ProcessingTimeTrigger(intervalMs) => + sinkBuilder.setProcessingTimeInterval(s"$intervalMs milliseconds") + case AvailableNowTrigger => + sinkBuilder.setAvailableNow(true) + case OneTimeTrigger => + sinkBuilder.setOnce(true) + case ContinuousTrigger(intervalMs) => + sinkBuilder.setContinuousCheckpointInterval(s"$intervalMs milliseconds") + } + this + } + + /** + * Specifies the name of the [[StreamingQuery]] that can be started with `start()`. + * This name must be unique among all the currently active queries in the associated SQLContext. + * + * @since 3.5.0 + */ + def queryName(queryName: String): DataStreamWriter[T] = { + sinkBuilder.setQueryName(queryName) + this + } + + /** + * Specifies the underlying output data source. + * + * @since 3.5.0 + */ + def format(source: String): DataStreamWriter[T] = { + sinkBuilder.setFormat(source) + this + } + + /** + * Partitions the output by the given columns on the file system. If specified, the output is + * laid out on the file system similar to Hive's partitioning scheme. As an example, when we + * partition a dataset by year and then month, the directory layout would look like: + * + * <ul> + * <li> year=2016/month=01/</li> + * <li> year=2016/month=02/</li> + * </ul> + * + * Partitioning is one of the most widely used techniques to optimize physical data layout. + * It provides a coarse-grained index for skipping unnecessary data reads when queries have + * predicates on the partitioned columns. In order for partitioning to work well, the number + * of distinct values in each column should typically be less than tens of thousands. + * + * @since 3.5.0 + */ + @scala.annotation.varargs + def partitionBy(colNames: String*): DataStreamWriter[T] = { + sinkBuilder.clearPartitioningColumnNames() + sinkBuilder.addAllPartitioningColumnNames(colNames.asJava) + this + } + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: String): DataStreamWriter[T] = { + sinkBuilder.putOptions(key, value) + this + } + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Boolean): DataStreamWriter[T] = option(key, value.toString) + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Long): DataStreamWriter[T] = option(key, value.toString) + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Double): DataStreamWriter[T] = option(key, value.toString) + + /** + * (Scala-specific) Adds output options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: scala.collection.Map[String, String]): DataStreamWriter[T] = { + this.options(options.asJava) + this + } + + /** + * Adds output options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: java.util.Map[String, String]): DataStreamWriter[T] = { + this.options(options.asScala) + this + } + + /** + * Starts the execution of the streaming query, which will continually output results to the given + * path as new data arrives. The returned [[StreamingQuery]] object can be used to interact with + * the stream. + * + * @since 3.5.0 + */ + def start(path: String): StreamingQuery = { + sinkBuilder.setPath(path) + start() + } + + /** + * Starts the execution of the streaming query, which will continually output results to the given + * path as new data arrives. The returned [[StreamingQuery]] object can be used to interact with + * the stream. Throws a `TimeoutException` if the following conditions are met: + * - Another run of the same streaming query, that is a streaming query + * sharing the same checkpoint location, is already active on the same + * Spark Driver + * - The SQL configuration `spark.sql.streaming.stopActiveRunOnRestart` + * is enabled + * - The active run cannot be stopped within the timeout controlled by + * the SQL configuration `spark.sql.streaming.stopTimeout` + * + * @since 3.5.0 + */ + @throws[TimeoutException] + def start(): StreamingQuery = { + val startCmd = Command + .newBuilder() + .setWriteStreamOperationStart(sinkBuilder.build()) + .build() + + val resp = ds.sparkSession.execute(startCmd).head + RemoteStreamingQuery.fromStartCommandResponse(ds.sparkSession, resp) + } + + /** + * TODO(SPARK-43133) Add foreach() API in Scala. + */ + // def foreach(writer: ForeachWriter[T]): DataStreamWriter[T] = { + + + /** + * :: Experimental :: + * + * (Scala-specific) Sets the output of the streaming query to be processed using the provided + * function. This is supported only in the micro-batch execution modes (that is, when the + * trigger is not continuous). In every micro-batch, the provided function will be called in + * every micro-batch with (i) the output rows as a Dataset and (ii) the batch identifier. + * The batchId can be used to deduplicate and transactionally write the output + * (that is, the provided Dataset) to external systems. The output Dataset is guaranteed + * to be exactly the same for the same batchId (assuming all operations are deterministic + * in the query). + * + * @since 3.5.0 + */ + @Evolving + def foreachBatch(function: (Dataset[T], Long) => Unit): DataStreamWriter[T] = { Review Comment: For unsupported Apis, I suggest removing the interface declaration first and adding the corresponding `ProblemFilters` rule in `CheckConnectJvmClientCompatibility` -- This is an automated message from the Apache Git Service. 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