hvanhovell commented on code in PR #40783: URL: https://github.com/apache/spark/pull/40783#discussion_r1169025739
########## connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala: ########## @@ -0,0 +1,242 @@ +/* + * 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.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] = { + sinkBuilder.putAllOptions(options) + 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 Review Comment: NIT: should the `RemoteStreamingQuery` be started instead of doing it here? -- 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. 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