You are viewing a plain text version of this content. The canonical link for it is here.
Posted to reviews@spark.apache.org by "LuciferYang (via GitHub)" <gi...@apache.org> on 2023/04/14 03:43:14 UTC
[GitHub] [spark] LuciferYang commented on a diff in pull request #40783: [SPARK-43129] Scala core API for streaming Spark Connect
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.
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-unsubscribe@spark.apache.org
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org