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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.
*/
package org.apache.spark.sql.streaming
import scala.collection.JavaConverters._
import org.apache.spark.annotation.Experimental
import org.apache.spark.sql.{AnalysisException, DataFrame, Dataset, ForeachWriter}
import org.apache.spark.sql.execution.datasources.DataSource
import org.apache.spark.sql.execution.streaming.{ForeachSink, MemoryPlan, MemorySink}
/**
* :: Experimental ::
* 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 2.0.0
*/
@Experimental
final class DataStreamWriter[T] private[sql](ds: Dataset[T]) {
private val df = ds.toDF()
/**
* :: Experimental ::
* Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.
* - `OutputMode.Append()`: only the new rows in the streaming DataFrame/Dataset will be
* written to the sink
* - `OutputMode.Complete()`: all the rows in the streaming DataFrame/Dataset will be written
* to the sink every time these is some updates
*
* @since 2.0.0
*/
@Experimental
def outputMode(outputMode: OutputMode): DataStreamWriter[T] = {
this.outputMode = outputMode
this
}
/**
* :: Experimental ::
* Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.
* - `append`: only the new rows in the streaming DataFrame/Dataset will be written to
* the sink
* - `complete`: all the rows in the streaming DataFrame/Dataset will be written to the sink
* every time these is some updates
*
* @since 2.0.0
*/
@Experimental
def outputMode(outputMode: String): DataStreamWriter[T] = {
this.outputMode = outputMode.toLowerCase match {
case "append" =>
OutputMode.Append
case "complete" =>
OutputMode.Complete
case _ =>
throw new IllegalArgumentException(s"Unknown output mode $outputMode. " +
"Accepted output modes are 'append' and 'complete'")
}
this
}
/**
* :: Experimental ::
* 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 2.0.0
*/
@Experimental
def trigger(trigger: Trigger): DataStreamWriter[T] = {
this.trigger = trigger
this
}
/**
* :: Experimental ::
* 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 2.0.0
*/
@Experimental
def queryName(queryName: String): DataStreamWriter[T] = {
this.extraOptions += ("queryName" -> queryName)
this
}
/**
* :: Experimental ::
* Specifies the underlying output data source. Built-in options include "parquet" for now.
*
* @since 2.0.0
*/
@Experimental
def format(source: String): DataStreamWriter[T] = {
this.source = 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:
*
* - year=2016/month=01/
* - year=2016/month=02/
*
* 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.
*
* This was initially applicable for Parquet but in 1.5+ covers JSON, text, ORC and avro as well.
*
* @since 1.4.0
*/
@scala.annotation.varargs
def partitionBy(colNames: String*): DataStreamWriter[T] = {
this.partitioningColumns = Option(colNames)
this
}
/**
* :: Experimental ::
* Adds an output option for the underlying data source.
*
* @since 2.0.0
*/
@Experimental
def option(key: String, value: String): DataStreamWriter[T] = {
this.extraOptions += (key -> value)
this
}
/**
* :: Experimental ::
* Adds an output option for the underlying data source.
*
* @since 2.0.0
*/
@Experimental
def option(key: String, value: Boolean): DataStreamWriter[T] = option(key, value.toString)
/**
* :: Experimental ::
* Adds an output option for the underlying data source.
*
* @since 2.0.0
*/
@Experimental
def option(key: String, value: Long): DataStreamWriter[T] = option(key, value.toString)
/**
* :: Experimental ::
* Adds an output option for the underlying data source.
*
* @since 2.0.0
*/
@Experimental
def option(key: String, value: Double): DataStreamWriter[T] = option(key, value.toString)
/**
* :: Experimental ::
* (Scala-specific) Adds output options for the underlying data source.
*
* @since 2.0.0
*/
@Experimental
def options(options: scala.collection.Map[String, String]): DataStreamWriter[T] = {
this.extraOptions ++= options
this
}
/**
* :: Experimental ::
* Adds output options for the underlying data source.
*
* @since 2.0.0
*/
@Experimental
def options(options: java.util.Map[String, String]): DataStreamWriter[T] = {
this.options(options.asScala)
this
}
/**
* :: Experimental ::
* 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 2.0.0
*/
@Experimental
def start(path: String): StreamingQuery = {
option("path", path).start()
}
/**
* :: Experimental ::
* 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 2.0.0
*/
@Experimental
def start(): StreamingQuery = {
if (source == "memory") {
assertNotPartitioned("memory")
if (extraOptions.get("queryName").isEmpty) {
throw new AnalysisException("queryName must be specified for memory sink")
}
val sink = new MemorySink(df.schema, outputMode)
val resultDf = Dataset.ofRows(df.sparkSession, new MemoryPlan(sink))
val query = df.sparkSession.sessionState.streamingQueryManager.startQuery(
extraOptions.get("queryName"),
extraOptions.get("checkpointLocation"),
df,
sink,
outputMode,
useTempCheckpointLocation = true,
recoverFromCheckpointLocation = false,
trigger = trigger)
resultDf.createOrReplaceTempView(query.name)
query
} else if (source == "foreach") {
assertNotPartitioned("foreach")
val sink = new ForeachSink[T](foreachWriter)(ds.exprEnc)
df.sparkSession.sessionState.streamingQueryManager.startQuery(
extraOptions.get("queryName"),
extraOptions.get("checkpointLocation"),
df,
sink,
outputMode,
useTempCheckpointLocation = true,
trigger = trigger)
} else {
val (useTempCheckpointLocation, recoverFromCheckpointLocation) =
if (source == "console") {
(true, false)
} else {
(false, true)
}
val dataSource =
DataSource(
df.sparkSession,
className = source,
options = extraOptions.toMap,
partitionColumns = normalizedParCols.getOrElse(Nil))
df.sparkSession.sessionState.streamingQueryManager.startQuery(
extraOptions.get("queryName"),
extraOptions.get("checkpointLocation"),
df,
dataSource.createSink(outputMode),
outputMode,
useTempCheckpointLocation = useTempCheckpointLocation,
recoverFromCheckpointLocation = recoverFromCheckpointLocation,
trigger = trigger)
}
}
/**
* :: Experimental ::
* Starts the execution of the streaming query, which will continually send results to the given
* [[ForeachWriter]] as as new data arrives. The [[ForeachWriter]] can be used to send the data
* generated by the [[DataFrame]]/[[Dataset]] to an external system.
*
* Scala example:
* {{{
* datasetOfString.writeStream.foreach(new ForeachWriter[String] {
*
* def open(partitionId: Long, version: Long): Boolean = {
* // open connection
* }
*
* def process(record: String) = {
* // write string to connection
* }
*
* def close(errorOrNull: Throwable): Unit = {
* // close the connection
* }
* }).start()
* }}}
*
* Java example:
* {{{
* datasetOfString.writeStream().foreach(new ForeachWriter() {
*
* @Override
* public boolean open(long partitionId, long version) {
* // open connection
* }
*
* @Override
* public void process(String value) {
* // write string to connection
* }
*
* @Override
* public void close(Throwable errorOrNull) {
* // close the connection
* }
* }).start();
* }}}
*
* @since 2.0.0
*/
@Experimental
def foreach(writer: ForeachWriter[T]): DataStreamWriter[T] = {
this.source = "foreach"
this.foreachWriter = if (writer != null) {
ds.sparkSession.sparkContext.clean(writer)
} else {
throw new IllegalArgumentException("foreach writer cannot be null")
}
this
}
private def normalizedParCols: Option[Seq[String]] = partitioningColumns.map { cols =>
cols.map(normalize(_, "Partition"))
}
/**
* The given column name may not be equal to any of the existing column names if we were in
* case-insensitive context. Normalize the given column name to the real one so that we don't
* need to care about case sensitivity afterwards.
*/
private def normalize(columnName: String, columnType: String): String = {
val validColumnNames = df.logicalPlan.output.map(_.name)
validColumnNames.find(df.sparkSession.sessionState.analyzer.resolver(_, columnName))
.getOrElse(throw new AnalysisException(s"$columnType column $columnName not found in " +
s"existing columns (${validColumnNames.mkString(", ")})"))
}
private def assertNotPartitioned(operation: String): Unit = {
if (partitioningColumns.isDefined) {
throw new AnalysisException(s"'$operation' does not support partitioning")
}
}
///////////////////////////////////////////////////////////////////////////////////////
// Builder pattern config options
///////////////////////////////////////////////////////////////////////////////////////
private var source: String = df.sparkSession.sessionState.conf.defaultDataSourceName
private var outputMode: OutputMode = OutputMode.Append
private var trigger: Trigger = ProcessingTime(0L)
private var extraOptions = new scala.collection.mutable.HashMap[String, String]
private var foreachWriter: ForeachWriter[T] = null
private var partitioningColumns: Option[Seq[String]] = None
}
