Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/22627#discussion_r222815845
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -1989,22 +2026,211 @@ head(sql("select * from aggregates"))
</div>
</div>
-##### Using Foreach
-The `foreach` operation allows arbitrary operations to be computed on the
output data. As of Spark 2.1, this is available only for Scala and Java. To use
this, you will have to implement the interface `ForeachWriter`
-([Scala](api/scala/index.html#org.apache.spark.sql.ForeachWriter)/[Java](api/java/org/apache/spark/sql/ForeachWriter.html)
docs),
-which has methods that get called whenever there is a sequence of rows
generated as output after a trigger. Note the following important points.
+##### Using Foreach and ForeachBatch
+The `foreach` and `foreachBatch` operations allow you to apply arbitrary
operations and writing
+logic on the output of a streaming query. They have slightly different use
cases - while `foreach`
+allows custom write logic on every row, `foreachBatch` allows arbitrary
operations
+and custom logic on the output of each micro-batch. Let's understand their
usages in more detail.
+
+###### ForeachBatch
+`foreachBatch(...)` allows you to specify a function that is executed on
+the output data of every micro-batch of a streaming query. Since Spark
2.4, this is supported in Scala, Java and Python.
+It takes two parameters: a DataFrame or Dataset that has the output data
of a micro-batch and the unique ID of the micro-batch.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+streamingDF.writeStream.foreachBatch { (batchDF: DataFrame, batchId: Long)
=>
+ // Transform and write batchDF
+}.start()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+streamingDatasetOfString.writeStream.foreachBatch(
+ new VoidFunction2<Dataset<String>, long> {
+ void call(Dataset<String> dataset, long batchId) {
+ // Transform and write batchDF
+ }
+ }
+).start();
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+def foreachBatchFunction(df, epochId):
+ # Transform and write batchDF
+ pass
+
+streamingDF.writeStream.foreachBatch(foreachBatchFunction).start()
+{% endhighlight %}
+
+</div>
+<div data-lang="r" markdown="1">
+R is not yet supported.
+</div>
+</div>
+
+With foreachBatch, you can do the following.
+
+- **Reuse existing batch data sources** - For many storage systems, there
may not be a streaming sink available yet,
+ but there may already exist a data writer for batch queries. Using
foreachBatch(), you can use the batch
+ data writers on the output of each micro-batch.
+- **Write to multiple locations** - If you want to write the output of a
streaming query to multiple locations,
+ then you can simply write the output DataFrame/Dataset multiple times.
However, each attempt to write can
+ cause the output data to be recomputed (including possible re-reading of
the input data). To avoid recomputations,
+ you should cache the output DataFrame/Dataset, write it to multiple
locations, and then uncache it. Here is an outline.
+
+ streamingDF.writeStream.foreachBatch { (batchDF: DataFrame, batchId:
Long) =>
+ batchDF.cache()
+ batchDF.write.format(...).save(...) // location 1
+ batchDF.write.format(...).save(...) // location 2
+ batchDF.uncache()
+ }
+
+- **Apply additional DataFrame operations** - Many DataFrame and Dataset
operations are not supported
+ in streaming DataFrames because Spark does not support generating
incremental plans in those cases.
+ Using foreachBatch() you can apply some of these operations on each
micro-batch output. However, you will have to reason about the end-to-end
semantics of doing that operation yourself.
+
+**Note:**
+- By default, `foreachBatch` provides only at-least-once write guarantees.
However, you can use the
+ batchId provided to the function as way to deduplicate the output and
get an exactly-once guarantee.
+- `foreachBatch` does not work with the continuous processing mode as it
fundamentally relies on the
+ micro-batch execution of a streaming query. If you write data in the
continuous mode, use `foreach` instead.
+
+
+###### Foreach
+If `foreachBatch` is not an option (for example, corresponding batch data
writer does not exist, or
+continuous processing mode), then you can express you custom writer logic
using `foreach`.
+Specifically, you can express the data writing logic by dividing it into
three methods: `open`, `process`, and `close`.
+Since Spark 2.4, `foreach` is available in Scala, Java and Python.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+In Scala, you have to extend the class `ForeachWriter`
([docs](api/scala/index.html#org.apache.spark.sql.ForeachWriter)).
+
+{% highlight scala %}
+streamingDF.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()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+In Java, you have to extend the class `ForeachWriter`
([docs](api/java/org/apache/spark/sql/ForeachWriter.html)).
+{% highlight java %}
+streamingDF.writeStream.foreach(
+ new ForeachWriter[String] {
+
+ boolean open(long partitionId, long version) {
+ // Open connection
+ }
+
+ void process(String record) {
+ // Write string to connection
+ }
+
+ void close(Throwable errorOrNull) {
+ // Close the connection
+ }
+ }
+).start();
+
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+In Python, you can invoke foreach in two ways: in a function or in an
object.
+The function offers a simple way to express your processing logic but does
not allow you to
+deduplicate generated data when failures cause reprocessing of some input
data.
+For that situation you must specify the processing logic in an object.
+
+1. The function takes a row as input.
+
+ {% highlight python %}
+ def processRow(row):
+ // Write row to storage
+ pass
+
+ query = streamingDF.writeStream.foreach(processRow).start()
+ {% endhighlight %}
+
+2. The object has a process method and optional open and close methods:
+
+ {% highlight python %}
+ class ForeachWriter:
+ def open(self, partition_id, epoch_id):
+ // Open connection. This method is optional in Python.
+
+ def process(self, row):
+ // Write row to connection. This method is NOT optional in
Python.
+
+ def close(self, error):
+ // Close the connection. This method in optional in Python.
--- End diff --
ditto
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