Use foreachBatch to write to arbitrary data sinks

Structured Streaming APIs provide two ways to write the output of a streaming query to data sources that do not have an existing streaming sink: foreachBatch() and foreach().

Reuse existing batch data sources with foreachBatch()

streamingDF.writeStream.foreachBatch(...) allows you to specify a function that is executed on the output data of every micro-batch of the streaming query. 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. With foreachBatch, you can:

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. Here are a few examples:

Many other batch data sources can be used from foreachBatch().

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.write.format(...).save(...)  // location 1
  batchDF.write.format(...).save(...)  // location 2


If you are running multiple Spark jobs on the batchDF, the input data rate of the streaming query (reported through StreamingQueryProgress and visible in the notebook rate graph) may be reported as a multiple of the actual rate at which data is generated at the source. This is because the input data may be read multiple times in the multiple Spark jobs per batch.

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. For example, you can use foreachBath() and the SQL MERGE INTO operation to write the output of streaming aggregations into a Delta table in Update mode. See more details in MERGE INTO.


  • 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. In either case, you will have to reason about the end-to-end semantics yourself.

  • 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 continuous mode, use foreach() instead.

An empty dataframe can be invoked with foreachBatch() and user code needs to be resilient to allow for proper operation. An example is shown here:

          (outputDf: DataFrame, bid: Long) => {
             // Process valid data frames only
             if (!outputDf.isEmpty) {
                // business logic

Write to any location using foreach()

If foreachBatch() is not an option (for example, you are using Databricks Runtime lower than 4.2, or corresponding batch data writer does not exist), then you can express your custom writer logic using foreach(). Specifically, you can express the data writing logic by dividing it into three methods: open(), process(), and close().

For examples, see Write to Amazon DynamoDB using foreach() in Scala and Python.

Using Scala or Java

In Scala or Java, you extend the class ForeachWriter:

  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

Using Python

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.

  • The function takes a row as input.

    def processRow(row):
      // Write row to storage
    query = streamingDF.writeStream.foreach(processRow).start()
  • The object has a process method and optional open and close methods:

    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 is optional in Python.
    query = streamingDF.writeStream.foreach(ForeachWriter()).start()

Execution semantics

When the streaming query is started, Spark calls the function or the object’s methods in the following way:

  • A single copy of this object is responsible for all the data generated by a single task in a query. In other words, one instance is responsible for processing one partition of the data generated in a distributed manner.

  • This object must be serializable, because each task will get a fresh serialized-deserialized copy of the provided object. Hence, it is strongly recommended that any initialization for writing data (for example, opening a connection or starting a transaction) is done after you call the open() method, which signifies that the task is ready to generate data.

  • The lifecycle of the methods are as follows:

    For each partition with partition_id:

    For each batch/epoch of streaming data with epoch_id:

    Method open(partitionId, epochId) is called.

    If open(...) returns true, for each row in the partition and batch/epoch, method process(row) is called.

    Method close(error) is called with error (if any) seen while processing rows.

  • The close() method (if it exists) is called if an open() method exists and returns successfully (irrespective of the return value), except if the JVM or Python process crashes in the middle.


The partitionId and epochId in the open() method can be used to deduplicate generated data when failures cause reprocessing of some input data. This depends on the execution mode of the query. If the streaming query is being executed in the micro-batch mode, then every partition represented by a unique tuple (partition_id, epoch_id) is guaranteed to have the same data. Hence, (partition_id, epoch_id) can be used to deduplicate and/or transactionally commit data and achieve exactly-once guarantees. However, if the streaming query is being executed in the continuous mode, then this guarantee does not hold and therefore should not be used for deduplication.