Structured Streaming Programming Guide

Overview

Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive. You can use the Dataset/DataFrame API in Scala, Java, Python or R to express streaming aggregations, event-time windows, stream-to-batch joins, etc. The computation is executed on the same optimized Spark SQL engine. Finally, the system ensures end-to-end exactly-once fault-tolerance guarantees through checkpointing and Write-Ahead Logs. In short, Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing without the user having to reason about streaming.

Internally, by default, Structured Streaming queries are processed using a micro-batch processing engine, which processes data streams as a series of small batch jobs thereby achieving end-to-end latencies as low as 100 milliseconds and exactly-once fault-tolerance guarantees. However, since Spark 2.3, we have introduced a new low-latency processing mode called Continuous Processing, which can achieve end-to-end latencies as low as 1 millisecond with at-least-once guarantees. Without changing the Dataset/DataFrame operations in your queries, you will be able to choose the mode based on your application requirements.

In this guide, we are going to walk you through the programming model and the APIs. We are going to explain the concepts mostly using the default micro-batch processing model, and then later discuss Continuous Processing model. First, let’s start with a simple example of a Structured Streaming query - a streaming word count.

Quick Example

Let’s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let’s see how you can express this using Structured Streaming. You can see the full code in Python/Scala/Java/R. And if you download Spark, you can directly run the example. In any case, let’s walk through the example step-by-step and understand how it works. First, we have to import the necessary classes and create a local SparkSession, the starting point of all functionalities related to Spark.

from pyspark.sql import SparkSession
from pyspark.sql.functions import explode
from pyspark.sql.functions import split

spark = SparkSession \
    .builder \
    .appName("StructuredNetworkWordCount") \
    .getOrCreate()
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession

val spark = SparkSession
  .builder
  .appName("StructuredNetworkWordCount")
  .getOrCreate()

import spark.implicits._
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.sql.*;
import org.apache.spark.sql.streaming.StreamingQuery;

import java.util.Arrays;
import java.util.Iterator;

SparkSession spark = SparkSession
  .builder()
  .appName("JavaStructuredNetworkWordCount")
  .getOrCreate();
sparkR.session(appName = "StructuredNetworkWordCount")

Next, let’s create a streaming DataFrame that represents text data received from a server listening on localhost:9999, and transform the DataFrame to calculate word counts.

# Create DataFrame representing the stream of input lines from connection to localhost:9999
lines = spark \
    .readStream \
    .format("socket") \
    .option("host", "localhost") \
    .option("port", 9999) \
    .load()

# Split the lines into words
words = lines.select(
   explode(
       split(lines.value, " ")
   ).alias("word")
)

# Generate running word count
wordCounts = words.groupBy("word").count()

This lines DataFrame represents an unbounded table containing the streaming text data. This table contains one column of strings named “value”, and each line in the streaming text data becomes a row in the table. Note, that this is not currently receiving any data as we are just setting up the transformation, and have not yet started it. Next, we have used two built-in SQL functions - split and explode, to split each line into multiple rows with a word each. In addition, we use the function alias to name the new column as “word”. Finally, we have defined the wordCounts DataFrame by grouping by the unique values in the Dataset and counting them. Note that this is a streaming DataFrame which represents the running word counts of the stream.

// Create DataFrame representing the stream of input lines from connection to localhost:9999
val lines = spark.readStream
  .format("socket")
  .option("host", "localhost")
  .option("port", 9999)
  .load()

// Split the lines into words
val words = lines.as[String].flatMap(_.split(" "))

// Generate running word count
val wordCounts = words.groupBy("value").count()

This lines DataFrame represents an unbounded table containing the streaming text data. This table contains one column of strings named “value”, and each line in the streaming text data becomes a row in the table. Note, that this is not currently receiving any data as we are just setting up the transformation, and have not yet started it. Next, we have converted the DataFrame to a Dataset of String using .as[String], so that we can apply the flatMap operation to split each line into multiple words. The resultant words Dataset contains all the words. Finally, we have defined the wordCounts DataFrame by grouping by the unique values in the Dataset and counting them. Note that this is a streaming DataFrame which represents the running word counts of the stream.

// Create DataFrame representing the stream of input lines from connection to localhost:9999
Dataset<Row> lines = spark
  .readStream()
  .format("socket")
  .option("host", "localhost")
  .option("port", 9999)
  .load();

// Split the lines into words
Dataset<String> words = lines
  .as(Encoders.STRING())
  .flatMap((FlatMapFunction<String, String>) x -> Arrays.asList(x.split(" ")).iterator(), Encoders.STRING());

// Generate running word count
Dataset<Row> wordCounts = words.groupBy("value").count();

This lines DataFrame represents an unbounded table containing the streaming text data. This table contains one column of strings named “value”, and each line in the streaming text data becomes a row in the table. Note, that this is not currently receiving any data as we are just setting up the transformation, and have not yet started it. Next, we have converted the DataFrame to a Dataset of String using .as(Encoders.STRING()), so that we can apply the flatMap operation to split each line into multiple words. The resultant words Dataset contains all the words. Finally, we have defined the wordCounts DataFrame by grouping by the unique values in the Dataset and counting them. Note that this is a streaming DataFrame which represents the running word counts of the stream.

# Create DataFrame representing the stream of input lines from connection to localhost:9999
lines <- read.stream("socket", host = "localhost", port = 9999)

# Split the lines into words
words <- selectExpr(lines, "explode(split(value, ' ')) as word")

# Generate running word count
wordCounts <- count(group_by(words, "word"))

This lines SparkDataFrame represents an unbounded table containing the streaming text data. This table contains one column of strings named “value”, and each line in the streaming text data becomes a row in the table. Note, that this is not currently receiving any data as we are just setting up the transformation, and have not yet started it. Next, we have a SQL expression with two SQL functions - split and explode, to split each line into multiple rows with a word each. In addition, we name the new column as “word”. Finally, we have defined the wordCounts SparkDataFrame by grouping by the unique values in the SparkDataFrame and counting them. Note that this is a streaming SparkDataFrame which represents the running word counts of the stream.

We have now set up the query on the streaming data. All that is left is to actually start receiving data and computing the counts. To do this, we set it up to print the complete set of counts (specified by outputMode("complete")) to the console every time they are updated. And then start the streaming computation using start().

 # Start running the query that prints the running counts to the console
query = wordCounts \
    .writeStream \
    .outputMode("complete") \
    .format("console") \
    .start()

query.awaitTermination()
// Start running the query that prints the running counts to the console
val query = wordCounts.writeStream
  .outputMode("complete")
  .format("console")
  .start()

query.awaitTermination()
// Start running the query that prints the running counts to the console
StreamingQuery query = wordCounts.writeStream()
  .outputMode("complete")
  .format("console")
  .start();

query.awaitTermination();
# Start running the query that prints the running counts to the console
query <- write.stream(wordCounts, "console", outputMode = "complete")

awaitTermination(query)

After this code is executed, the streaming computation will have started in the background. The query object is a handle to that active streaming query, and we have decided to wait for the termination of the query using awaitTermination() to prevent the process from exiting while the query is active.

To actually execute this example code, you can either compile the code in your own Spark application, or simply run the example once you have downloaded Spark. We are showing the latter. You will first need to run Netcat (a small utility found in most Unix-like systems) as a data server by using

$ nc -lk 9999

Then, in a different terminal, you can start the example by using

$ ./bin/spark-submit examples/src/main/python/sql/streaming/structured_network_wordcount.py localhost 9999
$ ./bin/run-example org.apache.spark.examples.sql.streaming.StructuredNetworkWordCount localhost 9999
$ ./bin/run-example org.apache.spark.examples.sql.streaming.JavaStructuredNetworkWordCount localhost 9999
$ ./bin/spark-submit examples/src/main/r/streaming/structured_network_wordcount.R localhost 9999

Then, any lines typed in the terminal running the netcat server will be counted and printed on screen every second. It will look something like the following.

# TERMINAL 1:
# Running Netcat

$ nc -lk 9999
apache spark
apache hadoop



















...
# TERMINAL 2: RUNNING structured_network_wordcount.py

$ ./bin/spark-submit examples/src/main/python/sql/streaming/structured_network_wordcount.py localhost 9999

-------------------------------------------
Batch: 0
-------------------------------------------
+------+-----+
| value|count|
+------+-----+
|apache|    1|
| spark|    1|
+------+-----+

-------------------------------------------
Batch: 1
-------------------------------------------
+------+-----+
| value|count|
+------+-----+
|apache|    2|
| spark|    1|
|hadoop|    1|
+------+-----+
...
# TERMINAL 2: RUNNING StructuredNetworkWordCount

$ ./bin/run-example org.apache.spark.examples.sql.streaming.StructuredNetworkWordCount localhost 9999

-------------------------------------------
Batch: 0
-------------------------------------------
+------+-----+
| value|count|
+------+-----+
|apache|    1|
| spark|    1|
+------+-----+

-------------------------------------------
Batch: 1
-------------------------------------------
+------+-----+
| value|count|
+------+-----+
|apache|    2|
| spark|    1|
|hadoop|    1|
+------+-----+
...
# TERMINAL 2: RUNNING JavaStructuredNetworkWordCount

$ ./bin/run-example org.apache.spark.examples.sql.streaming.JavaStructuredNetworkWordCount localhost 9999

-------------------------------------------
Batch: 0
-------------------------------------------
+------+-----+
| value|count|
+------+-----+
|apache|    1|
| spark|    1|
+------+-----+

-------------------------------------------
Batch: 1
-------------------------------------------
+------+-----+
| value|count|
+------+-----+
|apache|    2|
| spark|    1|
|hadoop|    1|
+------+-----+
...
# TERMINAL 2: RUNNING structured_network_wordcount.R

$ ./bin/spark-submit examples/src/main/r/streaming/structured_network_wordcount.R localhost 9999

-------------------------------------------
Batch: 0
-------------------------------------------
+------+-----+
| value|count|
+------+-----+
|apache|    1|
| spark|    1|
+------+-----+

-------------------------------------------
Batch: 1
-------------------------------------------
+------+-----+
| value|count|
+------+-----+
|apache|    2|
| spark|    1|
|hadoop|    1|
+------+-----+
...

Programming Model

The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let’s understand this model in more detail.

Basic Concepts

Consider the input data stream as the “Input Table”. Every data item that is arriving on the stream is like a new row being appended to the Input Table.

Stream as a Table

A query on the input will generate the “Result Table”. Every trigger interval (say, every 1 second), new rows get appended to the Input Table, which eventually updates the Result Table. Whenever the result table gets updated, we would want to write the changed result rows to an external sink.

Model

The “Output” is defined as what gets written out to the external storage. The output can be defined in a different mode:

Note that each mode is applicable on certain types of queries. This is discussed in detail later.

To illustrate the use of this model, let’s understand the model in context of the Quick Example above. The first lines DataFrame is the input table, and the final wordCounts DataFrame is the result table. Note that the query on streaming lines DataFrame to generate wordCounts is exactly the same as it would be a static DataFrame. However, when this query is started, Spark will continuously check for new data from the socket connection. If there is new data, Spark will run an “incremental” query that combines the previous running counts with the new data to compute updated counts, as shown below.

Model

Note that Structured Streaming does not materialize the entire table. It reads the latest available data from the streaming data source, processes it incrementally to update the result, and then discards the source data. It only keeps around the minimal intermediate state data as required to update the result (e.g. intermediate counts in the earlier example).

This model is significantly different from many other stream processing engines. Many streaming systems require the user to maintain running aggregations themselves, thus having to reason about fault-tolerance, and data consistency (at-least-once, or at-most-once, or exactly-once). In this model, Spark is responsible for updating the Result Table when there is new data, thus relieving the users from reasoning about it. As an example, let’s see how this model handles event-time based processing and late arriving data.

Handling Event-time and Late Data

Event-time is the time embedded in the data itself. For many applications, you may want to operate on this event-time. For example, if you want to get the number of events generated by IoT devices every minute, then you probably want to use the time when the data was generated (that is, event-time in the data), rather than the time Spark receives them. This event-time is very naturally expressed in this model – each event from the devices is a row in the table, and event-time is a column value in the row. This allows window-based aggregations (e.g. number of events every minute) to be just a special type of grouping and aggregation on the event-time column – each time window is a group and each row can belong to multiple windows/groups. Therefore, such event-time-window-based aggregation queries can be defined consistently on both a static dataset (e.g. from collected device events logs) as well as on a data stream, making the life of the user much easier.

Furthermore, this model naturally handles data that has arrived later than expected based on its event-time. Since Spark is updating the Result Table, it has full control over updating old aggregates when there is late data, as well as cleaning up old aggregates to limit the size of intermediate state data. Since Spark 2.1, we have support for watermarking which allows the user to specify the threshold of late data, and allows the engine to accordingly clean up old state. These are explained later in more detail in the Window Operations section.

Fault Tolerance Semantics

Delivering end-to-end exactly-once semantics was one of key goals behind the design of Structured Streaming. To achieve that, we have designed the Structured Streaming sources, the sinks and the execution engine to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing. Every streaming source is assumed to have offsets (similar to Kafka offsets, or Kinesis sequence numbers) to track the read position in the stream. The engine uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. The streaming sinks are designed to be idempotent for handling reprocessing. Together, using replayable sources and idempotent sinks, Structured Streaming can ensure end-to-end exactly-once semantics under any failure.

API using Datasets and DataFrames

Since Spark 2.0, DataFrames and Datasets can represent static, bounded data, as well as streaming, unbounded data. Similar to static Datasets/DataFrames, you can use the common entry point SparkSession (Python/Scala/Java/R docs) to create streaming DataFrames/Datasets from streaming sources, and apply the same operations on them as static DataFrames/Datasets. If you are not familiar with Datasets/DataFrames, you are strongly advised to familiarize yourself with them using the DataFrame/Dataset Programming Guide.

Creating streaming DataFrames and streaming Datasets

Streaming DataFrames can be created through the DataStreamReader interface (Python/Scala/Java docs) returned by SparkSession.readStream(). In R, with the read.stream() method. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc.

Input Sources

There are a few built-in sources.

Some sources are not fault-tolerant because they do not guarantee that data can be replayed using checkpointed offsets after a failure. See the earlier section on fault-tolerance semantics. Here are the details of all the sources in Spark.

Source Options Fault-tolerant Notes
File source path: path to the input directory, and common to all file formats.
maxFilesPerTrigger: maximum number of new files to be considered in every trigger (default: no max)
maxBytesPerTrigger: maximum total size of new files to be considered in every trigger (default: no max). maxBytesPerTrigger and maxFilesPerTrigger can't both be set at the same time, only one of two must be chosen. Note that a stream always reads at least one file so it can make progress and not get stuck on a file larger than a given maximum.
latestFirst: whether to process the latest new files first, useful when there is a large backlog of files (default: false)
fileNameOnly: whether to check new files based on only the filename instead of on the full path (default: false). With this set to `true`, the following files would be considered as the same file, because their filenames, "dataset.txt", are the same:
"file:///dataset.txt"
"s3://a/dataset.txt"
"s3n://a/b/dataset.txt"
"s3a://a/b/c/dataset.txt"
maxFileAge: Maximum age of a file that can be found in this directory, before it is ignored. For the first batch all files will be considered valid. If latestFirst is set to `true` and maxFilesPerTrigger or maxBytesPerTrigger is set, then this parameter will be ignored, because old files that are valid, and should be processed, may be ignored. The max age is specified with respect to the timestamp of the latest file, and not the timestamp of the current system.(default: 1 week)
maxCachedFiles: maximum number of files to cache to be processed in subsequent batches (default: 10000). If files are available in the cache, they will be read from first before listing from the input source.
discardCachedInputRatio: ratio of cached files/bytes to max files/bytes to allow for listing from input source when there is less cached input than could be available to be read (default: 0.2). For example, if there are only 10 cached files remaining for a batch but the maxFilesPerTrigger is set to 100, the 10 cached files would be discarded and a new listing would be performed instead. Similarly, if there are cached files that are 10 MB remaining for a batch, but the maxBytesPerTrigger is set to 100MB, the cached files would be discarded.
cleanSource: option to clean up completed files after processing.
Available options are "archive", "delete", "off". If the option is not provided, the default value is "off".
When "archive" is provided, additional option sourceArchiveDir must be provided as well. The value of "sourceArchiveDir" must not match with source pattern in depth (the number of directories from the root directory), where the depth is minimum of depth on both paths. This will ensure archived files are never included as new source files.
For example, suppose you provide '/hello?/spark/*' as source pattern, '/hello1/spark/archive/dir' cannot be used as the value of "sourceArchiveDir", as '/hello?/spark/*' and '/hello1/spark/archive' will be matched. '/hello1/spark' cannot be also used as the value of "sourceArchiveDir", as '/hello?/spark' and '/hello1/spark' will be matched. '/archived/here' would be OK as it doesn't match.
Spark will move source files respecting their own path. For example, if the path of source file is /a/b/dataset.txt and the path of archive directory is /archived/here, file will be moved to /archived/here/a/b/dataset.txt.
NOTE: Both archiving (via moving) or deleting completed files will introduce overhead (slow down, even if it's happening in separate thread) in each micro-batch, so you need to understand the cost for each operation in your file system before enabling this option. On the other hand, enabling this option will reduce the cost to list source files which can be an expensive operation.
Number of threads used in completed file cleaner can be configured with spark.sql.streaming.fileSource.cleaner.numThreads (default: 1).
NOTE 2: The source path should not be used from multiple sources or queries when enabling this option. Similarly, you must ensure the source path doesn't match to any files in output directory of file stream sink.
NOTE 3: Both delete and move actions are best effort. Failing to delete or move files will not fail the streaming query. Spark may not clean up some source files in some circumstances - e.g. the application doesn't shut down gracefully, too many files are queued to clean up.

For file-format-specific options, see the related methods in DataStreamReader (Python/Scala/Java/R). E.g. for "parquet" format options see DataStreamReader.parquet().

In addition, there are session configurations that affect certain file-formats. See the SQL Programming Guide for more details. E.g., for "parquet", see Parquet configuration section.
Yes Supports glob paths, but does not support multiple comma-separated paths/globs.
Socket Source host: host to connect to, must be specified
port: port to connect to, must be specified
No
Rate Source rowsPerSecond (e.g. 100, default: 1): How many rows should be generated per second.

rampUpTime (e.g. 5s, default: 0s): How long to ramp up before the generating speed becomes rowsPerSecond. Using finer granularities than seconds will be truncated to integer seconds.

numPartitions (e.g. 10, default: Spark's default parallelism): The partition number for the generated rows.

The source will try its best to reach rowsPerSecond, but the query may be resource constrained, and numPartitions can be tweaked to help reach the desired speed.
Yes
Rate Per Micro-Batch Source (format: rate-micro-batch) rowsPerBatch (e.g. 100): How many rows should be generated per micro-batch.

numPartitions (e.g. 10, default: Spark's default parallelism): The partition number for the generated rows.

startTimestamp (e.g. 1000, default: 0): starting value of generated time.

advanceMillisPerBatch (e.g. 1000, default: 1000): the amount of time being advanced in generated time on each micro-batch.

Yes
Kafka Source See the Kafka Integration Guide. Yes

Here are some examples.

spark = SparkSession. ...

# Read text from socket
socketDF = spark \
    .readStream \
    .format("socket") \
    .option("host", "localhost") \
    .option("port", 9999) \
    .load()

socketDF.isStreaming()    # Returns True for DataFrames that have streaming sources

socketDF.printSchema()

# Read all the csv files written atomically in a directory
userSchema = StructType().add("name", "string").add("age", "integer")
csvDF = spark \
    .readStream \
    .option("sep", ";") \
    .schema(userSchema) \
    .csv("/path/to/directory")  # Equivalent to format("csv").load("/path/to/directory")
val spark: SparkSession = ...

// Read text from socket
val socketDF = spark
  .readStream
  .format("socket")
  .option("host", "localhost")
  .option("port", 9999)
  .load()

socketDF.isStreaming    // Returns True for DataFrames that have streaming sources

socketDF.printSchema

// Read all the csv files written atomically in a directory
val userSchema = new StructType().add("name", "string").add("age", "integer")
val csvDF = spark
  .readStream
  .option("sep", ";")
  .schema(userSchema)      // Specify schema of the csv files
  .csv("/path/to/directory")    // Equivalent to format("csv").load("/path/to/directory")
SparkSession spark = ...

// Read text from socket
Dataset<Row> socketDF = spark
  .readStream()
  .format("socket")
  .option("host", "localhost")
  .option("port", 9999)
  .load();

socketDF.isStreaming();    // Returns True for DataFrames that have streaming sources

socketDF.printSchema();

// Read all the csv files written atomically in a directory
StructType userSchema = new StructType().add("name", "string").add("age", "integer");
Dataset<Row> csvDF = spark
  .readStream()
  .option("sep", ";")
  .schema(userSchema)      // Specify schema of the csv files
  .csv("/path/to/directory");    // Equivalent to format("csv").load("/path/to/directory")
sparkR.session(...)

# Read text from socket
socketDF <- read.stream("socket", host = hostname, port = port)

isStreaming(socketDF)    # Returns TRUE for SparkDataFrames that have streaming sources

printSchema(socketDF)

# Read all the csv files written atomically in a directory
schema <- structType(structField("name", "string"),
                     structField("age", "integer"))
csvDF <- read.stream("csv", path = "/path/to/directory", schema = schema, sep = ";")

These examples generate streaming DataFrames that are untyped, meaning that the schema of the DataFrame is not checked at compile time, only checked at runtime when the query is submitted. Some operations like map, flatMap, etc. need the type to be known at compile time. To do those, you can convert these untyped streaming DataFrames to typed streaming Datasets using the same methods as static DataFrame. See the SQL Programming Guide for more details. Additionally, more details on the supported streaming sources are discussed later in the document.

Since Spark 3.1, you can also create streaming DataFrames from tables with DataStreamReader.table(). See Streaming Table APIs for more details.

Schema inference and partition of streaming DataFrames/Datasets

By default, Structured Streaming from file based sources requires you to specify the schema, rather than rely on Spark to infer it automatically. This restriction ensures a consistent schema will be used for the streaming query, even in the case of failures. For ad-hoc use cases, you can reenable schema inference by setting spark.sql.streaming.schemaInference to true.

Partition discovery does occur when subdirectories that are named /key=value/ are present and listing will automatically recurse into these directories. If these columns appear in the user-provided schema, they will be filled in by Spark based on the path of the file being read. The directories that make up the partitioning scheme must be present when the query starts and must remain static. For example, it is okay to add /data/year=2016/ when /data/year=2015/ was present, but it is invalid to change the partitioning column (i.e. by creating the directory /data/date=2016-04-17/).

Operations on streaming DataFrames/Datasets

You can apply all kinds of operations on streaming DataFrames/Datasets – ranging from untyped, SQL-like operations (e.g. select, where, groupBy), to typed RDD-like operations (e.g. map, filter, flatMap). See the SQL programming guide for more details. Let’s take a look at a few example operations that you can use.

Basic Operations - Selection, Projection, Aggregation

Most of the common operations on DataFrame/Dataset are supported for streaming. The few operations that are not supported are discussed later in this section.

df = ...  # streaming DataFrame with IOT device data with schema { device: string, deviceType: string, signal: double, time: DateType }

# Select the devices which have signal more than 10
df.select("device").where("signal > 10")

# Running count of the number of updates for each device type
df.groupBy("deviceType").count()
case class DeviceData(device: String, deviceType: String, signal: Double, time: DateTime)

val df: DataFrame = ... // streaming DataFrame with IOT device data with schema { device: string, deviceType: string, signal: double, time: string }
val ds: Dataset[DeviceData] = df.as[DeviceData]    // streaming Dataset with IOT device data

// Select the devices which have signal more than 10
df.select("device").where("signal > 10")      // using untyped APIs
ds.filter(_.signal > 10).map(_.device)         // using typed APIs

// Running count of the number of updates for each device type
df.groupBy("deviceType").count()                          // using untyped API

// Running average signal for each device type
import org.apache.spark.sql.expressions.scalalang.typed
ds.groupByKey(_.deviceType).agg(typed.avg(_.signal))    // using typed API
import org.apache.spark.api.java.function.*;
import org.apache.spark.sql.*;
import org.apache.spark.sql.expressions.javalang.typed;
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder;

public class DeviceData {
  private String device;
  private String deviceType;
  private Double signal;
  private java.sql.Date time;
  ...
  // Getter and setter methods for each field
}

Dataset<Row> df = ...;    // streaming DataFrame with IOT device data with schema { device: string, type: string, signal: double, time: DateType }
Dataset<DeviceData> ds = df.as(ExpressionEncoder.javaBean(DeviceData.class)); // streaming Dataset with IOT device data

// Select the devices which have signal more than 10
df.select("device").where("signal > 10"); // using untyped APIs
ds.filter((FilterFunction<DeviceData>) value -> value.getSignal() > 10)
  .map((MapFunction<DeviceData, String>) value -> value.getDevice(), Encoders.STRING());

// Running count of the number of updates for each device type
df.groupBy("deviceType").count(); // using untyped API

// Running average signal for each device type
ds.groupByKey((MapFunction<DeviceData, String>) value -> value.getDeviceType(), Encoders.STRING())
  .agg(typed.avg((MapFunction<DeviceData, Double>) value -> value.getSignal()));
df <- ...  # streaming DataFrame with IOT device data with schema { device: string, deviceType: string, signal: double, time: DateType }

# Select the devices which have signal more than 10
select(where(df, "signal > 10"), "device")

# Running count of the number of updates for each device type
count(groupBy(df, "deviceType"))

You can also register a streaming DataFrame/Dataset as a temporary view and then apply SQL commands on it.

df.createOrReplaceTempView("updates")
spark.sql("select count(*) from updates")  # returns another streaming DF
df.createOrReplaceTempView("updates")
spark.sql("select count(*) from updates")  // returns another streaming DF
df.createOrReplaceTempView("updates");
spark.sql("select count(*) from updates");  // returns another streaming DF
createOrReplaceTempView(df, "updates")
sql("select count(*) from updates")

Note, you can identify whether a DataFrame/Dataset has streaming data or not by using df.isStreaming.

df.isStreaming()
df.isStreaming
df.isStreaming()
isStreaming(df)

You may want to check the query plan of the query, as Spark could inject stateful operations during interpret of SQL statement against streaming dataset. Once stateful operations are injected in the query plan, you may need to check your query with considerations in stateful operations. (e.g. output mode, watermark, state store size maintenance, etc.)

Window Operations on Event Time

Aggregations over a sliding event-time window are straightforward with Structured Streaming and are very similar to grouped aggregations. In a grouped aggregation, aggregate values (e.g. counts) are maintained for each unique value in the user-specified grouping column. In case of window-based aggregations, aggregate values are maintained for each window the event-time of a row falls into. Let’s understand this with an illustration.

Imagine our quick example is modified and the stream now contains lines along with the time when the line was generated. Instead of running word counts, we want to count words within 10 minute windows, updating every 5 minutes. That is, word counts in words received between 10 minute windows 12:00 - 12:10, 12:05 - 12:15, 12:10 - 12:20, etc. Note that 12:00 - 12:10 means data that arrived after 12:00 but before 12:10. Now, consider a word that was received at 12:07. This word should increment the counts corresponding to two windows 12:00 - 12:10 and 12:05 - 12:15. So the counts will be indexed by both, the grouping key (i.e. the word) and the window (can be calculated from the event-time).

The result tables would look something like the following.

Window Operations

Since this windowing is similar to grouping, in code, you can use groupBy() and window() operations to express windowed aggregations. You can see the full code for the below examples in Python/Scala/Java.

words = ...  # streaming DataFrame of schema { timestamp: Timestamp, word: String }

# Group the data by window and word and compute the count of each group
windowedCounts = words.groupBy(
    window(words.timestamp, "10 minutes", "5 minutes"),
    words.word
).count()
import spark.implicits._

val words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
val windowedCounts = words.groupBy(
  window($"timestamp", "10 minutes", "5 minutes"),
  $"word"
).count()
Dataset<Row> words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
Dataset<Row> windowedCounts = words.groupBy(
  functions.window(words.col("timestamp"), "10 minutes", "5 minutes"),
  words.col("word")
).count();
words <- ...  # streaming DataFrame of schema { timestamp: Timestamp, word: String }

# Group the data by window and word and compute the count of each group
windowedCounts <- count(
                    groupBy(
                      words,
                      window(words$timestamp, "10 minutes", "5 minutes"),
                      words$word))

Handling Late Data and Watermarking

Now consider what happens if one of the events arrives late to the application. For example, say, a word generated at 12:04 (i.e. event time) could be received by the application at 12:11. The application should use the time 12:04 instead of 12:11 to update the older counts for the window 12:00 - 12:10. This occurs naturally in our window-based grouping – Structured Streaming can maintain the intermediate state for partial aggregates for a long period of time such that late data can update aggregates of old windows correctly, as illustrated below.

Handling Late Data

However, to run this query for days, it’s necessary for the system to bound the amount of intermediate in-memory state it accumulates. This means the system needs to know when an old aggregate can be dropped from the in-memory state because the application is not going to receive late data for that aggregate any more. To enable this, in Spark 2.1, we have introduced watermarking, which lets the engine automatically track the current event time in the data and attempt to clean up old state accordingly. You can define the watermark of a query by specifying the event time column and the threshold on how late the data is expected to be in terms of event time. For a specific window ending at time T, the engine will maintain state and allow late data to update the state until (max event time seen by the engine - late threshold > T). In other words, late data within the threshold will be aggregated, but data later than the threshold will start getting dropped (see later in the section for the exact guarantees). Let’s understand this with an example. We can easily define watermarking on the previous example using withWatermark() as shown below.

words = ...  # streaming DataFrame of schema { timestamp: Timestamp, word: String }

# Group the data by window and word and compute the count of each group
windowedCounts = words \
    .withWatermark("timestamp", "10 minutes") \
    .groupBy(
        window(words.timestamp, "10 minutes", "5 minutes"),
        words.word) \
    .count()
import spark.implicits._

val words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
val windowedCounts = words
    .withWatermark("timestamp", "10 minutes")
    .groupBy(
        window($"timestamp", "10 minutes", "5 minutes"),
        $"word")
    .count()
Dataset<Row> words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
Dataset<Row> windowedCounts = words
    .withWatermark("timestamp", "10 minutes")
    .groupBy(
        window(col("timestamp"), "10 minutes", "5 minutes"),
        col("word"))
    .count();
words <- ...  # streaming DataFrame of schema { timestamp: Timestamp, word: String }

# Group the data by window and word and compute the count of each group

words <- withWatermark(words, "timestamp", "10 minutes")
windowedCounts <- count(
                    groupBy(
                      words,
                      window(words$timestamp, "10 minutes", "5 minutes"),
                      words$word))

In this example, we are defining the watermark of the query on the value of the column “timestamp”, and also defining “10 minutes” as the threshold of how late is the data allowed to be. If this query is run in Update output mode (discussed later in Output Modes section), the engine will keep updating counts of a window in the Result Table until the window is older than the watermark, which lags behind the current event time in column “timestamp” by 10 minutes. Here is an illustration.

Watermarking in Update Mode

As shown in the illustration, the maximum event time tracked by the engine is the blue dashed line, and the watermark set as (max event time - '10 mins') at the beginning of every trigger is the red line. For example, when the engine observes the data (12:14, dog), it sets the watermark for the next trigger as 12:04. This watermark lets the engine maintain intermediate state for additional 10 minutes to allow late data to be counted. For example, the data (12:09, cat) is out of order and late, and it falls in windows 12:00 - 12:10 and 12:05 - 12:15. Since, it is still ahead of the watermark 12:04 in the trigger, the engine still maintains the intermediate counts as state and correctly updates the counts of the related windows. However, when the watermark is updated to 12:11, the intermediate state for window (12:00 - 12:10) is cleared, and all subsequent data (e.g. (12:04, donkey)) is considered “too late” and therefore ignored. Note that after every trigger, the updated counts (i.e. purple rows) are written to sink as the trigger output, as dictated by the Update mode.

Some sinks (e.g. files) may not supported fine-grained updates that Update Mode requires. To work with them, we have also support Append Mode, where only the final counts are written to sink. This is illustrated below.

Note that using withWatermark on a non-streaming Dataset is no-op. As the watermark should not affect any batch query in any way, we will ignore it directly.

Watermarking in Append Mode

Similar to the Update Mode earlier, the engine maintains intermediate counts for each window. However, the partial counts are not updated to the Result Table and not written to sink. The engine waits for “10 mins” for late date to be counted, then drops intermediate state of a window < watermark, and appends the final counts to the Result Table/sink. For example, the final counts of window 12:00 - 12:10 is appended to the Result Table only after the watermark is updated to 12:11.

Types of time windows

Spark supports three types of time windows: tumbling (fixed), sliding and session.

The types of time windows

Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. An input can only be bound to a single window.

Sliding windows are similar to the tumbling windows from the point of being “fixed-sized”, but windows can overlap if the duration of slide is smaller than the duration of window, and in this case an input can be bound to the multiple windows.

Tumbling and sliding window use window function, which has been described on above examples.

Session windows have different characteristic compared to the previous two types. Session window has a dynamic size of the window length, depending on the inputs. A session window starts with an input, and expands itself if following input has been received within gap duration. For static gap duration, a session window closes when there’s no input received within gap duration after receiving the latest input.

Session window uses session_window function. The usage of the function is similar to the window function.

events = ...  # streaming DataFrame of schema { timestamp: Timestamp, userId: String }

# Group the data by session window and userId, and compute the count of each group
sessionizedCounts = events \
    .withWatermark("timestamp", "10 minutes") \
    .groupBy(
        session_window(events.timestamp, "5 minutes"),
        events.userId) \
    .count()
import spark.implicits._

val events = ... // streaming DataFrame of schema { timestamp: Timestamp, userId: String }

// Group the data by session window and userId, and compute the count of each group
val sessionizedCounts = events
    .withWatermark("timestamp", "10 minutes")
    .groupBy(
        session_window($"timestamp", "5 minutes"),
        $"userId")
    .count()
Dataset<Row> events = ... // streaming DataFrame of schema { timestamp: Timestamp, userId: String }

// Group the data by session window and userId, and compute the count of each group
Dataset<Row> sessionizedCounts = events
    .withWatermark("timestamp", "10 minutes")
    .groupBy(
        session_window(col("timestamp"), "5 minutes"),
        col("userId"))
    .count();

Instead of static value, we can also provide an expression to specify gap duration dynamically based on the input row. Note that the rows with negative or zero gap duration will be filtered out from the aggregation.

With dynamic gap duration, the closing of a session window does not depend on the latest input anymore. A session window’s range is the union of all events’ ranges which are determined by event start time and evaluated gap duration during the query execution.

from pyspark.sql import functions as sf

events = ...  # streaming DataFrame of schema { timestamp: Timestamp, userId: String }

session_window = session_window(events.timestamp, \
    sf.when(events.userId == "user1", "5 seconds") \
    .when(events.userId == "user2", "20 seconds").otherwise("5 minutes"))

# Group the data by session window and userId, and compute the count of each group
sessionizedCounts = events \
    .withWatermark("timestamp", "10 minutes") \
    .groupBy(
        session_window,
        events.userId) \
    .count()
import spark.implicits._

val events = ... // streaming DataFrame of schema { timestamp: Timestamp, userId: String }

val sessionWindow = session_window($"timestamp", when($"userId" === "user1", "5 seconds")
  .when($"userId" === "user2", "20 seconds")
  .otherwise("5 minutes"))

// Group the data by session window and userId, and compute the count of each group
val sessionizedCounts = events
    .withWatermark("timestamp", "10 minutes")
    .groupBy(
        Column(sessionWindow),
        $"userId")
    .count()
Dataset<Row> events = ... // streaming DataFrame of schema { timestamp: Timestamp, userId: String }

SessionWindow sessionWindow = session_window(col("timestamp"), when(col("userId").equalTo("user1"), "5 seconds")
  .when(col("userId").equalTo("user2"), "20 seconds")
  .otherwise("5 minutes"))

// Group the data by session window and userId, and compute the count of each group
Dataset<Row> sessionizedCounts = events
    .withWatermark("timestamp", "10 minutes")
    .groupBy(
        new Column(sessionWindow),
        col("userId"))
    .count();

Note that there are some restrictions when you use session window in streaming query, like below:

For batch query, global window (only having session_window in grouping key) is supported.

By default, Spark does not perform partial aggregation for session window aggregation, since it requires additional sort in local partitions before grouping. It works better for the case there are only few number of input rows in same group key for each local partition, but for the case there are numerous input rows having same group key in local partition, doing partial aggregation can still increase the performance significantly despite additional sort.

You can enable spark.sql.streaming.sessionWindow.merge.sessions.in.local.partition to indicate Spark to perform partial aggregation.

Representation of the time for time window

In some use cases, it is necessary to extract the representation of the time for time window, to apply operations requiring timestamp to the time windowed data. One example is chained time window aggregations, where users want to define another time window against the time window. Say, someone wants to aggregate 5 minutes time windows as 1 hour tumble time window.

There are two ways to achieve this, like below:

  1. Use window_time SQL function with time window column as parameter
  2. Use window SQL function with time window column as parameter

window_time function will produce a timestamp which represents the time for time window. User can pass the result to the parameter of window function (or anywhere requiring timestamp) to perform operation(s) with time window which requires timestamp.

words = ...  # streaming DataFrame of schema { timestamp: Timestamp, word: String }

# Group the data by window and word and compute the count of each group
windowedCounts = words.groupBy(
    window(words.timestamp, "10 minutes", "5 minutes"),
    words.word
).count()

# Group the windowed data by another window and word and compute the count of each group
anotherWindowedCounts = windowedCounts.groupBy(
    window(window_time(windowedCounts.window), "1 hour"),
    windowedCounts.word
).count()
import spark.implicits._

val words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
val windowedCounts = words.groupBy(
  window($"timestamp", "10 minutes", "5 minutes"),
  $"word"
).count()

// Group the windowed data by another window and word and compute the count of each group
val anotherWindowedCounts = windowedCounts.groupBy(
  window(window_time($"window"), "1 hour"),
  $"word"
).count()
Dataset<Row> words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
Dataset<Row> windowedCounts = words.groupBy(
  functions.window(words.col("timestamp"), "10 minutes", "5 minutes"),
  words.col("word")
).count();

// Group the windowed data by another window and word and compute the count of each group
Dataset<Row> anotherWindowedCounts = windowedCounts.groupBy(
  functions.window(functions.window_time("window"), "1 hour"),
  windowedCounts.col("word")
).count();

window function does not only take timestamp column, but also take the time window column. This is specifically useful for cases where users want to apply chained time window aggregations.

words = ...  # streaming DataFrame of schema { timestamp: Timestamp, word: String }

# Group the data by window and word and compute the count of each group
windowedCounts = words.groupBy(
    window(words.timestamp, "10 minutes", "5 minutes"),
    words.word
).count()

# Group the windowed data by another window and word and compute the count of each group
anotherWindowedCounts = windowedCounts.groupBy(
    window(windowedCounts.window, "1 hour"),
    windowedCounts.word
).count()
import spark.implicits._

val words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
val windowedCounts = words.groupBy(
  window($"timestamp", "10 minutes", "5 minutes"),
  $"word"
).count()

// Group the windowed data by another window and word and compute the count of each group
val anotherWindowedCounts = windowedCounts.groupBy(
  window($"window", "1 hour"),
  $"word"
).count()
Dataset<Row> words = ... // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
Dataset<Row> windowedCounts = words.groupBy(
  functions.window(words.col("timestamp"), "10 minutes", "5 minutes"),
  words.col("word")
).count();

// Group the windowed data by another window and word and compute the count of each group
Dataset<Row> anotherWindowedCounts = windowedCounts.groupBy(
  functions.window("window", "1 hour"),
  windowedCounts.col("word")
).count();
Conditions for watermarking to clean aggregation state

It is important to note that the following conditions must be satisfied for the watermarking to clean the state in aggregation queries (as of Spark 2.1.1, subject to change in the future).

Semantic Guarantees of Aggregation with Watermarking

Join Operations

Structured Streaming supports joining a streaming Dataset/DataFrame with a static Dataset/DataFrame as well as another streaming Dataset/DataFrame. The result of the streaming join is generated incrementally, similar to the results of streaming aggregations in the previous section. In this section we will explore what type of joins (i.e. inner, outer, semi, etc.) are supported in the above cases. Note that in all the supported join types, the result of the join with a streaming Dataset/DataFrame will be the exactly the same as if it was with a static Dataset/DataFrame containing the same data in the stream.

Stream-static Joins

Since the introduction in Spark 2.0, Structured Streaming has supported joins (inner join and some type of outer joins) between a streaming and a static DataFrame/Dataset. Here is a simple example.

staticDf = spark.read. ...
streamingDf = spark.readStream. ...
streamingDf.join(staticDf, "type")  # inner equi-join with a static DF
streamingDf.join(staticDf, "type", "left_outer")  # left outer join with a static DF
val staticDf = spark.read. ...
val streamingDf = spark.readStream. ...

streamingDf.join(staticDf, "type")          // inner equi-join with a static DF
streamingDf.join(staticDf, "type", "left_outer")  // left outer join with a static DF
Dataset<Row> staticDf = spark.read(). ...;
Dataset<Row> streamingDf = spark.readStream(). ...;
streamingDf.join(staticDf, "type");         // inner equi-join with a static DF
streamingDf.join(staticDf, "type", "left_outer");  // left outer join with a static DF
staticDf <- read.df(...)
streamingDf <- read.stream(...)
joined <- merge(streamingDf, staticDf, sort = FALSE)  # inner equi-join with a static DF
joined <- join(
            streamingDf,
            staticDf,
            streamingDf$value == staticDf$value,
            "left_outer")  # left outer join with a static DF

Note that stream-static joins are not stateful, so no state management is necessary. However, a few types of stream-static outer joins are not yet supported. These are listed at the end of this Join section.

Stream-stream Joins

In Spark 2.3, we have added support for stream-stream joins, that is, you can join two streaming Datasets/DataFrames. The challenge of generating join results between two data streams is that, at any point of time, the view of the dataset is incomplete for both sides of the join making it much harder to find matches between inputs. Any row received from one input stream can match with any future, yet-to-be-received row from the other input stream. Hence, for both the input streams, we buffer past input as streaming state, so that we can match every future input with past input and accordingly generate joined results. Furthermore, similar to streaming aggregations, we automatically handle late, out-of-order data and can limit the state using watermarks. Let’s discuss the different types of supported stream-stream joins and how to use them.

Inner Joins with optional Watermarking

Inner joins on any kind of columns along with any kind of join conditions are supported. However, as the stream runs, the size of streaming state will keep growing indefinitely as all past input must be saved as any new input can match with any input from the past. To avoid unbounded state, you have to define additional join conditions such that indefinitely old inputs cannot match with future inputs and therefore can be cleared from the state. In other words, you will have to do the following additional steps in the join.

  1. Define watermark delays on both inputs such that the engine knows how delayed the input can be (similar to streaming aggregations)

  2. Define a constraint on event-time across the two inputs such that the engine can figure out when old rows of one input is not going to be required (i.e. will not satisfy the time constraint) for matches with the other input. This constraint can be defined in one of the two ways.

    1. Time range join conditions (e.g. ...JOIN ON leftTime BETWEEN rightTime AND rightTime + INTERVAL 1 HOUR),

    2. Join on event-time windows (e.g. ...JOIN ON leftTimeWindow = rightTimeWindow).

Let’s understand this with an example.

Let’s say we want to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks. To allow the state cleanup in this stream-stream join, you will have to specify the watermarking delays and the time constraints as follows.

  1. Watermark delays: Say, the impressions and the corresponding clicks can be late/out-of-order in event-time by at most 2 and 3 hours, respectively.

  2. Event-time range condition: Say, a click can occur within a time range of 0 seconds to 1 hour after the corresponding impression.

The code would look like this.

from pyspark.sql.functions import expr

impressions = spark.readStream. ...
clicks = spark.readStream. ...

# Apply watermarks on event-time columns
impressionsWithWatermark = impressions.withWatermark("impressionTime", "2 hours")
clicksWithWatermark = clicks.withWatermark("clickTime", "3 hours")

# Join with event-time constraints
impressionsWithWatermark.join(
  clicksWithWatermark,
  expr("""
    clickAdId = impressionAdId AND
    clickTime >= impressionTime AND
    clickTime <= impressionTime + interval 1 hour
    """)
)
import org.apache.spark.sql.functions.expr

val impressions = spark.readStream. ...
val clicks = spark.readStream. ...

// Apply watermarks on event-time columns
val impressionsWithWatermark = impressions.withWatermark("impressionTime", "2 hours")
val clicksWithWatermark = clicks.withWatermark("clickTime", "3 hours")

// Join with event-time constraints
impressionsWithWatermark.join(
  clicksWithWatermark,
  expr("""
    clickAdId = impressionAdId AND
    clickTime >= impressionTime AND
    clickTime <= impressionTime + interval 1 hour
    """)
)
import static org.apache.spark.sql.functions.expr

Dataset<Row> impressions = spark.readStream(). ...
Dataset<Row> clicks = spark.readStream(). ...

// Apply watermarks on event-time columns
Dataset<Row> impressionsWithWatermark = impressions.withWatermark("impressionTime", "2 hours");
Dataset<Row> clicksWithWatermark = clicks.withWatermark("clickTime", "3 hours");

// Join with event-time constraints
impressionsWithWatermark.join(
  clicksWithWatermark,
  expr(
    "clickAdId = impressionAdId AND " +
    "clickTime >= impressionTime AND " +
    "clickTime <= impressionTime + interval 1 hour ")
);
impressions <- read.stream(...)
clicks <- read.stream(...)

# Apply watermarks on event-time columns
impressionsWithWatermark <- withWatermark(impressions, "impressionTime", "2 hours")
clicksWithWatermark <- withWatermark(clicks, "clickTime", "3 hours")

# Join with event-time constraints
joined <- join(
  impressionsWithWatermark,
  clicksWithWatermark,
  expr(
    paste(
      "clickAdId = impressionAdId AND",
      "clickTime >= impressionTime AND",
      "clickTime <= impressionTime + interval 1 hour"
)))
Semantic Guarantees of Stream-stream Inner Joins with Watermarking

This is similar to the guarantees provided by watermarking on aggregations. A watermark delay of “2 hours” guarantees that the engine will never drop any data that is less than 2 hours delayed. But data delayed by more than 2 hours may or may not get processed.

Outer Joins with Watermarking

While the watermark + event-time constraints is optional for inner joins, for outer joins they must be specified. This is because for generating the NULL results in outer join, the engine must know when an input row is not going to match with anything in future. Hence, the watermark + event-time constraints must be specified for generating correct results. Therefore, a query with outer-join will look quite like the ad-monetization example earlier, except that there will be an additional parameter specifying it to be an outer-join.

impressionsWithWatermark.join(
  clicksWithWatermark,
  expr("""
    clickAdId = impressionAdId AND
    clickTime >= impressionTime AND
    clickTime <= impressionTime + interval 1 hour
    """),
  "leftOuter"                 # can be "inner", "leftOuter", "rightOuter", "fullOuter", "leftSemi"
)
impressionsWithWatermark.join(
  clicksWithWatermark,
  expr("""
    clickAdId = impressionAdId AND
    clickTime >= impressionTime AND
    clickTime <= impressionTime + interval 1 hour
    """),
  joinType = "leftOuter"      // can be "inner", "leftOuter", "rightOuter", "fullOuter", "leftSemi"
 )
impressionsWithWatermark.join(
  clicksWithWatermark,
  expr(
    "clickAdId = impressionAdId AND " +
    "clickTime >= impressionTime AND " +
    "clickTime <= impressionTime + interval 1 hour "),
  "leftOuter"                 // can be "inner", "leftOuter", "rightOuter", "fullOuter", "leftSemi"
);
joined <- join(
  impressionsWithWatermark,
  clicksWithWatermark,
  expr(
    paste(
      "clickAdId = impressionAdId AND",
      "clickTime >= impressionTime AND",
      "clickTime <= impressionTime + interval 1 hour"),
  "left_outer"                 # can be "inner", "left_outer", "right_outer", "full_outer", "left_semi"
))
Semantic Guarantees of Stream-stream Outer Joins with Watermarking

Outer joins have the same guarantees as inner joins regarding watermark delays and whether data will be dropped or not.

Caveats

There are a few important characteristics to note regarding how the outer results are generated.

Semi Joins with Watermarking

A semi join returns values from the left side of the relation that has a match with the right. It is also referred to as a left semi join. Similar to outer joins, watermark + event-time constraints must be specified for semi join. This is to evict unmatched input rows on left side, the engine must know when an input row on left side is not going to match with anything on right side in future.

Semantic Guarantees of Stream-stream Semi Joins with Watermarking

Semi joins have the same guarantees as inner joins regarding watermark delays and whether data will be dropped or not.

Support matrix for joins in streaming queries
Left Input Right Input Join Type
Static Static All types Supported, since its not on streaming data even though it can be present in a streaming query
Stream Static Inner Supported, not stateful
Left Outer Supported, not stateful
Right Outer Not supported
Full Outer Not supported
Left Semi Supported, not stateful
Static Stream Inner Supported, not stateful
Left Outer Not supported
Right Outer Supported, not stateful
Full Outer Not supported
Left Semi Not supported
Stream Stream Inner Supported, optionally specify watermark on both sides + time constraints for state cleanup
Left Outer Conditionally supported, must specify watermark on right + time constraints for correct results, optionally specify watermark on left for all state cleanup
Right Outer Conditionally supported, must specify watermark on left + time constraints for correct results, optionally specify watermark on right for all state cleanup
Full Outer Conditionally supported, must specify watermark on one side + time constraints for correct results, optionally specify watermark on the other side for all state cleanup
Left Semi Conditionally supported, must specify watermark on right + time constraints for correct results, optionally specify watermark on left for all state cleanup

Additional details on supported joins:

In append output mode, you can construct a query having non-map-like operations e.g. aggregation, deduplication, stream-stream join before/after join.

For example, here’s an example of time window aggregation in both streams followed by stream-stream join with event time window:

clicksWindow = clicksWithWatermark.groupBy(
  clicksWithWatermark.clickAdId,
  window(clicksWithWatermark.clickTime, "1 hour")
).count()

impressionsWindow = impressionsWithWatermark.groupBy(
  impressionsWithWatermark.impressionAdId,
  window(impressionsWithWatermark.impressionTime, "1 hour")
).count()

clicksWindow.join(impressionsWindow, "window", "inner")
val clicksWindow = clicksWithWatermark
  .groupBy(window("clickTime", "1 hour"))
  .count()

val impressionsWindow = impressionsWithWatermark
  .groupBy(window("impressionTime", "1 hour"))
  .count()

clicksWindow.join(impressionsWindow, "window", "inner")
Dataset<Row> clicksWindow = clicksWithWatermark
  .groupBy(functions.window(clicksWithWatermark.col("clickTime"), "1 hour"))
  .count();

Dataset<Row> impressionsWindow = impressionsWithWatermark
  .groupBy(functions.window(impressionsWithWatermark.col("impressionTime"), "1 hour"))
  .count();

clicksWindow.join(impressionsWindow, "window", "inner");

Here’s another example of stream-stream join with time range join condition followed by time window aggregation:

joined = impressionsWithWatermark.join(
  clicksWithWatermark,
  expr("""
    clickAdId = impressionAdId AND
    clickTime >= impressionTime AND
    clickTime <= impressionTime + interval 1 hour
    """),
  "leftOuter"                 # can be "inner", "leftOuter", "rightOuter", "fullOuter", "leftSemi"
)

joined.groupBy(
  joined.clickAdId,
  window(joined.clickTime, "1 hour")
).count()
val joined = impressionsWithWatermark.join(
  clicksWithWatermark,
  expr("""
    clickAdId = impressionAdId AND
    clickTime >= impressionTime AND
    clickTime <= impressionTime + interval 1 hour
  """),
  joinType = "leftOuter"      // can be "inner", "leftOuter", "rightOuter", "fullOuter", "leftSemi"
)

joined
  .groupBy($"clickAdId", window($"clickTime", "1 hour"))
  .count()
Dataset<Row> joined = impressionsWithWatermark.join(
  clicksWithWatermark,
  expr(
    "clickAdId = impressionAdId AND " +
    "clickTime >= impressionTime AND " +
    "clickTime <= impressionTime + interval 1 hour "),
  "leftOuter"                 // can be "inner", "leftOuter", "rightOuter", "fullOuter", "leftSemi"
);

joined
  .groupBy(joined.col("clickAdId"), functions.window(joined.col("clickTime"), "1 hour"))
  .count();

Streaming Deduplication

You can deduplicate records in data streams using a unique identifier in the events. This is exactly same as deduplication on static using a unique identifier column. The query will store the necessary amount of data from previous records such that it can filter duplicate records. Similar to aggregations, you can use deduplication with or without watermarking.

streamingDf = spark.readStream. ...

# Without watermark using guid column
streamingDf.dropDuplicates("guid")

# With watermark using guid and eventTime columns
streamingDf \
  .withWatermark("eventTime", "10 seconds") \
  .dropDuplicates("guid", "eventTime")
val streamingDf = spark.readStream. ...  // columns: guid, eventTime, ...

// Without watermark using guid column
streamingDf.dropDuplicates("guid")

// With watermark using guid and eventTime columns
streamingDf
  .withWatermark("eventTime", "10 seconds")
  .dropDuplicates("guid", "eventTime")
Dataset<Row> streamingDf = spark.readStream(). ...;  // columns: guid, eventTime, ...

// Without watermark using guid column
streamingDf.dropDuplicates("guid");

// With watermark using guid and eventTime columns
streamingDf
  .withWatermark("eventTime", "10 seconds")
  .dropDuplicates("guid", "eventTime");
streamingDf <- read.stream(...)

# Without watermark using guid column
streamingDf <- dropDuplicates(streamingDf, "guid")

# With watermark using guid and eventTime columns
streamingDf <- withWatermark(streamingDf, "eventTime", "10 seconds")
streamingDf <- dropDuplicates(streamingDf, "guid", "eventTime")

Specifically for streaming, you can deduplicate records in data streams using a unique identifier in the events, within the time range of watermark. For example, if you set the delay threshold of watermark as “1 hour”, duplicated events which occurred within 1 hour can be correctly deduplicated. (For more details, please refer to the API doc of dropDuplicatesWithinWatermark.)

This can be used to deal with use case where event time column cannot be a part of unique identifier, mostly due to the case where event times are somehow different for the same records. (E.g. non-idempotent writer where issuing event time happens at write)

Users are encouraged to set the delay threshold of watermark longer than max timestamp differences among duplicated events.

This feature requires watermark with delay threshold to be set in streaming DataFrame/Dataset.

streamingDf = spark.readStream. ...

# deduplicate using guid column with watermark based on eventTime column
streamingDf \
  .withWatermark("eventTime", "10 hours") \
  .dropDuplicatesWithinWatermark("guid")
val streamingDf = spark.readStream. ...  // columns: guid, eventTime, ...

// deduplicate using guid column with watermark based on eventTime column
streamingDf
  .withWatermark("eventTime", "10 hours")
  .dropDuplicatesWithinWatermark("guid")
Dataset<Row> streamingDf = spark.readStream(). ...;  // columns: guid, eventTime, ...

// deduplicate using guid column with watermark based on eventTime column
streamingDf
  .withWatermark("eventTime", "10 hours")
  .dropDuplicatesWithinWatermark("guid");

Policy for handling multiple watermarks

A streaming query can have multiple input streams that are unioned or joined together. Each of the input streams can have a different threshold of late data that needs to be tolerated for stateful operations. You specify these thresholds using withWatermarks("eventTime", delay) on each of the input streams. For example, consider a query with stream-stream joins between inputStream1 and inputStream2.

inputStream1.withWatermark("eventTime1", "1 hour")
  .join(
    inputStream2.withWatermark("eventTime2", "2 hours"),
    joinCondition)

While executing the query, Structured Streaming individually tracks the maximum event time seen in each input stream, calculates watermarks based on the corresponding delay, and chooses a single global watermark with them to be used for stateful operations. By default, the minimum is chosen as the global watermark because it ensures that no data is accidentally dropped as too late if one of the streams falls behind the others (for example, one of the streams stops receiving data due to upstream failures). In other words, the global watermark will safely move at the pace of the slowest stream and the query output will be delayed accordingly.

However, in some cases, you may want to get faster results even if it means dropping data from the slowest stream. Since Spark 2.4, you can set the multiple watermark policy to choose the maximum value as the global watermark by setting the SQL configuration spark.sql.streaming.multipleWatermarkPolicy to max (default is min). This lets the global watermark move at the pace of the fastest stream. However, as a side effect, data from the slower streams will be aggressively dropped. Hence, use this configuration judiciously.

Arbitrary Stateful Operations

Many usecases require more advanced stateful operations than aggregations. For example, in many usecases, you have to track sessions from data streams of events. For doing such sessionization, you will have to save arbitrary types of data as state, and perform arbitrary operations on the state using the data stream events in every trigger. Since Spark 2.2, this can be done using the operation mapGroupsWithState and the more powerful operation flatMapGroupsWithState. Both operations allow you to apply user-defined code on grouped Datasets to update user-defined state. For more concrete details, take a look at the API documentation (Scala/Java) and the examples (Scala/Java).

Though Spark cannot check and force it, the state function should be implemented with respect to the semantics of the output mode. For example, in Update mode Spark doesn’t expect that the state function will emit rows which are older than current watermark plus allowed late record delay, whereas in Append mode the state function can emit these rows.

Unsupported Operations

There are a few DataFrame/Dataset operations that are not supported with streaming DataFrames/Datasets. Some of them are as follows.

In addition, there are some Dataset methods that will not work on streaming Datasets. They are actions that will immediately run queries and return results, which does not make sense on a streaming Dataset. Rather, those functionalities can be done by explicitly starting a streaming query (see the next section regarding that).

If you try any of these operations, you will see an AnalysisException like “operation XYZ is not supported with streaming DataFrames/Datasets”. While some of them may be supported in future releases of Spark, there are others which are fundamentally hard to implement on streaming data efficiently. For example, sorting on the input stream is not supported, as it requires keeping track of all the data received in the stream. This is therefore fundamentally hard to execute efficiently.

State Store

State store is a versioned key-value store which provides both read and write operations. In Structured Streaming, we use the state store provider to handle the stateful operations across batches. There are two built-in state store provider implementations. End users can also implement their own state store provider by extending StateStoreProvider interface.

HDFS state store provider

The HDFS backend state store provider is the default implementation of [[StateStoreProvider]] and [[StateStore]] in which all the data is stored in memory map in the first stage, and then backed by files in an HDFS-compatible file system. All updates to the store have to be done in sets transactionally, and each set of updates increments the store’s version. These versions can be used to re-execute the updates (by retries in RDD operations) on the correct version of the store, and regenerate the store version.

RocksDB state store implementation

As of Spark 3.2, we add a new built-in state store implementation, RocksDB state store provider.

If you have stateful operations in your streaming query (for example, streaming aggregation, streaming dropDuplicates, stream-stream joins, mapGroupsWithState, or flatMapGroupsWithState) and you want to maintain millions of keys in the state, then you may face issues related to large JVM garbage collection (GC) pauses causing high variations in the micro-batch processing times. This occurs because, by the implementation of HDFSBackedStateStore, the state data is maintained in the JVM memory of the executors and large number of state objects puts memory pressure on the JVM causing high GC pauses.

In such cases, you can choose to use a more optimized state management solution based on RocksDB. Rather than keeping the state in the JVM memory, this solution uses RocksDB to efficiently manage the state in the native memory and the local disk. Furthermore, any changes to this state are automatically saved by Structured Streaming to the checkpoint location you have provided, thus providing full fault-tolerance guarantees (the same as default state management).

To enable the new build-in state store implementation, set spark.sql.streaming.stateStore.providerClass to org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider.

Here are the configs regarding to RocksDB instance of the state store provider:

Config Name Description Default Value
spark.sql.streaming.stateStore.rocksdb.compactOnCommit Whether we perform a range compaction of RocksDB instance for commit operation False
spark.sql.streaming.stateStore.rocksdb.changelogCheckpointing.enabled Whether to upload changelog instead of snapshot during RocksDB StateStore commit False
spark.sql.streaming.stateStore.rocksdb.blockSizeKB Approximate size in KB of user data packed per block for a RocksDB BlockBasedTable, which is a RocksDB's default SST file format. 4
spark.sql.streaming.stateStore.rocksdb.blockCacheSizeMB The size capacity in MB for a cache of blocks. 8
spark.sql.streaming.stateStore.rocksdb.lockAcquireTimeoutMs The waiting time in millisecond for acquiring lock in the load operation for RocksDB instance. 60000
spark.sql.streaming.stateStore.rocksdb.maxOpenFiles The number of open files that can be used by the RocksDB instance. Value of -1 means that files opened are always kept open. If the open file limit is reached, RocksDB will evict entries from the open file cache and close those file descriptors and remove the entries from the cache. -1
spark.sql.streaming.stateStore.rocksdb.resetStatsOnLoad Whether we resets all ticker and histogram stats for RocksDB on load. True
spark.sql.streaming.stateStore.rocksdb.trackTotalNumberOfRows Whether we track the total number of rows in state store. Please refer the details in Performance-aspect considerations. True
spark.sql.streaming.stateStore.rocksdb.writeBufferSizeMB The maximum size of MemTable in RocksDB. Value of -1 means that RocksDB internal default values will be used -1
spark.sql.streaming.stateStore.rocksdb.maxWriteBufferNumber The maximum number of MemTables in RocksDB, both active and immutable. Value of -1 means that RocksDB internal default values will be used -1
spark.sql.streaming.stateStore.rocksdb.boundedMemoryUsage Whether total memory usage for RocksDB state store instances on a single node is bounded. false
spark.sql.streaming.stateStore.rocksdb.maxMemoryUsageMB Total memory limit in MB for RocksDB state store instances on a single node. 500
spark.sql.streaming.stateStore.rocksdb.writeBufferCacheRatio Total memory to be occupied by write buffers as a fraction of memory allocated across all RocksDB instances on a single node using maxMemoryUsageMB. 0.5
spark.sql.streaming.stateStore.rocksdb.highPriorityPoolRatio Total memory to be occupied by blocks in high priority pool as a fraction of memory allocated across all RocksDB instances on a single node using maxMemoryUsageMB. 0.1
spark.sql.streaming.stateStore.rocksdb.allowFAllocate Allow the rocksdb runtime to use fallocate to pre-allocate disk space for logs, etc... Disable for apps that have many smaller state stores to trade off disk space for write performance. true
spark.sql.streaming.stateStore.rocksdb.compression Compression type used in RocksDB. The string is converted RocksDB compression type through RocksDB Java API getCompressionType(). lz4
RocksDB State Store Memory Management

RocksDB allocates memory for different objects such as memtables, block cache and filter/index blocks. If left unbounded, RocksDB memory usage across multiple instances could grow indefinitely and potentially cause OOM (out-of-memory) issues. RocksDB provides a way to limit the memory usage for all DB instances running on a single node by using the write buffer manager functionality. If you want to cap RocksDB memory usage in your Spark Structured Streaming deployment, this feature can be enabled by setting the spark.sql.streaming.stateStore.rocksdb.boundedMemoryUsage config to true. You can also determine the max allowed memory for RocksDB instances by setting the spark.sql.streaming.stateStore.rocksdb.maxMemoryUsageMB value to a static number or as a fraction of the physical memory available on the node. Limits for individual RocksDB instances can also be configured by setting spark.sql.streaming.stateStore.rocksdb.writeBufferSizeMB and spark.sql.streaming.stateStore.rocksdb.maxWriteBufferNumber to the required values. By default, RocksDB internal defaults are used for these settings.

Note that the boundedMemoryUsage config will enable a soft limit on the total memory usage for RocksDB. So the total memory used by RocksDB can temporarily exceed this value if all blocks allocated to higher level readers are in use. Enabling a strict limit is not possible at this time since it will cause query failures and we do not support re-balancing of the state across additional nodes.

RocksDB State Store Changelog Checkpointing

In newer version of Spark, changelog checkpointing is introduced for RocksDB state store. The traditional checkpointing mechanism for RocksDB State Store is incremental snapshot checkpointing, where the manifest files and newly generated RocksDB SST files of RocksDB instances are uploaded to a durable storage. Instead of uploading data files of RocksDB instances, changelog checkpointing uploads changes made to the state since the last checkpoint for durability. Snapshots are persisted periodically in the background for predictable failure recovery and changelog trimming. Changelog checkpointing avoids cost of capturing and uploading snapshots of RocksDB instances and significantly reduce streaming query latency.

Changelog checkpointing is disabled by default. You can enable RocksDB State Store changelog checkpointing by setting spark.sql.streaming.stateStore.rocksdb.changelogCheckpointing.enabled config to true. Changelog checkpointing is designed to be backward compatible with traditional checkpointing mechanism. RocksDB state store provider offers seamless support for transitioning between two checkpointing mechanisms in both directions. This allows you to leverage the performance benefits of changelog checkpointing without discarding the old state checkpoint. In a version of spark that supports changelog checkpointing, you can migrate streaming queries from older versions of Spark to changelog checkpointing by enabling changelog checkpointing in the spark session. Vice versa, you can disable changelog checkpointing safely in newer version of Spark, then any query that already run with changelog checkpointing will switch back to traditional checkpointing. You would need to restart you streaming queries for change in checkpointing mechanism to be applied, but you won’t observe any performance degrade in the process.

Performance-aspect considerations
  1. You may want to disable the track of total number of rows to aim the better performance on RocksDB state store.

Tracking the number of rows brings additional lookup on write operations - you’re encouraged to try turning off the config on tuning RocksDB state store, especially the values of metrics for state operator are big - numRowsUpdated, numRowsRemoved.

You can change the config during restarting the query, which enables you to change the trade-off decision on “observability vs performance”. If the config is disabled, the number of rows in state (numTotalStateRows) will be reported as 0.

State Store and task locality

The stateful operations store states for events in state stores of executors. State stores occupy resources such as memory and disk space to store the states. So it is more efficient to keep a state store provider running in the same executor across different streaming batches. Changing the location of a state store provider requires the extra overhead of loading checkpointed states. The overhead of loading state from checkpoint depends on the external storage and the size of the state, which tends to hurt the latency of micro-batch run. For some use cases such as processing very large state data, loading new state store providers from checkpointed states can be very time-consuming and inefficient.

The stateful operations in Structured Streaming queries rely on the preferred location feature of Spark’s RDD to run the state store provider on the same executor. If in the next batch the corresponding state store provider is scheduled on this executor again, it could reuse the previous states and save the time of loading checkpointed states.

However, generally the preferred location is not a hard requirement and it is still possible that Spark schedules tasks to the executors other than the preferred ones. In this case, Spark will load state store providers from checkpointed states on new executors. The state store providers run in the previous batch will not be unloaded immediately. Spark runs a maintenance task which checks and unloads the state store providers that are inactive on the executors.

By changing the Spark configurations related to task scheduling, for example spark.locality.wait, users can configure Spark how long to wait to launch a data-local task. For stateful operations in Structured Streaming, it can be used to let state store providers running on the same executors across batches.

Specifically for built-in HDFS state store provider, users can check the state store metrics such as loadedMapCacheHitCount and loadedMapCacheMissCount. Ideally, it is best if cache missing count is minimized that means Spark won’t waste too much time on loading checkpointed state. User can increase Spark locality waiting configurations to avoid loading state store providers in different executors across batches.

State Data Source (Experimental)

Apache Spark provides a streaming state related data source that provides the ability to manipulate state stores in the checkpoint. Users can run the batch query with State Data Source to get the visibility of the states for existing streaming query.

As of Spark 4.0, the data source only supports read feature. See State Data Source Integration Guide for more details.

NOTE: this data source is currently marked as experimental - source options and the behavior (output) might be subject to change.

Starting Streaming Queries

Once you have defined the final result DataFrame/Dataset, all that is left is for you to start the streaming computation. To do that, you have to use the DataStreamWriter (Python/Scala/Java docs) returned through Dataset.writeStream(). You will have to specify one or more of the following in this interface.

Output Modes

There are a few types of output modes.

Different types of streaming queries support different output modes. Here is the compatibility matrix.

Query Type Supported Output Modes Notes
Queries with aggregation Aggregation on event-time with watermark Append, Update, Complete Append mode uses watermark to drop old aggregation state. But the output of a windowed aggregation is delayed the late threshold specified in withWatermark() as by the modes semantics, rows can be added to the Result Table only once after they are finalized (i.e. after watermark is crossed). See the Late Data section for more details.

Update mode uses watermark to drop old aggregation state.

Complete mode does not drop old aggregation state since by definition this mode preserves all data in the Result Table.
Other aggregations Complete, Update Since no watermark is defined (only defined in other category), old aggregation state is not dropped.

Append mode is not supported as aggregates can update thus violating the semantics of this mode.
Queries with mapGroupsWithState Update Aggregations not allowed in a query with mapGroupsWithState.
Queries with flatMapGroupsWithState Append operation mode Append Aggregations are allowed after flatMapGroupsWithState.
Update operation mode Update Aggregations not allowed in a query with flatMapGroupsWithState.
Queries with joins Append Update and Complete mode not supported yet. See the support matrix in the Join Operations section for more details on what types of joins are supported.
Other queries Append, Update Complete mode not supported as it is infeasible to keep all unaggregated data in the Result Table.

Output Sinks

There are a few types of built-in output sinks.

writeStream
    .format("parquet")        // can be "orc", "json", "csv", etc.
    .option("path", "path/to/destination/dir")
    .start()
writeStream
    .format("kafka")
    .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
    .option("topic", "updates")
    .start()
writeStream
    .foreach(...)
    .start()
writeStream
    .format("console")
    .start()
writeStream
    .format("memory")
    .queryName("tableName")
    .start()

Some sinks are not fault-tolerant because they do not guarantee persistence of the output and are meant for debugging purposes only. See the earlier section on fault-tolerance semantics. Here are the details of all the sinks in Spark.

Sink Supported Output Modes Options Fault-tolerant Notes
File Sink Append path: path to the output directory, must be specified.
retention: time to live (TTL) for output files. Output files which batches were committed older than TTL will be eventually excluded in metadata log. This means reader queries which read the sink's output directory may not process them. You can provide the value as string format of the time. (like "12h", "7d", etc.) By default it's disabled.

For file-format-specific options, see the related methods in DataFrameWriter (Python/Scala/Java/R). E.g. for "parquet" format options see DataFrameWriter.parquet()
Yes (exactly-once) Supports writes to partitioned tables. Partitioning by time may be useful.
Kafka Sink Append, Update, Complete See the Kafka Integration Guide Yes (at-least-once) More details in the Kafka Integration Guide
Foreach Sink Append, Update, Complete None Yes (at-least-once) More details in the next section
ForeachBatch Sink Append, Update, Complete None Depends on the implementation More details in the next section
Console Sink Append, Update, Complete numRows: Number of rows to print every trigger (default: 20)
truncate: Whether to truncate the output if too long (default: true)
No
Memory Sink Append, Complete None No. But in Complete Mode, restarted query will recreate the full table. Table name is the query name.

Note that you have to call start() to actually start the execution of the query. This returns a StreamingQuery object which is a handle to the continuously running execution. You can use this object to manage the query, which we will discuss in the next subsection. For now, let’s understand all this with a few examples.

# ========== DF with no aggregations ==========
noAggDF = deviceDataDf.select("device").where("signal > 10")

# Print new data to console
noAggDF \
    .writeStream \
    .format("console") \
    .start()

# Write new data to Parquet files
noAggDF \
    .writeStream \
    .format("parquet") \
    .option("checkpointLocation", "path/to/checkpoint/dir") \
    .option("path", "path/to/destination/dir") \
    .start()

# ========== DF with aggregation ==========
aggDF = df.groupBy("device").count()

# Print updated aggregations to console
aggDF \
    .writeStream \
    .outputMode("complete") \
    .format("console") \
    .start()

# Have all the aggregates in an in-memory table. The query name will be the table name
aggDF \
    .writeStream \
    .queryName("aggregates") \
    .outputMode("complete") \
    .format("memory") \
    .start()

spark.sql("select * from aggregates").show()   # interactively query in-memory table
// ========== DF with no aggregations ==========
val noAggDF = deviceDataDf.select("device").where("signal > 10")

// Print new data to console
noAggDF
  .writeStream
  .format("console")
  .start()

// Write new data to Parquet files
noAggDF
  .writeStream
  .format("parquet")
  .option("checkpointLocation", "path/to/checkpoint/dir")
  .option("path", "path/to/destination/dir")
  .start()

// ========== DF with aggregation ==========
val aggDF = df.groupBy("device").count()

// Print updated aggregations to console
aggDF
  .writeStream
  .outputMode("complete")
  .format("console")
  .start()

// Have all the aggregates in an in-memory table
aggDF
  .writeStream
  .queryName("aggregates")    // this query name will be the table name
  .outputMode("complete")
  .format("memory")
  .start()

spark.sql("select * from aggregates").show()   // interactively query in-memory table
// ========== DF with no aggregations ==========
Dataset<Row> noAggDF = deviceDataDf.select("device").where("signal > 10");

// Print new data to console
noAggDF
  .writeStream()
  .format("console")
  .start();

// Write new data to Parquet files
noAggDF
  .writeStream()
  .format("parquet")
  .option("checkpointLocation", "path/to/checkpoint/dir")
  .option("path", "path/to/destination/dir")
  .start();

// ========== DF with aggregation ==========
Dataset<Row> aggDF = df.groupBy("device").count();

// Print updated aggregations to console
aggDF
  .writeStream()
  .outputMode("complete")
  .format("console")
  .start();

// Have all the aggregates in an in-memory table
aggDF
  .writeStream()
  .queryName("aggregates")    // this query name will be the table name
  .outputMode("complete")
  .format("memory")
  .start();

spark.sql("select * from aggregates").show();   // interactively query in-memory table
# ========== DF with no aggregations ==========
noAggDF <- select(where(deviceDataDf, "signal > 10"), "device")

# Print new data to console
write.stream(noAggDF, "console")

# Write new data to Parquet files
write.stream(noAggDF,
             "parquet",
             path = "path/to/destination/dir",
             checkpointLocation = "path/to/checkpoint/dir")

# ========== DF with aggregation ==========
aggDF <- count(groupBy(df, "device"))

# Print updated aggregations to console
write.stream(aggDF, "console", outputMode = "complete")

# Have all the aggregates in an in memory table. The query name will be the table name
write.stream(aggDF, "memory", queryName = "aggregates", outputMode = "complete")

# Interactively query in-memory table
head(sql("select * from aggregates"))
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.

def foreach_batch_function(df, epoch_id):
    # Transform and write batchDF
    pass

streamingDF.writeStream.foreachBatch(foreach_batch_function).start()
streamingDF.writeStream.foreachBatch { (batchDF: DataFrame, batchId: Long) =>
  // Transform and write batchDF
}.start()
streamingDatasetOfString.writeStream().foreachBatch(
  new VoidFunction2<Dataset<String>, Long>() {
    public void call(Dataset<String> dataset, Long batchId) {
      // Transform and write batchDF
    }
  }
).start();

R is not yet supported.

With foreachBatch, you can do the following.

streamingDF.writeStream.foreachBatch { (batchDF: DataFrame, batchId: Long) =>
  batchDF.persist()
  batchDF.write.format(...).save(...)  // location 1
  batchDF.write.format(...).save(...)  // location 2
  batchDF.unpersist()
}

Note:

Foreach

If foreachBatch is not an option (for example, corresponding batch data writer does not exist, or continuous processing mode), 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. Since Spark 2.4, foreach is available in Scala, Java and 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.

  • First, the function takes a row as input.
def process_row(row):
    # Write row to storage
    pass

query = streamingDF.writeStream.foreach(process_row).start()
  • Second, 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.
        pass

    def process(self, row):
        # Write row to connection. This method is NOT optional in Python.
        pass

    def close(self, error):
        # Close the connection. This method in optional in Python.
        pass

query = streamingDF.writeStream.foreach(ForeachWriter()).start()

In Scala, you have to extend the class ForeachWriter (docs).

streamingDatasetOfString.writeStream.foreach(
  new ForeachWriter[String] {

    def open(partitionId: Long, version: Long): Boolean = {
      // Open connection
    }

    def process(record: String): Unit = {
      // Write string to connection
    }

    def close(errorOrNull: Throwable): Unit = {
      // Close the connection
    }
  }
).start()

In Java, you have to extend the class ForeachWriter (docs).

streamingDatasetOfString.writeStream().foreach(
  new ForeachWriter<String>() {

    @Override public boolean open(long partitionId, long version) {
      // Open connection
    }

    @Override public void process(String record) {
      // Write string to connection
    }

    @Override public void close(Throwable errorOrNull) {
      // Close the connection
    }
  }
).start();

R is not yet supported.

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

Streaming Table APIs

Since Spark 3.1, you can also use DataStreamReader.table() to read tables as streaming DataFrames and use DataStreamWriter.toTable() to write streaming DataFrames as tables:

spark = ...  # spark session

# Create a streaming DataFrame
df = spark.readStream \
    .format("rate") \
    .option("rowsPerSecond", 10) \
    .load()

# Write the streaming DataFrame to a table
df.writeStream \
    .option("checkpointLocation", "path/to/checkpoint/dir") \
    .toTable("myTable")

# Check the table result
spark.read.table("myTable").show()

# Transform the source dataset and write to a new table
spark.readStream \
    .table("myTable") \
    .select("value") \
    .writeStream \
    .option("checkpointLocation", "path/to/checkpoint/dir") \
    .format("parquet") \
    .toTable("newTable")

# Check the new table result
spark.read.table("newTable").show()
val spark: SparkSession = ...

// Create a streaming DataFrame
val df = spark.readStream
  .format("rate")
  .option("rowsPerSecond", 10)
  .load()

// Write the streaming DataFrame to a table
df.writeStream
  .option("checkpointLocation", "path/to/checkpoint/dir")
  .toTable("myTable")

// Check the table result
spark.read.table("myTable").show()

// Transform the source dataset and write to a new table
spark.readStream
  .table("myTable")
  .select("value")
  .writeStream
  .option("checkpointLocation", "path/to/checkpoint/dir")
  .format("parquet")
  .toTable("newTable")

// Check the new table result
spark.read.table("newTable").show()
SparkSession spark = ...

// Create a streaming DataFrame
Dataset<Row> df = spark.readStream()
  .format("rate")
  .option("rowsPerSecond", 10)
  .load();

// Write the streaming DataFrame to a table
df.writeStream()
  .option("checkpointLocation", "path/to/checkpoint/dir")
  .toTable("myTable");

// Check the table result
spark.read().table("myTable").show();

// Transform the source dataset and write to a new table
spark.readStream()
  .table("myTable")
  .select("value")
  .writeStream()
  .option("checkpointLocation", "path/to/checkpoint/dir")
  .format("parquet")
  .toTable("newTable");

// Check the new table result
spark.read().table("newTable").show();

Not available in R.

For more details, please check the docs for DataStreamReader (Python/Scala/Java docs) and DataStreamWriter (Python/Scala/Java docs).

Triggers

The trigger settings of a streaming query define the timing of streaming data processing, whether the query is going to be executed as micro-batch query with a fixed batch interval or as a continuous processing query. Here are the different kinds of triggers that are supported.

Trigger Type Description
unspecified (default) If no trigger setting is explicitly specified, then by default, the query will be executed in micro-batch mode, where micro-batches will be generated as soon as the previous micro-batch has completed processing.
Fixed interval micro-batches The query will be executed with micro-batches mode, where micro-batches will be kicked off at the user-specified intervals.
  • If the previous micro-batch completes within the interval, then the engine will wait until the interval is over before kicking off the next micro-batch.
  • If the previous micro-batch takes longer than the interval to complete (i.e. if an interval boundary is missed), then the next micro-batch will start as soon as the previous one completes (i.e., it will not wait for the next interval boundary).
  • If no new data is available, then no micro-batch will be kicked off.
One-time micro-batch(deprecated) The query will execute only one micro-batch to process all the available data and then stop on its own. This is useful in scenarios you want to periodically spin up a cluster, process everything that is available since the last period, and then shutdown the cluster. In some case, this may lead to significant cost savings. Note that this trigger is deprecated and users are encouraged to migrate to Available-now micro-batch, as it provides the better guarantee of processing, fine-grained scale of batches, and better gradual processing of watermark advancement including no-data batch.
Available-now micro-batch Similar to queries one-time micro-batch trigger, the query will process all the available data and then stop on its own. The difference is that, it will process the data in (possibly) multiple micro-batches based on the source options (e.g. maxFilesPerTrigger or maxBytesPerTrigger for file source), which will result in better query scalability.
  • This trigger provides a strong guarantee of processing: regardless of how many batches were left over in previous run, it ensures all available data at the time of execution gets processed before termination. All uncommitted batches will be processed first.
  • Watermark gets advanced per each batch, and no-data batch gets executed before termination if the last batch advances the watermark. This helps to maintain smaller and predictable state size and smaller latency on the output of stateful operators.
NOTE: this trigger will be deactivated when there is any source which does not support Trigger.AvailableNow. Spark will perform one-time micro-batch as a fall-back. Check the above differences for a risk of fallback.
Continuous with fixed checkpoint interval
(experimental)
The query will be executed in the new low-latency, continuous processing mode. Read more about this in the Continuous Processing section below.

Here are a few code examples.

# Default trigger (runs micro-batch as soon as it can)
df.writeStream \
  .format("console") \
  .start()

# ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream \
  .format("console") \
  .trigger(processingTime='2 seconds') \
  .start()

# One-time trigger (Deprecated, encouraged to use Available-now trigger)
df.writeStream \
  .format("console") \
  .trigger(once=True) \
  .start()

# Available-now trigger
df.writeStream \
  .format("console") \
  .trigger(availableNow=True) \
  .start()

# Continuous trigger with one-second checkpointing interval
df.writeStream
  .format("console")
  .trigger(continuous='1 second')
  .start()
import org.apache.spark.sql.streaming.Trigger

// Default trigger (runs micro-batch as soon as it can)
df.writeStream
  .format("console")
  .start()

// ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream
  .format("console")
  .trigger(Trigger.ProcessingTime("2 seconds"))
  .start()

// One-time trigger (Deprecated, encouraged to use Available-now trigger)
df.writeStream
  .format("console")
  .trigger(Trigger.Once())
  .start()

// Available-now trigger
df.writeStream
  .format("console")
  .trigger(Trigger.AvailableNow())
  .start()

// Continuous trigger with one-second checkpointing interval
df.writeStream
  .format("console")
  .trigger(Trigger.Continuous("1 second"))
  .start()
import org.apache.spark.sql.streaming.Trigger

// Default trigger (runs micro-batch as soon as it can)
df.writeStream
  .format("console")
  .start();

// ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream
  .format("console")
  .trigger(Trigger.ProcessingTime("2 seconds"))
  .start();

// One-time trigger (Deprecated, encouraged to use Available-now trigger)
df.writeStream
  .format("console")
  .trigger(Trigger.Once())
  .start();

// Available-now trigger
df.writeStream
  .format("console")
  .trigger(Trigger.AvailableNow())
  .start();

// Continuous trigger with one-second checkpointing interval
df.writeStream
  .format("console")
  .trigger(Trigger.Continuous("1 second"))
  .start();
# Default trigger (runs micro-batch as soon as it can)
write.stream(df, "console")

# ProcessingTime trigger with two-seconds micro-batch interval
write.stream(df, "console", trigger.processingTime = "2 seconds")

# One-time trigger
write.stream(df, "console", trigger.once = TRUE)

# Continuous trigger is not yet supported

Managing Streaming Queries

The StreamingQuery object created when a query is started can be used to monitor and manage the query.

query = df.writeStream.format("console").start()   # get the query object

query.id()          # get the unique identifier of the running query that persists across restarts from checkpoint data

query.runId()       # get the unique id of this run of the query, which will be generated at every start/restart

query.name()        # get the name of the auto-generated or user-specified name

query.explain()   # print detailed explanations of the query

query.stop()      # stop the query

query.awaitTermination()   # block until query is terminated, with stop() or with error

query.exception()       # the exception if the query has been terminated with error

query.recentProgress  # a list of the most recent progress updates for this query

query.lastProgress    # the most recent progress update of this streaming query
val query = df.writeStream.format("console").start()   // get the query object

query.id          // get the unique identifier of the running query that persists across restarts from checkpoint data

query.runId       // get the unique id of this run of the query, which will be generated at every start/restart

query.name        // get the name of the auto-generated or user-specified name

query.explain()   // print detailed explanations of the query

query.stop()      // stop the query

query.awaitTermination()   // block until query is terminated, with stop() or with error

query.exception       // the exception if the query has been terminated with error

query.recentProgress  // an array of the most recent progress updates for this query

query.lastProgress    // the most recent progress update of this streaming query
StreamingQuery query = df.writeStream().format("console").start();   // get the query object

query.id();          // get the unique identifier of the running query that persists across restarts from checkpoint data

query.runId();       // get the unique id of this run of the query, which will be generated at every start/restart

query.name();        // get the name of the auto-generated or user-specified name

query.explain();   // print detailed explanations of the query

query.stop();      // stop the query

query.awaitTermination();   // block until query is terminated, with stop() or with error

query.exception();       // the exception if the query has been terminated with error

query.recentProgress();  // an array of the most recent progress updates for this query

query.lastProgress();    // the most recent progress update of this streaming query
query <- write.stream(df, "console")  # get the query object

queryName(query)          # get the name of the auto-generated or user-specified name

explain(query)            # print detailed explanations of the query

stopQuery(query)          # stop the query

awaitTermination(query)   # block until query is terminated, with stop() or with error

lastProgress(query)       # the most recent progress update of this streaming query

You can start any number of queries in a single SparkSession. They will all be running concurrently sharing the cluster resources. You can use sparkSession.streams() to get the StreamingQueryManager (Python/Scala/Java docs) that can be used to manage the currently active queries.

spark = ...  # spark session

spark.streams.active  # get the list of currently active streaming queries

spark.streams.get(id)  # get a query object by its unique id

spark.streams.awaitAnyTermination()  # block until any one of them terminates
val spark: SparkSession = ...

spark.streams.active    // get the list of currently active streaming queries

spark.streams.get(id)   // get a query object by its unique id

spark.streams.awaitAnyTermination()   // block until any one of them terminates
SparkSession spark = ...

spark.streams().active();    // get the list of currently active streaming queries

spark.streams().get(id);   // get a query object by its unique id

spark.streams().awaitAnyTermination();   // block until any one of them terminates
Not available in R.

Monitoring Streaming Queries

There are multiple ways to monitor active streaming queries. You can either push metrics to external systems using Spark’s Dropwizard Metrics support, or access them programmatically.

Reading Metrics Interactively

You can directly get the current status and metrics of an active query using streamingQuery.lastProgress() and streamingQuery.status(). lastProgress() returns a StreamingQueryProgress object in Scala and Java and a dictionary with the same fields in Python. It has all the information about the progress made in the last trigger of the stream - what data was processed, what were the processing rates, latencies, etc. There is also streamingQuery.recentProgress which returns an array of last few progresses.

In addition, streamingQuery.status() returns a StreamingQueryStatus object in Scala and Java and a dictionary with the same fields in Python. It gives information about what the query is immediately doing - is a trigger active, is data being processed, etc.

Here are a few examples.

query = ...  # a StreamingQuery
print(query.lastProgress)

'''
Will print something like the following.

{u'stateOperators': [], u'eventTime': {u'watermark': u'2016-12-14T18:45:24.873Z'}, u'name': u'MyQuery', u'timestamp': u'2016-12-14T18:45:24.873Z', u'processedRowsPerSecond': 200.0, u'inputRowsPerSecond': 120.0, u'numInputRows': 10, u'sources': [{u'description': u'KafkaSource[Subscribe[topic-0]]', u'endOffset': {u'topic-0': {u'1': 134, u'0': 534, u'3': 21, u'2': 0, u'4': 115}}, u'processedRowsPerSecond': 200.0, u'inputRowsPerSecond': 120.0, u'numInputRows': 10, u'startOffset': {u'topic-0': {u'1': 1, u'0': 1, u'3': 1, u'2': 0, u'4': 1}}}], u'durationMs': {u'getOffset': 2, u'triggerExecution': 3}, u'runId': u'88e2ff94-ede0-45a8-b687-6316fbef529a', u'id': u'ce011fdc-8762-4dcb-84eb-a77333e28109', u'sink': {u'description': u'MemorySink'}}
'''

print(query.status)
'''
Will print something like the following.

{u'message': u'Waiting for data to arrive', u'isTriggerActive': False, u'isDataAvailable': False}
'''
val query: StreamingQuery = ...

println(query.lastProgress)

/* Will print something like the following.

{
  "id" : "ce011fdc-8762-4dcb-84eb-a77333e28109",
  "runId" : "88e2ff94-ede0-45a8-b687-6316fbef529a",
  "name" : "MyQuery",
  "timestamp" : "2016-12-14T18:45:24.873Z",
  "numInputRows" : 10,
  "inputRowsPerSecond" : 120.0,
  "processedRowsPerSecond" : 200.0,
  "durationMs" : {
    "triggerExecution" : 3,
    "getOffset" : 2
  },
  "eventTime" : {
    "watermark" : "2016-12-14T18:45:24.873Z"
  },
  "stateOperators" : [ ],
  "sources" : [ {
    "description" : "KafkaSource[Subscribe[topic-0]]",
    "startOffset" : {
      "topic-0" : {
        "2" : 0,
        "4" : 1,
        "1" : 1,
        "3" : 1,
        "0" : 1
      }
    },
    "endOffset" : {
      "topic-0" : {
        "2" : 0,
        "4" : 115,
        "1" : 134,
        "3" : 21,
        "0" : 534
      }
    },
    "numInputRows" : 10,
    "inputRowsPerSecond" : 120.0,
    "processedRowsPerSecond" : 200.0
  } ],
  "sink" : {
    "description" : "MemorySink"
  }
}
*/


println(query.status)

/*  Will print something like the following.
{
  "message" : "Waiting for data to arrive",
  "isDataAvailable" : false,
  "isTriggerActive" : false
}
*/
StreamingQuery query = ...

System.out.println(query.lastProgress());
/* Will print something like the following.

{
  "id" : "ce011fdc-8762-4dcb-84eb-a77333e28109",
  "runId" : "88e2ff94-ede0-45a8-b687-6316fbef529a",
  "name" : "MyQuery",
  "timestamp" : "2016-12-14T18:45:24.873Z",
  "numInputRows" : 10,
  "inputRowsPerSecond" : 120.0,
  "processedRowsPerSecond" : 200.0,
  "durationMs" : {
    "triggerExecution" : 3,
    "getOffset" : 2
  },
  "eventTime" : {
    "watermark" : "2016-12-14T18:45:24.873Z"
  },
  "stateOperators" : [ ],
  "sources" : [ {
    "description" : "KafkaSource[Subscribe[topic-0]]",
    "startOffset" : {
      "topic-0" : {
        "2" : 0,
        "4" : 1,
        "1" : 1,
        "3" : 1,
        "0" : 1
      }
    },
    "endOffset" : {
      "topic-0" : {
        "2" : 0,
        "4" : 115,
        "1" : 134,
        "3" : 21,
        "0" : 534
      }
    },
    "numInputRows" : 10,
    "inputRowsPerSecond" : 120.0,
    "processedRowsPerSecond" : 200.0
  } ],
  "sink" : {
    "description" : "MemorySink"
  }
}
*/


System.out.println(query.status());
/*  Will print something like the following.
{
  "message" : "Waiting for data to arrive",
  "isDataAvailable" : false,
  "isTriggerActive" : false
}
*/
query <- ...  # a StreamingQuery
lastProgress(query)

'''
Will print something like the following.

{
  "id" : "8c57e1ec-94b5-4c99-b100-f694162df0b9",
  "runId" : "ae505c5a-a64e-4896-8c28-c7cbaf926f16",
  "name" : null,
  "timestamp" : "2017-04-26T08:27:28.835Z",
  "numInputRows" : 0,
  "inputRowsPerSecond" : 0.0,
  "processedRowsPerSecond" : 0.0,
  "durationMs" : {
    "getOffset" : 0,
    "triggerExecution" : 1
  },
  "stateOperators" : [ {
    "numRowsTotal" : 4,
    "numRowsUpdated" : 0
  } ],
  "sources" : [ {
    "description" : "TextSocketSource[host: localhost, port: 9999]",
    "startOffset" : 1,
    "endOffset" : 1,
    "numInputRows" : 0,
    "inputRowsPerSecond" : 0.0,
    "processedRowsPerSecond" : 0.0
  } ],
  "sink" : {
    "description" : "org.apache.spark.sql.execution.streaming.ConsoleSink@76b37531"
  }
}
'''

status(query)
'''
Will print something like the following.

{
  "message" : "Waiting for data to arrive",
  "isDataAvailable" : false,
  "isTriggerActive" : false
}
'''

Reporting Metrics programmatically using Asynchronous APIs

You can also asynchronously monitor all queries associated with a SparkSession by attaching a StreamingQueryListener (Python/Scala/Java docs). Once you attach your custom StreamingQueryListener object with sparkSession.streams.addListener(), you will get callbacks when a query is started and stopped and when there is progress made in an active query. Here is an example,

spark = ...

class Listener(StreamingQueryListener):
    def onQueryStarted(self, event):
        print("Query started: " + queryStarted.id)

    def onQueryProgress(self, event):
        print("Query made progress: " + queryProgress.progress)

    def onQueryTerminated(self, event):
    	print("Query terminated: " + queryTerminated.id)


spark.streams.addListener(Listener())
val spark: SparkSession = ...

spark.streams.addListener(new StreamingQueryListener() {
    override def onQueryStarted(queryStarted: QueryStartedEvent): Unit = {
        println("Query started: " + queryStarted.id)
    }
    override def onQueryTerminated(queryTerminated: QueryTerminatedEvent): Unit = {
        println("Query terminated: " + queryTerminated.id)
    }
    override def onQueryProgress(queryProgress: QueryProgressEvent): Unit = {
        println("Query made progress: " + queryProgress.progress)
    }
})
SparkSession spark = ...

spark.streams().addListener(new StreamingQueryListener() {
    @Override
    public void onQueryStarted(QueryStartedEvent queryStarted) {
        System.out.println("Query started: " + queryStarted.id());
    }
    @Override
    public void onQueryTerminated(QueryTerminatedEvent queryTerminated) {
        System.out.println("Query terminated: " + queryTerminated.id());
    }
    @Override
    public void onQueryProgress(QueryProgressEvent queryProgress) {
        System.out.println("Query made progress: " + queryProgress.progress());
    }
});
Not available in R.

Reporting Metrics using Dropwizard

Spark supports reporting metrics using the Dropwizard Library. To enable metrics of Structured Streaming queries to be reported as well, you have to explicitly enable the configuration spark.sql.streaming.metricsEnabled in the SparkSession.

spark.conf.set("spark.sql.streaming.metricsEnabled", "true")
# or
spark.sql("SET spark.sql.streaming.metricsEnabled=true")
spark.conf.set("spark.sql.streaming.metricsEnabled", "true")
// or
spark.sql("SET spark.sql.streaming.metricsEnabled=true")
spark.conf().set("spark.sql.streaming.metricsEnabled", "true");
// or
spark.sql("SET spark.sql.streaming.metricsEnabled=true");
sql("SET spark.sql.streaming.metricsEnabled=true")

All queries started in the SparkSession after this configuration has been enabled will report metrics through Dropwizard to whatever sinks have been configured (e.g. Ganglia, Graphite, JMX, etc.).

Recovering from Failures with Checkpointing

In case of a failure or intentional shutdown, you can recover the previous progress and state of a previous query, and continue where it left off. This is done using checkpointing and write-ahead logs. You can configure a query with a checkpoint location, and the query will save all the progress information (i.e. range of offsets processed in each trigger) and the running aggregates (e.g. word counts in the quick example) to the checkpoint location. This checkpoint location has to be a path in an HDFS compatible file system, and can be set as an option in the DataStreamWriter when starting a query.

aggDF \
    .writeStream \
    .outputMode("complete") \
    .option("checkpointLocation", "path/to/HDFS/dir") \
    .format("memory") \
    .start()
aggDF
  .writeStream
  .outputMode("complete")
  .option("checkpointLocation", "path/to/HDFS/dir")
  .format("memory")
  .start()
aggDF
  .writeStream()
  .outputMode("complete")
  .option("checkpointLocation", "path/to/HDFS/dir")
  .format("memory")
  .start();
write.stream(aggDF, "memory", outputMode = "complete", checkpointLocation = "path/to/HDFS/dir")

Recovery Semantics after Changes in a Streaming Query

There are limitations on what changes in a streaming query are allowed between restarts from the same checkpoint location. Here are a few kinds of changes that are either not allowed, or the effect of the change is not well-defined. For all of them:

Types of changes

Asynchronous Progress Tracking

What is it?

Asynchronous progress tracking allows streaming queries to checkpoint progress asynchronously and in parallel to the actual data processing within a micro-batch, reducing latency associated with maintaining the offset log and commit log.

Async Progress Tracking

How does it work?

Structured Streaming relies on persisting and managing offsets as progress indicators for query processing. Offset management operation directly impacts processing latency, because no data processing can occur until these operations are complete. Asynchronous progress tracking enables streaming queries to checkpoint progress without being impacted by these offset management operations.

How to use it?

The code snippet below provides an example of how to use this feature:

val stream = spark.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
      .option("subscribe", "in")
      .load()
val query = stream.writeStream
     .format("kafka")
	.option("topic", "out")
     .option("checkpointLocation", "/tmp/checkpoint")
	.option("asyncProgressTrackingEnabled", "true")
     .start()

The table below describes the configurations for this feature and default values associated with them.

Option Value Default Description
asyncProgressTrackingEnabled true/false false enable or disable asynchronous progress tracking
asyncProgressTrackingCheckpointIntervalMs millisecond 1000 the interval in which we commit offsets and completion commits

Limitations

The initial version of the feature has the following limitations:

Switching the setting off

Turning the async progress tracking off may cause the following exception to be thrown

java.lang.IllegalStateException: batch x doesn't exist

Also the following error message may be printed in the driver logs:

The offset log for batch x doesn't exist, which is required to restart the query from the latest batch x from the offset log. Please ensure there are two subsequent offset logs available for the latest batch via manually deleting the offset file(s). Please also ensure the latest batch for commit log is equal or one batch earlier than the latest batch for offset log.

This is caused by the fact that when async progress tracking is enabled, the framework will not checkpoint progress for every batch as would be done if async progress tracking is not used. To solve this problem simply re-enable “asyncProgressTrackingEnabled” and set “asyncProgressTrackingCheckpointIntervalMs” to 0 and run the streaming query until at least two micro-batches have been processed. Async progress tracking can be now safely disabled and restarting query should proceed normally.

Continuous Processing

[Experimental]

Continuous processing is a new, experimental streaming execution mode introduced in Spark 2.3 that enables low (~1 ms) end-to-end latency with at-least-once fault-tolerance guarantees. Compare this with the default micro-batch processing engine which can achieve exactly-once guarantees but achieve latencies of ~100ms at best. For some types of queries (discussed below), you can choose which mode to execute them in without modifying the application logic (i.e. without changing the DataFrame/Dataset operations).

To run a supported query in continuous processing mode, all you need to do is specify a continuous trigger with the desired checkpoint interval as a parameter. For example,

spark \
  .readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
  .option("subscribe", "topic1") \
  .load() \
  .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") \
  .writeStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
  .option("topic", "topic1") \
  .trigger(continuous="1 second") \     # only change in query
  .start()
import org.apache.spark.sql.streaming.Trigger

spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("subscribe", "topic1")
  .load()
  .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .writeStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("topic", "topic1")
  .trigger(Trigger.Continuous("1 second"))  // only change in query
  .start()
import org.apache.spark.sql.streaming.Trigger;

spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("subscribe", "topic1")
  .load()
  .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .writeStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("topic", "topic1")
  .trigger(Trigger.Continuous("1 second"))  // only change in query
  .start();

A checkpoint interval of 1 second means that the continuous processing engine will record the progress of the query every second. The resulting checkpoints are in a format compatible with the micro-batch engine, hence any query can be restarted with any trigger. For example, a supported query started with the micro-batch mode can be restarted in continuous mode, and vice versa. Note that any time you switch to continuous mode, you will get at-least-once fault-tolerance guarantees.

Supported Queries

As of Spark 2.4, only the following type of queries are supported in the continuous processing mode.

See Input Sources and Output Sinks sections for more details on them. While the console sink is good for testing, the end-to-end low-latency processing can be best observed with Kafka as the source and sink, as this allows the engine to process the data and make the results available in the output topic within milliseconds of the input data being available in the input topic.

Caveats

Additional Information

Notes

Further Reading

Talks

Migration Guide

The migration guide is now archived on this page.