Github user ScrapCodes commented on a diff in the pull request:
https://github.com/apache/spark/pull/13945#discussion_r68920269
--- Diff: docs/structured-streaming-programming-guide.md ---
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+---
+layout: global
+displayTitle: Structured Streaming Programming Guide [Alpha]
+title: Structured Streaming Programming Guide
+---
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+# 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](sql-programming-guide.html) in
Scala, Java or Python 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.*
+
+**Spark 2.0 is the ALPHA RELEASE of Structured Streaming** and the APIs
are still experimental. In this guide, we are going to walk you through the
programming model and the APIs. First, let's start with a simple example - a
streaming word count.
+
+# Quick Example
+Letâs say you want to maintain a running word count of text data
received from a data server listening on a TCP socket. Letâs see how you can
express this using Structured Streaming. You can see the full code in
+[Scala]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredNetworkWordCount.scala)/
+[Java]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCount.java)/
+[Python]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/sql/streaming/structured_network_wordcount.py).
And if you
+[download Spark](http://spark.apache.org/downloads.html), you can directly
run the example. In any case, letâs 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.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+
+</div>
+<div data-lang="java" markdown="1">
+
+
+</div>
+<div data-lang="python" markdown="1">
+
+</div>
+</div>
+
+Next, letâs create a streaming DataFrame that represents text data
received from a server listening on localhost:9999, and transform the DataFrame
to calculate word counts.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.SparkSession
+
+val spark = SparkSession
+ .builder
+ .appName("StructuredNetworkWordCount")
+ .getOrCreate()
+{% endhighlight %}
+
+Next, letâs create a streaming DataFrame that represents text data
received from a server listening on localhost:9999, and transform the DataFrame
to calculate word counts.
+
+{% highlight scala %}
+// 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()
+{% endhighlight %}
+
+This `lines` DataFrame represents an unbounded table containing the
streaming text data. This table contains one column of strings named
âvalueâ, 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.
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+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();
+
+import spark.implicits._
+{% endhighlight %}
+
+Next, letâs create a streaming DataFrame that represents text data
received from a server listening on localhost:9999, and transform the DataFrame
to calculate word counts.
+
+{% highlight java %}
+// Create DataFrame representing the stream of input lines from connection
to localhost:9999
+Dataset<String> 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(
+ new FlatMapFunction<String, String>() {
+ @Override
+ public Iterator<String> call(String x) {
+ return Arrays.asList(x.split(" ")).iterator();
+ }
+ }, Encoders.STRING());
+
+// Generate running word count
+Dataset<Row> wordCounts = words.groupBy("value").count();
+{% endhighlight %}
+
+This `lines` DataFrame represents an unbounded table containing the
streaming text data. This table contains one column of strings named
âvalueâ, 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.
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import explode
+from pyspark.sql.functions import split
+
+spark = SparkSession\
+ .builder()\
+ .appName("StructuredNetworkWordCount")\
+ .getOrCreate()
+{% endhighlight %}
+
+Next, letâs create a streaming DataFrame that represents text data
received from a server listening on localhost:9999, and transform the DataFrame
to calculate word counts.
+
+{% highlight python %}
+# 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()
+{% endhighlight %}
+
+This `lines` DataFrame represents an unbounded table containing the
streaming text data. This table contains one column of strings named
âvalueâ, 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 âwordâ. 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.
+
+</div>
+</div>
+
+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(âcompleteâ)`) to the console every time they are updated. And
then start the streaming computation using `start()`.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+// Start running the query that prints the running counts to the console
+val query = wordCounts.writeStream
+ .outputMode("complete")
+ .format("console")
+ .start()
+
+query.awaitTermination()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+// Start running the query that prints the running counts to the console
+StreamingQuery query = wordCounts.writeStream()
+ .outputMode("complete")
+ .format("console")
+ .start();
+
+query.awaitTermination();
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+ # Start running the query that prints the running counts to the console
+query = wordCounts\
+ .writeStream\
+ .outputMode('complete')\
+ .format('console')\
+ .start()
+
+query.awaitTermination()
+{% endhighlight %}
+
+</div>
+</div>
+
+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
`query.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](quick-start.html#self-contained-applications), or
simply
+[run the example](index.html#running-the-examples-and-shell) 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
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight bash %}
+$ ./bin/run-example
org.apache.spark.examples.sql.streaming.StructuredNetworkWordCount localhost
9999
+{% endhighlight %}
+</div>
+<div data-lang="java" markdown="1">
+{% highlight bash %}
+$ ./bin/run-example
org.apache.spark.examples.sql.streaming.JavaStructuredNetworkWordCount
localhost 9999
+{% endhighlight %}
+</div>
+<div data-lang="python" markdown="1">
+ {% highlight bash %}
+$ ./bin/spark-submit
examples/src/main/python/sql/streaming/structured_network_wordcount.py
localhost 9999
+{% endhighlight %}
+</div>
+</div>
+
+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.
+
+<table width="100%">
+ <td>
+{% highlight bash %}
+# TERMINAL 1:
+# Running Netcat
+
+$ nc -lk 9999
+apache spark
+apache hadoop
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+...
+{% endhighlight %}
+ </td>
+ <td width="2%"></td>
+ <td>
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+{% highlight bash %}
+# 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|
++------+-----+
+...
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight bash %}
+# 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|
++------+-----+
+...
+{% endhighlight %}
+</div>
+<div data-lang="python" markdown="1">
+{% highlight bash %}
+# 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|
++------+-----+
+...
+{% endhighlight %}
+</div>
+</div>
+ </td>
+</table>
+
+
+# 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. Letâs understand this model in more detail.
+
+## Basic Concepts
+Consider the input data stream as the âInput Tableâ. Every data item
that is
+arriving on the stream is like a new row being appended to the Input Table.
+
+
+
+A query on the input will generate the âResult Tableâ. 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.
+
+
+
+The âOutputâ is defined as what gets written out to the external
storage. The output can be defined in different modes
+
+ - *Complete Mode* - The entire updated Result Table will be written to
the external storage. It is up to the storage connector to decide how to handle
writing of the entire table.
+
+ - *Append Mode* - Only the new rows appended in the Result Table since
the last trigger will be written to the external storage. This is applicable
only on the queries where existing rows in the Result Table are not expected to
change.
+
+ - *Update Mode* - Only the rows that were updated in the Result Table
since the last trigger will be written to the external storage (not available
yet in Spark 2.0). Note that this is different from the Complete Mode in that
this mode does not output the rows that are not changed.
+
+Note that each mode is applicable on certain types of queries. This is
discussed in detail [later](#output-modes).
+
+To illustrate the use of this model, letâs 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 âincrementalâ query that combines the
previous
+running counts with the new data to compute updated counts, as shown below.
+
+
+
+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,
letâs
+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 event every minute) to be just a special type of
grouping and aggregation on the even-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/cleaning up the aggregates when there is late
data. While not yet implemented in Spark 2.0, event-time watermarking will be
used to manage this data. These are explained later in more details in the
[Window Operations](#window-operations-on-event-time) 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 idempotant 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` (
+[Scala](api/scala/index.html#org.apache.spark.sql.SparkSession)/
+[Java](api/java/org/apache/spark/sql/SparkSession.html)/
+[Python](api/python/pyspark.sql.html#pyspark.sql.SparkSession) 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](sql-programming-guide.html).
+
+## Creating streaming DataFrames and streaming Datasets
+Streaming DataFrames can be created through the `DataStreamReader`
interface
+([Scala](api/scala/index.html#org.apache.spark.sql.streaming.DataStreamReader)/
+[Java](api/java/org/apache/spark/sql/streaming/DataStreamReader.html)/
+[Python](api/python/pyspark.sql.html#pyspark.sql.streaming.DataStreamReader)
docs) returned by `SparkSession.readStream()`. Similar to the read interface
for creating static DataFrame, you can specify the details of the source - data
format, schema, options, etc. In Spark 2.0, there are a few built-in sources.
+
+ - **File sources** - Reads files written in a directory as a stream of
data. Supported file formats are text, csv, json, parquet. See the docs of the
DataStreamReader interface for a more up-to-date list, and supported options
for each file format. Note that the files must be atomically placed in the
given directory, which in most file systems, can be achieved by file move
operations.
+
+ - **Socket source (for testing)** - Reads UTF8 text data from a socket
connection. The listening server socket is at the driver. Note that this should
be used only for testing as this does not provide end-to-end fault-tolerance
guarantees.
+
+Here are some examples.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+val spark: SparkSession = â¦
+
+// 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 parquet files
+ .csv("/path/to/directory") // Equivalent to
format("cv").load("/path/to/directory")
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+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 parquet files
+ .csv("/path/to/directory"); // Equivalent to
format("cv").load("/path/to/directory")
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+spark = SparkSession. â¦.
+
+# 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("cv").load("/path/to/directory")
+{% endhighlight %}
+
+</div>
+</div>
+
+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.
+
+## 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. Letâs 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.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+case class DeviceData(device: String, type: String, signal: Double, time:
DateTime)
+
+val df: DataFrame = ... // streaming DataFrame with IOT device data with
schema { device: string, type: 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("type").count() // using untyped API
+
+// Running average signal for each device type
+Import org.apache.spark.sql.expressions.scalalang.typed._
+ds.groupByKey(_.type).agg(typed.avg(_.signal)) // using typed API
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+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 type;
+ 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(new FilterFunction<DeviceData>() { // using typed APIs
+ @Override
+ public boolean call(DeviceData value) throws Exception {
+ return value.getSignal() > 10;
+ }
+}).map(new MapFunction<DeviceData, String>() {
+ @Override
+ public String call(DeviceData value) throws Exception {
+ return value.getDevice();
+ }
+}, Encoders.STRING());
+
+// Running count of the number of updates for each device type
+df.groupBy("type").count(); // using untyped API
+
+// Running average signal for each device type
+ds.groupByKey(new MapFunction<DeviceData, String>() { // using typed API
+ @Override
+ public String call(DeviceData value) throws Exception {
+ return value.getType();
+ }
+}, Encoders.STRING()).agg(typed.avg(new MapFunction<DeviceData, Double>() {
+ @Override
+ public Double call(DeviceData value) throws Exception {
+ return value.getSignal();
+ }
+}));
+{% endhighlight %}
+
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+
+df = ... # streaming DataFrame with IOT device data with schema {
device: string, type: string, signal: double, time: DateType }
+
+# Select the devices which have signal more than 11
+df.select("device").where("signal > 10")
+
+# Running count of the number of updates for each device type
+df.groupBy("type").count()
+{% endhighlight %}
+</div>
+</div>
+
+### Window Operations on Event Time
+Aggregations over a sliding event-time window are straightforward with
Structured Streaming. The key idea to understand about window-based
aggregations 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 the quick example is modified and the stream 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.
+
+
+
+Since this windowing is similar to grouping, in code, you can use
`groupBy()` and `window()` operations to express windowed aggregations.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+// Number of events in every 1 minute time windows
+df.groupBy(window(df.col("time"), "1 minute"))
+ .count()
+
+
+// Average number of events for each device type in every 1 minute time
windows
+df.groupBy(
+ df.col("type"),
+ window(df.col("time"), "1 minute"))
+ .avg("signal")
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import static org.apache.spark.sql.functions.window;
+
+// Number of events in every 1 minute time windows
+df.groupBy(window(df.col("time"), "1 minute"))
+ .count();
+
+// Average number of events for each device type in every 1 minute time
windows
+df.groupBy(
+ df.col("type"),
+ window(df.col("time"), "1 minute"))
+ .avg("signal");
+
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+{% highlight python %}
+from pyspark.sql.functions import window
+
+# Number of events in every 1 minute time windows
+df.groupBy(window("time", "1 minute")).count()
+
+# Average number of events for each device type in every 1 minute time
windows
+df.groupBy("type", window("time", "1 minute")).avg("signal")
+{% endhighlight %}
+
+</div>
+</div>
+
+
+Now consider what happens if one of the events arrives late to the
application.
+For example, a word that was generated at 12:04 but it was received at
12:11.
+Since this windowing is based on the time in the data, the time 12:04
should considered for windowing. This occurs naturally in our window-based
grouping --the late data is automatically placed in the proper windows and the
correct aggregates updated as illustrated below.
--- End diff --
Couple of minor corrections.
1) the time 12:04, should *be* considered for windowing.
2) grouping the late data
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