Github user koeninger commented on a diff in the pull request:
https://github.com/apache/spark/pull/13945#discussion_r68847747
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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
by
+thinking you are running a batch computation on a static dataset, and the
+Spark SQL engine takes care of running it incrementally and continuously
+updating the final result as streaming data keeps arriving. 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, *Stuctured 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, lets start with a simple example - a
streaming word count.
+
+# Quick Example
+Letâs say you want 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/Java/Python. And if you download Spark, you can directly run the example.
In any case, letâs walk through the example step-by-step and understand how
it is works. First, we have to import the names of 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">
+
+{% highlight scala %}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.SparkSession
+
+val spark = SparkSession
+ .builder
+ .appName("StructuredNetworkWordCount")
+ .getOrCreate()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import org.apache.spark.sql.*;
+import org.apache.spark.sql.streaming.StreamingQuery;
+
+SparkSession spark = SparkSession
+ .builder()
+ .appName("JavaStructuredNetworkWordCount")
+ .getOrCreate();
+{% endhighlight %}
+
+</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 %}
+
+</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 %}
+val lines = spark.readStream
+ .format("socket")
+ .option("host", "localhost")
+ .option("port", 9999)
+ .load()
+
+val words = lines.select(
+ explode(
+ split(lines.col("value"), " ")
+ ).alias("word")
+)
+
+val wordCounts = words.groupBy("word").count()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+Dataset<Row> lines = spark
+ .readStream()
+ .format("socket")
+ .option("host", "localhost")
+ .option("port", 9999)
+ .load();
+
+Dataset<Row> words = lines.select(
+ functions.explode(
+ functions.split(lines.col("value"), " ")
+ ).alias("word")
+);
+
+Dataset<Row> wordCounts = words.groupBy("word").count();
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+lines = spark\
+ .readStream\
+ .format('socket')\
+ .option('host', 'localhost')\
+ .option('port', 9999)\
+ .load()
+
+words = lines.select(
+ explode(
+ split(lines.value, ' ')
+ ).alias('word')
+)
+
+wordCounts = words.groupBy('word').count()
+{% endhighlight %}
+
+</div>
+</div>
+
+This `lines` DataFrame is like an unbounded table containing the streaming
+text data. This table contains one column of string named âvalueâ, and
each
+line in the streaming text data is like a row in this 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 to
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 running counts, by grouping
the `words`
+DataFrame by the column `word` and count on that grouping.
+
+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 output the counts to the console every time they are updated. In
+addition we are also going to set up additional details like checkpoint
+location. Donât worry about them for now, they are explained later in
the guide.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight java %}
+val query = wordCounts
+ .writeStream
+ .outputMode("complete")
+ .format("console")
+ .option("checkpointLocation", checkpointDir)
+ .start()
+
+query.awaitTermination()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+StreamingQuery query = wordCounts
+ .writeStream()
+ .outputMode("complete")
+ .format("console")
+ .option("checkpointLocation", checkpointDir)
+ .start();
+
+query.awaitTermination();
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+query = wordCounts\
+ .writeStream\
+ .outputMode('complete')\
+ .format('console')\
+ .option('checkpointLocation', checkpointDir)\
+ .start()
+
+query.awaitTermination()
+{% endhighlight %}
+
+</div>
+</div>
+
+Now the streaming computation has started in the background, and the
`query` object is a handle to that active streaming query. Note that we are
also waiting for the query to terminate, to prevent the process from finishing
while the query is active.
+To actually run this code, you can either compile 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
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+ $ ./bin/run-example
org.apache.spark.examples.sql.streaming.StructuredNetworkWordCount
+
+</div>
+<div data-lang="java" markdown="1">
+
+ $ ./bin/run-example
org.apache.spark.examples.sql.streaming.JavaStructuredNetworkWordCount
+
+</div>
+<div data-lang="python" markdown="1">
+
+ $ ./bin/spark-submit
examples/src/main/python/sql/streaming/structured_network_wordcount.py
+
+</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.
+
+# Programming Model
+
+The key idea is 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 details.
+
+## Basic Concepts
+Consider the input data stream as the âInput Tableâ. Every data items
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 gets appended to the Input Table,
which eventually updates the Result Table. Whenever the result table gets
updated, we would want 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.
+
+ - *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 queries where existing rows in the Result Table is not expected to
change.
+
+ - *Update Mode* - Only the rows that were updated in the Result Table
since the last trigger wil 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.
+
+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 system require the user to maintain running
+aggregations themselves, thus having the 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 do operate using 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 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
streaming dataset, 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 yet, event-time watermarking will
be used to manage this data. These are explained in more details 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)
+for 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, Structured Streaming can ensure **end-to-end
exactly-once semantics** under any failure.
+
+# API using Datasets and DataFrames
+Since Spark 2.0, DataFrames and can represent of static, bounded data, as
well as streaming, unbounded data. Similar to static Datasets/DataFrames, you
can use the common entry point SparkSession to create straming
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 Spark SQL Programming Guide.
+
+## Creating streaming DataFrames and streaming Datasets
+Streaming DataFrames can created through the DataStreamReader interface
`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 more up-to-date list, and supported options for
each file format. Note that the files needs to be atomically put in the
directory. In most file systems, this means that the files should be moved
atomically into the directory.
+
+ - **Socket source (for testing)** - Reads UTF8 text data from a socket
connection. The connection is only on the server. 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 has a
streaming source
+
+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 has a
streaming source
+
+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 has a
streaming source
+
+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, that is,
the schema of the DataFrame is not checked at compile time, only checked in
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 streaming DataFrames/Datasets - ranging for
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 operations 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">
+
+</div>
+<div data-lang="python" markdown="1">
+
+</div>
+</div>
+
+### Window Operations on Event Time
+Aggregations over a sliding window is very easy. Applying a sliding window
is simply a special type of grouping - each time window is a group and each row
can belong to multiple windows/groups. Hence aggregating over sliding windows
is same as aggregating over this special type of grouping. This is illustrated
below. Imagine the quick example is modified and the stream contains lines
along with the time when the line was generated. Lets say instead of running
word counts, we want to count windows within 10 minute windows, updating every
5 minutes. The result tables would look like the following. Note the window
notation 12:00 - 12:10 means data that arrived after 12:00 but before 12:10.
The counts are indexed by the combined group (window, cat)
+
+
+
+In code, you can use windows as the following.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+// 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(df.col("signal"))
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+</div>
+<div data-lang="python" markdown="1">
+
+</div>
+</div>
+
+This windowing based on group automatically handles late arriving data.
+
+
+
+### Joins Operations
+Streaming DataFrames can be joined with static DataFrames to create new
streaming DataFrames. Here are a few examples.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+val staticDf = spark.read. â¦
+val streamingDf = spark.readStream. â¦
+
+streamingDf.join(staticDf, âtypeâ) // inner
equi-join with a static DF
+streamingDf.join(staticDf, âtypeâ, âright_joinâ) // right outer
join with a static DF
+
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+</div>
+<div data-lang="python" markdown="1">
+
+</div>
+</div>
+
+### Unsupported Operations
+However, note that all of the operations applicable on static
DataFrames/Datasets are not supported in streaming DataFrames/Datasets yet.
While some of these unsupported operations will be supported in future releases
of Spark, there are others which are fundamentally hard to implement on
streaming data efficiently. As of Spark 2.0, some of the unsupported operations
are as follows
+
+- Multiple aggregations (i.e. a chain of aggregations on a streaming DF)
are not yet supported
+
+- Limit and take first N rows are not supported
+
+- Distinct and sorting operations are not supported
+
+- Stream-batch outer joins are conditionally supported
+
+ + Full outer join not allowed
+
+ + Left outer join with a streaming DF on the left is not supported
+
+ + Right outer join with a streaming DF on the right is not supported
+
+- Stream-stream joins are not yet supported
+
+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 makes sense on a streaming Dataset. Rather those
functionalities can be done by explicitly starting a streaming query (see the
next section regarding that).
+
+- `count()` - Cannot return a single count from a streaming Dataset.
Instead, use `ds.groupBy.count()` which returns a streaming Dataset containing
a running count.
+
+- `foreach()` - Instead use `ds.writeStream.foreach(...)` (see next
section).
+
+- `show()` - Instead use the console sink (see next section).
+
+If you try any of these operations, you will see an AnalysisException like
"operation XYZ is not supported with streaming DataFrames/Datasets".
+
+## Starting Streaming Queries
+Once you have defined the final result DataFrame/Dataset, all that is left
is for you start the StreamingQuery. To do that, you have to use the
`DataStreamWriter`, through you have to specify the following.
+
+- *Details of the output sink:* Data format, location, etc.
+
+- *Output mode:* Specify what gets written to the output sink.
+
+- *Query name:* Optionally, specify a unique name of the query for
identification.
+
+- *Trigger interval:* Optionally, specify the trigger interval. If its not
specified, the system will check for availability of new day as soon as the
previous processing has completed. If a trigger time is missed because the
previous processing has not completed, then the system will attempt to trigger
at the next trigger point, not immediately after the processing has completed.
+
+- *Checkpoint location:* For some output sinks where the end-to-end
fault-tolerance can be guaranteed, specify the location where the system will
write all the checkpoint information. This should be a directory in a
HDFS-compatible fault-tolerant file system. The semantics of checkpointing is
discussed in more detail in the next section.
+
+#### Output Modes
+There are two types of output mode currently implemented.
+
+- **Append mode (default)** - This is the default mode, where only the new
rows added to the result table since the last trigger will be outputted to the
sink. This is only applicable to queries that *do not have any aggregations*
(e.g. queries with only select, where, map, flatMap, filter, join, etc.).
+
+- **Complete mode** - The whole result table will be outputted to the
sink.This is only applicable to queries that *have aggregations*.
+
+#### Output Sinks
+There are a few types of built-in output sinks.
+
+- **File sink** - Stores the output to a directory. As of Spark 2.0, this
only supports Parquet file format, and Append output mode.
+
+- **Foreach sink** - Runs arbitrary computation on the records in the
output. See later in the section for more details.
+
+- **Console sink (for debugging)** - Prints the output to the
console/stdout every time there is a trigger. Both, Append and Complete output
modes, are supported.
+
+- **Memory sink (for debugging)** - The output is stored in memory as an
in-memory table. Both, Append and Complete output modes, are supported. Note
that this requires all the data to be stored in the memory of the driver and
does not scale well. Hence this should be used only for debugging.
+
+Here is a table of all the sinks, and the corresponding settings.
+
+<table class="table">
+ <tr>
+ <th>Sink</th>
+ <th>Supported Output Modes</th>
+ <th style="width:30%">Usage</th>
+ <th>Fault-tolerant</th>
+ <th>Notes</th>
+ </tr>
+ <tr>
+ <td><b>File Sink</b><br/>(only parquet in Spark 2.0)</td>
+ <td>Append</td>
+ <td><pre>writeStream<br/> .format(âparquetâ)<br/>
.start()</pre></td>
+ <td>Yes</td>
+ <td>Supports writes to partitioned tables. Partitioning by time may be
useful.</td>
+ </tr>
+ <tr>
+ <td><b>Foreach Sink</b></td>
+ <td>All modes</td>
+ <td><pre>writeStream<br/> .foreach(...)<br/> .start()</pre></td>
+ <td>Depends on ForeachWriter implementation</td>
+ <td>More details in the next section</td>
+ </tr>
+ <tr>
+ <td><b>Console Sink</b></td>
+ <td>Append, Complete</td>
+ <td><pre>writeStream<br/> .format(âconsoleâ)<br/>
.start()</pre></td>
+ <td>No</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td><b>Memory Sink</b></td>
+ <td>Append, Complete</td>
+ <td><pre>writeStream<br/> .format(âmemoryâ)<br/>
.queryName(âtableâ)<br/> .start()</pre></td>
+ <td>No</td>
+ <td>Saves the output data as a table, for interactive querying. Table
name is the query name.</td>
+ </tr>
+</table>
+
+Finally, you have to call `start()` to actually to 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, letâs understand all
this with a few examples.
+
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+// ========== 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
+ .parquet("path/to/destination/directory")
+ .start()
+
+// ========== DF with aggregation ==========
+val aggDF = df.groupBy(âdeviceâ).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
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+// ========== 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()
+ .parquet("path/to/destination/directory")
+ .start();
+
+// ========== DF with aggregation ==========
+Dataset[Row] aggDF = df.groupBy(âdeviceâ).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
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+# ========== 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()\
+ .parquet("path/to/destination/directory")\
+ .start();
+
+# ========== DF with aggregation ==========
+Dataset[Row] aggDF = df.groupBy(âdeviceâ).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
+{% endhighlight %}
+
+</div>
+</div>
+
+Using Foreach
+The `foreach` operation allows arbitrary operations to be computer on the
output data. As of Spark 2.0, this is available only for Scala and Java, not
Python. To use this, you will have to implement the interface `ForeachWriter`,
which has methods that gets called whenever there is a sequence of rows
generated as output after a trigger. It is important note the following
--- End diff --
operations to be computer => operations to be computed
methods that gets called => methods that get called
is important note the => is important to note the
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