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Posted to reviews@spark.apache.org by ahmed-mahran <gi...@git.apache.org> on 2016/07/17 00:06:16 UTC
[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
GitHub user ahmed-mahran opened a pull request:
https://github.com/apache/spark/pull/14234
[MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuations and grammar
## What changes were proposed in this pull request?
Minor fixes correcting some typos, punctuations, grammar.
Adding more anchors for easy navigation.
Fixing minor issues with code snippets.
## How was this patch tested?
`jekyll serve`
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/ahmed-mahran/spark b-struct-streaming-docs
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/14234.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #14234
----
commit 4b566b1b5d21af24032701c17b41c7e411659b92
Author: Ahmed Mahran <ah...@mashin.io>
Date: 2016-07-16T23:58:02Z
Fix minor typos, punctuations and grammar
Minor fixes correcting some typos, punctuations, grammar. Adding
more anchors for easy navigation.
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073746
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -410,26 +398,21 @@ 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.
+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
+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.
+([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 \u2013 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.
--- End diff --
Singular is the convention used around
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on the issue:
https://github.com/apache/spark/pull/14234
I can find a pptx at "docs/img/structured-streaming.pptx" where there is a corresponding slide for each image.
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/14234
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073700
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -82,8 +70,6 @@ SparkSession spark = SparkSession
.builder()
.appName("JavaStructuredNetworkWordCount")
.getOrCreate();
-
-import spark.implicits._
--- End diff --
Moved to `Scala` snippet
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on the issue:
https://github.com/apache/spark/pull/14234
The slides renders bad on "libre office" I have; I'll try something else and see.
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71077849
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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, let\u2019s walk through the example step-by-step and understand how it works.
<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, let\u2019s 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">
+First, we have to import the necessary classes and create a local SparkSession, the starting point of all functionalities related to Spark.
--- End diff --
Does not belong solely in the Scala block
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71080823
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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
--- End diff --
It changes "Scala/ Java/ Python" to "Scala/Java/Python"; when `/` is used as and/or, it comes with no spaces around.
This is happening everywhere, shall I revert it?
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on the issue:
https://github.com/apache/spark/pull/14234
Note, "img/structured-streaming-stream-as-a-table.png" needs to be regenerated; someone changed "new rows appended to **a** unbounded table" to "new rows appended to **an** unbounded table". I failed to regenerate with the same quality.
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71087402
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# 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
-[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, 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.
+[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
--- End diff --
Add spaces rather than remove them.
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71090763
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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, let\u2019s walk through the example step-by-step and understand how it works.
<div class="codetabs">
<div data-lang="scala" markdown="1">
-
--- End diff --
Done
Now it renders like
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073721
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -223,7 +211,7 @@ $ ./bin/run-example org.apache.spark.examples.sql.streaming.JavaStructuredNetwor
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
- {% highlight bash %}
--- End diff --
The trailing spaces add unnecessary line to the snippet
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073739
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -410,26 +398,21 @@ 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.
+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)/
--- End diff --
many cases like this later
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/14234
Yeah OK modulo the last round of comments here I think it's a good set of small fixes
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073736
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -410,26 +398,21 @@ 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.
+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)/
--- End diff --
( Scala/ Java/ Python docs) to (Scala/Java/Python docs)
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71080656
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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, let\u2019s walk through the example step-by-step and understand how it works.
<div class="codetabs">
<div data-lang="scala" markdown="1">
-
--- End diff --
It renders like this, is it okay?
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073714
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -65,11 +51,13 @@ val words = lines.as[String].flatMap(_.split(" "))
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 \u201cvalue\u201d, 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.
--- End diff --
`.as(Encoders.STRING())`, java's, changed to `.as[String]`, scala's
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/14234
Oh nice. It might be a matter of exporting the image at a higher resolution, but I still wouldn't worry if it's just a trivial typo and takes any non-trivial time to figure out. (You can fix and save the PPTX and add that here.)
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on the issue:
https://github.com/apache/spark/pull/14234
Fine, ignoring this
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/14234
Can one of the admins verify this patch?
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71077854
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -410,26 +398,21 @@ 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.
+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)/
--- End diff --
Add spaces rather than remove them
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073767
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -620,16 +603,14 @@ df.groupBy("type").count()
### 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 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).
--- End diff --
Added anchor `[quick example](#quick-example)`
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073691
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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, let\u2019s walk through the example step-by-step and understand how it works.
<div class="codetabs">
<div data-lang="scala" markdown="1">
-
--- End diff --
Removing empty `<div>` elements
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71090897
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -65,11 +51,13 @@ val words = lines.as[String].flatMap(_.split(" "))
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 \u201cvalue\u201d, 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.
--- End diff --
Scala snippet uses `.as[String]` while Java's uses `.as(Encoders.STRING())`.
In Scala context, it is weird to read "we have converted the DataFrame to a Dataset of String using `.as(Encoders.STRING())`" while *we* actually used `.as[String]`
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073763
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -519,10 +502,10 @@ csvDF = spark \
</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.
--- End diff --
Added link `[SQL Programming Guide](sql-programming-guide.html)`
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71081040
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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, let\u2019s walk through the example step-by-step and understand how it works.
<div class="codetabs">
<div data-lang="scala" markdown="1">
-
--- End diff --
Before | After
----------|---------
 | 
# Before
- "First, we have to ..." should be moved to the corresponding code block
- The "Next, let's create ..." paragraph is repeated twice
# After
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/14234
Merged to master, and to 2.0 because this fixes a relatively important problem with the display of the code snippets in this doc. I tested the doc changes by hand, note.
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/14234
Ah OK I now see the nature of the problem in the original code blocks. Great, that's an important fix. The rest look good. I wouldn't worry about the image just for that; I don't know where the source is. LGTM
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073776
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -1093,12 +1067,10 @@ spark.streams().awaitAnyTermination() # block until any one of them terminates
</div>
</div>
-Finally, for asynchronous monitoring of streaming queries, you can create and attach a `StreamingQueryListener` (
-[Scala](api/scala/index.html#org.apache.spark.sql.streaming.StreamingQueryListener)/
-[Java](api/java/org/apache/spark/sql/streaming/StreamingQueryListener.html) docs), which will give you regular callback-based updates when queries are started and terminated.
+Finally, for asynchronous monitoring of streaming queries, you can create and attach a `StreamingQueryListener` ([Scala](api/scala/index.html#org.apache.spark.sql.streaming.StreamingQueryListener)/[Java](api/java/org/apache/spark/sql/streaming/StreamingQueryListener.html) docs), which will give you regular callback-based updates when queries are started and terminated.
## 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) will be saved the checkpoint location. As of Spark 2.0, this checkpoint location has to be a path in a HDFS compatible file system, and can be set as an option in the DataStreamWriter when [starting a query](#starting-streaming-queries).
--- End diff --
Added anchor `[quick example](#quick-example)`
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71087433
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -65,11 +51,13 @@ val words = lines.as[String].flatMap(_.split(" "))
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 \u201cvalue\u201d, 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.
--- End diff --
Is that change valid -- not sure either way but can you show why? Both are valid in Java and Scala
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/14234
Most of this needs to be reverted. The minor typo fixes and obvious corrections can stay
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by ahmed-mahran <gi...@git.apache.org>.
Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073751
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -439,7 +422,7 @@ Here are some examples.
<div data-lang="scala" markdown="1">
{% highlight scala %}
-val spark: SparkSession = \u2026
--- End diff --
Using same convention; it is `...` everywhere
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71077847
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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
--- End diff --
This just put all this text on one line? we shoudln't do that.
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[GitHub] spark issue #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, punctuati...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/14234
Don't bother if you don't have Office and it's any trouble
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71087415
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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, let\u2019s walk through the example step-by-step and understand how it works.
<div class="codetabs">
<div data-lang="scala" markdown="1">
-
--- End diff --
OK the code still renders? hm, maybe these really are vestigial. The "First.." and "Next..." text does not belong within any code div. It belongs outside and between them.
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[GitHub] spark pull request #14234: [MINOR][SQL][STREAMING][DOCS] Fix minor typos, pu...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71077843
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -14,29 +14,13 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let\u2019s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let\u2019s 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, let\u2019s 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.
+[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, let\u2019s walk through the example step-by-step and understand how it works.
<div class="codetabs">
<div data-lang="scala" markdown="1">
-
--- End diff --
I think this breaks the page. These are the bits where plugins fill in the code.
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