[SPARK-44997][DOCS] Align example order (Python -> Scala/Java -> R) in all Spark Doc Content

docs/_layouts/global.html

@@ -71,9 +71,9 @@
                     <li class="nav-item dropdown">
                         <a href="#" class="nav-link dropdown-toggle" id="navbarAPIDocs" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">API Docs</a>
                         <div class="dropdown-menu" aria-labelledby="navbarAPIDocs">
+                            <a class="dropdown-item" href="api/python/index.html">Python</a>
                             <a class="dropdown-item" href="api/scala/org/apache/spark/index.html">Scala</a>
                             <a class="dropdown-item" href="api/java/index.html">Java</a>
-                            <a class="dropdown-item" href="api/python/index.html">Python</a>
                             <a class="dropdown-item" href="api/R/index.html">R</a>
                             <a class="dropdown-item" href="api/sql/index.html">SQL, Built-in Functions</a>
                         </div>

docs/index.md

@@ -120,9 +120,9 @@ options for deployment:
 
 **API Docs:**
 
+* [Spark Python API (Sphinx)](api/python/index.html)
 * [Spark Scala API (Scaladoc)](api/scala/org/apache/spark/index.html)
 * [Spark Java API (Javadoc)](api/java/index.html)
-* [Spark Python API (Sphinx)](api/python/index.html)
 * [Spark R API (Roxygen2)](api/R/index.html)
 * [Spark SQL, Built-in Functions (MkDocs)](api/sql/index.html)
 
@@ -163,7 +163,7 @@ options for deployment:
 * AMP Camps: a series of training camps at UC Berkeley that featured talks and
   exercises about Spark, Spark Streaming, Mesos, and more. [Videos](https://www.youtube.com/user/BerkeleyAMPLab/search?query=amp%20camp),
   are available online for free.
-* [Code Examples](https://spark.apache.org/examples.html): more are also available in the `examples` subfolder of Spark ([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+* [Code Examples](https://spark.apache.org/examples.html): more are also available in the `examples` subfolder of Spark ([Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python),
+ [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
  [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
- [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python),
  [R]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/r))

docs/ml-pipeline.md

@@ -238,9 +238,9 @@ notes, then it should be treated as a bug to be fixed.
 
 This section gives code examples illustrating the functionality discussed above.
 For more info, please refer to the API documentation
-([Scala](api/scala/org/apache/spark/ml/package.html),
-[Java](api/java/org/apache/spark/ml/package-summary.html),
-and [Python](api/python/reference/pyspark.ml.html)).
+([Python](api/python/reference/pyspark.ml.html),
+[Scala](api/scala/org/apache/spark/ml/package.html),
+and [Java](api/java/org/apache/spark/ml/package-summary.html)).
 
 ## Example: Estimator, Transformer, and Param
 

docs/quick-start.md

@@ -470,19 +470,19 @@ Congratulations on running your first Spark application!
 * For an in-depth overview of the API, start with the [RDD programming guide](rdd-programming-guide.html) and the [SQL programming guide](sql-programming-guide.html), or see "Programming Guides" menu for other components.
 * For running applications on a cluster, head to the [deployment overview](cluster-overview.html).
 * Finally, Spark includes several samples in the `examples` directory
-([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+([Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python),
+ [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
  [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
- [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python),
  [R]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/r)).
 You can run them as follows:
 
 {% highlight bash %}
-# For Scala and Java, use run-example:
-./bin/run-example SparkPi
-
 # For Python examples, use spark-submit directly:
 ./bin/spark-submit examples/src/main/python/pi.py
 
+# For Scala and Java, use run-example:
+./bin/run-example SparkPi
+
 # For R examples, use spark-submit directly:
 ./bin/spark-submit examples/src/main/r/dataframe.R
 {% endhighlight %}

docs/rdd-programming-guide.md

@@ -945,9 +945,9 @@ documentation](https://docs.oracle.com/javase/8/docs/api/java/lang/Object.html#h
 
 The following table lists some of the common transformations supported by Spark. Refer to the
 RDD API doc
-([Scala](api/scala/org/apache/spark/rdd/RDD.html),
+([Python](api/python/reference/api/pyspark.RDD.html#pyspark.RDD),
+ [Scala](api/scala/org/apache/spark/rdd/RDD.html),
  [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html),
- [Python](api/python/reference/api/pyspark.RDD.html#pyspark.RDD),
  [R](api/R/reference/index.html))
 and pair RDD functions doc
 ([Scala](api/scala/org/apache/spark/rdd/PairRDDFunctions.html),
@@ -1059,9 +1059,9 @@ for details.
 
 The following table lists some of the common actions supported by Spark. Refer to the
 RDD API doc
-([Scala](api/scala/org/apache/spark/rdd/RDD.html),
+([Python](api/python/reference/api/pyspark.RDD.html#pyspark.RDD),
+ [Scala](api/scala/org/apache/spark/rdd/RDD.html),
  [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html),
- [Python](api/python/reference/api/pyspark.RDD.html#pyspark.RDD),
  [R](api/R/reference/index.html))
 
 and pair RDD functions doc
@@ -1207,9 +1207,9 @@ In addition, each persisted RDD can be stored using a different *storage level*,
 to persist the dataset on disk, persist it in memory but as serialized Java objects (to save space),
 replicate it across nodes.
 These levels are set by passing a
-`StorageLevel` object ([Scala](api/scala/org/apache/spark/storage/StorageLevel.html),
-[Java](api/java/index.html?org/apache/spark/storage/StorageLevel.html),
-[Python](api/python/reference/api/pyspark.StorageLevel.html#pyspark.StorageLevel))
+`StorageLevel` object ([Python](api/python/reference/api/pyspark.StorageLevel.html#pyspark.StorageLevel),
+[Scala](api/scala/org/apache/spark/storage/StorageLevel.html),
+[Java](api/java/index.html?org/apache/spark/storage/StorageLevel.html))
 to `persist()`. The `cache()` method is a shorthand for using the default storage level,
 which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The full set of
 storage levels is:
@@ -1596,9 +1596,9 @@ as Spark does not support two contexts running concurrently in the same program.
 
 You can see some [example Spark programs](https://spark.apache.org/examples.html) on the Spark website.
 In addition, Spark includes several samples in the `examples` directory
-([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
+([Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python),
+ [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
  [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
- [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python),
  [R]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/r)).
 You can run Java and Scala examples by passing the class name to Spark's `bin/run-example` script; for instance:
 
@@ -1619,4 +1619,4 @@ For help on deploying, the [cluster mode overview](cluster-overview.html) descri
 in distributed operation and supported cluster managers.
 
 Finally, full API documentation is available in
-[Scala](api/scala/org/apache/spark/), [Java](api/java/), [Python](api/python/) and [R](api/R/).
+[Python](api/python/), [Scala](api/scala/org/apache/spark/), [Java](api/java/) and [R](api/R/).

docs/sql-getting-started.md

@@ -108,7 +108,7 @@ As an example, the following creates a DataFrame based on the content of a JSON
 
 ## Untyped Dataset Operations (aka DataFrame Operations)
 
-DataFrames provide a domain-specific language for structured data manipulation in [Scala](api/scala/org/apache/spark/sql/Dataset.html), [Java](api/java/index.html?org/apache/spark/sql/Dataset.html), [Python](api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) and [R](api/R/reference/SparkDataFrame.html).
+DataFrames provide a domain-specific language for structured data manipulation in [Python](api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html), [Scala](api/scala/org/apache/spark/sql/Dataset.html), [Java](api/java/index.html?org/apache/spark/sql/Dataset.html) and [R](api/R/reference/SparkDataFrame.html).
 
 As mentioned above, in Spark 2.0, DataFrames are just Dataset of `Row`s in Scala and Java API. These operations are also referred as "untyped transformations" in contrast to "typed transformations" come with strongly typed Scala/Java Datasets.
 

docs/sql-programming-guide.md

@@ -55,8 +55,9 @@ A DataFrame is a *Dataset* organized into named columns. It is conceptually
 equivalent to a table in a relational database or a data frame in R/Python, but with richer
 optimizations under the hood. DataFrames can be constructed from a wide array of [sources](sql-data-sources.html) such
 as: structured data files, tables in Hive, external databases, or existing RDDs.
-The DataFrame API is available in Scala,
-Java, [Python](api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame), and [R](api/R/index.html).
+The DataFrame API is available in
+[Python](api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame), Scala,
+Java and [R](api/R/index.html).
 In Scala and Java, a DataFrame is represented by a Dataset of `Row`s.
 In [the Scala API][scala-datasets], `DataFrame` is simply a type alias of `Dataset[Row]`.
 While, in [Java API][java-datasets], users need to use `Dataset<Row>` to represent a `DataFrame`.

docs/streaming-programming-guide.md

@@ -762,9 +762,9 @@ DStreams can be created with data streams received through custom receivers. See
 For testing a Spark Streaming application with test data, one can also create a DStream based on a queue of RDDs, using `streamingContext.queueStream(queueOfRDDs)`. Each RDD pushed into the queue will be treated as a batch of data in the DStream, and processed like a stream.
 
 For more details on streams from sockets and files, see the API documentations of the relevant functions in
-[StreamingContext](api/scala/org/apache/spark/streaming/StreamingContext.html) for
-Scala, [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html)
-for Java, and [StreamingContext](api/python/reference/api/pyspark.streaming.StreamingContext.html#pyspark.streaming.StreamingContext) for Python.
+[StreamingContext](api/python/reference/api/pyspark.streaming.StreamingContext.html#pyspark.streaming.StreamingContext) for Python,
+[StreamingContext](api/scala/org/apache/spark/streaming/StreamingContext.html) for Scala,
+and [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) for Java.
 
 ### Advanced Sources
 {:.no_toc}
@@ -1265,12 +1265,12 @@ JavaPairDStream<String, String> joinedStream = windowedStream.transform(rdd -> r
 
 In fact, you can also dynamically change the dataset you want to join against. The function provided to `transform` is evaluated every batch interval and therefore will use the current dataset that `dataset` reference points to.
 
-The complete list of DStream transformations is available in the API documentation. For the Scala API,
-see [DStream](api/scala/org/apache/spark/streaming/dstream/DStream.html)
+The complete list of DStream transformations is available in the API documentation. For the Python API,
+see [DStream](api/python/reference/api/pyspark.streaming.DStream.html#pyspark.streaming.DStream).
+For the Scala API, see [DStream](api/scala/org/apache/spark/streaming/dstream/DStream.html)
 and [PairDStreamFunctions](api/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.html).
 For the Java API, see [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html)
 and [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html).
-For the Python API, see [DStream](api/python/reference/api/pyspark.streaming.DStream.html#pyspark.streaming.DStream).
 
 ***
 
@@ -2564,21 +2564,21 @@ additional effort may be necessary to achieve exactly-once semantics. There are
     - [Custom Receiver Guide](streaming-custom-receivers.html)
 * Third-party DStream data sources can be found in [Third Party Projects](https://spark.apache.org/third-party-projects.html)
 * API documentation
+  - Python docs
+    * [StreamingContext](api/python/reference/api/pyspark.streaming.StreamingContext.html#pyspark.streaming.StreamingContext) and [DStream](api/python/reference/api/pyspark.streaming.DStream.html#pyspark.streaming.DStream)
   - Scala docs
     * [StreamingContext](api/scala/org/apache/spark/streaming/StreamingContext.html) and
   [DStream](api/scala/org/apache/spark/streaming/dstream/DStream.html)
     * [KafkaUtils](api/scala/org/apache/spark/streaming/kafka/KafkaUtils$.html),
-    [KinesisUtils](api/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.html),
+    [KinesisUtils](api/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.html)
   - Java docs
     * [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html),
     [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html) and
     [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html)
     * [KafkaUtils](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html),
     [KinesisUtils](api/java/index.html?org/apache/spark/streaming/kinesis/KinesisInputDStream.html)
-  - Python docs
-    * [StreamingContext](api/python/reference/api/pyspark.streaming.StreamingContext.html#pyspark.streaming.StreamingContext) and [DStream](api/python/reference/api/pyspark.streaming.DStream.html#pyspark.streaming.DStream)
 
-* More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming)
+* More examples in [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python/streaming)
+  and [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming)
   and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming)
-  and [Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python/streaming)
 * [Paper](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf) and [video](http://youtu.be/g171ndOHgJ0) describing Spark Streaming.