[GitHub] [spark] HyukjinKwon commented on a diff in pull request #40324: [SPARK-42496][CONNECT][DOCS] Adding Spark Connect to the Spark 3.4 documentation

["HyukjinKwon (via GitHub)" <gi...@apache.org>]

HyukjinKwon commented on code in PR #40324:
URL: https://github.com/apache/spark/pull/40324#discussion_r1136451520


##########
docs/spark-connect-overview.md:
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@@ -0,0 +1,259 @@
+---
+layout: global
+title: Spark Connect Overview
+license: |
+  Licensed to the Apache Software Foundation (ASF) under one or more
+  contributor license agreements.  See the NOTICE file distributed with
+  this work for additional information regarding copyright ownership.
+  The ASF licenses this file to You under the Apache License, Version 2.0
+  (the "License"); you may not use this file except in compliance with
+  the License.  You may obtain a copy of the License at
+ 
+     http://www.apache.org/licenses/LICENSE-2.0
+ 
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+---
+**Building client-side Spark applications**
+
+In Apache Spark 3.4, Spark Connect introduced a decoupled client-server
+architecture that allows remote connectivity to Spark clusters using the
+DataFrame API and unresolved logical plans as the protocol. The separation
+between client and server allows Spark and its open ecosystem to be
+leveraged from everywhere. It can be embedded in modern data applications,
+in IDEs, Notebooks and programming languages.
+
+To get started, see [Quickstart: Spark Connect](api/python/getting_started/quickstart_connect.html).
+
+<p style="text-align: center;">
+  <img src="img/spark-connect-api.png" title="Spark Connect API" alt="Spark Connect API Diagram" />
+</p>
+
+# How Spark Connect works
+
+The Spark Connect client library is designed to simplify Spark application
+development. It is a thin API that can be embedded everywhere: in application
+servers, IDEs, notebooks, and programming languages. The Spark Connect API
+builds on Spark's DataFrame API using unresolved logical plans as a
+language-agnostic protocol between the client and the Spark driver.
+
+The Spark Connect client translates DataFrame operations into unresolved
+logical query plans which are encoded using protocol buffers. These are sent
+to the server using the gRPC framework.
+
+The Spark Connect endpoint embedded on the Spark Server, receives and
+translates unresolved logical plans into Spark's logical plan operators.
+This is similar to parsing a SQL query, where attributes and relations are
+parsed and an initial parse plan is built. From there, the standard Spark
+execution process kicks in, ensuring that Spark Connect leverages all of
+Spark's optimizations and enhancements. Results are streamed back to the
+client via gRPC as Apache Arrow-encoded row batches.
+
+<p style="text-align: center;">
+  <img src="img/spark-connect-communication.png" title="Spark Connect communication" alt="Spark Connect communication" />
+</p>
+
+# Operational benefits of Spark Connect
+
+With this new architecture, Spark Connect mitigates several multi-tenant
+operational issues:
+
+**Stability**: Applications that use too much memory will now only impact their
+own environment as they can run in their own processes. Users can define their
+own dependencies on the client and don't need to worry about potential conflicts
+with the Spark driver.
+
+**Upgradability**: The Spark driver can now seamlessly be upgraded independently
+of applications, e.g. to benefit from performance improvements and security fixes.
+This means applications can be forward-compatible, as long as the server-side RPC
+definitions are designed to be backwards compatible.
+
+**Debuggability and Observability**: Spark Connect enables interactive debugging
+during development directly from your favorite IDE. Similarly, applications can
+be monitored using the application's framework native metrics and logging libraries.
+
+# How to use Spark Connect
+
+Starting with Spark 3.4, Spark Connect is available and supports PySpark and Scala
+applications. We will walk through how to run an Apache Spark server with Spark
+Connect and connect to it from a client application using the Spark Connect client
+library.
+
+## Download and start Spark server with Spark Connect
+
+First, download Spark from the
+[Download Apache Spark](https://spark.apache.org/downloads.html) page. Spark Connect
+was introduced in Apache Spark version 3.4 so make sure you choose 3.4.0 or newer in
+the release drop down at the top of the page. Then choose your package type, typically
+“Pre-built for Apache Hadoop 3.3 and later”, and click the link to download.
+
+Now extract the Spark package you just downloaded on your computer, for example:
+
+{% highlight bash %}
+tar -xvf spark-3.4.0-bin-hadoop3.tgz
+{% endhighlight %}
+
+In a terminal window, go to the `spark` folder in the location where you extracted
+Spark before and run the `start-connect-server.sh` script to start Spark server with
+Spark Connect, like in this example:
+
+{% highlight bash %}
+./sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0
+{% endhighlight %}
+
+Note that we include a Spark Connect package (`spark-connect_2.12:3.4.0`), when starting
+Spark server. This is required to use Spark Connect. Make sure to use the same version
+of the package as the Spark version you downloaded above. In the example here, Spark 3.4.0
+with Scala 2.12.
+
+Now Spark server is running and ready to accept Spark Connect sessions from client
+applications. In the next section we will walk through how to use Spark Connect
+when writing client applications.
+
+## Use Spark Connect in client applications
+
+When creating a Spark session, you can specify that you want to use Spark Connect
+and there are a few ways to do that as outlined below.
+
+If you do not use one of the mechanisms outlined here, your Spark session will
+work just like before, without leveraging Spark Connect, and your application code
+will run on the Spark driver node.
+
+### Set SPARK_REMOTE environment variable
+
+If you set the `SPARK_REMOTE` environment variable on the client machine where your
+Spark client application is running and create a new Spark Session as illustrated
+below, the session will be a Spark Connect session. With this approach, there is
+no code change needed to start using Spark Connect.
+
+In a terminal window, set the `SPARK_REMOTE` environment variable to point to the
+local Spark server you started on your computer above:
+
+{% highlight bash %}
+export SPARK_REMOTE="sc://localhost"
+{% endhighlight %}
+
+And start the Spark shell as usual:
+
+<div class="codetabs">
+
+<div data-lang="python"  markdown="1">
+{% highlight bash %}
+./bin/pyspark
+{% endhighlight %}
+
+The PySpark shell is now connected to Spark using Spark Connect as indicated in the welcome
+message.
+</div>
+
+</div>
+
+And if you write your own program, create a Spark session as shown in this example:
+
+<div class="codetabs">
+
+<div data-lang="python"  markdown="1">
+{% highlight python %}
+from pyspark.sql import SparkSession
+spark = SparkSession.builder.getOrCreate()
+{% endhighlight %}
+</div>
+
+</div>
+
+Which will create a Spark Connect session from your application by reading the
+`SPARK_REMOTE` environment variable we set above.
+
+### Specify Spark Connect when creating Spark session
+
+You can also specify that you want to use Spark Connect explicitly when you
+create a Spark session.
+
+For example, you can launch the PySpark shell with Spark Connect as
+illustrated here.
+
+<div class="codetabs">
+
+<div data-lang="python"  markdown="1">
+To launch the PySpark shell with Spark Connect, simply include the `remote`
+parameter and specify the location of your Spark server. We are using `localhost`
+in this example to connect to the local Spark server we started above.
+
+{% highlight bash %}
+./bin/pyspark --remote "sc://localhost"
+{% endhighlight %}
+
+And you will notice that the PySpark shell welcome message tells you that
+you have connected to Spark using Spark Connect.
+
+Now you can run PySpark code in the shell to see Spark Connect in action:
+
+{% highlight python %}
+>>> columns = ["id","name"]
+>>> data = [(1,"Sarah"),(2,"Maria")]
+>>> df = spark.createDataFrame(data).toDF(*columns)
+>>> df.show()
++---+-----+
+| id| name|
++---+-----+
+|  1|Sarah|
+|  2|Maria|
++---+-----+
+
+>>>

Review Comment:
   nit but I would remove this :-). Please make a followup PR if you find some time.



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