allanf-db commented on code in PR #40324: URL: https://github.com/apache/spark/pull/40324#discussion_r1130361771
########## docs/spark-connect-overview.md: ########## @@ -0,0 +1,108 @@ +--- +layout: global +title: Spark Connect Overview - Building client-side Spark applications +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. +--- + +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. + +<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 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 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 below, 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. + +Set SPARK_REMOTE environment variable: + +{% highlight bash %} + export SPARK_REMOTE="sc://localhost/" +{% endhighlight %} + +Start the PySpark CLI, for example: + +{% highlight bash %} + ./bin/pyspark +{% endhighlight %} + +And notice that the PySpark CLI is now connected to Spark using Spark Connect as indicated in the welcome message: “Client connected to the Spark Connect server at...”. + +And if you write your own Python program, create a Spark Session as shown in this example: + +{% highlight python %} + from pyspark.sql import SparkSession + spark = SparkSession.builder.getOrCreate() +{% endhighlight %} + +Which will create a Spark Connect session 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 when you create a Spark session explicitly. + +For example, when launching the PySpark CLI, simply include the remote parameter as illustrated here: + +{% highlight bash %} + ./bin/pyspark --remote "sc://localhost" +{% endhighlight %} + +And again you will notice that the PySpark welcome message tells you that you are connected to Spark using Spark Connect. + +Or, in your code, include the remote function with a reference to your Spark server when you create a Spark session, as in this example: + +{% highlight python %} + from pyspark.sql import SparkSession + spark = SparkSession.builder.remote("sc://localhost/").getOrCreate() +{% endhighlight %} + +# Client application authentication + +While Spark Connect does not have built-in authentication, it is designed to work seamlessly with your existing authentication infrastructure. Its gRPC HTTP/2 interface allows for the use of authenticating proxies, which makes it possible to secure Spark Connect without having to implement authentication logic in Spark directly. + +# What is included in Spark 3.4 + +In Spark 3.4, Spark Connect provides DataFrame API coverage for PySpark and +Spark Connect clients for other languages are planned for the future. Review Comment: I added this content, thanks -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
