HyukjinKwon commented on code in PR #40324: URL: https://github.com/apache/spark/pull/40324#discussion_r1131838714
########## docs/spark-connect-overview.md: ########## @@ -0,0 +1,244 @@ +--- +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 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, now 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. + +Set the `SPARK_REMOTE` environment variable to point to the Spark server we started +above: + +{% highlight bash %} +export SPARK_REMOTE="sc://localhost/" Review Comment: nit but I would remove `/` in the end at `sc://localhost/"` -> `sc://localhost"` -- 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
