HyukjinKwon commented on code in PR #48922: URL: https://github.com/apache/spark/pull/48922#discussion_r1853119784
########## docs/app-dev-spark-connect.md: ########## @@ -0,0 +1,239 @@ +--- +layout: global +title: Application Development with Spark Connect +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. +--- +**Spark Connect Overview** + +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 learn more about Spark Connect, see [Spark Connect Overview](spark-connect-overview.html). + +# Redefining Spark Applications using Spark Connect + +With its decoupled client-server architecture, Spark Connect simplifies how Spark Applications are +developed. +The notion of Spark Client Applications and Spark Server Libraries are introduced as follows: +* _Spark Client Applications_ are regular Spark applications that use Spark and its rich ecosystem for +distributed data processing. Examples include ETL pipelines, data preparation, and model training +and inference. +* _Spark Server Libraries_ build on, extend, and complement Spark's functionality, e.g. +[MLlib](ml-guide.html) (distributed ML libraries that use Spark's powerful distributed processing). Spark Connect +can be extended to expose client-side interfaces for Spark Server Libraries. + +With Spark 3.4 and Spark Connect, the development of Spark Client Applications is simplified, and +clear extension points and guidelines are provided on how to build Spark Server Libraries, making +it easy for both types of applications to evolve alongside Spark. As illustrated in Fig.1, Spark +Client applications connect to Spark using the Spark Connect API, which is essentially the +DataFrame API and fully declarative. + +<p style="text-align: center;"> + <img src="img/extending-spark-connect.png" title="Figure 1: Architecture" alt="Extending Spark +Connect Diagram" /> +</p> +Spark Server Libraries extend Spark. They typically provide additional server-side logic integrated +with Spark, which is exposed to client applications as part of the Spark Connect API, using Spark +Connect extension points. For example, the _Spark Server Library_ consists of custom +service-side logic (as indicated by the blue box labeled _Custom Library Plugin_), which is exposed +to the client via the blue box as part of the Spark Connect API. The client uses this API, e.g., +alongside PySpark or the Spark Scala client, making it easy for Spark client applications to work +with the custom logic/library. + +## Spark Client Applications + +Spark Client Applications are the _regular Spark applications_ that Spark users develop today, e.g., +ETL pipelines, data preparation, or model training or inference. These are typically built using +Sparks declarative DataFrame and DataSet APIs. With Spark Connect, the core behaviour remains the +same, but there are a few differences: +* Lower-level, non-declarative APIs (RDDs) can no longer be directly used from Spark Client +applications. Alternatives for missing RDD functionality are provided as part of the higher-level +DataFrame API. +* Client applications no longer have direct access to the Spark driver JVM; they are fully +separated from the server. + +Client applications based on Spark Connect can be submitted in the same way as any previous job. +In addition, Spark Client Applications based on Spark Connect have several benefits compared to +classic Spark applications using earlier Spark versions (3.4 and below): +* _Upgradability_: Upgrading to new Spark Server versions is seamless, as the Spark Connect API +abstracts any changes/improvements on the server side. Client- and server APIs are cleanly +separated. +* _Simplicity_: The number of APIs exposed to the user is reduced from 3 to 2. The Spark Connect API +is fully declarative and consequently easy to learn for new users familiar with SQL. +* _Stability_: When using Spark Connect, the client applications no longer run on the Spark driver +and, therefore don’t cause and are not affected by any instability on the server. +* _Remote connectivity_: The decoupled architecture allows remote connectivity to Spark beyond SQL +and JDBC: any application can now interactively use Spark “as a service”. +* _Backwards compatibility_: The Spark Connect API is code-compatible with earlier Spark versions, +except for the usage of RDDs, for which a list of alternative APIs is provided in Spark Connect. + +## Spark Server Libraries + +Until Spark 3.4, extensions to Spark (e.g., [Spark ML](ml-guide#:~:text=What%20is%20%E2%80%9CSpark%20ML%E2%80%9D%3F,to%20emphasize%20the%20pipeline%20concept.) +or [Spark-NLP](https://github.com/JohnSnowLabs/spark-nlp)) were built and deployed like Spark +Client Applications. With Spark 3.4 and Spark Connect, explicit extension points are offered to +extend Spark via Spark Server Libraries. These extension points provide functionality that can be +exposed to a client, which differs from existing extension points in Spark such as +[SparkSession extensions](api/java/org/apache/spark/sql/SparkSessionExtensions.html) or +[Spark Plugins](api/java/org/apache/spark/api/plugin/SparkPlugin.html). + +### Getting Started: Extending Spark with Spark Server Libraries + +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. + +A Spark Server Library consists of the following components, illustrated in Fig. 2: + +1. The Spark Connect protocol extension (blue box _Proto_ API) +2. A Spark Connect Plugin… Review Comment: is `…` a typo? or incomplete? 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