HyukjinKwon commented on code in PR #48922: URL: https://github.com/apache/spark/pull/48922#discussion_r1853115973
########## 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… +3. … with the application logic that extends Spark +4. The client package that exposes the Spark Server Library application logic to the Spark Client +Application, alongside PySpark or the Scala Spark Client. +<p style="text-align: center;"> + <img src="img/extending-spark-connect-labelled.png" title="Figure 2: Labelled Architecture" alt="Extending Spark +Connect Diagram - Labelled Steps" /> +</p> +#### (1) Spark Connect Protocol Extension +To extend Spark with a new Spark Server Library, developers can extend the three main operation +types in the Spark Connect protocol: _Relation_, _Expression_, and _Command_. + +{% highlight protobuf %} +message Relation { + oneof rel_type { + Read read = 1; + // ... + google.protobuf.Any extension = 998; + } +} + +message Expression { + oneof expr_type { + Literal literal = 1; + // ... + google.protobuf.Any extension = 999; + } +} + +message Command { + oneof command_type { + WriteCommand write_command = 1; + // ... + google.protobuf.Any extension = 999; + } +} +{% endhighlight %} +Their extension fields allow serializing arbitrary protobuf messages as part of the Spark Connect +protocol. These messages represent the parameters or state of the extension implementation. +To build a custom expression type, the developer first defines the custom protobuf definition +of the expression. + +{% highlight protobuf %} +message ExamplePluginExpression { + Expression child = 1; + string custom_field = 2; +} +{% endhighlight %} +#### (2) Spark Connect Plugin implementation with (3) custom application logic + +As a next step, the developer implements the _ExpressionPlugin_ class of Spark Connect with custom +application logic based on the input parameters of the protobuf message. +{% highlight protobuf %} +class ExampleExpressionPlugin extends ExpressionPlugin { + override def transform( + relation: protobuf.Any, + planner: SparkConnectPlanner): Option[Expression] = { + // Check if the serialized value of protobuf.Any matches the type + // of our example expression. + if (!relation.is(classOf[proto.ExamplePluginExpression])) { + return None + } + val exp = relation.unpack(classOf[proto.ExamplePluginExpression]) + Some(Alias(planner.transformExpression( + exp.getChild), exp.getCustomField)(explicitMetadata = None)) + } +} +{% endhighlight %} + +Once the application logic is developed, the code must be packaged as a jar and Spark must be +configured to pick up the additional logic. The relevant Spark configuration options are: +* _spark.jars_ which define the location of the Jar file containing the application logic built for +the custom expression. +* _spark.connect.extensions.expression.classes_ specifying the full class name +of each expression extension loaded by Spark. Based on these configuration options, Spark will +load the values at startup and make them available for processing. + +#### (4) Spark Server Library Client Package + +Once the server component is deployed, any client can use it with the right protobuf messages. +In the example above, the following message payload sent to the Spark Connect endpoint would be +enough to trigger the extension mechanism. +{% highlight json %} +{ + "project": { + "input": { + "sql": { + "query": "select * from samples.nyctaxi.trips" + } + }, + "expressions": [ + { + "extension": { + "typeUrl": "type.googleapis.com/spark.connect.ExamplePluginExpression", + "value": "\n\006\022\004\n\002id\022\006testval" + } + } + ] + } +} +{% endhighlight %} +To make the example available in Python, the application developer provides a Python library that +wraps the new expression and embeds it into PySpark. The easiest way to provide a function for any +expression is to take a PySpark column instance as an argument and return a new Column instance +with the expression applied. + +{% highlight python %} +from pyspark.sql.connect.column import Expression +from myxample.proto import ExamplePluginExpression +import pyspark.sql.connect.proto as proto + +# Internal class that satisfies the interface by the Python client +# of Spark Connect to generate the protobuf representation from +# an instance of the expression. +class ExampleExpression(Expression): Review Comment: nit but indentation would have to use 4 spaces instead of tabs -- 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. 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