spark

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The Apache Spark repository contains the core functionality of the Apache Spark framework, a powerful and versatile data processing engine that can handle a wide range of data processing tasks, from batch processing to real-time streaming. The repository is organized into several key directories, each of which focuses on a specific aspect of Spark's functionality.

The most important parts of the repository are the …/core, core, python, …/catalyst, and mllib directories. These directories contain the core implementation of Spark's SQL engine, the underlying Spark Core API, the Python-based PySpark library, the Catalyst module that powers Spark SQL's query optimization, and the Machine Learning Library (MLlib), respectively.

The …/core directory is responsible for the core functionality of the Spark SQL engine, including the implementation of various data sources, expressions, internal components, and the overall execution engine. It provides the foundation for Spark's powerful SQL capabilities, allowing users to perform complex data processing tasks using familiar SQL syntax. The key components in this directory include the implementation of the DataSource and DataSourceV2 APIs, the Expression and ExpressionEncoder classes for defining and evaluating SQL expressions, and the SparkPlan and WholeStageCodegenExec classes that handle the physical execution of SQL queries.

The core directory contains the core implementation of the Apache Spark framework, including the Java and Scala APIs, memory management, serialization, shuffle handling, storage management, and deployment and management of Spark applications. This directory is the foundation of the entire Spark ecosystem, providing the low-level building blocks that enable Spark's high-level functionality. Key components in this directory include the SparkContext and SparkSession classes, the MemoryManager and TaskMemoryManager classes for managing memory usage, the Serializer and SerializerInstance interfaces for pluggable serialization, and the BlockManager class for managing data storage and caching.

The python directory contains the core functionality of the PySpark library, which provides the Python API for Apache Spark. PySpark allows users to leverage the power of Spark's data processing capabilities using the familiar Python programming language. The PySpark library includes components for Spark Streaming, Spark SQL, Machine Learning, MLlib, Pandas-on-Spark, and various utility modules. The Broadcast class, SparkConf class, and SparkContext class are some of the key components in this directory.

The …/catalyst directory contains the core functionality of the Spark SQL Catalyst module, which is responsible for the analysis, optimization, and planning of SQL queries. The Catalyst module provides the foundation for the Spark SQL engine, handling tasks such as expression evaluation, logical plan optimization, and physical plan generation. Key components in this directory include the TreeNode class for representing and manipulating tree-like data structures, the QueryPlanner class for transforming logical plans into physical plans, and the ExpressionEncoder class for converting between JVM objects and Spark SQL's internal row format.

The mllib directory contains the core functionality of the Spark MLlib library, providing both a DataFrame-based machine learning API and an RDD-based machine learning API. MLlib includes a wide range of machine learning algorithms, feature transformers, evaluation metrics, and utilities, allowing users to easily incorporate advanced analytics into their Spark applications. The ClassificationModel trait, KMeans class, and ALS class are some of the key components in this directory.

Overall, the Apache Spark repository provides a comprehensive and powerful data processing framework that can handle a wide range of data processing tasks, from batch processing to real-time streaming. The repository is organized in a modular fashion, with each directory focusing on a specific aspect of Spark's functionality, making it easier for developers to understand and extend the framework to suit their needs.

References: sql/core

The …/core directory contains the core functionality of the Apache Spark SQL module. This directory is responsible for handling various aspects of Spark SQL, including data sources, expressions, internal components, and the overall execution engine.

References: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources, sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2

The core functionality for integrating various data sources into the Spark SQL engine is implemented in the …/datasources directory. This includes support for file-based formats like Parquet, CSV, JSON, and ORC, as well as JDBC data sources and other specialized data sources.

References: sql/core/src/main/scala/org/apache/spark/sql/expressions

The core functionality for defining and evaluating SQL expressions and operators in the Spark SQL engine is provided in the …/expressions directory. This includes support for user-defined functions (UDFs) and user-defined aggregate functions (UDAFs).

References: sql/core/src/main/scala/org/apache/spark/sql/execution, sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate, sql/core/src/main/scala/org/apache/spark/sql/execution/joins

The core components of the Spark SQL execution engine include physical planning, join algorithms, aggregation operators, and the overall execution flow.

References: sql/core/src/main/scala/org/apache/spark/sql/execution/streaming, sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous

The core functionality for the execution of streaming queries in the Spark SQL engine is implemented in the …/streaming directory. This subsection covers the key components and design choices in this implementation.

References: sql/core/src/main/scala/org/apache/spark/sql/execution/columnar, sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive

The …/columnar directory contains the core functionality for in-memory caching and query optimization in the Spark SQL engine.

References: core

The core directory contains the core implementation of the Apache Spark framework. This directory and its sub-directories provide a wide range of functionality, including:

References: core/src/main/java/org/apache/spark/api, core/src/main/scala/org/apache/spark/api

The core Java and Scala APIs provided by the Spark Core module include the following key components:

References: core/src/main/java/org/apache/spark/memory, core/src/main/scala/org/apache/spark/memory

The MemoryManager class in the org.apache.spark.memory package is responsible for managing the overall memory usage in Apache Spark. It enforces how memory is shared between execution (used for computation) and storage (used for caching and data propagation). The MemoryManager class has several concrete implementations, each with its own memory management policies.

References: core/src/main/java/org/apache/spark/serializer, core/src/main/scala/org/apache/spark/serializer

The org.apache.spark.serializer package in the Spark Core module provides a pluggable serialization mechanism for RDD and shuffle data. It allows users to specify a custom serializer to be used for serializing and deserializing data in Spark applications.

References: core/src/main/java/org/apache/spark/shuffle, core/src/main/scala/org/apache/spark/shuffle

The core functionality of the shuffle handling in Apache Spark is implemented across several key components:

References: core/src/main/java/org/apache/spark/storage, core/src/main/scala/org/apache/spark/storage

The BlockManager class is the central component responsible for managing the storage and caching of data in Apache Spark. It provides the following key functionality:

References: core/src/main/scala/org/apache/spark/deploy

The core functionality for deploying and managing Spark applications is implemented in the …/deploy directory. This includes the implementation of the Spark Master, Spark Worker, and Spark History Server.

References: core/src/main/java/org/apache/spark/util, core/src/main/scala/org/apache/spark/util

The ChildFirstURLClassLoader class is a custom class loader that gives preference to its own URLs over the parent class loader when loading classes and resources. It overrides the loadClass() method to first attempt to load the class using the parent class loader, and if that fails, it delegates the loading to the ParentClassLoader instance. It also overrides the getResources() and getResource() methods to prioritize the resources from the child class loader over the parent class loader.

References: python

The PySpark library provides the Python API for Apache Spark, offering a wide range of functionality for distributed data processing, machine learning, and real-time streaming. The key components of the PySpark library include:

References: spark

The …/broadcast.py file provides the implementation of the Broadcast class, which is used to efficiently distribute read-only data to all worker nodes in a Spark cluster.

References: spark

The …/conf.py file defines the SparkConf class, which is used to configure various parameters for a Spark application, such as the master URL, application name, and environment variables for executors.

References: spark

The SparkContext class is the main entry point for Spark functionality, representing the connection to a Spark cluster. It provides a wide range of methods for interacting with the Spark framework, including:

References: spark

The …/files.py file provides the SparkFiles class, which is used to manage the files that have been added to the Spark application's resources through SparkContext.addFile() or SparkContext.addPyFile().

References: spark

The …/status.py file defines the StatusTracker class, which provides low-level status reporting APIs for monitoring Spark job and stage progress.

References: spark

The …/streaming directory contains the core functionality for the Spark Streaming module in PySpark. The key components in this directory are:

References: spark

The …/sql directory contains the core functionality for the PySpark SQL module, which provides a DataFrame API for working with structured data in Apache Spark.

References: spark

The …/ml directory contains the core functionality for the machine learning pipeline in the PySpark library. This includes:

References: spark

The …/mllib directory contains the core functionality of the Machine Learning Library (MLlib) component in PySpark. This directory provides a comprehensive set of machine learning algorithms, feature engineering tools, and utility functions.

References: spark

The …/pandas directory contains the core functionality of the pandas-on-Spark library, which provides a pandas-like API for working with Apache Spark DataFrames.

References: spark

The …/errors directory contains the implementation of various custom exception classes and utilities used throughout the PySpark codebase to handle and report errors that can occur during the execution of Spark applications.

References: spark

The …/serializers.py file provides custom serializers for transferring data between the driver and executor processes in PySpark.

References: spark

The …/shuffle.py file provides functionality for handling data shuffling and aggregation during distributed data processing in PySpark.

References: sql/catalyst

The Spark SQL Catalyst module is the core component responsible for the analysis, optimization, and planning of SQL queries in the Apache Spark SQL engine. It provides a rich set of functionality and utilities for working with logical plans, expressions, data types, and various other aspects of SQL processing.

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions, sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate

The …/expressions directory contains the core functionality for defining and evaluating SQL expressions and operators in the Spark SQL Catalyst layer. This includes support for user-defined functions (UDFs) and user-defined aggregate functions (UDAFs).

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer

The core functionality for optimizing SQL queries in the Spark SQL Catalyst layer is implemented in several key classes and objects:

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis

The CheckAnalysis trait in the org.apache.spark.sql.catalyst.analysis package is responsible for performing various checks and validations on Spark SQL's logical plans. It throws user-facing errors when it encounters invalid queries that fail to analyze. The key functionality includes:

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans, sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical

The key components in the …/logical directory include:

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning

The core functionality for transforming logical query plans into efficient physical plans in the Spark SQL Catalyst layer is implemented in the …/planning directory. This includes the implementation of various physical planning strategies and utilities.

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog

The ExternalCatalog trait in the Spark SQL Catalyst layer defines the core interface for managing the system catalog, which includes databases, tables, partitions, and functions. It provides a set of methods for creating, dropping, and altering these catalog objects, as well as for retrieving information about them.

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/streaming

The StreamingRelationV2 class in the …/streaming directory serves as a bridge between the V2 data source and the streaming execution engine in Apache Spark SQL. It allows for the integration of continuous processing sources that only have V1 microbatch support.

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders

The ExpressionEncoder class in the …/ExpressionEncoder.scala file is responsible for converting JVM objects to and from Spark SQL's internal row format. This is a critical component of the Spark SQL Catalyst layer, as it allows for seamless integration between user-defined data types and the Spark SQL engine.

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl

The domain-specific language (DSL) provided in the Spark SQL Catalyst layer allows developers to easily construct and manipulate Catalyst data structures, including expressions, attributes, and logical plans. The DSL is defined in the …/package.scala file and includes the following key components:

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects

The …/objects directory contains a collection of classes and traits that handle various object-related operations within the Spark SQL catalyst layer.

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/variant

The core functionality for working with variant data types, including parsing JSON and extracting sub-values, within the Spark SQL Catalyst layer is implemented in the following files and directories:

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/xml

The …/xml directory contains the core functionality for working with XML data within the Spark SQL Catalyst layer. This includes the implementation of various XPath-related expressions that allow users to extract and manipulate XML data in Spark SQL queries.

References: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util

The org.apache.spark.sql.catalyst.util package provides a collection of utility classes and functionality that are used throughout the Spark SQL Catalyst module. This subsection covers the most important of these utilities, focusing on data type handling, string manipulation, and numeric value conversion.

References: sql/catalyst/src/main/scala/org/apache/spark/sql/execution/arrow

The …/arrow directory contains the core functionality for writing Spark SQL data to the Apache Arrow format. The main components in this directory are:

References: sql/catalyst/src/main/scala/org/apache/spark/sql/sources

The …/sources directory contains the core functionality for defining and managing filter predicates that can be pushed down to data sources in the Spark SQL Catalyst layer.

References: mllib

The Spark MLlib library provides both a DataFrame-based machine learning API (…/ml) and an RDD-based machine learning API (…/mllib). The key components in this directory include:

References: spark

The …/classification, …/regression, …/clustering, and …/recommendation directories contain the implementation of various classification, regression, clustering, and recommendation algorithms, respectively.

References: spark

The …/feature directory contains a collection of feature transformers. These transformers are used to extract, transform, and select features from data, which is a crucial step in the machine learning process.

References: spark

The …/evaluation directory contains classes for evaluating the performance of various machine learning models. The key classes in this directory are:

References: spark

The …/param, …/util, and …/python directories provide utilities for managing parameters, various utility classes and functions, and integrating Spark ML models and data structures with the Python environment, respectively.

References: sql/hive

The …/hive directory contains the core functionality for Spark SQL's integration with the Apache Hive data warehouse system. This includes the implementation of various Hive-specific features, such as:

References: spark

The …/execution directory contains a comprehensive set of test suites that verify the functionality of Spark SQL's integration with Hive, covering a wide range of features and use cases.

References: spark

The …/hive directory contains the core functionality for integrating Spark SQL with the Apache Hive ecosystem. The key components in this directory are:

References: spark

The …/execution subdirectory contains the implementation of various Hive-specific SQL operations within the Spark SQL engine.

References: spark

The …/hive directory includes the HiveFileFormat and HiveOutputWriter classes, which manage the writing of data to Hive tables.

References: spark

The …/hive directory contains the HiveExternalCatalog class, which provides a persistent implementation of the system catalog using the Hive metastore.

References: spark

The …/hive directory includes the HiveSessionCatalog and HiveSessionStateBuilder classes, which manage the Hive-specific session state and catalog within the Spark SQL ecosystem.

References: spark

The …/hive directory contains the HiveShim interface and its implementations, which provide a version-specific abstraction layer between the HiveClientImpl and the underlying Hive library.

References: spark

The …/hive directory includes the HiveUtils object, which provides various utility functions.

References: examples

The examples directory contains a comprehensive set of example applications that demonstrate the usage of various features and functionalities provided by the Apache Spark framework. These examples cover a wide range of topics, including:

References: examples/src/main/scala/org/apache/spark/examples/ml, examples/src/main/python/ml, examples/src/main/java/org/apache/spark/examples/ml

The key functionality in the …/ml directory includes:

References: examples/src/main/scala/org/apache/spark/examples/sql, examples/src/main/java/org/apache/spark/examples/sql, examples/src/main/python/sql

This subsection covers the examples that demonstrate the usage of Spark SQL and Structured Streaming, including integration with Hive, complex real-time data processing, data source options, and user-defined functions and aggregations.

References: examples/src/main/scala/org/apache/spark/examples/streaming, examples/src/main/java/org/apache/spark/examples/streaming, examples/src/main/python/streaming

The …/streaming directory contains several Scala-based Spark Streaming example applications that demonstrate various features and use cases of Spark Streaming.

References: examples/src/main/scala/org/apache/spark/examples/graphx

The GraphX Examples section covers the examples that demonstrate the usage of the GraphX library for graph processing, including PageRank computation, connected components, and triangle counting.

References: sql/api

The Spark SQL API provides the core Java and Scala interfaces and classes for working with structured data in Apache Spark. This includes functionality for defining custom functions, managing catalog objects, configuring streaming queries, working with data types, and controlling the behavior of DataFrame saving operations.

References: sql/api/src/main/java/org/apache/spark/sql/api/java, sql/api/src/main/scala/org/apache/spark/sql/catalyst/expressions

The Spark SQL API provides a set of Java and Scala interfaces for defining custom User-Defined Functions (UDFs) and User-Defined Aggregate Functions (UDAFs) that can be used in Spark SQL queries. These interfaces allow developers to extend the functionality of the Spark SQL engine by adding specialized logic that is not provided by the built-in SQL functions.

References: sql/api/src/main/java/org/apache/spark/sql/connector/catalog, sql/api/src/main/java/org/apache/spark/sql/connector

The …/catalog directory contains the core Java interfaces and implementations related to the catalog functionality in the Apache Spark SQL API.

References: sql/api/src/main/java/org/apache/spark/sql/streaming, sql/api/src/main/scala/org/apache/spark/sql/streaming

The Spark SQL API provides several Java and Scala interfaces for configuring and managing streaming queries, including support for state management, timeouts, and output modes.

References: sql/api/src/main/java/org/apache/spark/sql/types, sql/api/src/main/scala/org/apache/spark/sql/types

The Spark SQL API provides a comprehensive set of Java and Scala interfaces and classes for working with data types in the Spark SQL ecosystem. This subsection covers the core functionality for defining and manipulating various data types, including primitive types, complex types, and user-defined types.

References: spark

The core functionality for controlling the behavior of DataFrame saving operations in the Spark SQL API is provided by the classes in the …/connector and …/connector directories.

References: connector

The connector directory contains a collection of sub-directories that provide specialized functionality for integrating Apache Spark with various external systems and data sources.

References: connector/avro/src/main/scala/org/apache/spark/sql

The …/avro directory contains the implementation of the Avro data source for Apache Spark. This data source provides functionality for reading and writing Avro data files, including support for handling evolved Avro schemas, datetime rebasing, and various Avro data types and logical types.

References: connector/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010, connector/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010

The …/kafka010 directory provides both batch-oriented and stream-oriented interfaces for consuming data from Apache Kafka within Spark applications. The key functionality includes:

References: connector/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis

The Kinesis Connector in the Apache Spark repository provides a reliable and efficient way to integrate Apache Spark Streaming with Amazon Kinesis. The core functionality is implemented in the …/kinesis directory.

References: connector/protobuf/src/main/scala/org/apache/spark/sql/protobuf

The Spark Protobuf connector, located in the …/protobuf directory, provides seamless integration between Protobuf data and Spark SQL. It allows for the conversion of Protobuf data to Catalyst data types and vice versa.

References: connector/connect/server/src/main/scala/org/apache/spark/sql/connect, connector/connect/client/jvm/src/main/scala/org/apache/spark/sql

The Spark Connect server provides a gRPC-based interface for executing Spark SQL plans and managing Spark sessions. The core functionality is implemented in the …/connect directory.

References: spark

The …/kubernetes directory contains the core functionality for integrating Apache Spark with Kubernetes-based clusters. This includes components for submitting and configuring Spark applications, managing the execution of Spark applications, and implementing the Spark shuffle functionality for Kubernetes environments.

References: sql/hive-thriftserver

The Apache Hive Thrift Server (HiveServer2) is a key component of the Apache Spark SQL engine, responsible for handling various aspects of the Spark SQL Thrift Server's operation, including:

References: sql/hive-thriftserver/src/main/java/org/apache/hive/service/auth

The core functionality for handling authentication and authorization in the Apache Hive Thrift Server is implemented in the …/auth directory. This subsection covers the key components and design choices in this directory.

References: sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session

The core functionality for managing Hive sessions in the Apache Hive Thrift Server is implemented in the …/session directory.

References: sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/thrift

The Thrift-based CLI (Command-Line Interface) service in the Apache Hive Thrift Server is responsible for handling the setup and management of Thrift servers, client-side interactions, and HTTP-based Thrift request handling.

References: sql/hive-thriftserver/src/main/java/org/apache/hive/service/server

The core functionality for starting and managing the Apache Hive Thrift Server (HiveServer2) process is implemented in the …/HiveServer2.java file.

References: resource-managers/kubernetes

The …/kubernetes directory contains the core functionality for integrating Apache Spark with a Kubernetes-based cluster. This includes components for:

References: resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/features, resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/submit

The …/submit directory contains the core functionality for submitting Spark applications to run on a Kubernetes cluster.

References: resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s

The KubernetesClusterSchedulerBackend class is responsible for managing the lifecycle of executors in a Spark application running on a Kubernetes cluster. It handles tasks such as starting and stopping the application, decommissioning and killing executors, and setting up the necessary Kubernetes resources.

References: resource-managers/kubernetes/core/src/main/scala/org/apache/spark/shuffle

The core functionality of the Spark shuffle implementation for Kubernetes environments is handled in the …/shuffle directory. This directory contains the following key components:

References: resource-managers/kubernetes/integration-tests/src/test/scala/org/apache/spark/deploy/k8s/integrationtest, resource-managers/kubernetes/integration-tests/src/test/scala/org/apache/spark/deploy/k8s/integrationtest/backend

The …/integrationtest directory contains a comprehensive set of integration tests that verify the functionality of the Spark Kubernetes resource manager. These tests cover a wide range of scenarios, including: