spark
Auto-generated from apache/spark by Mutable.ai Auto WikiRevise
spark | |
---|---|
GitHub Repository | |
Developer | apache |
Written in | Scala |
Stars | 38k |
Watchers | 2.0k |
Created | 02/25/2014 |
Last updated | 04/04/2024 |
License | Apache License 2.0 |
Homepage | spark.apache.org |
Repository | apache/spark |
Auto Wiki | |
Revision | |
Software Version | 0.0.8Basic |
Generated from | Commit f6999d |
Generated at | 04/06/2024 |
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.
Spark SQL CoreRevise
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.
Data SourcesRevise
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.
Expressions and OperatorsRevise
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).
Execution EngineRevise
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.
StreamingRevise
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.
Caching and OptimizationRevise
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.
Spark CoreRevise
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:
Java and Scala APIsRevise
The core Java and Scala APIs provided by the Spark Core module include the following key components:
Memory ManagementRevise
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.
SerializationRevise
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.
Shuffle HandlingRevise
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:
Storage ManagementRevise
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:
Deployment and ManagementRevise
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.
UtilitiesRevise
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.
PySparkRevise
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:
Broadcast VariablesRevise
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.
Spark ConfigurationRevise
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.
Spark ContextRevise
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:
Spark File ManagementRevise
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()
.
Spark Status ReportingRevise
References: spark
The …/status.py
file defines the StatusTracker
class, which provides low-level status reporting APIs for monitoring Spark job and stage progress.
Spark StreamingRevise
References: spark
The …/streaming
directory contains the core functionality for the Spark Streaming module in PySpark. The key components in this directory are:
Spark SQLRevise
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.
Machine LearningRevise
References: spark
The …/ml
directory contains the core functionality for the machine learning pipeline in the PySpark library. This includes:
MLlibRevise
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.
Pandas-on-SparkRevise
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.
Error HandlingRevise
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.
Serialization and DeserializationRevise
References: spark
The …/serializers.py
file provides custom serializers for transferring data between the driver and executor processes in PySpark.
Shuffle HandlingRevise
References: spark
The …/shuffle.py
file provides functionality for handling data shuffling and aggregation during distributed data processing in PySpark.
Spark SQL CatalystRevise
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.
Expressions and OperatorsRevise
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).
OptimizationRevise
The core functionality for optimizing SQL queries in the Spark SQL Catalyst layer is implemented in several key classes and objects:
AnalysisRevise
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:
Logical PlansRevise
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:
Physical PlanningRevise
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.
Catalog ManagementRevise
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.
StreamingRevise
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.
EncodersRevise
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.
DSLRevise
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:
Object HandlingRevise
The …/objects
directory contains a collection of classes and traits that handle various object-related operations within the Spark SQL catalyst layer.
Variant Data TypesRevise
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:
XML SupportRevise
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.
UtilitiesRevise
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.
Arrow IntegrationRevise
The …/arrow
directory contains the core functionality for writing Spark SQL data to the Apache Arrow format. The main components in this directory are:
Data Source FiltersRevise
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.
Spark MLlibRevise
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:
AlgorithmsRevise
References: spark
The …/classification
, …/regression
, …/clustering
, and …/recommendation
directories contain the implementation of various classification, regression, clustering, and recommendation algorithms, respectively.
Feature TransformersRevise
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.
EvaluationRevise
References: spark
The …/evaluation
directory contains classes for evaluating the performance of various machine learning models. The key classes in this directory are:
UtilitiesRevise
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.
Spark SQL Hive IntegrationRevise
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:
Testing Hive IntegrationRevise
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.
Hive Client InteractionRevise
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:
Hive Execution CommandsRevise
References: spark
The …/execution
subdirectory contains the implementation of various Hive-specific SQL operations within the Spark SQL engine.
Hive File FormatsRevise
References: spark
The …/hive
directory includes the HiveFileFormat
and HiveOutputWriter
classes, which manage the writing of data to Hive tables.
Hive Metastore IntegrationRevise
References: spark
The …/hive
directory contains the HiveExternalCatalog
class, which provides a persistent implementation of the system catalog using the Hive metastore.
Hive Session ManagementRevise
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.
Hive Shim LayerRevise
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.
Spark ExamplesRevise
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:
Machine Learning ExamplesRevise
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:
Spark SQL and Structured Streaming ExamplesRevise
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.
Spark Streaming ExamplesRevise
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.
GraphX ExamplesRevise
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.
Spark SQL APIRevise
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.
Defining Custom FunctionsRevise
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.
Catalog ManagementRevise
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.
Streaming ConfigurationRevise
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.
Data TypesRevise
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.
DataFrame SavingRevise
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.
Spark ConnectorsRevise
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.
Avro ConnectorRevise
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.
Kafka ConnectorRevise
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:
Kinesis ConnectorRevise
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.
Protobuf ConnectorRevise
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.
Spark ConnectRevise
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.
Ganglia ConnectorRevise
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.
Spark Hive Thrift ServerRevise
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:
Session ManagementRevise
The core functionality for managing Hive sessions in the Apache Hive Thrift Server is implemented in the …/session
directory.
Thrift-based CLI ServiceRevise
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.
Server ManagementRevise
The core functionality for starting and managing the Apache Hive Thrift Server (HiveServer2) process is implemented in the …/HiveServer2.java
file.
Spark on KubernetesRevise
References: resource-managers/kubernetes
The …/kubernetes
directory contains the core functionality for integrating Apache Spark with a Kubernetes-based cluster. This includes components for:
Submitting and Configuring Spark Applications on KubernetesRevise
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.
Managing the Execution of Spark Applications on KubernetesRevise
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.
Spark Shuffle Functionality for KubernetesRevise
The core functionality of the Spark shuffle implementation for Kubernetes environments is handled in the …/shuffle
directory. This directory contains the following key components:
Integration Test Suite for Spark on KubernetesRevise
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: