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- Apache Spark is 100% open source, hosted at the vendor-independent Apache Software Foundation.
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The most widely-used engine for scalable computing. Thousands of companies, including 80% of the Fortune 500, use Apache Spark ™. Over 2,000 contributors to the open source project from industry and academia. Ecosystem.
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- Libraries
Spark SQL is developed as part of Apache Spark. It thus gets...
- Documentation
Spark Connect is a new client-server architecture introduced...
- Examples
Apache Spark ™ examples. This page shows you how to use...
- Community
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- Spark Streaming
Spark Structured Streaming makes it easy to build streaming...
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Try Apache Spark on the Databricks cloud for free. The Databricks Unified Analytics Platform offers 5x performance over open source Spark, collaborative notebooks, integrated workflows, and enterprise security — all in a fully managed cloud platform.
Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance.
- Overview
- Online Documentation
- Building Spark
- Interactive Scala Shell
- Interactive Python Shell
- Example Programs
- Running Tests
- A Note About Hadoop Versions
- Configuration
- Contributing
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
https://spark.apache.org/
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Spark is built using Apache Maven. To build Spark and its example programs, run:
(You do not need to do this if you downloaded a pre-built package.)
More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
The easiest way to start using Spark is through the Scala shell:
Try the following command, which should return 1,000,000,000:
Alternatively, if you prefer Python, you can use the Python shell:
And run the following command, which should also return 1,000,000,000:
Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example [params]. For example:
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be spark:// URL, "yarn" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:
Many of the example programs print usage help if no params are given.
Testing first requires building Spark. Once Spark is built, tests can be run using:
Please see the guidance on how to run tests for a module, or individual tests.
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
- Resilient Distributed Dataset (RDD) Resilient Distributed Datasets (RDDs) are fault-tolerant collections of elements that can be distributed among multiple nodes in a cluster and worked on in parallel.
- Directed Acyclic Graph (DAG) As opposed to the two-stage execution process in MapReduce, Spark creates a Directed Acyclic Graph (DAG) to schedule tasks and the orchestration of worker nodes across the cluster.
- DataFrames and Datasets. In addition to RDDs, Spark handles two other data types: DataFrames and Datasets. DataFrames are the most common structured application programming interfaces (APIs) and represent a table of data with rows and columns.
- Spark Core. Spark Core is the base for all parallel data processing and handles scheduling, optimization, RDD, and data abstraction. Spark Core provides the functional foundation for the Spark libraries, Spark SQL, Spark Streaming, the MLlib machine learning library, and GraphX graph data processing.
Apache Spark is an open source analytics engine used for big data workloads that can handle both batches as well as real-time analytics.
Spark is an open source framework focused on interactive query, machine learning, and real-time workloads. It does not have its own storage system, but runs analytics on other storage systems like HDFS, or other popular stores like Amazon Redshift , Amazon S3 , Couchbase, Cassandra, and others.
