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Apache Spark is a fast and general-purpose cluster computing system.It provides high-level APIs in Java, Scala, Python and R,and an optimized engine that supports general execution graphs.It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

Security in Spark is OFF by default. This could mean you are vulnerable to attack by default.Please see Spark Security before downloading and running Spark.

Get Spark from the downloads page of the project website. This documentation is for Spark version 2.4.3. Spark uses Hadoop’s client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions.Users can also download a “Hadoop free” binary and run Spark with any Hadoop versionby augmenting Spark’s classpath.Scala and Java users can include Spark in their projects using its Maven coordinates and in the future Python users can also install Spark from PyPI.

If you’d like to build Spark from source, visit Building Spark.

Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It’s easy to runlocally on one machine — all you need is to have java installed on your system PATH,or the JAVA_HOME environment variable pointing to a Java installation.

Spark runs on Java 8+, Python 2.7+/3.4+ and R 3.1+. For the Scala API, Spark 2.4.3uses Scala 2.12. You will need to use a compatible Scala version(2.12.x).

Click on the link above to download ApacheDS for Mac OS X. Double-click on the Apache Directory Server Installer.pkg file to open the installer. Follow the instructions of the installer to complete the installation. ApacheDS requires at least.

Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.Support for Scala 2.10 was removed as of 2.3.0. Support for Scala 2.11 is deprecated as of Spark 2.4.1and will be removed in Spark 3.0.

Spark comes with several sample programs. Scala, Java, Python and R examples are in theexamples/src/main directory. To run one of the Java or Scala sample programs, usebin/run-example <class> [params] in the top-level Spark directory. (Behind the scenes, thisinvokes the more generalspark-submit script forlaunching applications). For example,

You can also run Spark interactively through a modified version of the Scala shell. This is agreat way to learn the framework.

The --master option specifies themaster URL for a distributed cluster, or local to runlocally with one thread, or local[N] to run locally with N threads. You should start by usinglocal for testing. For a full list of options, run Spark shell with the --help option.

Spark also provides a Python API. To run Spark interactively in a Python interpreter, usebin/pyspark:

Example applications are also provided in Python. For example,

Spark also provides an experimental R API since 1.4 (only DataFrames APIs included).To run Spark interactively in a R interpreter, use bin/sparkR:

Example applications are also provided in R. For example,

The Spark cluster mode overview explains the key concepts in running on a cluster.Spark can run both by itself, or over several existing cluster managers. It currently provides severaloptions for deployment:

• Standalone Deploy Mode: simplest way to deploy Spark on a private cluster

Programming Guides:

• Quick Start: a quick introduction to the Spark API; start here!
• RDD Programming Guide: overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables
• Spark SQL, Datasets, and DataFrames: processing structured data with relational queries (newer API than RDDs)
• Structured Streaming: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)
• Spark Streaming: processing data streams using DStreams (old API)
• MLlib: applying machine learning algorithms
• GraphX: processing graphs

API Docs:

Deployment Guides:

• Cluster Overview: overview of concepts and components when running on a cluster
• Submitting Applications: packaging and deploying applications
• Deployment modes:
  • Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
  • Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager
  • Mesos: deploy a private cluster using Apache Mesos
  • YARN: deploy Spark on top of Hadoop NextGen (YARN)
  • Kubernetes: deploy Spark on top of Kubernetes

Other Documents:

• Configuration: customize Spark via its configuration system
• Monitoring: track the behavior of your applications
• Tuning Guide: best practices to optimize performance and memory use
• Job Scheduling: scheduling resources across and within Spark applications
• Security: Spark security support
• Hardware Provisioning: recommendations for cluster hardware
• Integration with other storage systems:
• Building Spark: build Spark using the Maven system
• Third Party Projects: related third party Spark projects

External Resources:

• Spark Community resources, including local meetups
• Mailing Lists: ask questions about Spark here
• AMP Camps: a series of training camps at UC Berkeley that featured talks andexercises about Spark, Spark Streaming, Mesos, and more. Videos,slides and exercises areavailable online for free.
• Code Examples: more are also available in the examples subfolder of Spark (Scala, Java, Python, R)

Downloading Apache Maven 3.6.3

Apache 2.4 Download Mac Iso

Apache Maven 3.6.3 is the latest release and recommended version for all users.

The currently selected download mirror is https://mirrors.ukfast.co.uk/sites/ftp.apache.org/. If you encounter a problem with this mirror, please select another mirror. If all mirrors are failing, there are backup mirrors (at the end of the mirrors list) that should be available. You may also consult the complete list of mirrors.

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System Requirements

Files

Maven is distributed in several formats for your convenience. Simply pick a ready-made binary distribution archive and follow the installation instructions. Use a source archive if you intend to build Maven yourself.

In order to guard against corrupted downloads/installations, it is highly recommended to verify the signature of the release bundles against the public KEYS used by the Apache Maven developers.

• Distributed under the Apache License, version 2.0

Previous Releases

It is strongly recommended to use the latest release version of Apache Maven to take advantage of newest features and bug fixes.

Apache 2.4 Download Mac Os

If you still want to use an old version you can find more information in the Maven Releases History and can download files from the archives for versions 3.0.4+ and legacy archives for earlier releases.