Sparkle 1.2.2

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V 1.2.2 Improvement: MiniFilter can now be downloaded; Bug fix: No more download message for VC4 products when user has already the latest release. V 1.2.1 Bug fixes: Analog Lab Lite can now be downloaded; No more 'Update' button when the plugin is up to date on OSX 10.11 and 10.12; V 1.2.0 Improvements. New 4 Diamante Sparkle Silver Heart Buttons 1/2' 2 holes Faux Rhinestone. Item Information. Bulk savings: Buy 1. Product Name: Red Heart Soft - All Colours. Brand Name: Red Heart. Brand: Red Heart. Yarn Name: Soft. Part Code: 9809670. Shade Count: 23. Yarn Weight: Aran. Apache Spark 2.2.1 documentation homepage. Apache Spark is a fast and general-purpose cluster computing system. Sparkle Tab by Radwimps with free online tab player. One accurate version. Recommended by The Wall Street Journal.

Here you can get the latest Ruby distributions in your favorite flavor.The current stable version is 2.7.2.Please be sure to read Ruby’s License.

Sparkle

Ways of Installing Ruby

We have several tools on each major platform to install Ruby:

  • On Linux/UNIX, you can use the package management system of yourdistribution or third-party tools (rbenv and RVM).
  • On macOS machines, you can use third-party tools (rbenv and RVM).
  • On Windows machines, you can use RubyInstaller.

See the Installation page for details on usingpackage management systems or third-party tools.

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Of course, you can also install Ruby from source on all major platforms.

Compiling Ruby — Source Code

Installing from the source code is a great solution for when you arecomfortable enough with your platform and perhaps need specific settingsfor your environment. It’s also a good solution in the event that thereare no other premade packages for your platform.

See the Installation page for details on buildingRuby from source. If you have an issue compiling Ruby, consider usingone of the third party tools mentioned above. They may help you.

  • Stable releases:
    • Ruby 2.7.2
      sha256: 6e5706d0d4ee4e1e2f883db9d768586b4d06567debea353c796ec45e8321c3d4
    • Ruby 2.6.6
      sha256: 364b143def360bac1b74eb56ed60b1a0dca6439b00157ae11ff77d5cd2e92291
  • Preview releases:
    • Ruby 3.0.0-preview2
      sha256: 9de8661565c2b1007d91a580e9a7e02d23f1e8fc8df371feb15a2727aa05fd9a
  • In security maintenance phase (will EOL soon!):
    • Ruby 2.5.8
      sha256: 6c0bdf07876c69811a9e7dc237c43d40b1cb6369f68e0e17953d7279b524ad9a
  • Not maintained anymore (EOL):
    • Ruby 2.4.10
      sha256: 93d06711795bfb76dbe7e765e82cdff3ddf9d82eff2a1f24dead9bb506eaf2d0
    • Ruby 2.3.8
      sha256: b5016d61440e939045d4e22979e04708ed6c8e1c52e7edb2553cf40b73c59abf
  • Snapshots:
    • Stable Snapshot of ruby_2_7 branch:This is a tarball of the latest snapshot of the current ruby_2_7 branch.
    • Stable Snapshot of ruby_2_6 branch:This is a tarball of the latest snapshot of the current ruby_2_6 branch.
    • Stable Snapshot of ruby_2_5 branch:This is a tarball of the latest snapshot of the current ruby_2_5 branch.
    • Nightly Snapshot:This is a tarball of whatever is in Git, made nightly.This may contain bugs or other issues, use at your own risk!

For more information about specific releases, particularly older releasesor previews, see the Releases page.Information about the current maintenance status of the variousRuby branches can be found on the Branches page.

For information about the Ruby Subversion and Git repositories, see ourRuby Core page.

The Ruby source is available from a worldwide set ofMirror Sites.Please try to use a mirror that is near you.

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.

Get Spark from the downloads page of the project website. This documentation is for Spark version 2.2.1. 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.

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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. Davinci resolve studio 15.3.0.8 for macos windows 10.

Spark runs on Java 8+, Python 2.7+/3.4+ and R 3.1+. For the Scala API, Spark 2.2.1uses Scala 2.11. You will need to use a compatible Scala version(2.11.x).

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.

Note that support for Scala 2.10 is deprecated as of Spark 2.1.0, and may be removed in Spark 2.3.0.

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,

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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:

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  • 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 (experimental): deploy Spark on top of Kubernetes

Other Documents:

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  • 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:

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  • 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)