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sparkle: Distributed Apache Spark applications in Haskell

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Deprecated

See README.md

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Modules

  • Control
    • Distributed
      • Control.Distributed.Spark
        • Control.Distributed.Spark.Closure
        • Control.Distributed.Spark.Context
        • ML
          • Feature
            • Control.Distributed.Spark.ML.Feature.CountVectorizer
            • Control.Distributed.Spark.ML.Feature.RegexTokenizer
            • Control.Distributed.Spark.ML.Feature.StopWordsRemover
          • Control.Distributed.Spark.ML.LDA
        • Control.Distributed.Spark.PairRDD
        • Control.Distributed.Spark.RDD
        • SQL
          • Control.Distributed.Spark.SQL.Context
          • Control.Distributed.Spark.SQL.DataFrame
          • Control.Distributed.Spark.SQL.Row

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Versions [RSS] 0.1, 0.1.0.1, 0.2, 0.3, 0.4, 0.4.0.1, 0.4.0.2, 0.5, 0.5.0.1, 0.6, 0.7, 0.7.1, 0.7.2, 0.7.2.1, 0.7.3, 0.7.4 (info)
Dependencies base (>=4.8 && <4.9), binary (>=0.7), bytestring (>=0.10), distributed-closure (>=0.3), filepath (>=1.4), inline-java (>=0.1), process (>=1.2), regex-tdfa (>=1.2), singletons (>=2.0), sparkle, text (>=1.2), vector (>=0.11), zip-archive (>=0.2) [details]
License BSD-3-Clause
Copyright 2016 EURL Tweag
Author Tweag I/O
Maintainer alp.mestanogullari@tweag.io
Category FFI, JVM, Java, Distributed Computing
Source repo head: git clone https://github.com/tweag/sparkle(sparkle)
Uploaded by MathieuBoespflug at 2016-06-12T23:04:22Z
Distributions
Executables sparkle
Downloads 9589 total (3 in the last 30 days)
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Status Docs not available [build log]
All reported builds failed as of 2016-11-23 [all 3 reports]

Readme for sparkle-0.1.0.1

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Sparkle: Apache Spark applications in Haskell

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Sparkle [spär′kəl]: a library for writing resilient analytics applications in Haskell that scale to thousands of nodes, using Spark and the rest of the Apache ecosystem under the hood.

This is an early tech preview, not production ready.

Getting started

The tl;dr using the hello app as an example on your local machine:

$ stack build hello
$ stack exec sparkle package sparkle-example-hello
$ spark-submit --master 'local[1]' sparkle-example-hello.jar

Requirements:

  • the Stack build tool;
  • either, the Nix package manager,
  • or, OpenJDK, Gradle and Spark >= 1.6 installed from your distro.

To run a Spark application the process is as follows:

  1. create an application in the apps/ folder, in-repo or as a submodule;
  2. add your app to stack.yaml;
  3. build the app;
  4. package your app into a deployable JAR container;
  5. submit it to a local or cluster deployment of Spark.

If you run into issues, read the Troubleshooting section below first.

To build:

$ stack [--nix] build

You can optionally pass --nix to all Stack commands to ask Nix to make Spark and Gradle available in a local sandbox for good build results reproducibility. Otherwise you'll need these installed through your OS distribution's package manager for the next steps (and you'll need to tell Stack how to find the JVM header files and shared libraries).

To package your app (omit the square bracket part entirely if you're not using --nix):

$ [stack --nix exec --] sparkle package <app-executable-name>

Finally, to run your application, for example locally:

$ [stack --nix exec --] spark-submit --master 'local[1]' <app-executable-name>.jar

See here for other options, including lauching a whole cluster from scratch on EC2.

Troubleshooting

jvm library or header files not found

You'll need to tell Stack where to find your local JVM installation. Something like the following in your ~/.stack/config.yaml should do the trick, but check that the paths match up what's on your system:

extra-include-dirs: [/usr/lib/jvm/java-7-openjdk-amd64/include]
extra-lib-dirs: [/usr/lib/jvm/java-7-openjdk-amd64/jre/lib/amd64/server]

Or use --nix: since it won't use your globally installed JDK, it will have no trouble finding its own locally installed one.

License

Copyright (c) 2015-2016 EURL Tweag.

All rights reserved.

Sparkle is free software, and may be redistributed under the terms specified in the LICENSE file.

About

Tweag I/O

Sparkle is maintained by Tweag I/O.

Have questions? Need help? Tweet at @tweagio.