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Monthly Downloads: 17
Programming language: Haskell
License: BSD 3-clause "New" or "Revised" License
Tags: Cloud     FFI     Distributed Computing     Jvm     Java    
Latest version: v0.7.4

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README

sparkle: Apache Spark applications in Haskell

CircleCI

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. See this blog post for the details.

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
$ stack exec -- spark-submit --master 'local[1]' --packages com.amazonaws:aws-java-sdk:1.11.253,org.apache.hadoop:hadoop-aws:2.7.2,com.google.guava:guava:23.0 sparkle-example-hello.jar

Using bazel

There is experimental support for bazel. This mechanism doesn't require executing sparkle package. Note however, that bazel evolves quickly and you'll need an old version (0.13.0) to use the following instructions.

$ bazel build //apps/hello:sparkle-example-hello_deploy.jar
$ bazel run spark-submit -- --packages com.amazonaws:aws-java-sdk:1.11.253,org.apache.hadoop:hadoop-aws:2.7.2,com.google.guava:guava:23.0 $(pwd)/bazel-bin/apps/hello/sparkle-example-hello_deploy.jar

How to use

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.

Build

Linux

Requirements

  • the Stack build tool (version 1.2 or above);
  • either, the Nix package manager,
  • or, OpenJDK, Gradle and Spark (version 1.6) installed from your distro.

To build:

$ stack build

You can optionally get Stack to download Spark and Gradle in a local sandbox (using Nix) for good build results reproducibility. This is the recommended way to build sparkle. Alternatively, 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 use Nix, set the following in your ~/.stack/config.yaml (or pass --nix to all Stack commands, see the Stack manual for more):

nix:
  enable: true

Other platforms

sparkle is not directly supported on non-Linux operating systems (e.g. Mac OS X or Windows). But you can use Docker to run sparkle natively inside a container on those platforms. First,

$ stack docker pull

Then, just add --docker as an argument to all Stack commands, e.g.

$ stack --docker build

By default, Stack uses the tweag/sparkle build and test Docker image, which includes everything that Nix does as in the Linux section. See the Stack manual for how to modify the Docker settings.

Integrating sparkle in another project

As sparkle interacts with the JVM, you need to tell ghc where JVM-specific headers and libraries are. It needs to be able to locate jni.h, jni_md.h and libjvm.so. Doing this with stack is explained in the Troubleshooting section below.

sparkle uses inline-java to embed fragments of Java code in Haskell modules, which requires running the javac compiler, which must be available in the PATH of the shell. Moreover, javac needs to find the Spark classes that inline-java quotations refer to. Therefore, these classes need to be added to the CLASSPATH when building sparkle. Dependending on your build system, how to do this might vary. In this repo, we use gradle to install Spark, and we query gradle to get the paths we need to add to the CLASSPATH. This is done with Cabal hooks (see [./Setup.hs](./Setup.hs)).

Package

To package your app as a JAR directly consumable by Spark:

$ stack exec -- sparkle package <app-executable-name>

Submit

Finally, to run your application, for example locally:

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

The <app-executable-name> is any executable name as given in the .cabal file for your app. See apps in the [apps/](apps/) folder for examples.

See here for other options, including launching a whole cluster from scratch on EC2. This blog post shows you how to get started on the Databricks hosted platform and on Amazon's Elastic MapReduce.

How it works

sparkle is a tool for creating self-contained Spark applications in Haskell. Spark applications are typically distributed as JAR files, so that's what sparkle creates. We embed Haskell native object code as compiled by GHC in these JAR files, along with any shared library required by this object code to run. Spark dynamically loads this object code into its address space at runtime and interacts with it via the Java Native Interface (JNI).

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.

Can't build sparkle on OS X

OS X is not a supported platform for now. There are several issues to make sparkle work on OS X, tracked in this ticket.

Gradle <= 2.12 incompatible with JDK 9

If you're using JDK 9, note that you'll need to either downgrade to JDK 8 or update your Gradle version, since Gradle versions up to and including 2.12 are not compatible with JDK 9.

Anonymous classes in inline-java quasiquotes fail to deserialize

When using inline-java, it is recommended to use the Kryo serializer, which is currently not the default in Spark but is faster anyways. If you don't use the Kryo serializer, objects of anonymous class, which arise e.g. when using Java 8 function literals,

foo :: RDD Int -> IO (RDD Bool)
foo rdd = [java| $rdd.map((Integer x) -> x.equals(0)) |]

won't be deserialized properly in multi-node setups. To avoid this problem, switch to the Kryo serializer by setting the following configuration properties in your SparkConf:

do conf <- newSparkConf "some spark app"
   confSet conf "spark.serializer" "org.apache.spark.serializer.KryoSerializer"
   confSet conf "spark.kryo.registrator" "io.tweag.sparkle.kryo.InlineJavaRegistrator"

See #104 for more details.

java.lang.UnsatisfiedLinkError: /tmp/sparkle-app...: failed to map segment from shared object

Sparkle unzips the Haskell binary program in a temporary location on the filesystem and then loads it from there. For loading to succeed, the temporary location must not be mounted with the noexec option. Alternatively, the temporary location can be changed with

spark-submit --driver-java-options="-Djava.io.tmpdir=..." \
             --conf "spark.executor.extraJavaOptions=-Djava.io.tmpdir=..."

java.io.IOException: No FileSystem for scheme: s3n

Spark 2.2 requires explicitly specifying extra JAR files to spark-submit in order to work with AWS. To work around this, add an additional 'packages' argument when submitting the job:

spark-submit --packages com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.2,com.google.guava:guava:12.0

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](LICENSE) file.

Sponsors

         Tweag I/O              LeapYear

sparkle is maintained by Tweag I/O.

Have questions? Need help? Tweet at @tweagio.


*Note that all licence references and agreements mentioned in the sparkle README section above are relevant to that project's source code only.