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.
This is an early tech preview, not production ready.
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]' sparkle-example-hello.jar
To run a Spark application the process is as follows:
- create an application in the
apps/folder, in-repo or as a submodule; - add your app to
stack.yaml; - build the app;
- package your app into a deployable JAR container;
- submit it to a local or cluster deployment of Spark.
If you run into issues, read the Troubleshooting section below first.
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: truesparkle 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.
To package your app as a JAR directly consumable by Spark:
$ stack exec -- sparkle package <app-executable-name>
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/ 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.
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).
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.
OS X is not a supported platform for now. There are several issues to make sparkle work on OS X, tracked in this ticket.
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.
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.
sparkle is maintained by Tweag I/O.
Have questions? Need help? Tweet at @tweagio.