sparkle: Apache Spark applications in Haskell
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.
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
How to use
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.
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.
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/ 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 file.
Sponsors
sparkle is maintained by Tweag I/O.
Have questions? Need help? Tweet at
@tweagio.