Joblib-hadoop

This package provides parallel and store backends for joblib that can be use on a Hadoop cluster.

If you don't know joblib already, user documentation is located on https://pythonhosted.org/joblib

Joblib-hadoop supports Python 2.7, 3.4 and 3.5.

Getting the latest code

To get the latest code use git:

git clone git://github.com/joblib/joblib-hadoop.git

Installing joblib-hadoop

We recommend using Python Anaconda 3 distribution for full support of the HDFS store backends.

1. Create an Anaconda environment (use python 2.7, 3.4 or 3.5) and activate it:

$ conda create -n joblibhadoop-env python==3.5 libhdfs3 -c conda-forge
$ . activate joblibhadoop-env

We recommend using anaconda because it provides a pre-built version of libhdfs3. See build_libhdfs3 if you want to install it using pip.

2. From the joblibhadoop-env environment, perform installation using pip:

$ cd joblib-hadoop
$ pip install -r requirements.txt .

Using joblib-hadoop on a Hadoop cluster

1. Use a HDFS storage backend with Joblib memory to cache results (replace 'namenode' with the name of the HDFS namenode):

import numpy as np
from joblib import Memory
from joblibhadoop.hdfs import register_hdfs_store_backend

if __name__ == '__main__':
    register_hdfs_store_backend()

    mem = Memory(location='joblib_cache_hdfs', backend='hdfs',
                 verbose=100, compress=True
                 store_options=dict(host='namenode', port=8020, user='test'))

    multiply = mem.cache(np.multiply)
    array1 = np.arange(10000)
    array2 = np.arange(10000)

    result = multiply(array1, array2)

    # Second call should return the cached result
    result = multiply(array1, array2)
    print(result)

2. Use a YARN backend with Joblib parallel to parallelize computations:

from math import sqrt
from joblib import (Parallel, delayed,
                    register_parallel_backend, parallel_backend)
from joblibhadoop.yarn import YarnBackend

if __name__ == '__main__':
    register_parallel_backend('yarn', YarnBackend)

    # Run in parallel using Yarn backend
    with parallel_backend('yarn', n_jobs=5):
        print(Parallel(verbose=100)(
            delayed(sqrt)(i**2) for i in range(100)))

    # Should be executed in parallel locally
    print(Parallel(verbose=100, n_jobs=5)(
        delayed(sqrt)(i**2) for i in range(100)))

The YARN parallel backend example only works on a host where Hadoop is installed and correctly configured.

All examples are available in the examples directory.

Developping with joblibhadoop

In order to run the test suite, you need to setup a local hadoop cluster inside Docker containers. This can be achieved very easily using the recipes available in the docker directory and with the provided Makefile targets.

To avoid problems when accessing an Hadoop cluster using localhost, joblib-hadoop provides the joblib-hadoop-client container. This container has Hadoop 2.7.0 installed and is thus fully functionnal for playing locally with the hadoop cluster.

Another important point is that the root directory of this project is shared with the /shared directory inside the Hadoop client container. Thanks to this trick, one can code on the host and test in the container without having to rebuild it.

Prerequisites

There are some prerequisites to check before going further.

1. Install docker-engine:

You have to be able to run the hello-world container:

$ docker run hello-world

2. Install docker-compose with pip:

$ pip install docker-compose

3. Start your hadoop cluster using docker-compose:

$ cd joblib-hadoop/docker
$ docker-compose up

Running the test suite

The test suite has to be launched from the joblib-hadoop-client container of the docker-compose configuration. This is achieved very easily with docker-test Makefile target.

1. First, ensure your hadoop cluster is already started:

$ cd joblib-hadoop/docker
$ docker-compose up -d
$ docker-compose ps

Your containers should all be in the state Up except joblib-hadoop-client that should have exited with code 0.

2. You can now start the test suite with:

$ cd joblib-hadoop
$ make docker-test

If you want to access the container directly and test some customizations or run examples. We provided the other following targets to be run from your host:

• make run-container: start an interactive shell in the joblib-hadoop-client container
• make run-examples: start a new container, install joblib-hadoop and run the examples

Here we list the helpers to be run from the container:

• make install: install joblib-hadoop in the container once logged in (you need to be in the container with make run-container first)
• make run-hdfs-example: run the HDFS Memory multiply example with the cluster.
• make run-yarb-example: run the YARN parallel backend example on the cluster.

Building and installing the hdfs3 package by hand

For the moment hdfs3 cannot be directly installed using pip : the reason is because hdfs3 depends on a C++ based library that is not available in the Linux distros and that one needs to build by hand first.

The following notes are specific to Ubuntu 16.04 but can also be adapted to Fedora (packages names are slightly different).

1. Clone libhdfs3 from github:

$ sudo mkdir /opt/hdfs3
$ sudo chown <login>:<login> /opt/hdfs3
$ cd /opt/hdfs3
$ git clone [email protected]:Pivotal-Data-Attic/pivotalrd-libhdfs3.git libhdfs3

2. Install required packages

$ sudo apt-get install cmake cmake-curses-gui libxml2-dev libprotobuf-dev \
libkrb5-dev uuid-dev libgsasl7-dev protobuf-compiler protobuf-c-compiler \
build-essential -y

3. Use CMake to configure and build

$ cd /opt/hdfs3/libhdfs3
$ mkdir build
$ cd build
$ ../bootstrap
$ make
$ make install

4. Add the following to your ~/.bashrc environment file:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/hdfs3/libhdfs3/dist

5. reload your environment:

$ source ~/.bashrc

6. Use pip to install hdfs3 (use sudo if needed):

$ pip install hdfs3