Getting Started

You’ll need:

Running Hail locally

Hail uploads distributions to Google Storage as part of our continuous integration suite. You can download a pre-built distribution from the below links. Make sure you download the distribution that matches your Spark version!

Unzip the distribution after you download it. Next, edit and copy the below bash commands to set up the Hail environment variables. You may want to add these to the appropriate dot-file (we recommend ~/.profile) so that you don’t need to rerun these commands in each new session.

Here, fill in the path to the un-tarred Spark package.

export SPARK_HOME=???

Here, fill in the path to the unzipped Hail distribution.

export HAIL_HOME=???
export PATH=$PATH:$HAIL_HOME/bin/

Once you’ve set up Hail, we recommend that you run the Python tutorials to get an overview of Hail functionality and learn about the powerful query language. To try Hail out, run the below commands to start a Jupyter Notebook server in the tutorials directory.

cd $HAIL_HOME/tutorials
jhail

You can now click on the “hail-overview” notebook to get started!

Building Hail from source

On a Debian-based Linux OS like Ubuntu, run:

$ sudo apt-get install g++ cmake

On Mac OS X, install Xcode, available through the App Store, for the C++ compiler. CMake can be downloaded from the CMake website or through Homebrew. To install with Homebrew, run

$ brew install cmake

  • The Hail source code. To clone the Hail repository using Git, run

    $ git clone --branch 0.1 https://github.com/broadinstitute/hail.git
$ cd hail

    You can also download the source code directly from Github.

    You may also want to install Seaborn, a Python library for statistical data visualization, using conda install seaborn or pip install seaborn. While not technically necessary, Seaborn is used in the tutorials to make prettier plots.

The following commands are relative to the hail directory.

The single command

$ ./gradlew -Dspark.version=2.0.2 shadowJar

creates a Hail JAR file at build/libs/hail-all-spark.jar. The initial build takes time as Gradle installs all Hail dependencies.

Add the following environmental variables by filling in the paths to SPARK_HOME and HAIL_HOME below and exporting all four of them (consider adding them to your .bashrc):

$ export SPARK_HOME=/path/to/spark
$ export HAIL_HOME=/path/to/hail
$ export PYTHONPATH="$PYTHONPATH:$HAIL_HOME/python:$SPARK_HOME/python:`echo $SPARK_HOME/python/lib/py4j*-src.zip`"
$ export SPARK_CLASSPATH=$HAIL_HOME/build/libs/hail-all-spark.jar

Running on a Spark cluster

Hail can run on any cluster that has Spark 2 installed. For instructions specific to Google Cloud Dataproc clusters and Cloudera clusters, see below.

For all other Spark clusters, you will need to build Hail from the source code.

To build Hail, log onto the master node of the Spark cluster, and build a Hail JAR and a zipfile of the Python code by running:

$ ./gradlew -Dspark.version=2.0.2 shadowJar archiveZip

You can then open an IPython shell which can run Hail backed by the cluster with the ipython command.

$ SPARK_HOME=/path/to/spark/ \
  HAIL_HOME=/path/to/hail/ \
  PYTHONPATH="$PYTHONPATH:$HAIL_HOME/build/distributions/hail-python.zip:$SPARK_HOME/python:$SPARK_HOME/python/lib/py4j-*-src.zip" \
  ipython

Within the interactive shell, check that you can create a HailContext by running the following commands. Note that you have to pass in the existing SparkContext instance sc to the HailContext constructor.

>>> from hail import *
>>> hc = HailContext()

Files can be accessed from both Hadoop and Google Storage. If you’re running on Google’s Dataproc, you’ll want to store your files in Google Storage. In most on premises clusters, you’ll want to store your files in Hadoop.

To convert sample.vcf stored in Google Storage into Hail’s .vds format, run:

>>> hc.import_vcf('gs:///path/to/sample.vcf').write('gs:///output/path/sample.vds')

To convert sample.vcf stored in Hadoop into Hail’s .vds format, run:

>>> hc.import_vcf('/path/to/sample.vcf').write('/output/path/sample.vds')

It is also possible to run Hail non-interactively, by passing a Python script to spark-submit. In this case, it is not necessary to set any environment variables.

For example,

$ spark-submit --jars build/libs/hail-all-spark.jar \
               --py-files build/distributions/hail-python.zip \
               hailscript.py

runs the script hailscript.py (which reads and writes files from Hadoop):

import hail
hc = hail.HailContext()
hc.import_vcf('/path/to/sample.vcf').write('/output/path/sample.vds')

Running on a Cloudera Cluster

These instructions explain how to install Spark 2 on a Cloudera cluster. You should work on a gateway node on the cluster that has the Hadoop and Spark packages installed on it.

Once Spark is installed, building and running Hail on a Cloudera cluster is exactly the same as above, except:

  • On a Cloudera cluster, when building a Hail JAR, you must specify a Cloudera version of Spark. The Cloudera Spark version string is the Spark version string followed by “.cloudera”. For example, to build a Hail JAR compatible with Cloudera Spark version 2.0.2, execute:

    ./gradlew shadowJar -Dspark.version=2.0.2.cloudera1

    Similarly, a Hail JAR compatible with Cloudera Spark version 2.1.0 is built by executing:

    ./gradlew shadowJar -Dspark.version=2.1.0.cloudera1

  • On a Cloudera cluster, SPARK_HOME should be set as: SPARK_HOME=/opt/cloudera/parcels/SPARK2/lib/spark2,

  • On Cloudera, you can create an interactive Python shell using pyspark2:

    $ pyspark2 --jars build/libs/hail-all-spark.jar \
           --py-files build/distributions/hail-python.zip \
           --conf spark.sql.files.openCostInBytes=1099511627776 \
           --conf spark.sql.files.maxPartitionBytes=1099511627776 \
           --conf spark.hadoop.parquet.block.size=1099511627776

  • Cloudera’s version of spark-submit is called spark2-submit.

Running in the cloud

Google and Amazon offer optimized Spark performance and exceptional scalability to many thousands of cores without the overhead of installing and managing an on-prem cluster.

Hail publishes pre-built JARs for Google Cloud Platform’s Dataproc Spark clusters. If you would prefer to avoid building Hail from source, learn how to get started on Google Cloud Platform by reading this forum post. You can use cloudtools to simplify using Hail on GCP even further, including via interactive Jupyter notebooks (also discussed here).

Building with other versions of Spark 2

Hail is compatible with Spark 2.0.x and 2.1.x. To build against Spark 2.1.0, modify the above instructions as follows:

  • Set the Spark version in the gradle command

    $ ./gradlew -Dspark.version=2.1.0 shadowJar

  • SPARK_HOME should point to an installation of the desired version of Spark, such as spark-2.1.0-bin-hadoop2.7

  • The version of the Py4J ZIP file in the hail alias must match the version in $SPARK_HOME/python/lib in your version of Spark.

BLAS and LAPACK

Hail uses BLAS and LAPACK optimized linear algebra libraries. These should load automatically on recent versions of Mac OS X and Google Dataproc. On Linux, these must be explicitly installed; on Ubuntu 14.04, run

$ apt-get install libatlas-base-dev

If natives are not found, hail.log will contain the warnings

Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK
Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS

See netlib-java for more information.

Running the tests

Several Hail tests have additional dependencies:

Other recent versions of QCTOOL and R should suffice, but PLINK 1.7 will not.

To execute all Hail tests, run

$ ./gradlew -Dspark.home=$SPARK_HOME test