- The Java 8 JDK.
- Spark 2.0.2. Hail should work with other versions of Spark 2, see below.
- Python 2.7 and Jupyter Notebooks. We recommend the free Anaconda distribution.
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
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.
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¶
$ 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 seabornor
pip install seaborn. While not technically necessary, Seaborn is used in the tutorials to make prettier plots.
The following commands are relative to the
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
$ 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
sc to the
>>> 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:
To convert sample.vcf stored in Hadoop into Hail’s .vds format, run:
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
$ 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_HOMEshould be set as:
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
Running in the cloud¶
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 should work with other versions of Spark 2. To build against a different version, such as 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_HOMEshould 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/libin 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