This image is based off the python:2.7 image and contains Hadoop, Sqoop and Spark binaries. Installs OpenJDK 7.

This is used as a base image for airflow-pipeline, a simplified setup for Airflow to launch Hadoop and Spark jobs.

Useful packages included for Spark and Sqoop:

• Spark-csv
• Spark-avro
• Spark graphframes
• MySQL JDBC driver
• PostgreSQL JDBC driver
• MS SQL JDBC driver

Kerberos support has been added, but derivative images would need to update the kerberos configuration file in /etc/krb5.conf. To use spark with kerberos, the environment variable PYSPARK_SUBMIT_ARGS should also be modified to specify the kerberos principal and location of the corresponding keytab. Derivative images are responsible for putting in the keytab by mounting the volume containing the keytab into the image (suggested location: /etc/kerberos). So for example, if the principal is kerbuser and the keytab is kerbuser.keytab in the default location, then PYSPARK_SUBMIT_ARGS would be

[other spark parameters] --principal kerbuser --keytab /etc/kerberos/kerbuser.keytab pyspark-shell

Credentials are managed by docker login. Login with docker login

Assuming that the Python version we want to build is 2.7, the Hadoop version is 2.7.3 and the Spark version is 2.1.0, then we can build the images as such:

docker build -t datagovsg/python-spark:2.7-2.1 -f python2/spark2.1/Dockerfile .
docker push datagovsg/python-spark:2.7-2.1

• latest: Python 2.7 with Spark 1.6.1 (Dockerfile)
• 2.7-1.6: Python 2.7 with Spark 1.6.1 (Dockerfile)
• 2.7-2.1: Python 2.7 with Spark 2.1.0 (Dockerfile)
• 3.6-2.1: Python 3.6 with Spark 2.1.0 (Dockerfile)

• PYTHONPATH and PYSPARK_SUBMIT_ARGS
• Spark packages installed into the Ivy cache
• Spark 1.6.1 is good up to Cloudera CDH 5.9
• Spark 1.6.2 is good up to Cloudera CDH 5.10