Various Docker resources for using data analytics tools like Spark, etc., in teaching. They are not intended for production use!
A standard installation of Apache Spark (2.3) that can start up either as a master or a worker node as required. Each worker is limited to a maximum of two cores (change this in spark-defaults.conf
and rebuild the image if necessary).
A standard installation of Apache Kafka (2.2) that uses the built-in Zookeeper instance, and is configured to listen for plain text.
Based on the Spark image, so that the Spark libraries are available.
A standard installation of PySpark, that includes a PySpark kernel for Jupyter. The default user is pyspark
, with a working directory of /home/pyspark/work
.
It also installs the sparkmonitor extension, but that doesn’t always seem to work properly. The project hasn’t been updated since June 2018. TODO: PixieDust looks like a more robust and supported solution, but requires a new kernel to be installed.
Based on the Spark image, so that the Spark libraries are available. (Kafka is not included.)
The compose file sets up a Spark cluster and associated Kafka and PySpark instances, running on the network spark-network
. It defines four services:
spark-master
: Creates a single Spark master node with the hostname spark-master
, exposing ports 7077 and 8080.spark-worker
: Creates a Spark worker node with 2 GB of memory (set by environment variable SPARK_WORKER_MEMORY
). Scalable as required.kafka
: Creates a single Kafka node with the hostname kafka
, using its built-in Zookeeper instance.pyspark
: Creates a PySpark/Jupyter instance, exposing port 8888. Scalable as required. All four services map /mnt/sparkdata
to ~/tmp/sparkdata
on the host.
You can, of course, run any combination of these services as desired. Examples:
docker-compose up --scale spark-worker=2
will create a complete Spark + Kafka + PySpark stack with two Spark worker nodes.docker-compose up pyspark
will run a standalone PySpark instance.