Various Docker resources for using data analytics tools like Spark, etc., in teaching

examples Added relevant documentation links 3 years ago
kafka Refactored and added per-image makefiles 4 months ago
pyspark Refactored and added per-image makefiles 4 months ago
spark Refactored and added per-image makefiles 4 months ago
spark-pyspark-kafka Reimplemented using build stages 4 months ago
.gitignore Ignored .tgz files 3 years ago
Makefile Refactored and added per-image makefiles 4 months ago
README.md Updated to match modern Compose 4 months ago
docker-compose.yml Added graphframes for PySpark 2 years ago
docker.make Switched to containerised builder 4 months ago
README.md

docker-analytics

Various Docker resources for using data analytics tools like Spark, etc., in teaching. They are not intended for production use!

Spark

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).

Kafka

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.

PySpark

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.)

Compose

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.