# 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](https://github.com/krishnan-r/sparkmonitor) extension, but that doesn’t always seem to work properly. The project hasn’t been updated since June 2018. **TODO:** [PixieDust](https://github.com/pixiedust/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.
