What is it?
Apache Spark is an open-source, distributed computing system used for big data processing. It can process large amounts of data quickly and efficiently, and handle both batch and streaming data. Spark uses the in-memory computing concept, which allows it to process data much faster than traditional disk-based systems.
Spark supports a wide range of programming languages including Java, Python, and Scala. It provides a number of high-level libraries and APIs, such as Spark SQL, Spark Streaming, and MLlib, that make it easy for developers to perform complex data processing tasks. Spark SQL allows for querying structured data using SQL and the DataFrame API, Spark Streaming allows for processing real-time data streams, and MLlib is a machine learning library for building and deploying machine learning models. Spark also supports graph processing and graph computation through GraphX and GraphFrames.
Working with Spark on OpenShift
Spark can be fully containerized. Therefore a standalone Spark cluster can of course be installed on OpenShift. However, it sorts of breaks the cloud-native approach brought by Kubernetes of ephemeral workloads. There are in fact many ways to work with Spark on OpenShift, either with Spark-on-Kubernetes operator, or directly through PySpark or spark-submit commands.
In this Spark on OpenShift repository, you will find all the instructions to work with Sparl on OpenShift.
- pre-built UBI-based Spark images including the drivers to work with S3 storage,
- instructions and examples to build your own images (to include your own libraries for example),
- instructions to deploy the Spark history server to gather your processing logs,
- instructions to deploy the Spark on Kubernetes operator,
- Prometheus and Grafana configuration to monitor your data processing and operator in real time,
- instructions to work without the operator, from a Notebook or a Terminal, inside or outside the OpenShit Cluster,
- various examples to test your installation and the different methods.