Apache Spark 4.1 on Ubuntu by cloudimg

## Apache Spark 4.1 on Ubuntu 22.04 by cloudimg

Production ready unified analytics engine on Microsoft Azure, bundled as a single node standalone cluster with the Spark master and worker daemons pre configured under systemd and started automatically on first boot. PySpark, SparkSQL, spark-shell, and spark-submit are immediately usable under a dedicated spark system user. Java 17 and Python 3 are aligned to the exact versions Spark 4.1 expects, eliminating the compatibility work that normally eats the first day of any Spark deployment.

Why Choose cloudimg?

* 24/7 Expert Support with guaranteed 24 hour response for all requests and one hour average for critical issues. Contact support@cloudimg.co.uk

* Production Ready from Launch Pre configured, security patched, and validated before publication

* Azure Native Integration Built with Azure Linux Agent, cloud init, and Gen2 Hyper V support

* Zero Compatibility Fight Java 17, Python 3, and Spark 4.1 are pinned to versions that are known to work together so you spend your time on analytics, not packaging

What's Included

* Apache Spark 4.1.0 (built for Hadoop 3) installed to /opt/spark

* OpenJDK 17 headless runtime

* Python 3 with pip for PySpark driver and worker

* Pre configured spark-defaults.conf and spark-env.sh

* systemd units for spark-master.service and spark-worker.service, both enabled at boot

* Helper scripts in the spark user home: setEnv.sh, start_spark.sh, stop_spark.sh

* Dedicated /var/log/spark log directory

* tmpfiles.d entry that recreates /var/run/spark at every boot

* First boot apt-get update and upgrade hygiene that self deletes after running once

Key Ports

* 8080 Spark Master Web UI

* 7077 Spark Master RPC (standalone cluster communication, keep internal)

* 4040 Per application Web UI while a driver is running

* 8081 Worker Web UI

Use Cases

* ETL pipelines over Parquet, CSV, JSON, and other file formats

* Batch analytics against datasets staged on an attached Premium SSD

* Interactive data exploration via PySpark or spark-shell

* Ad hoc Spark SQL queries against warehouse tables

* MLlib pipeline prototyping before scaling out to a multi node cluster

* Data engineering evaluation and training environments

Getting Started

1. Deploy from the Azure Marketplace in your preferred region

2. Connect via SSH using your chosen admin user and SSH key

3. Switch to the spark user and source the setEnv.sh shim

4. Verify the Master Web UI on port 8080 shows one ALIVE worker

5. Run the SparkPi example with spark-submit to prove the standalone cluster

6. Launch pyspark or spark-sql for interactive work

Technical Specifications

* Operating System: Ubuntu 22.04 LTS

* Apache Spark: 4.1.0 (bin-hadoop3)

* Install Path: /opt/spark (symlink to /opt/spark-4.1.0-bin-hadoop3)

* Java: OpenJDK 17 headless

* Python: Python 3 with pip

* Default OS User: your chosen admin user

* Spark OS User: spark (owns SPARK_HOME and runs the daemons)

* Recommended Size: Standard_D4s_v3 (4 vCPU, 16 GB RAM)

* VM Generation: Hyper V Gen2 with UEFI boot

* Filesystem: Default Ubuntu gallery LVM layout

Security

* Latest CVE patches applied at build time, with a first boot apt-get update and upgrade for any patches published between build and deployment

* SSH hardened with key based authentication

* Spark authentication disabled by default; the deployment guide includes the exact steps to enable spark.authenticate with a shared secret before production

* Spark History Server not enabled by default to minimise attack surface; the guide documents post deploy enablement

* Recommended to restrict 8080 and 4040 to trusted operator IP addresses and keep 7077 internal to the VM

Support

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Visit www.cloudimg.co.uk/guides/apache-spark-4-1-on-ubuntu-22-04-azure for the full user guide.

Apache Spark is a registered trademark of the Apache Software Foundation. This image is a repackaged upstream distribution provided by cloudimg. Additional charges apply for build, maintenance, and 24/7 support.