Server host requirements and recommendations

For a small-scale installation: 

• 4GB of RAM 

For a large-scale installation:

• 16 GB of RAM

64-bit system.

For a small-scale installation: 

• 2 cores

For a large-scale installation: 

• 8 cores

In addition, for a server installation from a Docker image: 

• amd64 architecture or arm64-based Apple Silicon 

Depends on how much code you analyze with SonarQube.

For a small-scale installation:

• 30 GB

• Free disk space is an absolute requirement.
  Elasticsearch implements a safety mechanism to prevent the disk from being flooded with index data that locks all indices in read-only mode when a 95% disk usage watermark is reached.
• Disk access can easily become the bottleneck of Elasticsearch. If you can afford SSDs, they are by far superior to any spinning media. SSD-backed nodes see boosts in both query and indexing performance. If you use spinning media, try to obtain the fastest disks possible (high-performance server disks 15,000 RPM drives).
• Using RAID 0 is an effective way to increase disk speed, for both spinning disks and SSD. There is no need to use mirroring or parity variants of RAID because of Elasticsearch replicas and database primary storage.
• Do not use remote-mounted storage, such as NFS, SMB/CIFS, or network-attached storage (NAS). They are often slower, display larger latencies with a wider deviation in average latency, and are a single point of failure. 
• You may put <sonarqubeHome>/Data (where sonarqubeHome is the SonarQube installation directory; it is recommended to use /opt/sonarqube for this directory) into a separate partition to help alleviate the single point of failure mentioned above.

It is recommended that you give 50% of the available memory to Elasticsearch heap while leaving the other 50% free. The reason is that Lucene (used by Elasticsearch) is designed to leverage the underlying OS for caching in-memory data structures. 

• Don't allocate more than 32GB. 

See the following Elasticsearch articles for more details:

• Elasticsearch Guide: Heap Sizing
• A Heap of Trouble
• Elasticsearch Reference: JVM heap size

If you need to choose between faster CPUs or more cores, then choose more cores. The extra concurrency that multiple cores offer will far outweigh a slightly faster clock speed.

By nature, data is distributed on multiple nodes, so execution time depends on the slowest node. It's better to have multiple medium boxes than one fast and one slow.

If you use SSD, do not use the CFQ (Completely Fair Queuing) I/O scheduler (this is the defaul I/O scheduler under most Unix distributions). Use either the deadline or the NOOP scheduler instead.

When you write data to disk, the I/O Scheduler decides when that data is actually sent to the disk. The CFQ  allocates "time slices" to each process, and then optimizes the delivery of these various queues to the disk. It is optimized for spinning media: the nature of rotating platters means it is more efficient to write data to disk based on physical layout. The deadline scheduler optimizes based on how long writes have been pending, while NOOP is just a simple FIFO queue. 

They should have excellent read and write performance. 

Most importantly, the "data" folder houses the Elasticsearch indices on which a huge amount of I/O will be done when the server is up and running. Read and write hard drive performance will therefore have a big impact on the overall SonarQube server performance.

• Microsoft Edge: latest version
• Mozilla Firefox: latest version
• Google Chrome: latest version
• Safari: latest version

Applies only to a server installation from the ZIP file.

• Oracle JRE or OpenJDK
• Java version 17
• Recommendation: Use Java CPU (critical patch update) releases.

Note: SonarQube is able to analyze any kind of Java source files regardless of the version of Java they comply with.