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Mark Felder 2020-11-06 13:12:13 -06:00
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docs/configuration/optimizing_beam.md
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# Optimizing the BEAM # Optimizing the BEAM
Pleroma is built upon the Erlang/OTP VM known as BEAM. The BEAM VM is highly optimized for latency, but this has drawbacks in environments without dedicated hardware. One of the tricks used by the BEAM VM is [busy waiting](https://en.wikipedia.org/wiki/Busy_waiting). This allows the application to pretend to be busy working so the OS kernel does not pause the application process and switch to another process waiting for the CPU to execute its workload. It does this by spinning for a period of time which inflates the apparent CPU usage of the application as seen in utilities like **top(1)** so it is immediately ready to execute another task. Switching between procesess is a rather expensive operation and also clears CPU caches which further affects latency and performance. Pleroma is built upon the Erlang/OTP VM known as BEAM. The BEAM VM is highly optimized for latency, but this has drawbacks in environments without dedicated hardware. One of the tricks used by the BEAM VM is [busy waiting](https://en.wikipedia.org/wiki/Busy_waiting). This allows the application to pretend to be busy working so the OS kernel does not pause the application process and switch to another process waiting for the CPU to execute its workload. It does this by spinning for a period of time which inflates the apparent CPU usage of the application so it is immediately ready to execute another task. This can be observed with utilities like **top(1)** which will show consistently high CPU usage for the process. Switching between procesess is a rather expensive operation and also clears CPU caches further affecting latency and performance. The goal of busy waiting is to avoid this penalty.
This strategy is very successful in making a performant and responsive application, but is not desirable on Virtual Machines or hardware with few CPU cores. Pleroma instances are often deployed on the same server as the required PostgreSQL database which can lead to situations where the Pleroma application is holding the CPU in a busy-wait loop and as a result the database cannot process requests in a timely manner. The fewer CPUs available, the more this problem is exacerbated. The latency is further amplified by the OS being installed on a Virtual Machine as the Hypervisor uses CPU time-slicing to pause the entire OS and switch between other tasks. This strategy is very successful in making a performant and responsive application, but is not desirable on Virtual Machines or hardware with few CPU cores. Pleroma instances are often deployed on the same server as the required PostgreSQL database which can lead to situations where the Pleroma application is holding the CPU in a busy-wait loop and as a result the database cannot process requests in a timely manner. The fewer CPUs available, the more this problem is exacerbated. The latency is further amplified by the OS being installed on a Virtual Machine as the Hypervisor uses CPU time-slicing to pause the entire OS and switch between other tasks.