What's the relationship between clients and CPU cores?

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Hi Folks,

I found that rally uses Actor model to generate load under the hood. From the documentation, I understand that target throughput is achieved with all clients together. However, I do not understand the way number of clients set in a race and CPU cores are related. Can someone help me understand how these two are related? Basically the way number of clients set in a rally schedule are mapped to Actors and inturn to CPU threads to generate target-throughput.

If there are any documentation that's already there regarding these, please point me to it.

Thanks

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Hi @Dhineshkumar_R,

These are largely implementation details, and as a user of Rally you shouldn't really need to be aware of them, but let me try and explain.

Rally is written in Python, which has a mechanism called the GlobalInterpreterLock to prevent multiple threads of the same Python process from running on CPU in parallel.

This 'GIL' limits the total throughput we could achieve using a single Python process, so to work around this we use a model of allocating one 'Actor' per-available core, where an Actor is separate Python process responsible for running a single asyncio event loop.

Then, the number of clients defined in a specific task's client property are evenly distributed across the available 'Actors' (i.e. Python processes/asyncio event loops).

Brad

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Thanks Brad. This helps me imagine the flow. One followup question. I read that the latency metrics reported in the summary report includes the time spent by a request waiting in a queue after creation but before sent to the cluster. Where is this queue in the picture? Is this queue part of each client within an Actor or the Actor itself?

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This really is an implementation detail, but right now it is per client within an Actor.

If you're interested in understanding this in more detail, take a look at these files for a start:

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