This document provides information for some common tasks within Chronicle Map.
I am investigating Chronicle Map as a potential replacement of Redis; with concurrency in mind.
In our architecture, we would be looking to replace a "large" Redis instance that currently has multiple clients connecting to it, causing latency pileups due to Redis's blocking nature.
The issue is that we need to make requests in random batches of ~1000. With Redis we are able to make a single request via a Lua script (or multi-get / multi-set commands) and receive a single response. In the documentation on Chronicle Map`s stateless client, I see that the remote calls are blocking, and can be made only one key at a time; so for us the solution is not obvious.
While I am considering passing off each individual key task to a threadpool running x blocking threads at a time, I wonder if there might be a better solution, that could take advantage of doing RPC in batches, and perhaps work asynchronously. As I do not see this available currently, my question is whether this is an enhancement that you might consider, or if you could perhaps point me to if/how we could write our own solution for doing this?
Also, is there a reason these 1000 gets have be done serially in one thread? Why not submit 1000 get() tasks to a pool of say 20 threads. Wouldn’t this improve throughput and reduce latency?
The stateless client has been replaced with Chronicle Engine. The stateless client is not supported in ChronicleMap 3.x. For more information, please refer to Chronicle Engine FAQ’s.
For get()s, parallelizing will reduce costs. For put()s, if you have concurrency requirements, that is, multi-key lock before updating all of them, it should be in one thread.
You would not gain a performance benefit by using batches, unless you are compressing the batch of data. All the data will have to be sent via TCP, even if it is in a batch.
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Note
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Under high load, the Chronicle Map stateless client consolidates many small TCP requests into a single request when run with a number of threads. |
Yes; Chronicle Map uses memory mapped files see https://en.wikipedia.org/wiki/Memory-mapped_file. This works the same way on both Linux and Windows.
Will Chronicle Map work in Docker environments “out-of-the-box”. For example, two java programs running on dockers?
While Chronicle Map has not been tested with Docker, we are not aware of any reason why it would not if you use Chronicle Map for IPC (https://en.wikipedia.org/wiki/Inter-process_communication). In other words, if you distribute data between two or more instances of the same map, then the same memory mapped file which holds your map data would have to be available to all the instances of Chronicle Map. We assume this would have to be in the same docker. Alternatively, you could replicate the data between your maps using Chronicle Map Enterprise.