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The rate limiting solution uses the process' memory to store the token buckets.
If for some reason, the service becomes popular and a lot of clients target it, the number of buckets will start to grow, and so does the memory consumption.
I think that we should timestamp the buckets with the time they were last used, and have a background job like reap that runs every N seconds to flush the stale buckets out of memory.
The text was updated successfully, but these errors were encountered:
The rate limiting solution uses the process' memory to store the token buckets.
If for some reason, the service becomes popular and a lot of clients target it, the number of buckets will start to grow, and so does the memory consumption.
I think that we should timestamp the buckets with the time they were last used, and have a background job like
reap
that runs every N seconds to flush the stale buckets out of memory.The text was updated successfully, but these errors were encountered: