Storage Memory Utilization Benchmark

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Revision as of 20:49, 17 September 2025 by KaceyRenner (talk | contribs) (Created page with "<br>Two elements of etcd storage devour bodily memory. The etcd process allocates an in-memory index to hurry key lookup. The process’s page cache, managed by the operating system, shops not too long ago-accessed data from disk for quick re-use. The in-memory index holds all of the keys in a B-tree data structure, along with pointers to the on-disk data (the values). Each key within the B-tree might include multiple pointers, pointing to completely different variations...")
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Two elements of etcd storage devour bodily memory. The etcd process allocates an in-memory index to hurry key lookup. The process’s page cache, managed by the operating system, shops not too long ago-accessed data from disk for quick re-use. The in-memory index holds all of the keys in a B-tree data structure, along with pointers to the on-disk data (the values). Each key within the B-tree might include multiple pointers, pointing to completely different variations of its values. 1 is the key metadata overhead and c2 is the model metadata overhead. The graph reveals the detailed structure of the in-memory index B-tree. Web page cache memory is managed by the working system and is not coated intimately in this doc. On this check, we solely benchmark the memory utilization of the in-memory index. The objective is to find c1 and c2 talked about above and to know the onerous limit of memory consumption of the storage.



We calculate the memory usage consumption via the Go runtime.ReadMemStats. We calculate the total allocated bytes difference earlier than creating the index and after creating the index. It cannot perfectly reflect the memory usage of the in-memory index itself however can show the rough consumption sample. 30bytes. We only need two units of knowledge to calculate c1 and c2, since they are the one unknown variable within the formula. 30bytes are the average worth of the 4 sets of c1 and c2 we calculated. The key metadata overhead remains to be relatively nontrivial (50%) for small key-worth pairs. Nonetheless, that is a significant improvement over the previous retailer, which had at least 1000% overhead. The overall memory usage captures how much RSS etcd consumes with the storage. The value measurement ought to have little or no influence on the general memory usage of etcd, since we keep values on disk and solely retain hot values in memory, managed by the OS page cache. Based on the consequence, we all know the worth size doesn't significantly influence the memory consumption. There is some minor improve because of more information held in the OS page cache. Was this page useful? Glad to listen to it! Please tell us how we are able to enhance memory retention. Sorry to listen to that. Please tell us how we are able to improve.



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