If yes, what would be the reaction time here.īesides #1 and #2 also, if you have any idea to quickly decrease heap usage in an emergency scenario, please let me know. In case memory usage goes too high, can we rely on stopping the queries to suggester to bring the memory usage down ? Assuming that ElasticSearch will remove the FST from memory. This has the advantage to be flexible, you don't need to know all possible contexts at query time but it also makes the suggester very slow since the context of the suggestions are added as a prefix at. I know that FST is loaded into memory on first query for completion. When querying a context awarecompletion field it is possible to omit the context and search for all possible contexts for the suggestions. Queries like above go against this design. This design facilitates faster searches through in-memory FST. In case memory due to completion suggester just occupies a lot of heap, is there any emergency way to turn off completion suggester for the entire index / cluster quickly through some API call ? As already discussed, suggestions are stored in a separate data-structure - in-memory FST, whereas other fields are stored on disk. I know that node-stats give direct os-> mem indication but since we've multiple indices in cluster, its hard to isolate measurements for any single index. The field in stats response that seems closest to overall memory is "segments"-> "memory_in_bytes"īut if I go by that field, 99.39% of RAM is being captured by FST for our index which is shockingly high. However, for overall RAM usage of index, I'm not finding any metric from index-stats. I am trying to compare RAM usage of FST vs overall RAM usage for a given index.įor FST, confirmed that the "completion" -> "size_in_bytes" metric is heap metric in reply to my post here Here are a few questions I had in that regard: However, there is growing concern due to memory usage as our data increases. Expanding the example from the docs, i want to do something like: POST place/sea. We're using elasticsearch for our search use-case and have an index that serves both regular queries as well as autocompletion.įor autocompletion, I've enabled completion suggester on it. I want to use the context suggester from elasticSearch, but my suggestion results need to match 2 context values.
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