![]() ![]() 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. I know that FST is loaded into memory on first query for completion. Adumbrate is a formal verb with several meanings that all have to do with figurative shadows. 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 ? Implement autocomplete using one of three methods: Prefix matching Edge N-gram matching Completion. Elasticsearch allows you to design autocomplete that updates with each keystroke, provides a few relevant suggestions, and tolerates typos. still requires you to know and specify all contexts in every query, plus know the magic token. These suggestions preempt your user’s intention and lead them to a possible search term more quickly. somehow collect a list of all possible values of a context and send the entire list with every request to /suggest. 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. add '' (or any other token) to every 'category' context for every record and then use that as your catchall. 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. 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.
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