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. Schema free: Some definitions, such as index, class, and field type, are not required before the indexing procedure, and when an object is indexed. I know that FST is loaded into memory on first query for completion. Term Suggester, Phrase Suggester, Completion Suggester, Context Suggester are the core components of its auto-completion and instant search capability.
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 ?
#Context suggester elasticsearch how to#
Understand how to configure suggestions at index time and how to order the suggestions. 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. Learn how to enable auto-completion for search terms for your data, with results returned in less than a millisecond. 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.
#Context suggester elasticsearch plus#
still requires you to know and specify all contexts in every query, plus know the magic token. somehow collect a list of all possible values of a context and send the entire list with every request to /suggest. 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 add '' (or any other token) to every 'category' context for every record and then use that as your catchall. Here are a few questions I had in that regard: Restart methods differ depending on whether you installed Grafana using Homebrew. 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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