I'm trying to improve the performance of my facet queries. My typical facet
query looks a bit more complicated (two facets with regex and a more
complex query), but I have reduced it to a very simple example:
The answer is:
"terms": [ ... ],
As you can see, there are a lot of documents. My index has 23 GiB size.
This query takes ~ 200ms, but it takes < 5ms without the facet. The
question is: how can I improve its performance? It should be around 10ms...
(1) I am thinking if rewriting my query could improve it.
"_tokens._all._facet" is a non-analyzed string field, whereas
"_tokens._all._text.ngram" is ICU tokenized, ICU folded and ngram analyzed.
There are several values for each document. Is there anything wrong there I
(2) I don't know if it could be possible to "index" or "cache" somehow the
values of "_tokens._all._facet". It is the field that is used by the facets
all the time, so it gets constantly accessed.
(3) If I use a cluster, could a high number of nodes and shards improve my
performance? Would Elasticsearch perform a parallel facetting in the data
(4) Finally, can you give me advice on how to test if I am having problems
with data access (like I/O blocks)?
Thanks in advance!
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