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Query Optimization in ElasticSearch

Authors: Jančič, Andrej;

Query Optimization in ElasticSearch

Abstract

In the graduation thesis, I present database, its history of origin, and where it is placed from the perspective of cases of use on the software market. I make a short overview of examples of use from the real world, and shortly research trends of its popularity compared to related products and market as a whole. Based on my own experience, literature, official documentation, and experience of other users, I examine the cases which caused problematic operation of the database. For each of the cases I examine the possibility and advisability of solving the problem with automatic optimisation of queries. I examine the case also historically, since Elasticsearch has in the last year significantly changed. I note that automation of queries is not advisable, since the developers in Elasticsearch solved most of the cases with architectural changes, internal optimisation, and a change of query language, which takes away from the user ambiguity in expressing the queries. I establish that the most important feature of well-functioning cluster is a proper size of shards, which cannot be easily changed. For that, an experimental planning of activities is necessary, which I also describe. In addition to optimum size of shard, there are some bad practices, which I also describe in the thesis; with them we can collapse cluster, and it is important that they are known by the user.

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Keywords

Computer and Information Science, 004

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
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