Elasticsearch supports a wide range of queries. The query begins with a query keyword, which is followed by conditions and filters in the form of a JSON object. Here are a few of the queries:

• Match All Query: This is a basic query that retrieves all the documents in the specified index.
• Full-text queries: There are high-level queries for executing full-text searches over full-text fields. Full-text queries usually work depending on the analyzer associated with a particular document or index. Full-text queries can be of different types, such as match query, multi-match query, query-string query, ETC.
• Term Level Queries: Instead of full-text field searches, term-level queries deal with structured data like numbers, enums, dates, etc. Term level queries can be of different types, such as range, exists, prefix, wildcard, fuzzy, type, etc.

In recent years, it has evolved into one of the most popular search engines that are USED for business analytics, LOG analytics, security intelligence, operational intelligence, full-text searches, etc. Research has shown that Elasticsearch has a market share of about 0.24%, so there is a lot of opportunity for many renowned companies. Thus, you are still able to advance in your career as an ElasticSearch Engineer. Almost every area of ElasticSearch, along with the ELK stack, has been covered in the interview questions, including questions about the analyzers, filters, tokenizers, index, token filters, and APIs used in ElasticSearch.

Here, we have compiled a LIST of insightful interview questions that give ample information vital to the interview process. Being familiar with these frequently ASKED interview questions increases your chances of getting hired.

Hopefully, we have answered any questions or concerns that you may have had. All the best with your future endeavours.

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