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Elasticsearch Query DSL in details with real time project scenario

Disclaimer: This is a personal summary and interpretation based on a YouTube video. It is not official material and not endorsed by the original creator. All rights remain with the respective creators.

This document summarizes the key takeaways from the video. I highly recommend watching the full video for visual context and coding demonstrations.

Before You Get Started

  • I summarize key points to help you learn and review quickly.
  • Simply click on Ask AI links to dive into any topic you want.

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Introduction to Query DSL

Query DSL comes in two types: leaf query clauses for single fields and compound for multiple. Focus on practical use over theory, especially full-text queries like match, match_phrase, match_phrase_prefix, and multi_match for handling searches like comments or email bodies that are tough in RDBMS.

Key takeaway: Use full-text queries for efficient searching in large text fields; match queries treat terms as OR by default.

Ask AI: Introduction to Query DSL

Match Query

Match queries search a single field with OR logic across terms. For example, searching "quick brown dog" in a comments field returns results containing any of the words.

Key takeaway: It's flexible for broad searches but not precise; all matching documents get similar scores regardless of term count.

Ask AI: Match Query

Match Phrase Query and Slop

Match phrase requires exact phrase matching for precision. Add slop to allow gaps between words—e.g., slop of 4 permits up to 4 intervening words between "brown" and "dog".

Key takeaway: Without slop, it's strict; with slop, it balances precision and flexibility, counting all words and gaps in distance.

Ask AI: Match Phrase Query and Slop

Match Phrase Prefix

Similar to match phrase but allows prefix matching on the last term, requiring terms to appear together in order.

Key takeaway: Avoid for large platforms like search engines due to strictness; better for targeted, non-customer-facing queries.

Ask AI: Match Phrase Prefix

Query String Query

Handles searches across fields with default OR operator; switch to AND for stricter matches. For integer fields like age, use .text mapping. Supports multiple fields in an array for combined searches.

Key takeaway: Check mappings first—if a field is integer, append .text to treat it as string for querying.

{
  "query": {
    "query_string": {
      "fields": ["first_name", "last_name"],
      "query": "Olivia Brown",
      "default_operator": "AND"
    }
  }
}

Ask AI: Query String Query

Implementing Search Box

For a search box querying multiple fields (e.g., first_name, last_name), use query_string with fields array for combined values or separate queries per field. Elasticsearch speeds up results for trillions of records. Handle single vs. combined terms based on UI needs.

Key takeaway: For UI showing "John Smith" but stored separately, group fields in one query_string for AND matching across them; sort results with .keyword for text fields.

{
  "query": {
    "query_string": {
      "fields": ["employee_first_name", "employee_last_name"],
      "query": "Olivia Brown",
      "default_operator": "AND"
    }
  },
  "sort": [{ "employee_last_name.keyword": "asc" }]
}

Ask AI: Implementing Search Box

Type Ahead Search

Provides suggestions as you type, like autocomplete. Use query_string on a field, add sort for ordered results, and _source to limit returned fields. Combine with bool must for conditions like gender.

Key takeaway: For female names only, nest query_string in bool must with a match on gender.

{
  "query": {
    "bool": {
      "must": [
        { "query_string": { "default_field": "employee_first_name", "query": "O*" } },
        { "match": { "gender": "female" } }
      ]
    }
  },
  "sort": [{ "employee_first_name.keyword": "asc" }],
  "_source": ["employee_first_name", "gender"]
}

Ask AI: Type Ahead Search

Term and Terms Queries

Term requires exact full-term match, unlike partials in query_string. Terms allows multiple values. Use in filters combined with searches for precise results, like gender or last names.

Key takeaway: In bool must, combine with query_string for filtered suggestions; use _source to control output fields.

{
  "query": {
    "bool": {
      "must": [
        { "query_string": { "default_field": "employee_first_name", "query": "O*" } },
        { "term": { "gender": "female" } }
      ]
    }
  },
  "sort": [{ "employee_first_name.keyword": "asc" }]
}

Ask AI: Term and Terms Queries

Range Query

Filters numeric or date fields with ranges using gte (greater than equal), gt (greater than), lte, lt. Apply to ages or timestamps.

Key takeaway: For ages 10-30 inclusive: gte:10, lte:30; remove 'e' for exclusive boundaries.

Ask AI: Range Query

Prefix Query

Matches documents where a field starts with the given prefix, simple field-value setup.

Key takeaway: For last names starting with "bro", it returns "brown" matches.

Ask AI: Prefix Query

Wildcard Query

Allows * for any characters, like "bro*n" for "brown". Handles before/after patterns but avoid due to high resource use—like a for-loop iterating all possibilities.

Key takeaway: Fails on spaces (e.g., "brown wilson" won't match "brown* wilson"), unlike query_string; it's heavy, so prefer alternatives for performance.

Ask AI: Wildcard Query

About the summarizer

I'm Ali Sol, a Backend Developer. Learn more: