Introduction to Elasticsearch Searching
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The document provides an introduction to searching techniques in Elasticsearch, covering key concepts like relevancy, scoring, and various types of queries. It explains query string and query DSL methods, detailing how to construct leaf and compound queries for effective document ranking. Additionally, it discusses specialized query types such as full-text, term level, nested, and geo queries, highlighting their applications in a search context.
Introduction to Elasticsearch Searching
- 1. INTRODUCTION TO SEARCHINGby Bo Andersen - codingexplained.com
- 2. OUTLINE ➤ Relevancy & scoring ➤ Ways of searching ➤ Query string ➤ Query DSL ➤ Types of queries ➤ Leaf & compound ➤ Full text ➤ Term level ➤ Compound
- 3. RELEVANCY & SCORING ➤ To rank documents for a query, a score is calculated for each document that matches a query ➤ The higher the score, the more relevant the document is to the search query ➤ Queries in query context affect the scores of matching documents ➤ "How well does the document match?" ➤ Queries in filter context do not affect the scores of matching documents ➤ "Does the document match"
- 5. QUERY STRING ➤ Search by sending search parameters through the REST request URI (as query parameters) ➤ Used for simple queries and ad-hoc queries on the command line ➤ Also supports advanced queries ➤ Example ➤ GET http://localhost:9200/ecommerce/product/_search?q=pasta
- 6. QUERY DSL ➤ Search by defining queries within the request body in JSON ➤ Supports more features than the query string approach ➤ Used for more advanced queries ➤ Often easier to read, as queries are defined in JSON GET http://localhost:9200/ecommerce/product/_search { "query": { "match": { "name": "pasta" } } }
- 8. LEAF & COMPOUND QUERIES ➤ Leaf ➤ Look for particular values in particular fields, for instance "pasta" in product names ➤ Can be used by themselves within a query, without being part of a compound query ➤ Can also be used within compound queries to construct more advanced queries ➤ Compound ➤ Wrap leaf clauses or even other compound query clauses ➤ Used to combine multiple queries in a logical fashion (usually with boolean logic) ➤ Can also be used to alter the behavior of queries
- 9. FULL TEXT ➤ Used for running full text queries on full text fields ➤ E.g. a product name or description ➤ Values are analyzed when adding documents or modifying values ➤ E.g. removing stop words, tokenizing and lowercasing ➤ Will apply each field's analyzer to the query string before executing
- 10. TERM LEVEL ➤ Used for exact matching of values ➤ Usually used for structured data like numbers and dates, rather than full text fields ➤ E.g. finding persons born between year 1980 and 2000 ➤ Search queries are not analyzed before executing
- 11. JOINING QUERIES ➤ Performing joins in a distributed system is expensive ➤ Elasticsearch offers two forms of joins that are designed to scale horizontally ➤ Nested query ➤ Documents may contains fields of type nested with arrays of objects ➤ Each object can be queried with the nested query as an independent document ➤ has_child and has_parent queries ➤ A parent-child relationship can exist between two document types within a single index ➤ The has_child query returns parent documents whose child documents match the query ➤ The has_parent query returns child documents whose parent document matches the query
- 12. GEO QUERIES ➤ Elasticsearch supports two types of geo fields ➤ geo_point (lat/lon pairs) ➤ geo_shape (points, lines, circles, polygons, etc.) ➤ Various geo queries use these fields to perform geographical searches ➤ E.g. finding points of interest near GPS coordinates