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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"

4. WAYS OF
SEARCHING

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"
}
}
}

7. TYPES OF
QUERIES

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

13. THANK YOU FOR
WATCHING!