This document discusses Elasticsearch queries and filters. It explains that filters are used for exact matches and are faster than queries. It provides examples of common filters like term, terms, range and bool filters. It also discusses query types like match, multi_match and bool queries. Finally, it explains how to combine queries and filters to filter query results.

1. Elasticsearch
(Presentation 02)
By
Maruf Hassan
Email: maruf571@gmail.com
Date: 06 April, 2015

2. ContentContent
● Query DSL
● Queries Vs Filters
● Filters
– term Filter
– terms Filter
– range Filter
– exists and missing Filters
– bool Filter
● Query
– match_all Query
– match Query
– multi_match Query
– Bool Query
● Queries with Filters

3. Query DSL
There are two types of (query) DSL. filter DSL & query DSL
filter DSL: A filter asks yes/no question of every document and is used for fields that contain exact
values.
Example:
● Is the `created` date in the range `2013` - `2014`?
● Does the `status` field contain the term `published`?
● Is the `lat_lon` field within `10km` of a specified point?
query DSL :A query is similar to a filter, but also asks the question: How well does this document
match? A query calculates how relevant each document is to the query, and assigns it a relevance
`_score`.
Example:
● Best matching the words `full text search`
● Containing the word `run`, but maybe also matching `runs`, `running`, `jog`, or `sprint`
● Containing the words `quick`, `brown`, and `fox`; the closer together they are, the more relevant the
document

4. Queries Vs Filters
Filter:The output from most filter clauses
--a simple list of the documents that match the filter
--is quick to calculate and easy to cache in memory
Queries: Queries have to not only find matching documents --but also
--calculate how relevant each document is
--query results are not cache.
When to Use Which
● Use query clauses for full-text search or for any condition that should affect the
relevance score
● And use filter clauses for everything else
● Filter is faster :)

5. Filters
Most important filters
● term Filter
● terms Filter
● range Filter
● exists and missing Filters
● bool Filter

6. term Filter
The `term` filter is used to filter by exact values,
be they numbers, dates, Booleans, or
not_analyzed` exact-value string fields
Example:
● { "term": { "age": 26 }}
● { "term": { "date": "2014-09-01" }}
● { "term": { "public": true }}
● { "term": { "tag": "full_text" }}

7. terms Filter
Allows you to specify multiple values to match.
If the field contains any of the specified values,
the document matches
Example:
{ "terms":
{ "tag": [ "search", "full_text", "nosql" ] }
}

8. range Filter
The `range` filter allows you to find numbers or dates that fall
into a specified range
Example:
{
"range": {
"age": {
"gte": 20,
"lt": 30
}}}

9. exists and missing Filters
The `exists` and `missing` filters are used to find documents
in which the specified field either has one or more values or
doesn't have any values
Example:
{
"exists": {
"field": "title"
}
}

10. bool Filter
The `bool` filter is used to combine multiple filter clauses using Boolean logic. It accepts three
parameters
●
must: These clauses must match, like and
●
must_not: These clauses must not match, like not.
●
should: At least one of these clauses must match, like or.
Example:
{ "bool": {
"must": { "term": { "folder": "inbox" }},
"must_not": { "term": { "tag": "spam" }},
"should": [
{ "term": { "starred": true }},
{ "term": { "unread": true }}
]
}}

11. Query
most important queries
● match_all Query
● match Query
● multi_match Query
● bool Query

12. match_all Query
The `match_all` query simply matches all
documents. It is the default query that is used if
no query has been specified
Example:
{ "match_all": {}}

13. match Query
The `match` query should be the standard query that you reach for whenever you want to
query for a full-text or exact value in almost any field.
Example:
● (need analyze the query)
{ "match": { "tweet": "About Search" }}
● (not_analyzed)
{ "match": { "age": 26 }}
{ "match": { "date": "2014-09-01" }}
{ "match": { "public": true }}
{ "match": { "tag": "full_text" }}

14. multi_match Query
The `multi_match` query allows to run the same `match` query on
multiple fields
Example:
{
"multi_match": {
"query": "full text search",
"fields": [ "title", "body" ]
}
}

15. bool Query
The `bool` query used to combine multiple query clauses.This query accepts the following parameters:
●
`must`: Clauses that must_match for the document to be included.
●
`must_not`:Clauses that must not match for the document to be included.
● `should`:If these clauses match, they increase the `_score`;
otherwise, they have no effect.
{
"bool": {
"must": { "match": { "title": "how to make millions" }},
"must_not": { "match": { "tag": "spam" }},
"should": [
{ "match": { "tag": "starred" }},
{ "range": { "date": { "gte": "2014-01-01" }}}
]
}
}

16. Queries with Filters
●
It is often useful to apply a filter to a query or, less frequently, to use a full-text query as a filter.
Filtering a Query
Match Query:
{ "query": { "match": { "interests": internet" }} }
Term filter:
{ "filter": { "range": { "age": { "gte": 18 }}}
Filtering query
GET /cefalo/employee/_search
{ "query": {
"filtered": {
"query": { "match": { "interests": "internet" } },
"filter": { "range": { "age": { "gte": 25 }}
}}}}

17. Queries with Filters
Just as a Filter
Query: { "query": { "match_all": {}}
Filter: "filter": { "term": { "age": "28" } }
Just filter in a all document
GET /cefalo/employee/_search
{
"query": {
"filtered": {
"query": { "match_all": {}},
"filter": { "term": { "age": "28" } }
}}}

18. Thanks