This is the second presentation about elasticsearch

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