This document discusses Elasticsearch and its uses for search. It describes how Elasticsearch can be used for intelligent search engines, autocomplete, geo-search, and search by colors. It then covers the basics of how Elasticsearch works, including its architecture with nodes, shards, and replicas. The document also outlines how to map and index documents, perform searches using queries and filters, and generate analytics through aggregations.

1. Elasticsearch
Guide to search #1
Antoni Orfin
antoniorfin@gmail.com

2. USE CASES
1. Intelligent search engines
…learning on users behaviour
„Search for cats
that I would love
from 3M database”
…forgiving spelling mistakes
„Search for Mihael Jakson photos
and show Michael Jackson photos”

3. USE CASES
2. Autocomplete
„Show the most relevant suggestions
that starts with search…”

4. USE CASES
3. Geo-search (Geospatial)
„Search for restaurants
that are nearest to ”

5. USE CASES
4. Search by colors (ColorSearch)
„Search for flowers
that are ”

6. OLD SCHOOL
Searching in MySQL
SELECT * FROM photos WHERE title LIKE ”%cat%”
SELECT * FROM photos WHERE title LIKE ”%cats%”
Id [PK] title
1 Cute cat and dog
2 Cat plays with a dog
3 Cats playing piano
… ….
3 000 000 Hidden cat

7. SEARCH THEORY
Building Inverted Index
Cute cat
and dog
#1
Cats
playing
piano
#3
Term [PK] Id
cute 1
cat 1, 2, 3
dog 1, 2
play 2, 3
… ….
Cat plays
with
a dog
#2

8. SEARCH THEORY
Text Analysis
Puppy and kitten with guinea pig
1. Tokenization
[Puppy] [and] [kitten] [with] [guinea] [pig]
2. Filtering tokens
[dog] [cat] [guinea] [pig]
Two separate
tokens? L

9. ASCII Folding – róża à roza
Lowercase - Cat à cat
Synonyms –
kitten à cat
puppy à dog
Stopwords – common words to remove
and, what, with, or
Stemming - reducing inflected words to their base
form
cats -> cat
fishing, fisher, fished -> fish
SEARCH THEORY
Text Analysis

10. Lekarz Chorób Wewnętrznych
stemming
Lekarz Choroba Wewnętrzny
asciifolding, lowercase
lekarz choroba wewnetrzny
synonyms
internista
SEARCH THEORY
Text Analysis

11. TECHNOLOGIES
Search Engines Overview

12. SOLUTION
Elasticsearch is a flexible and powerful open-
source, distributed, real-time search and analytics
engine.

13. ELASTICSEARCH
Architecture
Node 1
Shard 1
Shard 2
Replica 3
Replica 4
Shard 3
Shard 4
Replica 1
Replica 2
Node 2
4 shards
1 replica

14. Elasticsearch MySQL
Node Instance
Index Database
Type Table
Document Row
Attribute Column
ELASTICSEARCH
Nomenclature

15. PUT [localhost:9200]/pixers/photos/_mapping
{
"photos" : {
"properties" : {
"title" : {"type" : "string", "analyzer" : "pl"},
”categories" : {"type" : ”nested”, ...}
}
}
}
Types
string, float, double, byte, short, integer, long, date
nested
geo_point
geo_shape … etc
…
ELASTICSEARCH
Mapping

16. localhost:9200/{index}/{type}/{document id}
PUT [localhost:9200]/pixers/photos/1
{
"title" : "Cute cat and dog sitting on books",
"keywords": ["cat", "dog"]
}
GET [localhost:9200]/pixers/photos/1
DELETE [localhost:9200]/pixers/photos/1
ELASTICSEARCH
REST API

17. Searching
GET /pixers/photos/_search
{
"query" : {
"match" : {
"title" : "cat"
}
}
}
Real life query > >
ELASTICSEARCH
REST API

18. Query vs Filter
Query String
„likes:[10 to *] and title:(+cat –dog)”
Match – „funny cat”
Fuzzy – „funy cad”
More Like This
ELASTICSEARCH
Searching

19. Query vs Filter
Terms – [some, tags]
Range – likes > 10
Geo Distance
Lat=50; Lon=20; Distance=200m
ELASTICSEARCH
Searching

20. Query vs Filter
Nested
Bool
MUST/MUST NOT/SHOULD/SHOULD NOT
Function Score
ELASTICSEARCH
Searching

21. Aggregations
Get likes stats and histogram of
created_at date grouped by
categories.
terms: category
- stats: likes
- histogram: created_at
ELASTICSEARCH
Analytics

22. Contact me at:
antoniorfin@gmail.com
linkedin.com/in/antoniorfin
twitter.com/antoniorfin
www.pixersize.com
Thank you!
Questions & Answers