Elasticsearch is a distributed, schema-free document store designed for real-time data indexing and analytics, built on Apache Lucene. It provides full-text search capabilities, supports JSON documents, and features a RESTful API for operations, ensuring data safety and quick access. The platform is open-source under the Apache 2.0 license, making it highly extensible and accessible for various applications.

1. introduction to
elasticsearch.
Ruslan Zavacky
@ruslanzavacky | ruslan.zavacky@gmail.com

2. Released in 2010
In 2014, 70$ million in Series C
funding
2

3. A cluster can host multiple indices which can be queried
independently or as a group. Index aliases allow you to
add indexes on the fly, while being transparent to your
application.
multi-tenancy
Elasticsearch clusters are resilient - they will detect and
remove failed nodes, and reorganise themselves to ensure
that your data is safe and accessible.
high availability
real time data
Data flows into your system all the time. The question is …
how quickly can that data become an insight? With
Elasticsearch, real-time is the only time.
Search isn’t just free text search anymore - it’s about
exploring your data. Understanding it. Gaining insights
that will make your business better or improve your
product.
real time analytics
3

4. full text search
Elasticsearch uses Lucene under the covers to provide the
most powerful full text search capabilities available in any
open source product. Search comes with multi-language
support, a powerful query language, support for
geolocation, context aware did-you-mean suggestions,
autocomplete and search snippets.
document oriented
Store complex real world entities in Elasticsearch as
structured JSON documents. All fields are indexed by
default, and all the indices can be used in a single query,
to return results at breath taking speed.
conflict management
Optimistic version control can be used where needed to
ensure that data is never lost due to conflicting changes
from multiple processes
Elasticsearch allows you to get started easily. Toss it a
JSON document and it will try to detect the data structure,
index the data and make it searchable. Later, apply your
domain specific knowledge of your data to customise how
your data is indexed.
schema free
4

5. Elasticsearch is API driven. Almost any action can be
performed using a simple RESTful API using JSON over
HTTP. An API already exists in the language of your
choice.
restful api
Elasticsearch puts your data safety first. Document
changes are recorded in transaction logs on multiple
nodes in the cluster to minimise the chance of any data
loss.
per-operation persistence
Elasticsearch can be downloaded, used and modified free
of charge. It is available under the Apache 2 license, one
of the most flexible open source licenses available.
apache 2 open source license build on top of apache lucene™
Apache Lucene is a high performance, full-featured
Information Retrieval library, written in Java. Elasticsearch
uses Lucene internally to build its state of the art
distributed search and analytics capabilities.
5

6. who
6

7. I
7

8. 8

9. Unstructured search
9

10. Structured search
10

11. Enrichment
11

12. Sorting
12

13. Pagination
13

14. Aggregation
14

15. Suggestions
15

16. Elasticsearch in 10 seconds
• Schema-free, REST & JSON based distributed
document store
• Open Source: Apache License 2.0
• Zero configuration
• Written in Java, extensible
16

17. The most
important question
17

18. 18

19. Exploding kittens
on Kickstarter
> 195,794 bakers
> $7,840,830 pledged
… and yes, Kickstarter use
elasticsearch
19

20. Capabilities
20

21. Capabilities
Store schema less data
Or create a schema for your data
Manipulate your data record by record
Or use Multi-document APIs to do Bulk ops
Perform Queries/Filters on your data for insights
Or if you are DevOps person, use APIs to monitor
Do not forget about built-in Full-Text search and analysis
Document API Search APIs Indices API Cat APIs Cluster API Query DSL
Validate API Search API More Like This API Mapping Analysis Modules
21

22. Auto Completion
SELECT name
FROM product
WHERE name LIKE ‘d%’
1k records 500k records 20m records
22

23. Auto Completion
Yea, sure…
23

24. Auto Completion: FST
24

25. Auto Completion
Multiple Inputs
Single Unified Output
Scoring
Payloads
Synonyms
Ignoring stopwords
Going fuzzy
Statistics
25

26. Auto Completion
curl -X PUT localhost:9200/hotels/hotel/2 -d '
{
"name" : "Hotel Monaco",
"city" : "Munich",
"name_suggest" : {
"input" : [
"Monaco Munich",
"Hotel Monaco"
],
"output": "Hotel Monaco",
"weight": 10
}
}'
26

27. Faceted Navigation
27

28. Aggregation & Filtering
Documents
28

29. Aggregation & Filtering
Documents
Query
29

30. Aggregation & Filtering
Documents
Query
Buckets
30

31. Aggregation & Filtering
Documents
Query
Buckets
31

32. Aggregation & Filtering
Documents
Query
Buckets
Metrics 123 344 545
32

33. Faceted Navigation
33

34. Snapshot / Restore
34
curl -XPUT "localhost:9200/_snapshot/my_backup/snapshot_1?wait_for_completion=true"
curl -XPOST "localhost:9200/_snapshot/my_backup/snapshot_1/_restore"
Snapshot
Restore

35. Percolate API
35
Store queries in ElasticSearch.
Pass documents as queries.
Observe matched queries.
WUT?

36. Percolate API
36
Use Case
You tell customer, that you will notify them
when Plane ticket will be available and
cheaper.
Solution
Store customer criteria about desired flight
- departure, destination, max price
When you store flight data, match it against
saved percolators.

37. Percolate API
37
curl -XPUT 'localhost:9200/my-index/.percolator/1' -d '{
"query" : {
"match" : {
"message" : "bonsai tree"
}
}
}'
Store Query
Match document
curl -XGET 'localhost:9200/my-index/my-type/_percolate'
-d '{
"doc" : {
"message" : "A new bonsai tree in the office"
}
}'

38. Percolate API
38
{
"took" : 19,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"total" : 1,
"matches" : [
{
"_index" : "my-index",
"_id" : "1"
}
]
}

39. More like this API
39
curl -XGET 'http://localhost:9200/memes/meme/1/_mlt?mlt_fields=face&min_doc_freq=1'

40. scalability
40

41. Distributed & scalable
Replication
Read scalability
Removing SPOF
Sharding
Split logical data over several machines
Write scalability
Control data flows
41

42. Distributed & scalable
node 1
1 2
3 4
orders
1 2
products
curl -X PUT localhost:9200/orders -d ’{
“settings.index.number_of_shards" : 4
“settings.index.number_of_replicas”: 1
}'
curl -X PUT localhost:9200/products -d ’{
“settings.index.number_of_shards" : 2
“settings.index.number_of_replicas”: 0
}'
42

43. Distributed & scalable
node 1
1 2
3 4
orders
1
products
node 2
1 2
3 4
orders
2
products
43

44. Distributed & scalable
node 1
1 2
4
orders
1
products
node 2
2
orders
2
products
node 3
1
3 4
orders
products
3
44

45. API tour
45

46. Create
» curl -X PUT localhost:9200/books/book/1 -d '
{
"title" : "Elasticsearch - The definitive guide",
"authors" : "Clinton Gormley",
"started" : "2013-02-04",
"pages" : 230
}'
46

47. Update
» curl -X PUT localhost:9200/books/book/1 -d '
{
"title" : "Elasticsearch - The definitive guide",
"authors" : [ "Clinton Gormley", "Zachary Tong"],
"started" : "2013-02-04",
"pages" : 230
}'
47

48. Delete
» curl -X DELETE localhost:9200/books/book/1
» curl -X GET localhost:9200/books/book/1
Get
48

49. Search
» curl -X GET localhost:9200/books/_search?q=elasticsearch
{
"took" : 2, "timed_out" : false,
"_shards" : { "total" : 5, "successful" : 5, "failed" : 0 },
"hits" : {
"total" : 1, "max_score" : 0.076713204,
"hits" : [ {
"_index" : “books", "_type" : “book", "_id" : "1",
"_score" : 0.076713204, "_source" : {
"title" : "Elasticsearch - The definitive guide",
"authors" : [ "Clinton Gormley", "Zachary Tong" ],
"started" : “2013-02-04", "pages" : 230
}
}]
}
}
49

50. Search Query DSL
» curl -XGET ‘localhost:9200/books/book/_search' -d '{
"query": {
"filtered" : {
"query" : {
"match": {
"text" : {
"query" : “To Be Or Not To Be",
"cutoff_frequency" : 0.01
}
}
},
"filter" : {
"range": {
"price": {
"gte": 20.0
"lte": 50.0
…
}
}'
» curl -XGET ‘localhost:9200/books/book/_search' -d '{
"query": {
"filtered" : {
"query" : {
"match": {
"text" : {
"query" : “To Be Or Not To Be",
"cutoff_frequency" : 0.01
}
}
},
"filter" : {
"range": {
"price": {
"gte": 20.0
"lte": 50.0
…
}
}'
50

51. Use case: Product Search Engine
51

52. Just index all your products and be happy?
Product Search Engine
Synonyms, Suggestions, Faceting, De-compounding,
Custom scoring, Analytics, Price agents,
Query optimisation, beyond search
Search is not that easy
52

53. Neutrality? Really?
Is full-text search relevancy really your
preferred scoring algorithm?
Possible influential factors
Age of the product, been ordered in last 24h
In stock?
Special offer
Provision
No shipping costs
Rating (product, seller)
Returns
….
53

54. Neutrality? Really?
54

55. Neutrality? Really?
55

56. ecosystem
56

57. Ecosystem
• Plugins
• Clients for many languages
• Kibana
• Logstash
• Hadoop integration
• Marvel
57

58. Ecosystem
• Plugins
• Clients for many languages
• Kibana
• Logstash
• Hadoop integration
• Marvel
58

59. spoiler alert!
59

60. what is data?
60

61. Whatever
provides value for
your business.
61

62. Domain data Application data
Internal
Orders
products
External
Social media streams
email
Log files
Metrics
62

63. 63

64. Logstash
• Managing events and logs
• Collect data
• Parse data
• Enrich data
• Store data (search and visualising)
64

65. Why collect and centralise data?
• Access log files without system access
• Shell scripting: Too limited or slow
• Using unique ids for errors, aggregate it across
your stack
• Reporting (everyone can create his/her own report)
• Bonus points: Unify your data to make it easily
searchable
65

66. Unify dates
• apache
• unix timestamp
• log4j
• postfix.log
• ISO 8601
[19/Feb/2015:19:00:00 +0000]
1424372400
[2015-02-19 19:00:00,000]
Feb 19 19:00:00
2015-02-19T19:00:00+02:00
66

67. Logstash
• Managing events and logs
• Collect data
• Parse data
• Enrich data
• Store data (search and visualise)
Input
Filter
Output
}
}
}
67

68. kibana
68

69. Kibana
69

70. Kibana
70

71. Kibana
71

72. Kibana
72

73. Thank You!
73

74. Feedback
☺ ☹!

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