The document provides an overview of Elasticsearch, detailing its key features like real-time data availability, distributed architecture, and document-oriented storage. It explores the components of the Elastic Stack, including Logstash and Beats, which facilitate data collection and processing, and emphasizes Elasticsearch's role in search, logging, and analytics for enterprise applications. Additionally, it highlights use cases in Oracle systems and showcases Elasticsearch's capabilities in data ingestion, search functionalities, and integration with various platforms.

1. Vinay Kumar, ORACLE ACE
@vinaykuma201
Sangam18-Bengaluru
1

2. 2
• O RACL E ACE
• Enterp ris e Arc h itec t
• Au th or of Book “Beginning Oracle
Web Center p ortal 1 2 c”
• O rac le c ertified p ro fes s io n al
• B lo g ger-http ://w w w.tech artifact. com/b logs
• So ftware Con s u ltant
• JAVA EE GUARDI AN

4. 4
• Un der st an din g En terprise S earch
• Elast ic S earch in t rodu ct ion
• Elast ic St ack Arch itect u re
• Elast ic S earch core con cept s
• Elast ic S earch AP Is
• In tegration of Elastic Search with Oracle F MW
• Elast ic S earch plu gin s
• Demo

5. 5Source-Wikipedia

6. 6

7. 7
Understanding Elastic
Search

8. 8

9. Elastic Search Key Features
9
• Document- oriented : stores complex entities as structured JSON documents and indexes all fields by
default
• RESTful API : API driven, actions can be performed using a simple Restful API.
• Real-Time Data availability and analytics : As soon as data is indexed, it is made available for search and
analytics. It's all real-time.
• Distributed : Allows us to set up as many nodes we need for our requirement. Cluster will manage
everything and it can grow horizontally to a large number.
• Highly available : The cluster is smart enough to detect a new node or failed node to add/remove from
the cluster..
• Full-text & Fuzzy Search
• Multitenancy : In Elasticsearch, an alias for index can be created. Usually a cluster contains multiple
indices. These aliases allow a filtered view of an index to achieve multitenancy.

10. ELK Stack
10

11. ELK Stack
11
• Logstash helps in centralizing event data such as logs, metrics, or any other data in any format. It can
perform a number of transformations before indexing.
• Elastic search is at the heart of Elastic Stack. It stores all your data and provides search and analytic
capabilities in a scalable way. Elastic search can be used without using any other components to power
your application in terms of search and analytics.
• Kibana is the visualization tool of Elastic Stack which can help you gain powerful insights about your
data in Elastic search.

12. Logstash
12
• Logstash is a plugin-based data collection and processing engine. The Logstash event processing
pipeline has three stages, they are: Inputs, Filters and Outputs.
• Inputs create events, Filters modify the input events, and Outputs ship them to the destination. Inputs
and outputs support codecs which enable you to encode or decode the data as and when it enters or
exits the pipeline without having to use a separate filter. Logstash uses in-memory bounded queues
between pipeline stages by default (Input to Filter and Filter to Output) to buffer events

13. Logstash- available plugins
13

14. Beats
14
• Beats is a platform of open source lightweight data shippers.
• Beats has a role on the client side whereas logstash is a server side.
• Beats consists of a core library, libbeat, which provides an API for shipping data from the
source, configuring the input options, and implementing logging

15. Beats vs Logstash
15
Beats Logstash
Beats requires fewer resources and consumes
low memory
consumes a lot of memory and requires a
higher amount of resources
Beats are created based on the Go language. Logstash is based on Java requiring JVM
Beats are lightweight data shippers that will
ship your data from multiple systems.
Heavy to install on all the systems from which
you want to collect the logs,
• Beats are data shippers shipping data from a variety of inputs such as files, data streams, or
logs whereas Logstash is a data parser. Though Logstash can ship data, it's not its primary
usage.
• Logstash provides capabilities of ETL (Extract, Transform, and Load), whereas Beats are
lightweight shippers that ship the data.

16. What is Elastic Search
16
“Software that makes massive amounts of
structured and unstructured data usable
for search, logging, analytics, and more
in mission critical system and
application…..”

17. What is Elastic Search
17

18. What is Elastic Search
18
• Full text search engine.
• NoSql Database
• Analytics Engine
• Lucene Based
• Inverted indices
• Easy to Scale
• RESTFUL interface (JSON/HTTP)
• Schemaless
• Real time

19. Why Elastic Search
19

20. Elastic Search - Core concepts
20
Type
Document
Type
Index

21. Elastic Search - Core concepts – Node, Type, Document
21
• An index contains one or multiple types.
• A type can be thought of as a table in a relational database. A type has one or more documents.
• A Document a group of fields. Field is key value pair. Document can be thought of as a table as row in
relational database. Its JSON data structure. It is with key value pair.

22. Elastic Search -– Node , Cluster
22
• A Node node is a single server of Elasticsearch ,part of a larger cluster of nodes. It participates
in indexing, searching, and performing other operations supported by Elasticsearch.
• A cluster is formed by one or more nodes. Every Elasticsearch node is always part of a cluster, even if it
is just a single node cluster. A cluster hosts one or more indices and is responsible for providing
operations such as searching, indexing, and aggregations.

23. Elastic Search – Shards
23
• A Shard help in dividing the documents of a single index over multiple nodes. It distribute the data into
multiple node. The process of dividing the data among shards is called sharding.
- It helps in utilizing storage across different nodes of the cluster
- It helps in utilizing the processing power of different nodes of the cluster
- Deafult 5 shards per index, and this is configurable.

24. Elastic Search – Replica
24
• A Replica is copy of shard. It is useful for the failover of any node.
- Each shard in an index can be configured to have zero or more replica shards.
- Replica shards are extra copies of the original or primary shard and provide a high availability of data.
- Also manage the query work load execution across replicas

25. Elastic Search – Inverted Index
25
• A Inverted index is the core data structure of
Elasticsearch.
• It is very similar to index at end of every book.
• Building block for performing fast searches.
• Easy to look up how many occurrences of
terms are present in the index. This is a simple
count aggregation.
• It caters to both search and analytics.
• Elastic search builds an inverted index on all
the fields in the document.

26. Elastic Search – Inverted Index- Continued
26
Document ID Document
1 This is the best
session in Sangam
2 Sangam is cool
3 This is your choice.
Term Frequency Document
This 2 1,3
Sangam 2 1,2
is 3 1,2,3
best 1 1
in 1 1
cool 1 2
your 1 3
the 1 1
choice 1 3
Inverted Index in ESInput Strings

27. Elastic Search – Core concepts - Summary
27
• Nodes get together to form a cluster.
• Clusters provide a physical layer of services on which multiple indexes can be created
• An index may contain one or more types, with each type containing millions or billions of
documents.
• Indexes are split into shards, which are partitions of underlying data within an index. Shards
are distributed across the nodes of a cluster.
• Replicas are copies of primary shards and provide high availability and failover.
• ES stores documents in terms in the inverted index for search and analytics.

28. Core concepts – Data type
28
• Text data
• Numbers
• Booleans
• Binary objects
• Arrays, objects
• Nested types
• Geo-points
• Geo-shapes
• IPv4 and IPv6 addresses.

29. Elastic Search Client – Polyglot
29
• Java
• Javascript
• Java REST client
• Groovy
• .NET
• PERL
• PHP
• PYTHON
• Ruby

30. Elastic Search- index API
30
Create Index API Index API

31. Elastic Search- GET API
31

32. Elastic Search- Update API
32

33. Elastic Search- Query API
33
RDBMS ES Kibana Java API

34. Kibana
34
• Kibana, which provides us with an interface to visualize the data we collect and store

35. Elastic Search Use case
35

36. Elastic Search & Oracle
• WebCenter Portal uses Elastic Search
• PeopleSoft uses Elastic search…
• Cloud Apps…?
• Custom integration … Yes, why not….
Opportunity……………….. ?

37. Logging & Monitor with Oracle Fusion Middleware
Problem -
• Each log has to be monitored manually.
• Single place to see application log, system errors, user errors, network metrics etc.
• Requires Ops Admin with special access privileges to access the file
• Normal devs or testers cannot view the data generated in staging or productions environments
• Not a single place to monitor and search the logs.
• Custom user experience according to organization UI standards.
• Great quick search experience.
Solution -
Oracle Enterprise Manager

38. Logging & Monitor with Oracle fusion Middleware
ADF Log Server
OSB Log Server
SOA Log Server
WCP Log Server
BPM Log Server
……. Log Server
Filebeat
Filebeat pull
the log file
Logstash
Parses &
pushes
updates
Elastic search
Transform
& pushes
data to ES
Monitor &
visualization
OFMW
……. Log Server

39. Document search in WebCenter Content
Problem -
• Search for documents with keywords, document number etc.
• Search for text in documents/PDF, Autocad files etc.
• Google like search experience.
• Full text search
- Stemming (developing for mobile matches results for develop for mobile and vice versa.)
- fuzzy matching (service workers matches results for Service Worker)
• Quick and performant search.
• One Search field to search for all document

40. Document search with Oracle WebCenter Content
Elastic
search
WCC
Filters
Oracle
ADF/WCP
RIDC Client/WCC
API
Oracle JET
Oracle ADF
Oracle
WebCenterBrowser
Users Ingesting
document via
User interface
Ingest file and other
information in to ES
Insert
Search
Document
Search in Elastic
Search with text,
keyword
Ingest Attachment
plugin to store
document
Return result with
documnt id to
Java API
Java
Code
Find doc with DocId
Return result doc
1
2
4
3
5
6
7
8
9
10
Insert document with
WCC console or
desktop/API etc
OTS

41. Document search in WebCenter Content
To ingest document Elastic search uses Attachment processor plugin- This
plugin uses Tika library, which is a toolkit developed by Apache, and can
extract metadata and text from a number of file types. Using Tika, this plugin
helps Elasticsearch to extract details from attachments. Common attachment
formats include--PPT, PDF, XLS, and many more.

42. Web Search with ERPs
Problem -
• Multiple sources of data i.e. 3 ERPs – IFS, Oracle E Business, MS Dynamic
• Single Search screen to retrieve result from 3 ERP in WebCenter Portal Screen.
• Google like search experience.
• Full text search .
• Quick and performant search.
• One Search field to search across the ERPs.

43. Web application search with Oracle fusion Middleware
Data
Sync
Elastic
search
Schedular
IFS Ingesting
data via ES
java API
Oracle
ADF
Oracle
JET
WebCenter
Portal
Portal/
User
interface
Users
Oracle JET
Oracle ADF
Oracle
WebCenter
Data ship
via logstash
plugin
Browser
JSON Data

44. Web application search with Oracle JET
ADF Log Server
OSB Log Server
SOA Log Server
WCP Log Server
BPM Log Server
……. Log Server
Filebeat
Filebeat pull
the log file
Logstash
Parses &
pushes
updates
Elastic search
Transform
& pushes
data to ES
Monitor &
visualization
OFMW
Search
Filters

45. Elastic Search plugins

46. Elastic Search plugins – Part 2

47. Elastic Search plugins –Elasticsearch SQL
• Elasticsearch speaks SQL
• Use traditional database syntax

48. 48

49. 49

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THANK YOU
Vinay Kumar
@Vinaykuma201
mail2vinayku@gmail.com
www.techartifact.com/blogs
medium.com/@vinaykuma201