DevOps Classroomnotes 24/Feb/2022

Mappings and datatypes

ElasticSearch is schema less, i.e. we can store documents with any number of fields and types of fields.
There are always some set of fields common across all documents in a type.
Core Data Types
- String Data types
  - text: The text datatype is useful for supporting full-text search for fields that contain a description or lenghty text values. Thes fields are analyzed before indexing to support full-text search
  - keyword: This type enable analytics on string fields. Fields of this type support sorting, filtering and aggregations
- Numeric data types:
  - byte, short, integer and long
  - float and double
  - half_float
  - scaled_float
- Date datatype:
  - data: Date with an optional timestamp
- Boolean datatype:
  - boolean:
- Binary datatype:
  - binary: Allows us to store binary with Base64 encoding
- Range datatypes:
  - integer_range, float_range, long_range, double_range, data_range
Complex Data types:
- Array datatype:
- Object data types:
- Nested data type:
Other datatypes:
- Geo-point datatype
- Geo-shape datatype
- IP datatype

Document ID	Document
1	Its Friday tomorrow
2	Friday night is beginining of Weekend
3	The best day of the week is your choice
4	This week is best for sales

Elastic search builds a data structure from the three documents that have been indexed. This data structure is called as inverted index
Documents will be broken down into terms after removing puncutation and placing them in LowerCase
Terms are sorted alphabetically
When searching for terms in the documents, it is blazingly fast to locate the document in which given terms appear.

To understand how to perform CRUD Operations, we will look into following APIs under document API
Index API
Get API
Update API
Delete API

Adding a document to a type within an index of elastic search is called as indexing.
There are two ways to index a document
Indexing a document by providing an ID
Indexing a document by without providing an ID