DevOps Classroom Series – 20/Sept/2021

Mappings and Datatypes

• Elastic search supports wide variety of data types
  • Core datatypes:
    • String datatypes:
      • text: The text data type is useful for supporting full-text search for fields that contain a description or lengthy text values.
      • keyword: The keyword type enables analytics on string fields to support sorting, filtering and aggregations
    • Numeric datatypes:
      • byte (8), short (16),integer(32) and long(64)
      • float (32) and double (64)
      • half_float (16)
      • scaled_float
    • Date Datatype
      • date
    • Boolean datatype
      • boolean
    • Binary data type:
      • binary
    • Range datatypes
      • integer_range, float_range, long_range, double_range and date_range
  • Complex datatypes
    • Array datatype
    • Object datatype
    • Nested datatype
  • Other datatypes:
    • Geo-point
    • Geo-shape
    • Ip datatype
• Get a mapping for the index
• Lets create an index dummyproducts with some mapping
• Now create a document in the dummy products with extra fields not specified in mapping
• Now lets get mapping for dummy products
• Lets try to update mappings Refer Here

Inverted Index

• An inverted index is the core data structure of Elastic search.
• Lets assume we have following documents created

• Elastic search builds a data structure from the above three documents which is called as inverted index
• When searching for terms in documents, it fast to locate the documents in which given term appears and inverted index is created.
• By default Elastic search builds an inverted index on all the fields in the document

CRUD Operations

• To understand how to perform CRUD operations lets try to understand the following APIs
  • Index API
  • Get API
  • Update API
  • Delete API

Index API

• In Elastic search terminology, adding a document to a type with in an index is called indexing operation.
• We have already learnt, indexing involves
  • adding the document to the index by parsing all the fields within the document
  • building an inverted index
• There are two ways we can index a document
  • Indexing a document with ID
  • Indexing a document without provding ID