DevOps Classroom Series – 17/Apr/2021


  • JSON documents are first class citizens of Elastic Search.
  • A document consists of multiple fields
  • Each field in JSON document is of particular type
  • In addition to the fields that are sent by user in the document , Elastic search adds the following metafields
    • _id: This is unique identifier of the document with in the type
    • _type: This field consists of type of the document
    • _index: This field consists of index name of the document Preview Preview


  • Elastic search node is a single server of Elastic Search, which is part of large cluster of nodes.
  • Nodes participate in indexing, searching and performing other operations supported by Elastic Search


  • A cluster hosts one or more indexes and is responsible for providing operations such as searching, indexing and aggregrations.
  • A cluster can be formed by one or more nodes.
  • Every Elastic search node is always part of the cluster Preview

Shards and replicas

  • Lets assume we have elastic search cluster with 3 nodes Preview
  • Shards help in distributing a index over the cluster (dividing the documents of single index over multiple nodes). This process of dividing the data among shards s called as sharding Preview
  • Now lets imagine the Node 2 is failed Preview
  • Distribute systems such as elastic search are expected to run inspite of hardware failures, this is addressed by replica shards or replicas.
  • Each shard in an index can be configured to have zero or more replicas. Preview
  • Now lets see what happens when one node i.e. node2 fails Preview
  • We still have the data available add more nodes will make elastic search more available and fault tolerant
  • By default, every index is configured to have five shards Preview

Datatypes in Elastic Search

  • Core data types:
    • String data types
      • text: This datatype is used of supporting full-text search for fields that contain a description or lengthy text values
      • keyword: This type enables analytics of string fields. Fields of this type support sorting, filtering and aggregation
    • Numeric datatypes:
      • byte, short, integer and long
      • float, double
      • half_float
      • scaled_float
    • Date datatypes
      • date
    • Boolean datatype:
      • boolean
    • Binary datatype
      • binary
    • Range data types
      • integer_range, float_range, long_range, double_range and date_range
  • Complex data types
    • Array data type
    • Object data type
    • Nested Data type
  • Other data types
    • Geo-point datatype
    • Geo-shape datatype
    • IP datatype


  • We have inserted two records into the elastic search the following tasks were performed by Elastic search
    • Creating an index with name library
    • Defining the mapping of the type of documents stored in the index’s default type _doc
  • Operations performed
PUT /library/_doc/1
    "title": "Mind Hacking, Unfck Yourself, Rich Dad Poor Dad, Smarter Faster Better 4 Books Collection Set",
    "ISBN-10": "1612680178",
    "Authors": [
        "Sir John Hargrave", "Gary John Bishop", "Charles Duhigg",  "Robert T. Kiyosaki"
    "Edition": 2,
    "Binding": "Paperback",
    "List Price": "0.17$",
    "Published": "January 2020"


PUT /library/_doc/2
    "title": "Who Moved My Cheese",
    "ISBN-13": "9780399144462",
    "Author": "Johnson, Spencer",
    "Edition": 1,
    "Binding": "HardCover",
    "Published": "September 1998"



  • Elastic search automatically adds mapping to fields in document so that elastic search operations such as analyzing and aggregations can be done on this.

Inverted Index

  • This is core data structure of Elastic Search and supports full text search.
  • Lets assume we have 3 simple documents as shown below Preview
  • Elasticsearch builds a datastructure from these 3 documents that have been index and this data structure is called as inverted index
  • Notice the following
    • Documents were broken down into terms after removing punctuation and placing them in lowercase
    • Terms are sorted alphabetically
    • The Frequency column captures how many times the term appears in the entire document set
    • The third column captures the documents in which term was found Preview

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