DevOps Classroom Series – 16/Apr/2021

Fix for kibana server not ready yet

• Change the following in the /etc/elasticsearch/elasticsearch.yml
• Note: add the private ip of your elastic search server in the above highlighted section and restart elastic search and kibana

Elastic Search

• Elastic search plays the central role of search and analytics engine
• Elastic search is built on Apache Lucene Refer Here
• Benifits of Elastic Search
  • Schemaless and document oriented:
    • Elastic search doesn’t impose a strict structure on your & you can store any json documents
    • For learning about json Refer Here
    • Example documents

    {
        "name": "Ram",
        "address": " Near mythrivanam, Hyderabad",
        "courses": ["DevOps"]
    }
    

    {
        "name": "Robert",
        "email": "robertatqt@gmail.com"
    }
    

  • Searching Capability:
    • The core strength of Elastic search lies in its text searching capabilities. This also implements Full text search
  • Analytics:
    • Elastic search supports a wide variety of aggregations for analytics. These aggregations are quite powerfull and can be applied for various data types
  • Rich Client library support and the REST API
    • Elastic search has very rich client library support to make it accesible to many programming languages (Java, C#, Python, JavaScript, PHP, Ruby)
    • Elastic search has a Very RICH REST API (Which works on http)
  • Easy to operate and Easy to scale:
    • Elastic search can run on single node and easily scale to hundreds of nodes
  • Ligthning Fast
  • Fault-tolerant

Setting up kibana

Core Concepts of Elastic Search

• Following are the core concepts of Elastic Search
  • Indexes
  • Types
  • Documents
  • Clusters
  • Nodes
  • Shards & replicas
  • Mappings & Types
  • Inverted Indexes
• Example: Add the following using kibana console

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"

}

• Elastic search can also be interacted using curl

Indexes

• An index is a container that stores and manages documents of single type in elastic search
• The concept of index in Elastic search is roughly analogues to the database schema in relational database. Going by this analogy, a type in Elasticsearch is equivalent to table and document is equivalent to record in the table.

Types

• In our example of library, the document that was indexed was of library type. Each document stored in the library type represent one book
• Typically documents with mostly common set of fields are grouped under one type

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"

}

• Next Steps:
  • Documents