A Basic CRUD Operation Demo On Elasticsearch Using Kibana

In this article, we will understand the way to implement Elasticsearch CRUD operation using the Kibana tool.

Elasticsearch:

Elasticsearch is a distributed, free, and open search and analytics engine for all types of data, including textual, numerical, geospatial, structured, and unstructured. It provides simple REST APIs, distributed nature, speed, and scalability.

Elasticsearch stores data in JSON format. so each JSON format data record in elastic search is called a document. So documents are queried or searched by the Index, Index holds the reference to respective documents.

Elasticsearch use cases:

Application Search
Website Search
Enterprise search
Loggin and log analytics
Infrastructure metrics and container monitoring
Application performance monitoring

Kibana:

Kibana is a free and open frontend application that sits on top of the Elastic Stack, providing search and data visualization capabilities for data indexed in Elasticsearch.

Run Elasticsearch And Kibana Docker Containers:

Create a network first that can help to connect our services like 'Elasticsearch' & 'Kibana' under it.

Command to create a network:
docker network create your_network_name_any_name

Let's pull and create the Elasticsearch docker container.

Command To Create Elasticsearch Docker Container:

docker run -d --name your_container_name_any_name --net network_name_just_created -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.16.3

[ -d ] run the docker command in detach mode, which means runs as a background service.
[ --name your_container_name_any_name] define the name to the docker container.
[ --net network_name_just_created] specify the network name under which our service should run.
[ -p 9200:9200] right-hand side port number(fixed port number) is the default port number for the 'ElasticSearch', the left-hand side port number is exposing port number we can define any of our custom port numbers.
[-e "discovery.type=singlenode"] setting the environment variable to run our Elastic search on a single node. it can be changed for production applications.
[ elasticsearch:7.16.3] name of service and its version.

Let's pull and create the Kibana docker container.

Command To Create Kibana Docker Container:

docker run -d --name your_container_name_any_name --net network_name_just_created -p 5601:5601 kibana:7.16.3

[ -d ] run the docker command in detach mode, which means runs as a background service.
[ --name your_container_name_any_name] define the name to the docker container.
[ --net network_name_just_created] specify the network name under which our service should run.
[ -p 5601:5601] right-hand side port number(fixed port number) is the default port number for the 'Kibana', the left-hand side port number is exposing port number we can define any of our custom port numbers.
[ kibana:7.16.3] name of service and its version.

After running the Kibana container then wait for a couple of minutes then try to access the Kibana UI tool at the URL http://localhost:5601. So this Kibana UI tool we will implement all our elastic search CRUD operations.

Cluster Health Check:

Query to check the elastic search cluster health check.

GET _cluster/health

Nodes In A Cluster:

Query to get the information about the nodes in the cluster.

GET _nodes/stats

Create An Index:

In elastic search, 'Index' will hold the reference to the documents. To compare with SQL, the 'Index' will be equivalent to the 'Table'.

Syntax to create an index.

PUT Name_of_Your_Index

Sample query to create an index.

PUT user_info

Document Create Operation With HTTP Verbs POST Or PUT:

(1) Creating a document using HTTP POSt makes elastic search to auto-generate the document's unique identifier.

The syntax for creating a document using the HTTP POST

POST Name_Of_Your_Index/_doc { "field": "value" }

Sample Query to create a document using HTTP POST

POST user_info/_doc
{
"name":"naveen",
"age": "28"
}

'POST' - Http verb
'user_info' - index name.
'_doc' - endpoint represents looking for a document
'name' & 'age' - properties of payload to save as new document.

(2) The HTTP PUT can be used to create a document when we want to specify the id(unique identifier of the document) explicitly. This will either create a new document or update the document with the specified argument.

The syntax for creating a document using HTTP PUT

PUT Name_of_your_Index/_doc/id_your_document
{
"field": "value"
}

Sample query to creating a document using HTTP PUT

PUT user_info/_doc/1
{
"name":"hemanth",
"age": "28"
}

'PUT' - HTTP verb
'user_info' - index name.
'_doc' - keyword represents document endpoint.
'1' - identifier value specified by us for the document while creating it.
'name', 'age' - payload properties to save as a document.

Document Create Operation With '_create' Endpoint:

The '_create' endpoint restricts the overriding of the document. It will throw an error if the document already exists, wherein the above step the 'HTTP PUT' will override the document.

The syntax for creating a document using the '_create' endpoint.

PUT Name_of_your_index/_create/id
{
"field": "value"
}

A Sample query for creating the document using the '_create' endpoint.

PUT user_info/_create/2
{
"name":"Kumar",
"age": "28"
}

'PUT' - HTTP verb.
'user_info' - index name.
'_create' - keyword represent create endpoint.
'2' - id specified to create a document.
'name', 'age' - payload to save as a document.

(1)Creating a new record

(2)Try to create a new record with an identifier of an already existing document.

Document Read Operation:

Let's try to fetch the documents of the elastic search.

The syntax for the document read operation

GET Name_of_your_Index/_doc/id

Sample query for the document read operation

GET user_info/_doc/1

'GET' - HTTP verb
'userInfo' - index name,
_doc - keyword represents document.
'1' - id of the document.

Document Update Operation:

Using '_update' endpoint we can update the properties of the document.

The syntax for updating the document properties with '_update' endpoint.

POST Name_of_your_Index/_update/id
{
"doc":
{
"field1": "value",
"field2": "value",
}
}

The sample query for updating the document properties with '_update' endpoint.

POST user_info/_update/1
{
"doc":{
"name":"hemanth kumar"
}
}

'POST' - Http verb
'user_infor' - index name
'_udpate' - keyword represent update endpoint.
'1' - id of the document.
'doc' - inside of the 'doc' object define our properties that need to update in the document.

Document Delete Operation:

Let's try to delete the document from the elastic search.

The syntax for deleting the document.

DELETE Name_of_your_Index/_doc/id

The sample query for deleting the document.

DELETE user_info/_doc/1

'DELETE' - HTTP verb
'user_info' - index name
'_doc' - keyword represent document
'1' - id of the document.

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