Next blogs on Elasticsearch of this series:
Elasticsearch is a full-text search engine that can be used as a NoSQL database and can be used as an analytics engine. It is easy to scale, schema-less, near real-time and provides a restful interface for different operations. It is schema-less and uses an inverted index for data storage. Elasticsearch is created in Java and built on top of Lucene. We can explain Elasticsearch by following terms:
- Full-text Search Engine
- NoSQL Database
- Analytics Engine
- Easy to Scale
- RESTFul interface
- Inverted Index
- Near Real-Time
- Elastic Stack
These are the characteristics of Elasticsearch and we can use them in the following ways:
- Elasticsearch as the primary backend for your website.
- Adding Elasticsearch to an existing system running through an existing data source.
- Use Elasticsearch for monitoring and analysis of the existing application without affecting the behavior of the current application.
Elasticsearch can be used in different applications as it has different language clients through which we can integrate it in any application. Some of the clients are as follows:
We can have different use cases to use Elasticsearch like:
- Online Web Store
- Price Alerting Platform
- Analytics / Business-intelligence
- Central Log Management
- Fraud Management
- System Monitoring
- E-commerce Search Solutions
- Visualizing Data
There are the following components of Elasticsearch:
An index is a collection of documents that have somewhat similar characteristics. For example, you can have an index for customer data, another index for a product catalog, and yet another index for order data. It is a logical namespace to store similar types of documents.
Just take an example here: Let's say we have an Elasticsearch cluster with two nodes, now we want to index a data set with 2 primary shards and one replica shard. On two nodes data will be stored in a way that we are not going to loose any data, in case one machine fails. Please refer to the below diagram:
In the above diagram, P1 and P2 are primary shards while R1 and R2 are replica shards. Now in any node we have complete data so even if one machine goes down, we can still fetch the complete set of data.
Other Blogs on Elastic Stack:
Introduction to Elasticsearch
Elasticsearch Installation and Configuration on Ubuntu 14.04
Log analysis with Elastic stack
Elasticsearch Rest API
Basics of Data Search in Elasticsearch
Elasticsearch Rest API
Wildcard and Boolean Search in Elasticsearch
Configure Logstash to push MySQL data into Elasticsearch
Metrics Aggregation in Elasticsearch
Bucket Aggregation in Elasticsearch
How to create Elasticsearch Cluster
If you found this article interesting, then you can explore “Mastering Kibana 6.0”, “Kibana 7 Quick Start Guide”, “Learning Kibana 7”, and “Elasticsearch 7 Quick Start Guide” books to get more insight about Elastic Stack, how to perform data analysis, and how you can create dashboards for key performance indicators using Kibana.
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Apr 15, 2018, 11:26:01 AM
Sir, can you please elaborate all terms like cluster, node, index, type, document, shard in different blogs ..
Apr 15, 2018, 3:15:30 PM
Sure I will do that wait for couple of days as I am little busy for a presentation.
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