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Introduction

⭐ Real world use cases of ElasticSearch

⭐ General Use Cases of Search-Indexes

Key Features of ElasticSearch

Feature Remarks
Near-time search Elasticsearch has near real-time search - document changes are not visible to search immediately, but will become visible within this timeframe. Read more
REST API interface We can interact with ElasticSearch through REST APIs.
GraphQL support ElasticSearch also supports GraphQL in its APIs.
Default Config An Elasticsearch index has 5 shards and 1 replica by default.
Mapping Mapping is the process of defining how a document, and the fields it contains, are stored and indexed.
Sample Search Query Read more
Ingest Node Ingest node is used for pre-process documents before the actual document indexing happens.
- It helps to intercepts bulk and index requests.
Data Storage Settings, index mapping, alternative cluster states, and other metadata are saved to Elasticsearch files outside the Lucene environment. This is to prevent excessive disk I/O.
Elastic Stack For data analysis, it operates alongside Kibana, and Logstash to form the ELK stack.
- ElasticSearch data streaming is used for the logs.
Data Streaming A data stream lets you store append-only time series data (like logs) across multiple indices while giving you a single named resource for requests.
Pricing Read more
Auto Scaling Auto-expand indices based on search queries. Read more.

Data types supported

Data Type
Textual
Numerical
GeoSpatial (geo-point, geo-shape data types etc.)
Unstructured etc.

ElasticSearch Cluster

Read more

Tools

References