The official summary is as follows:

Elasticsearch is a distributed search and analysis engine located in the Elastic Stack center. Logstach and Beats help you collect, aggregate, and enrich your data and store it in Elasticsearch. Kibana allows you to interactively explore, visualize and share insights into data, as well as monitor the Elastic Stack. Elasticsearch is the magic of indexing, searching, and analysis.

Elasticsearch provides near real-time search and analysis for a wide variety of data types. Whether you have structured or unstructured text, digital data, or geospatial data, Elasticsearch can efficiently store and index it in a way that supports fast searching. You can find trends and patterns in your data far beyond simple data retrieval and aggregation. Also, as your data and query volume grows, Elasticsearch’s distributed nature allows your deployment to grow and match seamlessly with it.

While not every problem is a search problem, Elasticsearch provides speed and flexibility in handling data across a large number of instances:

  • Add a search box to your app or website
  • Store and analyze log, metrics, and security event data
  • Use machine learning to automatically model your data behavior in real time
  • Automate business workflow using Elasticsearch as the storage engine
  • Manage, integrate, and analyze spatial information using Elasticsearch as a geographic Information system (GIS), and process genetic data using Elasticsearch as a biological information research tool

We’re always amazed by the novel ways people use search. But whether your instance is similar to one of these or you are using Elasticsearch to solve a new problem, the way you handle data, documents, and indexes in Elasticsearch is the same.

What is Elasticsearch?

What is Elasticsearch?

More introduction:

Data input: documents and indexes

Information output: search and analysis

Scalability and resilience

In addition to Elasticsearch, There are Logstash and Beats for Elasticseach data collection and aggregation, while Kibana provides an interface for data display and manipulation.

We can see a few key descriptions:

  1. Elasticsearch is a distributed search and analysis engine
  2. Elasticsearch provides near real-time search and analysis for a wide variety of data types
  3. Elasticsearch supports a variety of data

There are a few features of Elasticsearch that we can only vaguely understand:

  • Elasticsearch is distributed, you can have multiple Elasticsearch nodes in a cluster, and any node can access the documents in the node.
  • Elasticsearch supports primary/secondary clusters and relies on cross-cluster replication (CCR)
  • Elasticsearch can serialize various data into A JSON document and store it for search.
  • Elasticsearch uses an inverted index data structure to support fast searches.
  • Elasticsearch,fieldIs a key-value pair that contains data,The documentIs a collection of fields, andThe indexIs an optimized collection of documents (index -> document -> Field -> data).
  • When Dynamic mapping is enabled for Elasticsearch, new fields are automatically added to the index or you can define your own mapping.
  • An index is a logical group of one or more physical fragments; Documents in the index are distributed over multiple shards and those shards are distributed over multiple nodes
  • The number of master shards is specified when the index is created, and the replica shards can change at any time
  • The configuration of the number of fragments requires trade-offs

In further documentation, you can see. Elasticsearch is based on Apache Lucene. Elasticsearch also provides REST apis and supports clients in multiple languages.

Furthermore, the Elasticsearch REST API supports structured queries, full-text queries, and a combination of both.

  • Structured query, type SQL
  • Full-text query that matches the string of the query

Search, in addition to the search of a single word, also support phrase search, similarity search and prefix search.

Elasticsearch also has a feature aggregation that enables you to build complex summaries of your data and drill down into key metrics, patterns, and trends.

That’s all you need to know about Elasticsearch. Stay tuned for the next article.

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