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This article will explain in detail how to understand the principle of Elasticsearch. The content of the article is of high quality, so the editor will share it for you as a reference. I hope you will have a certain understanding of the relevant knowledge after reading this article.

Search is a necessary basic function of modern software, and Elasticsearch is a powerful open source distributed search and data analysis engine.

It can quickly find relevant information from huge amounts of data, and there are few competitors in the same field-- in the database evaluation of DBRanking in the past two years, ES has always ranked first in the field of search engines.

When you search on GitHub, Elasticsearch can achieve code-level search and highlight; when you are shopping online, ES can recommend your favorite products; when you take a taxi home from work, ES can help the platform optimize scheduling by locating nearby passengers and drivers.

Elasticsearch is also widely used in big data's near real-time analysis, including log analysis, index monitoring, information security and other fields.

It can explore large amounts of structured and unstructured data, create visual reports on demand, set alarm thresholds for monitoring data, and even automatically identify abnormal conditions by using machine learning.

As the most popular open source search engine, ES has been downloaded more than 350 million times around the world. Tencent, Didi, Jinri Toutiao, ele.me, 360Security, Xiaomi, vivo and many other domestic well-known companies are using Elasticsearch.

What are the characteristics of Elasticsearch?

Elasticsearch is very easy to use, with out-of-the-box features, you can set up the development environment in a very short time, and then quickly get started, and then achieve PB-level data processing on hundreds of servers.

Although it is easy to get started, it is not so simple to understand deeply and use it efficiently, such as:

1. Why can't I find my data? there are obviously some!

two。 What the hell? Why are these pieces of data at the top of the search results?

3. How many machines do I need in the production environment, and how to set the number of fragments in the index is reasonable?

4. Which indicators should be paid attention to to ensure that the cluster runs healthily and efficiently?

5. For log applications, how to set up Hot & Warm Architecture to save costs, and how to manage and optimize index data based on time series in order to improve the overall performance of the cluster?

4. Why is my cluster brain cracked? How can I recover after the data is corrupted?

In fact, if you want to master Elasticsearch, you should not only understand the principle of its distributed architecture, but also understand some knowledge and related skills in the field of information retrieval.

Give you a core knowledge graph of Elasticsearch. Only by carefully combing and deeply understanding each knowledge point, can you solve the practical problems in your work.

If you know something about Elasticsearch, you will find that the iteration speed of ES products is very fast, many of the old API have been abandoned and no longer used, and most of the parameter configurations have changed.

On how to understand the principle of Elasticsearch to share here, I hope that the above content can be of some help to you, can learn more knowledge. If you think the article is good, you can share it for more people to see.

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