Shulou(Shulou.com)06/01 Report--

This article mainly introduces what is the difference between elasticsearch and redis, which can be used for reference by friends who need it. I hope you will learn a lot after reading this article. Next, let the editor take you to learn about it.

Redis is now the most popular key-value database. It was released in 2009 with MongoDB, and it is also a database masterpiece of the early big data era.

The biggest feature of Redis is, of course, the simplicity and high performance that key-value storage brings.

The so-called key-value storage means that each record contains only one Key for querying data, and the corresponding value for storing data, just like house numbers and households in real life, without the complex concepts necessary in conventional databases such as tables and fields, and all queries only rely on key values. Therefore, the key-value database is the simplest data structure in the database, and thanks to this simple structure, coupled with the fact that Redis will load all data into memory, Redis can get much higher read and write performance than conventional databases such as MongoDB. Of course, the function of Redis is not so simple as key-value storage. Compared with its key-value predecessor Memcached,Redis, it also supports data persistence, list, set and other data structures, and some column functions such as master-slave replication and backup, so Redis can definitely be called the most comprehensive and easy-to-use model in key-value database.

Redis's key-valule storage brings performance advantages, but it also brings a lot of limitations to complex queries. Due to the castration of important features such as data tables and fields, and all queries rely on key, Redis can not provide complex query functions such as multi-column query and section query that conventional databases have. At the same time, because Redis needs to store data in memory, which greatly limits the amount of data that Redis can store, which also determines that Redis is difficult to be used in application scenarios with large data scale.

Elasticsearch (ES)

ES, which was released a year later, may be less well-known than MongoDB and Redis, but ES's reputation in the search engine field is definitely strong. Compared with other high-end database products, ES has a lot of losers. Shay Banon, the founder of ES, used to be an unemployed loser programmer who created ES to make it easier for his wife to search recipes when he had nothing to do (not ES at the time, of course). Unexpectedly, inadvertently inserted willow into shade, the achievement of today's most popular search engine database, sure enough, the girl is the biggest driving force for programmers to work! ES has also set up its own Elastic company, which has received hundreds of millions of dollars in financing, and the failed programmer Shay Banon has already counterattacked into CEO and reached the pinnacle of his life. After reading this story, have you programmers begun to imagine the day when you become CEO and marry Bai Fumei?

The feature of ES, as its name suggests, is search. Strictly speaking, ES is not a database, but a search engine, and all aspects of ES are designed around search. ES supports full-text search. Here is a brief explanation of what full-text search is: for data such as "I work for an Internet company in Beijing", if the keywords such as "Beijing", "Internet" and "work" can hit this data, this is full-text search. Baidu and Google, which you use every day, belong to full-text search. It is worth mentioning that ES's full-text search also has good support for Chinese (there are many kinds of Chinese word separators alone), which can absolutely meet the full-text search needs of most people in China. In addition to searching, ES will automatically index all fields for you to achieve high-performance complex aggregate queries, so as long as the data stored in ES, no matter how complex aggregate queries can get good performance, and you no longer have to worry about how to build a variety of complex indexes.

Difference

If you have extremely high requirements for reading and writing data, and your data is small and does not need long-term storage, choose redis

If you need to build a search engine or you want to build a high-end data visualization platform, and your data has some analytical value, or your boss is a tuhao, choose ElasticSearch

Thank you for reading this article carefully. I hope it will be helpful for everyone to share the difference between elasticsearch and redis. At the same time, I also hope you will support us, pay attention to the industry information channel, and find out if you encounter problems. Detailed solutions are waiting for you to learn!

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