Difference Between Redis and Elasticsearch (With Table)

Redis and Elasticsearch are two significant technologies for data solutions used in the IT field. These are versatile and flexible technologies that can be used for a variety of purposes. Both have concrete differences between them and, there are different ways of integrating them into a program.

Redis vs Elasticsearch

The difference between Redis and Elasticsearch is that Redis is a data structure store that acts as a NoSQL Database. It is a popular in-memory data platform. Elasticsearch was developed as a search and analytics engine to store data and help in its analysis.

Redis can be used as a cache, data broker, and database. It can be deployed on-premises, across clouds, and in hybrid environments. It is an open-source key-value store that was initially released in 2009. It offers a schema-free data scheme and is developed in the language C.

Elasticsearch is an open-source, distributed, modern search and analytics engine that offers real-time index search and analysis. Initially released in 2010, it is developed in Java and has a SQL-like query language. It is capable of executing multitenant searches. Elasticsearch uses an HTTP web interface and is self and schema-free.

Comparison Table Between Redis and Elasticsearch

Parameters of ComparisonRedisElasticsearch
DefinitionRedis is an open-sourced, BSD licensed, advanced key value store.Elasticsearch was developed as a modern search and analytics engine for data and its analysis.
Memory TechnologyIt runs natively on DRAM and persistent memory.It is disk-based with caching option.
Programming LanguageIt is C-based and is extremely optimized.It is developed in Java.
ProtocolIt uses the optimized RESP (REdis Serialization Protocol).It uses HTTP.
PerformanceRedis tends to be faster than Elasticsearch while indexing and when performing searches on the indexed data set.It is a great feature-rich search product but has a lower performance compared to Redis.

What is Redis?

The primary database model of Redis was that of a key-value store. But the secondary database models included different types of DBMS, document store and, a search engine. Redis is supported on almost all the major server operating systems like Linux, Windows, BSD, etc. Redis has eventual consistency and follows the partition method of sharding.

It is used by large enterprises, startups, and government organizations. Common use cases include real-time fraud detection, global user session management, real-time inventory management, feature store for AI/ML, claims processing, and more.

Typical application scenarios of Redis include:

  • Real-time analytics
  • Caching
  • High-speed transaction
  • Distributed messaging
  • Data ingest, etc.

Some of the notable customers are Vodafone, Samsung, Nokia, Trip Advisor, etc.

Redis uses the RESP( REdis Serialization Protocol) for TCP connections within it. Apart from eventual consistency, strong consistency can be achieved by using Redis raft and strong eventual consistency can be achieved by using Active-Active and its databases. An advantage of Redis is that it can be deployed anywhere and can be managed by public clouds, multi-cloud, and other hybrid architecture to ensure that the data can be accessed from anywhere without any lock-in.

Redis was the most loved database for four years in a row, according to the Stack Overflow Annual Developer survey. It was also termed as one of the 20 fastest-growing technical skill sets in 2020.

What is Elasticsearch?

Elasticsearch stores data in the form of indexes and provides powerful searching capabilities. It is developed in Java and hence, is supported in all Operating Systems with Java. It is available for free under Apache 2.0 License, which also offers paid consulting services. In addition to offering real-time index search and analysis, other advantages of Elasticsearch include it being a distributed, resilient, and horizontally scalable engine.

The primary database model included only a search engine, while its secondary database models include spatial DBMS and a Document Store. Elasticsearch uses a powerful API called RESTful API that uses an HTTP web interface. Elasticsearch, along with Logstash and Kibana, forms the ELK Stack, which is a popular software stack for managing and transporting data. These tools are not just used by developers alone but also by data analysts and engineers.

The common case uses of Elastic search include search experiences for workplaces, websites, and apps; observability of APM, logging, metrics, and uptime. It also offers security and is used in Endpoint and SIEM. Many world-famous organizations use Elasticsearch to power their critical systems. Some of them are Cisco, eBay, Wikipedia, Goldman Sachs, NASA, Microsoft, and The New York Times.

Main Differences Between Redis and Elasticsearch

  1. Redis is an open-sourced, advanced key-value store while, Elasticsearch is a distributed, RESTful modern search and analytics engine based on Apache Lucene.
  2. Redis natively runs on Dynamic-RAM and Persistent Memory. Elasticsearch has a disk-based memory with caching option.
  3. Redis is developed in language C and is highly optimized. Elasticsearch is developed in Java.
  4. Redis uses the optimized RESP(REdis Serialization Protocol) while Elasticsearch uses an HTTP web interface.
  5. Redis tends to be faster than Elasticsearch when it comes to indexing and while performing searches on the indexed dataset. Even though Elasticsearch is a feature-rich search product compared to Redis, it has a lower performance than Redis.


It is clear from the above data that Redis is significantly faster than Elasticsearch and hence, is the most useful tool from the two of them. However, the poor performance of Elasticsearch can be solved by introducing a Redis caching layer to the code. It created a boost in the performance of Elastic search, although the downside to it is finding the appropriate key. Finding the appropriate key in Redis can be challenging.

No tool is perfect; it all depends upon how we use it. By manipulating it to suit our needs, we can achieve a near-perfect model of it. The best example of this is overlapping Elasticsearch with Redis to increase its performance. It is also necessary to note the skills of developers in each tool to compensate for the technical shortcomings provided by each tool.


  1. https://www.net.in.tum.de/fileadmin/TUM/NET/NET-2017-05-1.pdf#page=49
  2. https://books.google.com/books?hl=en&lr=&id=OANwDQAAQBAJ&oi=fnd&pg=PP1&dq=redis+and+elasticsearch&ots=D7UAgrWKes&sig=Fx6FGOd1rPSnNZ_Op4B-jxJda9E
  3. https://books.google.com/books?hl=en&lr=&id=d19aBgAAQBAJ&oi=fnd&pg=PR3&dq=elasticsearch&ots=NAe_rSyshC&sig=8oovVg7DsCVCS-nYiG6BXmt4Nuo
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