Redis, or otherwise Remote Dictionary Server, is an open-source data store, a much faster option among the in-memory, key-value data stores. Along with its utility as a database, Redis can also be a cache, queue, and message broker. Redis can deliver query responses in sub-milliseconds’ time and enable millions of requests each second in real-time heavy applications like gaming, financial services, ad-tech, healthcare, machine learning, and IoT, etc. Redis is also a very common choice in session management, caching, leaderboards, gaming, real-time data analytics, geospatial apps, chat and messaging, ride-hailing, media streaming, etc.

Working of Redis

Unlike the databases with storage on disk or SSDs, Redis data primarily resides in-memory, eliminating the need to access the disks. The in-memory data stores like Redis avoid any time delay for data access, which is done in microseconds. Redis also features versatile data structures with high availability, Lua scripting, geospatial, on-disk persistence, transactions, and cluster support, making it so simpler to build internet-scale applications with real-time functions.

Offerings from the latest Redis 5.0

The latest version of Redis now is Redis 5.0.3, which features the Redis open source’s latest GA version. Even though its first release was in 2009, Redis as an open-source DB had beyond the simple cache technology to a must faster and easy to use data store, which now offers a very versatile DBMS structure quicker responses in milliseconds. With the latest 5.0 release, Redis had reached some major milestones with many new improvements and advancements. The major catch in the latest version of Redis is Streams, a fresh data structure offered by Redis after HyperLogLog. This latest release also added more commands for the Sorted Sets and new capabilities for Module APIs.

Benefits and features of new Redis

Let’s discuss some of the major features of the latest Redis.

  1. In-memory store

All the data on Redis are stored in the server’s main memory and not on the SSDs or HDDs as in Cassandra, PostgreSQL, MongoDB, etc. Compared to the traditional disk storage databases in which most of the operations need a roundtrip to fetch data, the in-memory stores, as Redis offer, will not need such a penalty of time delay due to it. So, it can support a lot of operations by ensuring a faster response time. As a result, you can rely on Redis for amazingly faster performance. An average read/write operation may take only less than a millisecond, thereby supporting millions of operations every second. This makes it an ideal data store for read/write intensive DBs and Big Data Applications. You can check options from RemoteDBA.com

  • A very flexible data structure

Usually, key-value data stores may only offer very limited data structures. On the other hand, Redis offers a wide range of data structures in different varieties to meet your application needs. Different types of data on Redis include, but not limited to:

  • Strings – These are binary data or text data, which is up to the size of 512 MB.
  • Lists – Collection of Strings in its order of addition.
  • Sets – This is an unordered string collection that can intersect, combine, and diff the other Sets.
  • Hashes – Another data structure to store a list of different values and fields.
  • Bitmaps – Data type offering bit-level operations.
  • HyperLogLogs – A unique data structure that helps estimate unique items in data.
  • Ease of use and simplicity

Along with the above features, Redis also helps to simplify the code by letting the users write a few custom codes to access, store, delete, and use data for your enterprise applications. For example, if there is an application that stores data in a hashmap, then the users can use the Redis hash for data storage. A similar data structure for other databases without hash data may demand many lines of codes to achieve the same.

So, Redis features many such native data structures with different options to interact and manipulate your data in various formats. There are more than 100 open source clients available for Redis developers. There are also many languages supported as Python, Java, PHP, C, CPP, Node.js, JavaScript, R, Ruby, etc.

  • Persistence and replication

Redis can follow an architecture of a primary replica and support some asynchronous replication in which data can be easily replicated in different replica servers. Such an approach will help improve Redis’s performance in read operations and ensure a faster recovery even when the primary server faces an outage. To ensure persistence, Redis can support point-in-time backups, i.e., copying data to the disks.

  • High scalability and availability

As discussed, the primary-replica architecture of Redis is a clustered topology or single node primary. Such architecture will help you ensure high availability solutions by offering consistent performance and reliability. While you have to adjust the cluster size, there may be various options for scaling up, in, or out. This will let the given cluster to grow based on the user demands.

  • Extensibility

A strong community supports Redis as being an open-source project. There is no technology lock or vendors in between, and it supports many open data formats and boasts of an impressive set of clients.

  • Caching

As we have already discussed, Redis is a wonderful choice to implement an in-memory cache for decreasing the data access latency. It also enables increased throughput and can also ease the load off your NoSQL or relational databases. As shown above, Redis can serve frequent requests with sub-millisecond responses and enable the users to scale up easily to handle higher loads without acquiring any costlier hardware. Query caching, session caching, web page caching, image caching, metadata caching, etc., are examples of the Redis caching approach.

Redis is now successfully used for streaming solutions like Amazon Kinesis and Apache Kafka to store, process, and quickly analyze data for real-time applications with the sub-millisecond response time. So, Redis is now an apt choice for real-time analytics applications like ad targeting, social media analytics, IoT, etc.

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