Top Redis Use Cases by Core Data Structure Types

Redis, short for Remote Dictionary Server, is a BSD-licensed, open-source in-memory key-value data structure store written in C language by Salvatore Sanfillipo and was first released on May 10, 2009. Depending on how it is configured, Redis can act like a database, a cache or a message broker. It’s important to note that Redis is a NoSQL database system. This implies that unlike SQL (Structured Query Language) driven database systems like MySQL, PostgreSQL, and Oracle, Redis does not store data in well-defined database schemas which constitute tables, rows, and columns. Instead, Redis stores data in data structures which makes it very flexible to use. In this blog, we outline the top Redis use cases by the different core data structure types.

Data Structures in Redis

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2019 Open Source Database Report: Top Databases, Public Cloud vs. On-Premise, Polyglot Persistence

Ready to transition from a commercial database to open source, and want to know which databases are most popular in 2019? Wondering whether an on-premise vs. public cloud vs. hybrid cloud infrastructure is best for your database strategy? Or, considering adding a new database to your application and want to see which combinations are most popular? We found all the answers you need at the Percona Live event last month, and broke down the insights into the following free trends reports:

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Intro to Redis Cluster Sharding – Advantages, Limitations, Deploying & Client Connections

Redis Cluster is the native sharding implementation available within Redis that allows you to automatically distribute your data across multiple nodes without having to rely on external tools and utilities. At ScaleGrid, we recently added support for Redis Clusters on our platform through our fully managed Redis hosting plans. In this post, we’re going to introduce you to the advanced Redis Cluster sharding opportunities, discuss its advantages and limitations, when you should deploy, and how to connect to your Redis Cluster.

Sharding with Redis Cluster

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RebornDB: the Next Generation Distributed Key-Value Store

There are many key-value stores in the world and they are widely used in many systems. E.g, we can use a Memcached to store a MySQL query result for later same query, use MongoDB to store documents for better searching, etc.

For different scenarios, we should choose different key-value store. There is no silver-bullet key-value store for all solutions. But if you just want a simple key-value store, easy to use, very fast, supporting many powerful data structures, redis may be a good choice for your start.  

Redis is advanced key-value cache and store, under BSD license. It is very fast, has many data types(String, Hash, List, Set, Sorted Set …), uses RDB or AOF persistence and replication to guarantee data security, and supplies many language client libraries.

Most of all, market chooses Redis. There are many companies using Redis and it has proved its worth.

Although redis is great, it still has some disadvantages, and the biggest one is memory limitation.  Redis keeps all data in memory, which limits the whole dataset size and lets us save more data impossibly.

The official redis cluster solves this by splitting data into many redis servers, but it has not been proven in many practical environments yet. At the same time, it need us to change our client libraries to support “MOVED” redirection and other special commands, this is unacceptable in running production too. So redis cluster is not a good solution now.

QDB

We like redis, and want to go beyond its limitation, so we building a service named QDB, which is compatible with redis, saves data in disk to exceed memory limitation and keeps hot data in memory for performance.

Introduction

QDB is a redis like, fast key-value store.It has below good features:

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