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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Azure Redis Cache vs Redis

Azure Redis Cache vs Redis

OverviewComparisonAlternatives

Overview

Redis
Redis
Stacks61.9K
Followers46.5K
Votes3.9K
GitHub Stars42
Forks6
Azure Redis Cache
Azure Redis Cache
Stacks58
Followers124
Votes7

Azure Redis Cache vs Redis: What are the differences?

Azure Redis Cache and Redis are both in-memory data stores. Let's explore the key differences between the two.

  1. Scalability: One major difference between Azure Redis Cache and Redis is the scalability. Azure Redis Cache provides the ability to scale up and down based on demand without any downtime. It offers different pricing tiers with varying sizes of cache instances. On the other hand, Redis is a self-managed solution, and the scalability depends on the infrastructure it is deployed on. Users need to manually manage and scale Redis clusters to meet their requirements.

  2. Managed Service: Azure Redis Cache is a managed service provided by Microsoft. This means that Microsoft takes care of the infrastructure, updates, backups, and monitoring of the cache. Users can focus on using the cache without worrying about the underlying infrastructure. Redis, on the other hand, is an open-source solution that needs to be managed by the users themselves. Users are responsible for setting up the infrastructure, managing updates, backups, and monitoring of the Redis instances.

  3. Integration with Azure services: Azure Redis Cache seamlessly integrates with various Azure services like Azure Functions, Azure App Service, and Azure Logic Apps. This allows developers to easily incorporate caching in their applications and leverage other Azure services for building scalable and performant applications. Redis, being a standalone solution, does not have such direct integrations with Azure services and requires custom integration efforts.

  4. Security and Compliance: Azure Redis Cache provides built-in security features like SSL/TLS encryption, authentication, and access control policies. It also complies with various certifications and regulations like ISO, SOC, and HIPAA. Redis, being a self-managed solution, relies on the user's implementation for security measures. Users are responsible for implementing encryption, access control, and compliance measures as per their requirements.

  5. Monitoring and Metrics: Azure Redis Cache provides comprehensive monitoring and metrics through Azure Monitor. Users can easily monitor cache performance, get insights, and set up alerts. Redis, on the other hand, requires users to set up their own monitoring and metrics solutions. Users need to configure tools like Prometheus or Grafana to monitor and gather metrics from Redis instances.

  6. Pricing Model: Azure Redis Cache offers a pay-as-you-go pricing model based on cache size and performance tier. Users can choose the appropriate pricing tier based on their requirements and only pay for the resources they consume. Redis, being an open-source solution, does not have any specific pricing. Users can deploy Redis on their preferred infrastructure and need to manage the costs associated with the infrastructure, maintenance, and support.

In summary, Azure Redis Cache is a managed service provided by Microsoft, offering scalability, integration with Azure services, built-in security and compliance features, monitoring and metrics through Azure Monitor, and a pay-as-you-go pricing model. Redis, on the other hand, is a self-managed solution with users needing to manage scalability, infrastructure, security, monitoring, and pricing on their own.

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Detailed Comparison

Redis
Redis
Azure Redis Cache
Azure Redis Cache

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.

-
Enterprise-grade security; Flexible scaling; Improve application throughput and latency; Speed up applications with a distributed cache
Statistics
GitHub Stars
42
GitHub Stars
-
GitHub Forks
6
GitHub Forks
-
Stacks
61.9K
Stacks
58
Followers
46.5K
Followers
124
Votes
3.9K
Votes
7
Pros & Cons
Pros
  • 888
    Performance
  • 542
    Super fast
  • 514
    Ease of use
  • 444
    In-memory cache
  • 324
    Advanced key-value cache
Cons
  • 15
    Cannot query objects directly
  • 3
    No secondary indexes for non-numeric data types
  • 1
    No WAL
Pros
  • 4
    Cache-cluster
  • 3
    Redis
Integrations
No integrations available
Spring Boot
Spring Boot
Java
Java

What are some alternatives to Redis, Azure Redis Cache?

Hazelcast

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Aerospike

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

SAP HANA

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

VoltDB

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Tarantool

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

KeyDB

KeyDB

KeyDB is a fully open source database that aims to make use of all hardware resources. KeyDB makes it possible to breach boundaries often dictated by price and complexity.

LokiJS

LokiJS

LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser.

BuntDB

BuntDB

BuntDB is a low-level, in-memory, key/value store in pure Go. It persists to disk, is ACID compliant, and uses locking for multiple readers and a single writer. It supports custom indexes and geospatial data. It's ideal for projects that need a dependable database and favor speed over data size.

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