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

Aerospike vs Azure Redis Cache

OverviewComparisonAlternatives

Overview

Aerospike
Aerospike
Stacks200
Followers288
Votes48
GitHub Stars1.3K
Forks196
Azure Redis Cache
Azure Redis Cache
Stacks58
Followers124
Votes7

Aerospike vs Azure Redis Cache: What are the differences?

# Introduction

Key differences between Aerospike and Azure Redis Cache:

1. **Data Model**: Aerospike stores data in a hybrid memory architecture, while Azure Redis Cache uses an in-memory key-value store. This means that Aerospike can handle larger datasets with fast access times, while Azure Redis Cache is optimized for high-speed data access. 
2. **Replication**: Aerospike supports cross data-center replication for data redundancy and fault tolerance, while Azure Redis Cache offers replication across multiple nodes within the same data center for high availability. 
3. **Consistency**: Aerospike provides strong consistency guarantees with its distributed ACID-compliant transactions, ensuring data integrity even in complex systems. Azure Redis Cache, on the other hand, relies on eventual consistency, which may lead to temporary inconsistencies under certain conditions. 
4. **Pricing Model**: Aerospike offers a flexible pricing model based on the number of nodes and capacity needed, while Azure Redis Cache has a more straightforward pricing structure based on the cache tier and data transfer costs. 
5. **Monitoring and Management**: Aerospike provides comprehensive monitoring and management tools, including a web-based console and APIs for automation, while Azure Redis Cache offers basic monitoring capabilities with options for integration with Azure Monitor for more advanced monitoring needs. 

# Summary 

In Summary, Aerospike and Azure Redis Cache differ in their data models, replication strategies, consistency guarantees, pricing models, and monitoring capabilities. 

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Detailed Comparison

Aerospike
Aerospike
Azure Redis Cache
Azure Redis Cache

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.

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.

99% of reads/writes complete in under 1 millisecond.;Predictable low latency at high throughput – second to none. Read the YCSB Benchmark.;The secret sauce? A thousand things done right. Server code in ‘C’ (not Java or Erlang) precisely tuned to avoid context switching and memory copies. Highly parallelized multi-threaded, multi-core, multi-cpu, multi-SSD execution.;Indexes are always stored in RAM. Pure RAM mode is backed by spinning disks. In hybrid mode, individual tables are stored in either RAM or flash.
Enterprise-grade security; Flexible scaling; Improve application throughput and latency; Speed up applications with a distributed cache
Statistics
GitHub Stars
1.3K
GitHub Stars
-
GitHub Forks
196
GitHub Forks
-
Stacks
200
Stacks
58
Followers
288
Followers
124
Votes
48
Votes
7
Pros & Cons
Pros
  • 16
    Ram and/or ssd persistence
  • 12
    Easy clustering support
  • 5
    Easy setup
  • 4
    Acid
  • 3
    Scale
Pros
  • 4
    Cache-cluster
  • 3
    Redis
Integrations
No integrations available
Spring Boot
Spring Boot
Java
Java

What are some alternatives to Aerospike, Azure Redis Cache?

Redis

Redis

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.

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.

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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