Azure Redis Cache vs CQEngine

Overview

Azure Redis Cache

Stacks58

Followers124

Votes7

CQEngine

Stacks3

Followers22

Votes0

GitHub Stars1.8K

Forks255

Azure Redis Cache vs CQEngine: What are the differences?

Scalability: Azure Redis Cache is a fully managed, in-memory data store that can handle high throughput and low-latency applications easily. On the other hand, CQEngine is an in-memory indexed query engine suitable for small to medium-sized datasets with lower scalability capabilities.
Data Persistence: Azure Redis Cache offers the option to persist data to disk, ensuring data durability in case of a cache restart or failure. In contrast, CQEngine does not provide built-in data persistence features, relying solely on in-memory data storage.
Query Language: Azure Redis Cache supports a rich set of data structures and provides a versatile query language, allowing for complex data manipulation and retrieval operations. CQEngine, on the other hand, offers a simpler query language tailored specifically for querying indexed collections efficiently.
Integration with Other Services: Azure Redis Cache seamlessly integrates with other Azure services such as Azure Functions, Azure App Service, and Azure Kubernetes Service, enabling easy development of cloud-based applications. In contrast, CQEngine is a standalone library that needs to be integrated manually into the application stack.
Support for Transactions: Azure Redis Cache supports transactions, allowing for multiple operations to be executed atomically, ensuring data consistency. CQEngine does not offer built-in support for transactions, requiring developers to implement custom logic for maintaining data integrity in multi-operation scenarios.

In Summary, Azure Redis Cache and CQEngine differ in terms of scalability, data persistence, query language, integration with other services, and support for transactions.

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Detailed Comparison

Azure Redis Cache	CQEngine
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.	It is a NoSQL indexing and Query Engine, for retrieving objects matching SQL-like queries from Java collections, with ultra-low latency
Enterprise-grade security; Flexible scaling; Improve application throughput and latency; Speed up applications with a distributed cache	Ultra-fast; Query engine; No SQL
Statistics
GitHub Stars -	GitHub Stars 1.8K
GitHub Forks -	GitHub Forks 255
Stacks 58	Stacks 3
Followers 124	Followers 22
Votes 7	Votes 0
Pros & Cons
Pros 4 Cache-cluster 3 Redis	No community feedback yet
Integrations
Spring Boot Java	MongoDB PostgreSQL Fastify MySQL

What are some alternatives to Azure Redis Cache, CQEngine?

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

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 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 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

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

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 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

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 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 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.

Related Comparisons

Azure Redis Cache vs CQEngine: What are the differences?

Scalability: Azure Redis Cache is a fully managed, in-memory data store that can handle high throughput and low-latency applications easily. On the other hand, CQEngine is an in-memory indexed query engine suitable for small to medium-sized datasets with lower scalability capabilities.
Data Persistence: Azure Redis Cache offers the option to persist data to disk, ensuring data durability in case of a cache restart or failure. In contrast, CQEngine does not provide built-in data persistence features, relying solely on in-memory data storage.
Query Language: Azure Redis Cache supports a rich set of data structures and provides a versatile query language, allowing for complex data manipulation and retrieval operations. CQEngine, on the other hand, offers a simpler query language tailored specifically for querying indexed collections efficiently.
Integration with Other Services: Azure Redis Cache seamlessly integrates with other Azure services such as Azure Functions, Azure App Service, and Azure Kubernetes Service, enabling easy development of cloud-based applications. In contrast, CQEngine is a standalone library that needs to be integrated manually into the application stack.
Support for Transactions: Azure Redis Cache supports transactions, allowing for multiple operations to be executed atomically, ensuring data consistency. CQEngine does not offer built-in support for transactions, requiring developers to implement custom logic for maintaining data integrity in multi-operation scenarios.

In Summary, Azure Redis Cache and CQEngine differ in terms of scalability, data persistence, query language, integration with other services, and support for transactions.

Azure Redis Cache vs CQEngine

Overview