Hazelcast vs Redis

Overview

Redis

Stacks61.9K

Followers46.5K

Votes3.9K

GitHub Stars42

Forks6

Hazelcast

Stacks427

Followers474

Votes59

GitHub Stars6.4K

Forks1.9K

Hazelcast vs Redis: What are the differences?

Introduction

Markdown code comparing the key differences between Hazelcast and Redis.

In-Memory Data Grid vs. NoSQL Database: Hazelcast is primarily an in-memory data grid (IMDG) that stores and processes data in memory, providing fast access and low latency. On the other hand, Redis is a NoSQL database that persists data on disk and also supports in-memory caching. This fundamental difference influences their use cases, with Hazelcast being better suited for scenarios requiring real-time data processing and Redis for data persistence and caching.
Data Models: Hazelcast supports a wide range of data structures such as distributed maps, queues, sets, and lists, allowing for complex data models and distributed computations. Redis, on the other hand, focuses on simpler data structures like strings, hashes, lists, sets, and sorted sets, emphasizing efficiency and simplicity.
Consistency and Durability: Hazelcast provides strong data consistency guarantees through its distributed data structures. It ensures that data is always synchronized across the cluster, even in the presence of failures. Redis, on the other hand, offers eventual consistency by default, where data updates may take some time to propagate across nodes. Additionally, Redis allows users to choose different persistence mechanisms like snapshots or append-only logs for data durability.
Programming Language Support: Hazelcast provides official clients for multiple programming languages, including Java, .NET, C++, Python, and Node.js, enabling developers to integrate it seamlessly into their application stack. Redis also offers official clients for a wide range of programming languages, making it accessible for different development environments.
Distributed Computing Capabilities: Hazelcast includes a distributed computing framework, enabling distributed processing of data across the cluster using parallel algorithms. It supports distributed tasks, computing grid, event-driven programming, and distributed execution of user-defined functions. Redis, on the other hand, does not provide built-in distributed computing capabilities, focusing more on data operations and caching.
Scalability and Fault-Tolerance: Both Hazelcast and Redis support horizontal scalability, allowing users to add or remove nodes dynamically to accommodate growing or shrinking workloads. However, Hazelcast provides automatic data replication, ensuring fault-tolerance even in the event of node failures. Redis requires users to configure replication manually to achieve fault-tolerance.

In summary, Hazelcast is an in-memory data grid with strong consistency and distributed computing capabilities, while Redis is a NoSQL database with a focus on simplicity, efficiency, and caching.

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CLI (Node.js)

Manual

Detailed Comparison

Redis	Hazelcast
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.	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.
-	Distributed implementations of java.util.{Queue, Set, List, Map};Distributed implementation of java.util.concurrent.locks.Lock;Distributed implementation of java.util.concurrent.ExecutorService;Distributed MultiMap for one-to-many relationships;Distributed Topic for publish/subscribe messaging;Synchronous (write-through) and asynchronous (write-behind) persistence;Transaction support;Socket level encryption support for secure clusters;Second level cache provider for Hibernate;Monitoring and management of the cluster via JMX;Dynamic HTTP session clustering;Support for cluster info and membership events;Dynamic discovery, scaling, partitioning with backups and fail-over
Statistics
GitHub Stars 42	GitHub Stars 6.4K
GitHub Forks 6	GitHub Forks 1.9K
Stacks 61.9K	Stacks 427
Followers 46.5K	Followers 474
Votes 3.9K	Votes 59
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 11 High Availibility 6 Distributed Locking 6 Distributed compute 5 Sharding 4 Load balancing Cons 4 License needed for SSL
Integrations
No integrations available	Java Spring

What are some alternatives to Redis, Hazelcast?

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

Azure Redis Cache

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.

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.

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.

Related Comparisons

Hazelcast vs Redis: What are the differences?

Introduction

Markdown code comparing the key differences between Hazelcast and Redis.

In-Memory Data Grid vs. NoSQL Database: Hazelcast is primarily an in-memory data grid (IMDG) that stores and processes data in memory, providing fast access and low latency. On the other hand, Redis is a NoSQL database that persists data on disk and also supports in-memory caching. This fundamental difference influences their use cases, with Hazelcast being better suited for scenarios requiring real-time data processing and Redis for data persistence and caching.
Data Models: Hazelcast supports a wide range of data structures such as distributed maps, queues, sets, and lists, allowing for complex data models and distributed computations. Redis, on the other hand, focuses on simpler data structures like strings, hashes, lists, sets, and sorted sets, emphasizing efficiency and simplicity.
Consistency and Durability: Hazelcast provides strong data consistency guarantees through its distributed data structures. It ensures that data is always synchronized across the cluster, even in the presence of failures. Redis, on the other hand, offers eventual consistency by default, where data updates may take some time to propagate across nodes. Additionally, Redis allows users to choose different persistence mechanisms like snapshots or append-only logs for data durability.
Programming Language Support: Hazelcast provides official clients for multiple programming languages, including Java, .NET, C++, Python, and Node.js, enabling developers to integrate it seamlessly into their application stack. Redis also offers official clients for a wide range of programming languages, making it accessible for different development environments.
Distributed Computing Capabilities: Hazelcast includes a distributed computing framework, enabling distributed processing of data across the cluster using parallel algorithms. It supports distributed tasks, computing grid, event-driven programming, and distributed execution of user-defined functions. Redis, on the other hand, does not provide built-in distributed computing capabilities, focusing more on data operations and caching.
Scalability and Fault-Tolerance: Both Hazelcast and Redis support horizontal scalability, allowing users to add or remove nodes dynamically to accommodate growing or shrinking workloads. However, Hazelcast provides automatic data replication, ensuring fault-tolerance even in the event of node failures. Redis requires users to configure replication manually to achieve fault-tolerance.

In summary, Hazelcast is an in-memory data grid with strong consistency and distributed computing capabilities, while Redis is a NoSQL database with a focus on simplicity, efficiency, and caching.

Hazelcast vs Redis

Overview

Hazelcast vs Redis: What are the differences?