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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. ActiveMQ vs Hazelcast

ActiveMQ vs Hazelcast

OverviewComparisonAlternatives

Overview

ActiveMQ
ActiveMQ
Stacks879
Followers1.3K
Votes77
GitHub Stars2.4K
Forks1.5K
Hazelcast
Hazelcast
Stacks427
Followers474
Votes59
GitHub Stars6.4K
Forks1.9K

ActiveMQ vs Hazelcast: What are the differences?

Introduction

This Markdown code provides a comparison between ActiveMQ and Hazelcast, highlighting their key differences.

  1. Scalability: ActiveMQ is a message broker that allows for distributing messages across multiple machines, providing scalability for handling large volumes of data. On the other hand, Hazelcast is an in-memory data grid that enables the distribution of data and computation across a cluster of machines, allowing for horizontal scalability. While both solutions offer scalability, ActiveMQ focuses on message distribution, while Hazelcast focuses on data distribution.

  2. Data Replication: ActiveMQ supports data replication through its network of brokers, allowing for high availability and fault tolerance. In contrast, Hazelcast provides data replication by replicating the distributed objects across its cluster, ensuring high availability even in case of node failures. The approach to data replication differs between ActiveMQ, which replicates messages, and Hazelcast, which replicates distributed objects.

  3. Clustering Approach: ActiveMQ implements a master-slave clustering model, where a master broker handles the message distribution while slave brokers act as backups. In the event of a master failure, one of the slave brokers takes over as the new master. In Hazelcast, clustering follows a peer-to-peer model, where all the cluster members share the same responsibilities without any dedicated master node. Each member in Hazelcast can act as a client or server and can initiate or handle requests.

  4. JMS Compliance: ActiveMQ fully supports the Java Message Service (JMS) standard, which provides an interface for Java applications to create, send, receive, and read messages. Hazelcast, on the other hand, does not provide native support for JMS, focusing more on distributed data structures and computing functionalities. While ActiveMQ is directly integrated with JMS, Hazelcast can be used alongside JMS implementations.

  5. Programming Language Support: ActiveMQ primarily targets Java applications and provides extensive support for integration with various Java frameworks and libraries. Hazelcast, on the other hand, offers support for multiple programming languages, including Java, .NET, C++, Python, and Node.js. This wider language support makes Hazelcast more versatile for applications developed using different programming languages.

  6. Data Persistence: ActiveMQ provides persistent message storage using a variety of persistence adapters, including databases and file systems. Messages are stored and can be recovered in case of system failure. In contrast, Hazelcast primarily focuses on in-memory data storage and does not provide built-in support for long-term data persistence. While Hazelcast can be integrated with external systems for durable data storage, ActiveMQ offers more comprehensive built-in persistence options.

In summary, ActiveMQ is a message broker with scalability, data replication, and JMS compliance, while Hazelcast is an in-memory data grid with horizontal scalability, a peer-to-peer clustering model, and support for multiple programming languages. ActiveMQ focuses on message distribution and provides comprehensive persistence options, while Hazelcast emphasizes data distribution and in-memory processing.

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Detailed Comparison

ActiveMQ
ActiveMQ
Hazelcast
Hazelcast

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

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.

Protect your data & Balance your Load; Easy enterprise integration patterns; Flexible deployment
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
2.4K
GitHub Stars
6.4K
GitHub Forks
1.5K
GitHub Forks
1.9K
Stacks
879
Stacks
427
Followers
1.3K
Followers
474
Votes
77
Votes
59
Pros & Cons
Pros
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
Cons
  • 1
    Low resilience to exceptions and interruptions
  • 1
    ONLY Vertically Scalable
  • 1
    Support
  • 1
    Difficult to scale
Pros
  • 11
    High Availibility
  • 6
    Distributed compute
  • 6
    Distributed Locking
  • 5
    Sharding
  • 4
    Load balancing
Cons
  • 4
    License needed for SSL
Integrations
No integrations available
Java
Java
Spring
Spring

What are some alternatives to ActiveMQ, Hazelcast?

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.

Kafka

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

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.

Gearman

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

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