ActiveMQ vs Memcached: What are the differences?
Developers describe ActiveMQ as "A message broker written in Java together with a full JMS client". 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. On the other hand, Memcached is detailed as "High-performance, distributed memory object caching system". Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.
ActiveMQ belongs to "Message Queue" category of the tech stack, while Memcached can be primarily classified under "Databases".
"Open source" is the top reason why over 9 developers like ActiveMQ, while over 133 developers mention "Fast object cache" as the leading cause for choosing Memcached.
ActiveMQ and Memcached are both open source tools. It seems that Memcached with 9K GitHub stars and 2.6K forks on GitHub has more adoption than ActiveMQ with 1.51K GitHub stars and 1.05K GitHub forks.
Facebook, Instagram, and Dropbox are some of the popular companies that use Memcached, whereas ActiveMQ is used by Intuit, Wix, and SoFi. Memcached has a broader approval, being mentioned in 755 company stacks & 267 developers stacks; compared to ActiveMQ, which is listed in 33 company stacks and 17 developer stacks.
What is ActiveMQ?
What is Memcached?
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What are the cons of using ActiveMQ?
What are the cons of using Memcached?
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As part of the cacheing system within Drupal.
Memcached mainly took care of creating and rebuilding the REST API cache once changes had been made within Drupal.
Distributed cache exposed through Google App Engine APIs; use to stage fresh data (incoming and recently processed) for faster access in data processing pipeline.
Memcache caches database results and articles, reducing overall DB load and allowing seamless DB maintenance during quiet periods.
Remote broker and local client for incoming data feeds. Local broker for republishing data feeds to other systems.
Used to cache most used files for our clients. Connected with CloudFlare Railgun Optimizer.