At early stages, we constructed our distributed messaging middleware based on ActiveMQ 5.x(prior to 5.3). Our multinational business uses it for asynchronous communication, search, social network activity stream, data pipeline, even in its trade processes. As our trade business throughput rises, pressure originating from our messaging cluster also becomes urgent.
Why RocketMQ ?
Based on our research, with increased queues and virtual topics in use, ActiveMQ IO module reaches a bottleneck. We tried our best to solve this problem through throttling, circuit breaker or degradation, but it did not work well. So we begin to focus on the popular messaging solution Kafka at that time. Unfortunately, Kafka can not meet our requirements especially in terms of low latency and high reliability, see here for details.
In this context, we decided to invent a new messaging engine to handle a broader set of use cases, ranging from traditional pub/sub scenarios to high volume real-time zero-loss tolerance transaction system. We believe this solution can be beneficial, so we would like to open source it to the community. Today, more than 100 companies are using the open source version of RocketMQ in their business.
The following table demonstrates the comparison between RocketMQ, ActiveMQ and Kafka (Apache’s most popular messaging solutions according to awesome-java):
RocketMQ vs. ActiveMQ vs. Kafka
Please note this documentation is written by the RocketMQ team. Although the ideal is a disinterested comparison of technology and features, the authors’ expertise and biases obviously favor RocketMQ.
The table below is a handy quick reference for spotting the differences among RocketMQ and its most popular alternatives at a glance.
|Messaging Product||Client SDK||Protocol and Specification||Ordered Message||Scheduled Message||Batched Message||BroadCast Message||Message Filter||Server Triggered Redelivery||Message Storage||Message Retroactive||Message Priority||High Availability and Failover||Message Track||Configuration||Management and Operation Tools|
|ActiveMQ||Java, .NET, C++ etc.||Push model, support OpenWire, STOMP, AMQP, MQTT, JMS||Exclusive Consumer or Exclusive Queues can ensure ordering||Supported||Not Supported||Supported||Supported||Not Supported||Supports very fast persistence using JDBC along with a high performance journal，such as levelDB, kahaDB||Supported||Supported||Supported, depending on storage,if using levelDB it requires a ZooKeeper server||Not Supported||The default configuration is low level, user need to optimize the configuration parameters||Supported|
|Kafka||Java, Scala etc.||Pull model, support TCP||Ensure ordering of messages within a partition||Not Supported||Supported, with async producer||Not Supported||Supported, you can use Kafka Streams to filter messages||Not Supported||High performance file storage||Supported offset indicate||Not Supported||Supported, requires a ZooKeeper server||Not Supported||Kafka uses key-value pairs format for configuration. These values can be supplied either from a file or programmatically.||Supported, use terminal command to expose core metrics|
|RocketMQ||Java, C++, Go||Pull model, support TCP, JMS, OpenMessaging||Ensure strict ordering of messages,and can scale out gracefully||Supported||Supported, with sync mode to avoid message loss||Supported||Supported, property filter expressions based on SQL92||Supported||High performance and low latency file storage||Supported timestamp and offset two indicates||Not Supported||Supported, Master-Slave model, without another kit||Supported||Work out of box,user only need to pay attention to a few configurations||Supported, rich web and terminal command to expose core metrics|