Overview
Apache ActiveMQ is a popular open-source message broker used by organisations to enable reliable and asynchronous communication between different applications.
How it works
ActiveMQ acts as an intermediary, receiving and then delivering messages, ensuring that none are not lost even if the receiving application is temporarily unavailable.
ActiveMQ supports various messaging protocols, including JMS, AMQP, and MQTT, allowing for interoperability between diverse systems.
Resilience and availability
ActiveMQ is built for high availability, meaning it can keep running even if one part fails. It does this using a main server and a backup server (master/slave) and by storing data in a shared location. More traffic can be handled by connecting multiple ActiveMQ servers together in a cluster.
Security
Security is handled by making sure only authorised users can access the system (authentication and authorisation) and by encrypting messages so they can’t be read by unauthorised people.
Use cases
Organisations use ActiveMQ in situations that require reliable and guaranteed message delivery, such as:
- Banking or Financial Transactions: Processing payments, transferring funds between accounts, and handling stock or share trades - where data accuracy and delivery are required. For example, ensuring that a payment is processed exactly once and that no transaction data is lost.
- Inventory Management: Tracking stock levels, managing orders, and coordinating shipments across a supply chain. For example, ensuring that an order is only processed once and that inventory levels are updated accurately in real-time.
- Application Integration: Connecting disparate business applications, allowing them to exchange data and coordinate workflows. For example, integrating an e-commerce platform with a warehouse management system to automate order fulfillment.
- Logging and Monitoring: Centralising logs from multiple applications for analysis and troubleshooting. ApacheMQ can reliably deal with the collection and processing of large volumes of log data.
- Real-time Data Streaming: Distributing real-time data to multiple consumers, such as in financial markets or sensor networks.