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The pub/sub pattern scales well for small networks with a small number of publisher and subscriber nodes and low message volume. However, as the number of nodes and messages grows, the likelihood of instabilities increases, limiting the maximum scalability of a pub/sub network. Example throughput instabilities at large scales include:
Apache Kafka is a distributed event store and stream-processing platform. It is an open-source system developed by the Apache Software Foundation written in Java and Scala . The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Sequence diagram for depicting the Message Broker pattern. A message broker (also known as an integration broker or interface engine [1]) is an intermediary computer program module that translates a message from the formal messaging protocol of the sender to the formal messaging protocol of the receiver.
The Data Distribution Service (DDS) for real-time systems is an Object Management Group (OMG) machine-to-machine (sometimes called middleware or connectivity framework) standard that aims to enable dependable, high-performance, interoperable, real-time, scalable data exchanges using a publish–subscribe pattern.
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Events can be implemented through various mechanisms such as callbacks, message objects, signals, or interrupts, and events themselves are distinct from the implementation mechanisms used. Event propagation models, such as bubbling, capturing, and pub/sub, define how events are distributed and handled within a system.
Kafka: a message broker software; Karaf: an OSGi distribution for server-side applications. Kibble: a suite of tools for collecting, aggregating and visualizing activity in software projects. Knox: a REST API Gateway for Hadoop Services; Kudu: a distributed columnar storage engine built for the Apache Hadoop ecosystem
Recent examples of its use include the Github load balancer, [10] the Apache Ignite distributed database, [11] and by the Twitter EventBus pub/sub platform. [18] Consistent hashing operates by mapping sites uniformly and randomly to points on a unit circle called tokens.