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BlazingMQ Network Topology


This document introduces readers to BlazingMQ’s network topology and describes how its unique topology enables BlazingMQ to provide flexible deployments, high bandwidth savings and very high fan-out ratio (10,000+ and beyond).

Network Topology

As seen in the Clustering section of another article, a BlazingMQ setup can be deployed in a typical or an alternative topology.

Typical Deployment

The above figure shows a typical deployment of BlazingMQ. The four nodes in the middle represent a BlazingMQ cluster. The blue node in the cluster represents a queue’s primary node, while the orange nodes represent replicas. Producer/consumer applications can connect to any node in the BlazingMQ cluster instead of always requiring to connect to the primary node.

Alternative Deployment

Above figure shows an alternate deployment of BlazingMQ. As can be seen, applications (green nodes) connect to local BlazingMQ agents (yellow nodes), which then connect to any node in the BlazingMQ cluster. These local BlazingMQ agents are known as proxies and are optional in a BlazingMQ deployment.

An important conclusion can be made from above – client applications don’t need to directly connect to a queue’s primary, and there can be any number of hops between an application and the primary node. This leads to an interesting design in BlazingMQ as discussed in the next section.

Distribution Tree

There is another more generic way to redraw the above two figures for a given queue:

Distribution Tree

In the above figure, blue node is queue’s primary, orange nodes are the replicas, yellow nodes are proxies and green nodes are client applications. As mentioned previously, the case of producers is simple, so we will assume that all green nodes are consumer applications.

As can be seen from the figure, for every queue, BlazingMQ builds a distribution tree rooted at queue’s primary node, and as new links are established or existing links are torn down due to nodes (primary, replica, proxy, application) starting or stopping, BlazingMQ readjusts the routes in the affected sub-tree as necessary. This readjustment of routes occurs dynamically – every node at a given level attempts to establish a healthy connection with at least one node from the upper level.

Message Flow in the Distribution Tree

An interesting feature of the topology shown in the previous figure is that it optimizes message flow for queues in fan-out and broadcast modes. Consider this setup:

Distribution Tree for Broadcast Queue

In this figure, there are hundreds of consumers for a broadcast queue. In the absence of a distribution tree, all consumers would connect directly with the primary node, which would then need to carry out the fan-out to all those consumers by itself. While this approach works up to a certain number of consumers (as well as the traffic rate), it does not scale well. After a certain threshold, primary node’s network gets saturated and/or consumers start seeing higher latency.

In a distribution tree, however, the primary node (and in fact, every intermediate node) carry out a fan-out to only a handful of nodes connected to it from the lower level, thereby ensuring a message is disseminated to all consumers in an efficient way. This approach effectively implements application level multicast (note that we are using the term ‘multicast’ in a logical sense; all communication occurs over TCP).

This topology ensures that BlazingMQ can achieve very high (theoretically infinite) fan-out ratio. If at any time, we notice that the fan-out ratio is putting pressure on bandwidth or latency for nodes in particular level (yellow nodes), we can simply add another level of nodes (gray nodes) below the affected level in the tree, thereby reducing the fan-out ratio at all nodes in the affected level.

This tree-based topology has been very successful at Bloomberg for supporting applications which demand very large number of consumers for a queue in fan-out or broadcast mode. BlazingMQ’s topology provides tremendous bandwidth saving in such scenarios, and also helps cut down the latency of message delivery to consumers, as each node has to do minimal fan-out of a message.

This approach is used to run queues with over 6,000 consumers in some Bloomberg production environments.

Another benefit of a tree based architecture is efficient fail-overs. Imagine a scenario where primary node crashes or goes down gracefully. In the approach where all producer and consumer applications are connecting directly to the primary, they will now fail over to the new primary, and the new primary will see a deluge of incoming connections and requests (thousands or more). This can slow down the fail over process and introduce undesirable latency for applications. In the case of BlazingMQ’s tree based approach, fail overs are very fast as only the affected sub-tree needs readjustment. So in the scenario where primary node crashes, the new primary receives fail over requests only from the replica nodes and the nodes connected to it from one level below, instead of thousands of requests from all producer and consumer applications.

A question that may arise in this setup is the effect of multi-hop architecture on message latency. Since a message has to travel through several hops, additional latency is unavoidable. In most set ups, BlazingMQ provides an end to end median latency in single digit milliseconds. Ultimately, it comes down to carrying out end to end benchmarking in a production-like setup, reviewing the latency and bandwidth numbers, and then adjusting number of consumers or number of levels in the tree.


The distribution tree based approach works very well for certain high fan-out use cases. While this approach comes at the cost of additional deployment footprint, it makes up for that by leading to network bandwidth savings for high traffic and high fan-out queues.

How BlazingMQ manages to prevent or minimize disruption in message flow for applications in distribution tree like topology is an interesting topic. Please see this article for details about BlazingMQ’s high availability design.