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High Availability in BlazingMQ


This article introduces readers to a high level design and implementation of high availability in BlazingMQ. Readers are encouraged to read the Network Topology article and also get familiar with various message routing patterns in BlazingMQ before reading this article.


BlazingMQ Network Topology

As described in the Network Topology article and shown in the figure below, BlazingMQ builds a distribution tree for every queue. The tree is rooted at a queue’s primary node. Producer and consumer applications are represented as leaf nodes.

Distribution Tree for Broadcast Queue

This topology structure helps BlazingMQ achieve bandwidth savings in very high fan-out ratio setups (a scenario where every message posted on the queue needs to go to every consumer attached to that queue).

What is High Availability?

Failure is inevitable in any distributed system. While total failure can be minimized or even avoided in a well designed distributed system, partial failures occur all the time as a result of misconfigurations, software bugs, machine crashes, network or disk issues, etc. Any formidable distributed system must shield applications from such transient failures and allow them to work seamlessly while healing itself in the background.

While there is no agreed upon definition for a “highly available” system, such a system generally remains available for an extended period of time to its users and minimizes downtime (both scheduled and unscheduled). Often, availability is expressed in nines and is calculated as a percentage of time the system was functional from users’ perspective throughout a given year. The more nines a system has, the more available it is to the users.


In message queuing systems like BlazingMQ, high availability is achieved via clustering and replication. Every queue in BlazingMQ is stored on disk and replicated across a group of machines such that the queue continues to be accessible to applications in case some machines in the cluster become unavailable. Theoretically, as long as at least one machine in the cluster is available, queue remains accessible.

In a BlazingMQ cluster, a node is dynamically assigned as primary for a queue such that all writes to the queue go through that node. Primary node also ensures that queue is replicated to other nodes (replicas) in the cluster. We believe that there is already enough literature available on the topic of clustering and replication and we won’t go into more details about it in this article.

While clustering and replication solve the problem of high availability to an extent, BlazingMQ network topology provides an additional challenge. As can be seen in the distribution tree in figure above, failure of any node or link in the tree directly affects BlazingMQ’s availability to the applications. The higher the failing node in the tree, the higher the impact on BlazingMQ’s availability to users. In fact, a node which is simply restarting also affects BlazingMQ’s availability.

The rest of the article documents BlazingMQ’s data life cycle and approach to seamlessly handle failing or restarting nodes in the distribution tree.


Before we go further, lets quickly go over some BlazingMQ terminology which will be useful in the rest of the article.

  • PUT: A message sent (or “posted”) by the producer to the queue.

  • ACK: An acknowledgement sent by BlazingMQ to the producer, informing whether the PUT message was successfully accepted or not. BlazingMQ tries as much as possible to accept the PUT message and send successful ACKs. However there are scenarios where BlazingMQ may not accept PUT messages sent by a producer: the queue getting full, a long standing network issue, etc. BlazingMQ guarantees is that producer will receive an ACK for every PUT message.

  • PUSH: A message sent by BlazingMQ to the consumer. This is effectively the same message as the corresponding PUT.

  • CONFIRM: A message sent by the consumer to BlazingMQ indicating that it has processed a specific PUSH message. Once a CONFIRM message is received, BlazingMQ is free to mark that message as deleted in the queue.

  • GUID: A globally unique identifier assigned to every PUT message by BlazingMQ framework. A GUID is used to identify a message throughout its lifetime, and as such, the ACK message sent to the producer by BlazingMQ contains the GUID that was assigned to the corresponding PUT message. Additionally, PUSH message sent by BlazingMQ to the consumer contains the GUID that was assigned to the corresponding PUT message, and lastly, CONFIRM message sent by consumer to BlazingMQ contains the same GUID. Each GUID is 16 bytes in length.

  • Upstream: Direction from a BlazingMQ client application to queue’s primary node. PUT and CONFIRM messages always travel upstream.

  • Downstream: Direction from a queue’s primary node to a BlazingMQ client application. ACK and PUSH messages always travel downstream.

BlazingMQ Data Life Cycle

BlazingMQ Data Path

The figure above shows the path for various data messages in BlazingMQ. PUT and CONFIRM messages travel from client applications to primary node (i.e., upstream) while ACK and PUSH messages travel from primary node to client applications (i.e., downstream).

Several things can go wrong in this data path:

  • TCP connection(s) can drop due to network issues, missing heartbeats, etc.

  • BlazingMQ nodes can crash.

  • BlazingMQ nodes can undergo graceful shutdown or restart.

In the absence of any buffering and retransmission logic in BlazingMQ nodes, any of the above conditions will lead to:

  • Lost PUT messages and thus producers receiving an ACK message indicating failure.

  • Lost ACK messages and thus producers not receiving any ACK messages for the PUT messages that they posted on the queue.

  • Duplicate PUT messages being posted on the queue if a producer application sends the same PUT message again upon receiving a failed ACK message.

  • Lost CONFIRM messages leading to duplicate PUSH messages to consumers.

Clearly, none of the above scenarios are acceptable. Let’s see in the following sections how BlazingMQ handles these scenarios.

High Availability in BlazingMQ

All problems listed in the previous section have been solved by introducing these three principles in BlazingMQ nodes:

  • Buffering and retransmission when node to the right is failing (see previous figure).

  • Maintaining a history of message identifiers (GUIDs) of the PUT messages posted on the queue.

  • Introducing a shutdown sequence for nodes stopping gracefully.

Let’s look into each of the above in detail.

Buffering and Retransmission

Looking at the previous figure, one can see that in case of a failing node or link, the node to the left of it is in the best position to buffer and retry any messages until the failing node heals or a fail-over event occurs (i.e., left node connects to another healthy node on the right).

As an example, lets assume that replica 1 in the figure is failing. One can see that in such case, PUT messages sent by proxy 1 to replica 1 run the risk of getting lost. If such scenario occurs, producer application will forever keep waiting for an ACK for its PUT message. This is an extremely unpleasant user experience, since BlazingMQ is not only failing to accept a PUT message from the producer, but also failing to notify producer about it.

One way to solve this would be for proxy 1 to keep a list of GUIDs of unacknowledged PUTs. If upstream node fails, explicitly send a failed ACK for every GUID in the unacknowledged list. This will ensure that producer application now gets notified of BlazingMQ’s failure to accept the PUT message. While this is an improvement, it is still not ideal as the producer application now has to resend the PUT message (potentially several times).

A better way would be for proxy 1 to retransmit unacknowledged PUT messages once replica 1 becomes healthy again, or when proxy 1 fails over to another replica, whichever occurs first. This approach will provide seamless user experience for producer applications, and increase the chances of a successful ACK instead of a failed ACK.

BlazingMQ adopts the following approach where every node along the path of a PUT message from producer to primary node keeps an in-memory retransmission buffer for PUT messages. A new PUT message is buffered unconditionally, is purged upon receiving an ACK message and retransmitted if a fail-over event occurs. This buffer is bound by both size and time, to ensure that a BlazingMQ node’s memory does not grow beyond the configured limit, and so that producer applications can expect an ACK (success or failure) within the configured time interval.

The presence of this retransmission buffer ensures that producer applications don’t see any transient issues in the BlazingMQ back-end. In fact, today the entire BlazingMQ cluster (all replicas and primary node) can disappear from the network for a few minutes, and producer applications won’t notice any failure from BlazingMQ APIs. Now this’s high availability!

On the PUSH message path, the replicated persistent storage acts as the retransmission buffer, and as such, there is no need for a separate in-memory retransmission buffer for PUSH messages. Consider a scenario in the figure where proxy 2 fails, leading to loss of in-flight PUSH messages. In this case, once a new path is established between replica 2 and the consumer, replica 2 can simply use the replicated persistent storage to retrieve messages that need to be sent to the consumer.

History of GUIDs

Readers may have noticed that if a node retransmits a PUT message after a fail-over scenario, there is a risk of duplicate messages i.e., same message could appear twice in the queue. In above figure, this can occur if replica 1 failed right after forwarding a PUT message to the primary node. Depending upon the sequence of events, a primary node may or may not receive the message. If it receives it, it will replicate and “commit” it in the queue. However, once proxy 1 fails over to another replica, it will retransmit this PUT message, leading to the same message being posted twice on the queue.

BlazingMQ solves this problem by having the primary node maintain a history of GUIDs of PUT messages previously posted on the queue. This history is bound by a configurable time interval. At a high level, history is represented as a custom hash table where GUID is the key. This logic ensures downstream nodes can retransmit PUT messages without creating duplicates, thereby providing a better user experience.

Shutdown Sequence of Stopping Nodes

Nodes in BlazingMQ can undergo graceful shutdown or restart, and we want BlazingMQ to provide a seamless user experience in this scenario as well. Specifically, we don’t want producer or consumer applications to notice any failure when a node along the PUT or PUSH data paths is restarted or shutting down. Additionally, we want to ensure that the node shutting down does not take any additional work and drains any pending work in a timely manner.

Shutdown sequence is implemented with the help of pairs of StopRequest/StopResponse. A node which is shutting down informs its peer nodes (both upstream and downstream) of this action by sending them StopRequest. Upon receiving a StopRequest, peer nodes stop sending any PUT and PUSH messages to the node. Peer nodes keep any new PUT messages in the retransmission buffer (described above) and PUSH messages stay in the replicated storage. This ensures that the node shutting down does not get any new work. ACK and CONFIRM messages are still sent to the node by peers. This is necessary to ensure that any pending PUT and PUSH messages no longer remain pending and remaining work at the node shutting down is drained.

Lets walk through as example. In above figure, if replica 1 is shutting down, it will send StopRequests to proxy 1 and primary nodes. Upon receiving the request, proxy 1 (the downstream node) will stop sending PUT messages and keep them in retransmission buffer. It will continue to forward CONFIRM messages, if any, to the replica. On the other side, the primary node will stop sending PUSH messages to the replica (primary may send them along another route, if applicable). The primary node will, however, continue to send ACK messages, which replica will forward to proxy 1. After a while, all PUSH messages which are pending (i.e., unconfirmed) along the route will be confirmed by consumer(s), and at that time, all pending work will be drained. At this time proxy 1 will send StopResponse to the replica and the link between them will be considered ‘frozen’. In other words, once StopResponse has been sent, no further communication will occur between the two nodes.

In the scenario shown in above figure, there is only one downstream (proxy 1) and one upstream node (primary) for replica 1. However, in practice, there can be tens or hundreds of downstream and more than one upstream nodes for the node shutting down. In such case, there will be a StopRequest/StopResponse pair for every such combination.

Peers are required to respond back with StopResponse within a configured time interval. The chosen time interval has interesting effect – a low value may not be enough to receive CONFIRM messages from consumers for all pending PUSH messages (some consumers take several seconds or even minutes to process and confirm messages, which is the whole point of a message queue!). In such scenario, those unconfirmed PUSH messages will be sent by primary node to another consumer, if any, on a different route, and as such, the same PUSH message could end up being processed twice by two different consumers in an application ecosystem. This is within contract, as BlazingMQ provides at least once delivery guarantees. On the other hand, choosing a higher time interval for StopResponse will lead to delayed fail over and delayed delivery of ACK and PUSH messages to producers and consumers respectively. In practice, the value of time interval varies for each BlazingMQ cluster, and is chosen keeping in mind the latency requirements and typical CONFIRM times of applications using that BlazingMQ cluster.

Extending High Availability to Client Libraries

Readers may have noticed that the high availability support described above ensures uninterrupted service to BlazingMQ users in cases when a replica or a primary node fails or restarts, but not when a proxy node fails or restarts. This is indeed a correct observation. If currently if BlazingMQ proxy node (i.e., the node immediately connected to the client) goes bad, producer applications will receive failed ACKs messages. Additionally, when such producers attempt to post the same message again after conditions have improved, the message may appear twice in the queue. This is because the first copy of the message, which was posted while the proxy node failed, may or may not have reached the primary node. Moreover, the second copy of the message will be assigned a new GUID by the proxy (recall that GUIDs are assigned by the BlazingMQ node immediately connected to the client).

A simple solution for this is to update BlazingMQ client libraries to have the same high availability logic that exists in BlazingMQ nodes. And this is exactly what has been done! BlazingMQ C++ client library has been updated to contain a retransmission buffer, generate GUIDs and support StopRequest/StopResponse work flow. More details about it can be found in this article.


Addition of high availability support in BlazingMQ has undoubtedly improved BlazingMQ user experience and has helped provide uninterrupted service to users in case of transient issues in BlazingMQ back-end, including network and hardware. Applications can rely on BlazingMQ to buffer and retry messages seamlessly. Additionally, BlazingMQ nodes can be restarted at any time without service interruption, which helps BlazingMQ maintainers during software upgrades, machine or network maintenance, etc.