Bringing High Availability to Node-RED

How we are tackling the hard problems of HA in FlowFuse

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Many companies look to deploy Node-RED into use cases that require the application to have a high degree of availability, reliability, and scalability. Following up our previous post on the subject, in this post I’m going to look at some of the technical details of achieving HA, the approaches available and what that means for the work we’re doing at FlowFuse and upstream in Node-RED.

Everyone we speak to has a different set of requirements for this topic. To help with the discussion, I’m going to look at two ways of approaching it:

  • The hot-spare approach where you have a second instance of the application ready to take over when the primary fails. This achieves availability but doesn’t contribute to scalability.
  • The load-balanced approach where you have a second active instance of the application and work is shared between them. If either fails, the other continues running. A side-effect of this approach is a higher potential through-put and scalability; although in practice you need to ensure capacity to tolerate an instance failing.

To consider which approach is most appropriate in the context of Node-RED, we need to look at the benefits and complications of each approach. It comes down to two factors; statefulness and how work is routed.


There are two types of state to consider when thinking about a Node-RED flow: explicit and implicit state.

Explicit state is what is programmed into the flow. For example, a flow may store state in Context or use an external database service. Within FlowFuse we provide two types of context - the default in-memory context store and a database-backed persistent store. Currently the database-backed store includes a memory-caching layer to provide better performance and interoperability. That gets tricky when you want to have multiple instances sharing the same store. The context API doesn’t provide a way to atomically update values - so you can get into classic concurrency issues around two applications trying to update the same value.

The other type of state is that which is implicitly maintained in a flow - even if the user hasn’t explicitly configured it. For example, the Smooth node can be used to calculate a running average value of messages passing through it. The node does that by keeping in memory the recent values so it can recalculate the average with each update. If you have multiple instances, then the node will be calculating the average for just the message its instances sees.

Another example of implicit state is the Batch node that can be used to group messages into batches. Again - it will only be able to do that for the selection of messages the instance receives.

It very much depends on the requirements of a flow and what nodes it uses, as to how the state can be handled.

In the hot-spare approach, as only one instance is active at any time, a lot of the explicit state handling will work as expected. However the implicit state remains bound to the individual Node-RED instances.

In the load-balanced approach, care has to be taken to ensure any state generated by the flow is done in a way that copes with multiple instances accessing it at the same time.

A key take-away from this being that a flow has to be created with HA and/or scaling in mind.

Routing work

Node-RED makes it easy to integrate with lots of different sources of events. A couple of the most common being HTTP and MQTT. When considering how to handle multiple instances of an application we need to think about how work is routed to those instances.

HTTP is the most well understood; you put a load-balancing proxy in front of the Node-RED instances and it takes care of sharing out the incoming requests. In the hot-spare scenario, the proxy needs to know which instance is active - that requires some coordination within the platform to track that properly.

MQTT is commonly used with Node-RED, but unlike HTTP which is in-bound, MQTT works by having Node-RED create an out-bound connection to a broker and then subscribing to the topics of interest. In the early days of MQTT that would mean each instance would subscribe to the same set of topics and receive every message. That doesn’t really fit any HA model.

With the publication of MQTTv5, the concept of Shared Subscriptions was added; the ability for a group of clients to connect, subscribe to the same topic and have the broker distribute messages between them. At this point you do get load balancing across your Node-RED instances - as long as the MQTT nodes are suitably configured.

There are lots of other nodes that can be used to trigger flows, whether by listening for events on an API, connecting to locally attached hardware and many things in between. Typically, those that are more cloud-aligned, such as messaging systems like Kafka and AMQP will have very well established ways of doing load balancing.

Managing out-bound connections gets more complicated in the hot-spare scenario.

If we only had to deal with in-bound connections, the hot-spare instance can just sit there waiting for work to be passed its way. But once you have out-bound connections, then you have a problem. The hot-spare instance should only create its out-bound connections when it becomes the active instance. In real terms, that means the Node-RED flows should only be started when the instance becomes active.

With our goal to minimize the Mean Time To Recovery (MTTR), we need to find a way to get that spare instance running as quickly as possible; if it takes just as long to start the spare instance as it does to restart the failed primary instance, then it isn’t much of an improvement.

The key here is that Node-RED allows you to start the runtime without the flows running. That gets everything loaded and the runtime ready ahead of time. It can then start the flows at a moment's notice with a simple call to the runtime admin API.

Detecting failure

A key requirement of the hot-spare approach to HA is knowing when to failover to the spare.

This requires close monitoring of the active instance to know whether it's still working. How quickly you can detect failure is key to reducing the time to recovery. This is where you have to think about the different ways an instance could fail - has it crashed, has it hung, has it got ‘stuck’?

Detecting failure usually involves some combination of heartbeat ‘pings’ between the instances to check each is able to respond to requests. The spare instance then needs to be able to decide for itself whether it should become the active instance - and do so safely. You do not want to accidentally have two instances active at the same time. This can get quite complicated to achieve safely, but there are a number of approaches that can be used. We’ll be exploring them as we continue our journey towards HA.

Editing Flows

Within the Node-RED architecture, each instance also serves up its own editor. This is what you get when you point your web browser at it.

In a HA world, once you have multiple instances running behind an HTTP load balancer, there is a tricky question of how you edit the flows. If each request hits a different instance, just loading the editor will result in different bits coming from different instances. That can typically be solved at the load balancer level by creating sticky-sessions; ensuring for a given client, each request is routed to a consistent instance. That solves part of the issue, but the next challenge is what to do when the Deploy button is pressed. That is how new flows are passed from the editor to the runtime. When you have multiple instances, we need to make sure that they all get updated. That is quite a tricky problem to solve with the current Node-RED APIs - and something we’ll be working on both in FlowFuse and in the upstream Node-RED project to resolve.

That said, a more immediate solution could well be to take advantage of separate development/production instances. You develop in a single instance and, when happy with what you’ve got, roll it out to your HA-ready production instance. This bypasses the need to edit the flows in the HA environment at all.

Whichever method is used, there is a question of how you minimize downtime whilst deploying an update. In a purely in-bound environment, solutions can be built where the new application is deployed alongside the old version and, when everything is ready, the in-bound events are redirected to the new version. But that isn’t feasible when you have out-bound connections to deal with as well. For some users, having a scheduled maintenance window for doing updates will be completely acceptable.

As with the hot-spare approach to failover, a similar method could be used that starts new instances of Node-RED alongside the old, but with the flows all stopped. Then, once everything is ready, the old instances are stopped and the new instances started - minimizing the downtime, although not completely removing it.

Continuing the HA journey at FlowFuse

So the question is how are we going to apply all of this to what we’re building at FlowFuse. We cannot do everything at once, so we have to prioritize which scenarios we’re going to address first. Consequently, drawing from customer feedback, we have chosen to start with the scaling side of high availability - allowing multiple copies of an instance to be run with appropriate load balancing put in front of it.

We are building FlowFuse as an open platform with the ability to run on top of Docker Compose and Kubernetes. As we get into some of these HA features, we will need to look carefully at where we can lean on these underlying technologies - we don’t want to reinvent the wheel here.

Our initial focus is going to be when running in a Kubernetes environment - just as we do with our hosted FlowFuse Cloud platform. Kubernetes provides lots of the building blocks for creating a scalable and highly available solution, but it certainly doesn’t do all of the work for you.

We've identified our initial set of tasks and changes to how we'll run Node-RED instance with the k8s environment. You can follow our progress with this issue on our backlog.

I hope this post has given some useful insight into the problems we’re looking to solve at FlowFuse. As it's such an important requirement for many users we’ll keep you updated as we make progress.

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