Node-RED: Lecture 3 – Basic nodes and flows

In this lecture you will get to see a few of the more commonly used nodes and build on some of the things you learnt in previous lectures. You’ll start off with a series of examples based around the popular MQTT protocol that shows how to wire together a set of basic but very useful message processing nodes. Then you’ll briefly look at other ways to get messages into and out of your flows using protocols such as TCP, UDP and websockets.

In this lecture you will get to see a few of the more commonly used nodes and build on some of the things you learnt in previous lectures. You’ll start off with a series of examples based around the popular MQTT protocol that shows how to wire together a set of basic but very useful message processing nodes. Then you’ll briefly look at other ways to get messages into and out of your flows using protocols such as TCP, UDP and websockets.

At the end of this lecture you will have a better understanding of some of the basic nodes used in Node-Red flows. You will also have seen how easy it is to build sophisticated processing flows that take real-world events, process them and generate responses that use popular Internet protocols to communicate the results outside of your flow.


We use a cloud hosted version of Node-RED for these lectures called FRED. Sign up for a free account at FRED. Examples in the early lectures will work with other installations of Node-RED, later lectures use nodes you will need to install yourself if you don’t use FRED.



Example 3.1 Receiving JSON via an MQTT message

Example 3.2 Using the switch node to handle a JSON object

Example 3.3 Using a change node to change or manipulate a message payload

Example 3.4 Using the rbe (report by exception) node

Example 3.5 Scaling input with the range node

Example 3.6 Using an mqtt output node to test the flow

Example 3.7 Using Websockets with Node-RED

Example 3.8 Sending TCP requests.

Example 3.1 Receiving JSON via an MQTT message

The following series of examples builds on the mqtt node, which provides a convenient way to take input from an MQTT broker. For those not familiar with MQTT, it is an example of a publish/subscribe system (usually shortened to pub/sub system) which lets sensors publish updates that all delivered to client subscribed to that sensor. MQTT uses a topic model allowing publishers (eg sensors) to create topics and publish data to the topics Equally, others can subscribe to a topic and will receive asynchronous notification of data posted to the topic.

Pub/Sub systems are a great way to connect loosely coupled distributed systems and they map well to typical IoT patterns where devices or things generate events that you want to share. The MQTT protocol, apart from being asynchronous, is also lightweight and doesn’t have as high an overhead as HTTP; which for resource-constrained devices is often an important advantage. MQTT was originally developed in the late 1990s and has been used in a variety of IoT settings. MQTT became an OASIS standard in 2014 and is a standard part of many IoT toolboxes. MQTT actually stands for Message Queueing Telemetry Transport.

To use the mqtt node, you need to have access to a broker. There are a number of free MQTT servers running, for example, or the one that will be used in this lecture, Using the broker address and the topic, you can configure the mqtt input node to subscribe on that topic, causing it to generate a new message whenever new data is published on that topic. The message will contain information on the published data, including the data itself in msg.payload and the MQTT broker topic in msg.topic.

To get you started with the mqtt node, you’ll be using the free mqqt broker hivemq – which is available via ( Of course you can use any MQTT broker, including your own, if you have installed one.

First, drag and drop an mqtt input node and configure it for the broker. Don’t forget to configure the topic to something unique, in the case of this example we are using noderedlecture/sensor but you should use your own unique topic, i.e. <your name here>/sensor

Figure 3.1 Configuring the mqtt node with broker address and topic

There are many ways you can send mqtt messages to hivemq. You can use their websockets client showcase (, the mqtt dashboard ( or your own library. You’ll be using their websocket client in this example, so navigate to that page and connect to the broker. You will publish a JSON encoded string to the topic you configured to see both the use of the mqtt node and the json node.

Figure 3.2 Sending an MQTT message (publishing) using the HiveMQ client page.

Since you are sending a JSON string, you will need to parse the message that the mqtt node generates when it receives the MQTT message. To do that, you’ll need to drag and drop a json node and connect it to the mqtt node’s output.

Node-RED’s json node is a sort of convenience function, in that it parses the incoming message and tries to convert it to/from JSON. So if you send it a JSON string, it will convert it to a JavaScript object, and vice versa.

If you wire up the usual debug node to the json node and deploy, then use the HiveMQ dashboard to send the JSON string {“analyze”:false, “value”:10} as shown in Fig 3.2. You will see it printing in the debug tab (Fig 3.3).


Figure 3.3 Receiving and parsing an MQTT message sent as a JSON string

If you look closely at the output, you can see that the msg.payload contains an object, which itself has two fields, analyze and value, each with their own values. As you saw in lecture 2, you can access these fields via msg.payload.analyze and msg.payload.value. Let’s take a look at a node that can do that.


You can find the node-red description of this flow at:

Example 3.2 Using the switch node to handle a JSON object

One of the nice features of having a JSON object is that you can easily act on its properties. A useful node for this is the switch node. Its role is to ‘switch’ or route messages depending on the incoming message properties.  For example, you can check the msg.payload.analyze property and, depending on its value (true/false), decide to route a message to one of the switch node’s outputs.

Drag a switch node and double-click on it. Configure it to evaluate the property “msg.payload.analyze”. If true, send the message to the first output; if false, send it to the second output as shown in in Fig 3.4.

Figure 3.4 Configuring the switch node to route based on a message property

Now you can connect two debug nodes as shown in Fig 3.5 – when you set up multiple outputs for a node, they are numbered from the top, so output 1 is the top output and output 2 is at the bottom in  Fig. 3.5.

Figure 3.5 Connecting a switch node to two debug nodes

If you now go back to the HiveMQ input page and send the MQTT message {“analyze”:true, “value”:6}, you will see that the first (top) output is activated and the incoming messages is routed, or ‘switched’, to output 1. If you send the original message {“analyze”:false, “value”:10}, the switch node will activate output 2 and the original debug node will fire. Hovering the pointer over the debug message will show which debug node is printing out the message as shown in Fig. 3.6.


Figure 3.6 Confirming the operation of the switch node

As you can see, this provides you with a built-in Node-RED node that allows you to quickly determine the contents of incoming messages and direct the message through to different parts of the flow depending on the input.


You can find the node-red description of this flow at:

Example 3.3 Using a change node to change or manipulate a message payload

Another useful node is the change node, which will allow you to change a message payload or add new properties. You can use this node to affect the properties in a message, either by changing existing ones, deleting them or adding new properties.

In this example, you’ll continue with your MQTT theme and see how, now that you have successfully ‘switched’ the message flow based on the incoming MQTT message, you can add a new message property msg.payload.note.

First, let’s drag and drop a change node and connect it to the second output of the switch node (Fig 3.7). As you will remember, this is the output that fires when msg.payload.analyze is set to false.

Figure 3.7 Adding in a change node to set a new message property.


Now configure it to set the property msg.payload.note to “this is not being analyzed” as shown in Fig 3.8.


Figure 3.8 Using a change node to add a new message property


When you receive a message that the switch node sends on the 2nd output, it will be modified to contain a “note” element with the string “this is not being analyzed”. If you deploy and test the flow by sending the MQTT message from HiveMQ, you’ll see the output as shown in Fig 3.9.


Figure 3.9 The result of switching and then changing the message


You can find the node-red description of this flow at:

Example 3.4 Using the rbe (report by exception) node

In this example, you’ll continue your message analysis theme and add nodes to the part of the flow that is used when you determine that the flow should be analyzed. You’ll be using the rbe (report by exception) node which only passes on data if it has changed. You can set it to examine a message payload and either block until a message changes (rbe mode) or when a messages changes by a specified amount (deadband mode). In rbe mode, it works on numbers and strings. In deadband mode, it works on numbers only and uses the configured deadband as a + or – ‘band’, so that the incoming value can fluctuate within a range before it fires.

You’ll start by adding in another change node which you’ll connect to output 1 of the switch node. You’ll then connect an rbe node to the switch node as shown in Fig 3.10.

Let’s connect a change node, and an rbe node like this. To remind us that this output deals with the flag “analyze”, add a comment node and write “Analyze = true”. Comments are useful when writing complex flows.

Figure 3.10 Adding an rbe node to check if our input data has changed by more than 20%

Edit the change node to set the msg.payload to msg.payload.value. This will set the output of this node to the value found in the msg.payload.value element of the input received (Fig 3.11).

Figure 3.11 Using a change node to set payload

Since you want to determine if this value has changed by 20% or more, you’ll need to double-click on the rbe node and configure it to block unless the value changes by more than 20%.

Fig 3.12 Setting the rbe node to check msg.payload.value

To test the flow, deploy this flow and then return to the HiveMQ page and send a series of messages. Firstly, you’ll need to set the analyze value to true so that the switch node sends through a message on output 1. If you use the original message value of 6, this will fail to pass the rbe node. If you then send a second message of value 10, the rbe node will evaluate the difference between 6 and 10, see it is greater than 20%, and send on a message to the final debug node which will print on the debug pane as shown in Fig 3.13.

Fig 3.13 Confirming that 10 is more than 20% larger than 6 using an rbe node.


You can find the node-red description of this flow at:

Example 3.5 Scaling input with the range node

When dealing with real world input from sensors and other devices, an ability to scale input data is often required. Node-RED provides the scale node to support this and allows you to scale (linearly) an input value. Let’s assume you want to scale your value (originally in the range 0-10) to a range (0-255) when you aren’t doing any analysis. This means we are dealing with the lower part of the flow fired when the switch node evaluates the Analyze property as false.

To do this, select the change node you configured above (set msg.payload) and copy it with ctrl+c, then ctrl+v. Attach a range node as shown in Fig 3.14.


Figure 3.14 Scaling input values using the range node.

Double-click on it, and configure it to map the input from 0-10 to 0-255 as shown in Fig 3.15.

The scale node has three options set by the action field. The default will scale according to the mapping given but will happily scale values outside the given ranges, using the same mapping. Scale and limit to target range means that the result will never be outside the range specified within the result range. A third option, Scale and wrap within the target range means that the result will essentially be a “modulo-style” wrap-around within the result range.

Then return to the HiveMQ test page and post {“analyze”:false, “value”:10} as a new MQTT message to the same topic.

Figure 3.15 Setting the scaling ranges for input and output of the scale node

If you return to your Node-RED window, you will see that the debug node associated with the lower part of the flow has fired, showing that the msg.payload.value property that you set as 10 when you published it to MQTT, has been scaled up 255 as shown in Fig 3.16.

Figure 3.16 Final scaled output when analysis is turned off


You can find the node-red description of this flow at:

Example 3.6 Using an mqtt output node to test the flow

As an alternative to using the HiveMQ test page to publish on the MQTT topic, we can configure an mqtt output node. This is the mirror of the mqtt input node and allows you to configure an MQTT service and the topic you are publishing on. You can then send the node messages with the exact same JSON string we’ve been sending via the HiveMQ test page.

To try that, drag and drop three inject nodes and an mqtt output node as shown in Fig 3.17, and configure and wire up as shown

Figure 3.17 using an mqtt node to publish on the noderedlecture/sensor topic

You can now test the flow you created to analyze the MQTT message directly from the workspace by clicking on the three inject nodes in sequence. The first will exercise the part of the flow handling the case when analysis is turned off; the 2nd two messages will cause the path with the rbe node to be exercised.


You can find the node-red description of this flow at:

Example 3.7 Using Websockets with Node-RED

Websockets are another useful communication capability that is built into Node-RED via the the websocket node. Websockets provide a duplex TCP connection and were designed to allow web browsers and servers to maintain a ‘backchannel’ that could be used to augment traditional HTTP interactions, allowing servers to update web pages without the client making a new pull request.

The websocket node comes in two flavours, input and output, allowing you to listen for incoming data (input) or to send (output) on a websocket. The output version is designed to check to see if the output payload originated at a websocket in a node, in which case it responds to the original sender. Otherwise it will broadcast the payload to all connected websockets.

In addition, both input and output websocket nodes can be configured as either server or client – in server mode they ‘listen on’ a URL, and in client mode they connect to a specified IP address.

To see how the websocket nodes work, you’ll use a public websockets echo server which runs on the public site: (

Drag an inject, websocket in, websocket out and a debug node onto the workspace and connect them as shown in figure 3.18.

Figure 3.18 using websockets to communicate with a Node-RED flow

Configure the inject node to send a string payload of “Hello There” (Fig 3.19)

Figure 3.19 Configuring an inject node to send on a websocket

Configure the websocket nodes to connect to wss:// as shown in Fig 3.20.

Figure 3.20 Configuring the websocket to send to a public echo server. Do the same for the websocket out node.

Deploy. When you click on the inject node you will see the message printed out as shown in Fig 3.21


Figure 3.21 Output from a websocket listening for incoming data


You can find the node-red description of this flow at:

Example 3.8 Sending TCP requests.

This example shows you how to send TCP requests using the tcp node. In this case you will make an HTTP request following the specifications in (

This example shows the use of the tcp node. It could equally be configured with the udp or http nodes in a similar manner.

To get started, let’s connect an inject, function, tcp request, and debug nodes as shown in Fig 3.22.

Figure 3.22 building a TCP request and sending on a tcp output node

Edit the first function node to add a function that sets the string “GET / HTTP/1.1\r\n\r\nHost:” as payload as shown in Fig 3.23.

This string is a standard HTTP request, indicating it is a GET request, the protocol is HTTP 1.1 and the host is The \r\n\r\n is two return/newline pairs which is required in the HTTP protocol.

Figure 3.23 Building a TCP request in a function node

Configure the tcp request node to connect to the server, on port 80. Configure it to close the connection after 1 second (1000 ms) as shown in Fig 3.24.

Figure 3.24 Configuring the end-point for a TCP request

The tcp request node response is a buffer and needs to be parsed. Configure the second function node to parse the tcp request node response as shown in Fig 3.25

Figure 3.25. A function node to parse a response buffer into a string

If you deploy the flow and click on inject, you will make a request to Google and will get a TCP response. The debug node will print the response as a string as shown in Fig 3.26.

Figure 3.26 Printing out the response to a well formed HTTP request sent over a TCP connection.

Some of you may be wondering why you need to use a function node to build the HTTP request that we sent over TCP. Why not just use the inject node to input the string? The reason is that the inject node ‘escapes’ the string it uses, causing the return/newline you inserted to be removed. This in turn confuses the receiving server (Google) into not returning a response as it waits for the missing return/newlines. So instead, you build the string in a function node. This is one of those ‘gotchas’ that trips up even experienced Node-RED programmers, so always read the info pane for nodes to make sure you understand any limitations or constraints.


You can find the node-red description of this flow at:


In this lecture you have seen a series of small examples that use a number of the processing and communication nodes that are available in Node-RED. As you have seen, it is both easy and fast to wire together basic flows that take real-world input, carry out some processing, such as simple data analysis, and return results.

In these examples, you have done little or no coding, but still have been able to build quite complex programs – this is the power of Node-RED.

The next lecture is a quick summary of the basic set of nodes that are available in both vanilla Node-RED as well as the extended set that the FRED service provides. You can read through the lecture to understand the default capabilities, or you can treat the lecture as a reference and use it to look up examples of each node as used in this lecture series.



About Sense Tecnic: Sense Tecnic Systems Inc have been building IoT applications and services since 2010. We provide these lectures, and FRED, cloud hosted Node-RED as a service to the community. We also offer a commercial version to our customers, as well as professional services. Learn more.

© Lea, Blackstock, Calderon

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.



Node-RED: Lecture 2 – Building your first flows

This lecture will introduce you to the Node-RED visual tool and get you started building your first flow. You’ll learn how to create simple flows, use debug nodes to track messages through the flows and how to use the function node to write simple JavaScript code that tailors the node to your specific needs.

This lecture will introduce you to the Node-RED visual tool and get you started building your first flow. You’ll learn how to create simple flows, use debug nodes to track messages through the flows and how to use the function node to write simple JavaScript code that tailors the node to your specific needs.

To get you started, you’ll be using a free cloud service that provides you with a preconfigured Node-RED setup and a wide range of built-in nodes. Later lectures will explain how to set-up Node-RED for yourself on devices such as a Raspberry PI.

At the end of this lecture you’ll be able to create your own simple flows, you’ll know your way around the Node-RED UI and you’ll be familiar with some of the basic built-in nodes that Node-RED offers.

FRED – Front end for Node-RED

FRED is a cloud service that we’ve created that allows you to sign up and use Node-RED without the hassle of downloading and installing on a device such as a PI. It makes it easy to get started with Node-RED by managing an instance of Node-RED for you, keeping the instance up to date with the latest code releases and applying bug fixes or patches. Also included is a set of extra Nodes that are contributed by the community, which add to the standard basic set you get when you install Node-RED yourself. However, FRED is just a wrapper for Node-RED, so anything you write in FRED will work on a Node-RED instance running on a Pi or a Beagleboard.

To begin our examples, make sure you have created your own Node-RED instance in the cloud by signing up for a free account at

After registering, make sure to activate your account via your email. You will not be able to log in until you validate your account.

The Node-RED UI

Once you have logged in to your FRED account, you’ll see the standard Node-RED UI which consists of three main panes, as shown in Fig 2.1

Screen Shot 2015-10-22 at 4.53.21 PM.png

Figure 2.1 The Node-RED UI  – showing the node palette (left), flow canvas (centre) and output pane (right)

The main pane is the flow creation workspace in the middle. This is where you drag and drop nodes and connect them with wires. Along the top of the workspace pane is a set of tabs. Each tab opens a previously created workspace and shows any flows created using that workspace.

On the left is the node pane that contains all the built-in nodes that your instance of Node-RED supports. In the example above, you are seeing the FRED node set which contains a large selection that has been added to the basic set that comes with Node-RED. In later lectures you will learn about these nodes and use them to develop new flows. As you can see, nodes are grouped into categories. Opening up a category shows the individual nodes.

On the right-hand side is the output pane that can be toggled between the info and debug tabs. When info is selected, documentation for the selected node is shown there.  When debug is selected, it will display the output of debug nodes, errors and warnings.

Above these three main panes is the usual toolbar, and on the right-hand side are three widgets, a deploy button, a user info icon and a pulldown menu for admin and control. You’ll look in more detail at the pulldown later in these lectures. The user info icon allows you to return to the FRED front page with links to tutorials, your account information, status and other information as well as to log out of the FRED service.

The Deploy button is used when a flow has been constructed and causes the flow to be deployed onto the Node-RED system and executed. You’ll be introduced to the details of what’s actually happening under the covers in lecture 5 and beyond. For now just treat the Deploy button as the way to get your flow running.

A quick tour of Node-RED nodes and messages

As you saw in lecture 1, Node-RED allows you to wire together nodes to create flows which carry out your programming task. Messages pass between nodes, moving from input nodes through processing nodes to output nodes. Let’s take a brief look at nodes, flows and messages.

There are three main  types of nodes:

  1. Input Nodes (e.g. inject)
  2. Output Nodes (e.g. debug)
  3. Processing Nodes (e.g. function)


Figure 2.2 The main types of nodes: input, output and processing

Input nodes allow you to input data into a Node-RED application or “flow”. They have at least one output endpoint represented by the small grey square only on their right side. You use input nodes to connect data from other services, for example the  Twitter, Google, serial, websockets or tcp nodes, or to manually input data into a flow using the inject node.

Output nodes allow you to send data outside of a Node-RED flow. They have a single input endpoint on their left side. You use output nodes to send data to other services, for example via Twitter, tcp, serial or email nodes, or to use the debug node to output to the debug pane.

Processing nodes allow you to process data. They have an input endpoint and one or more output endpoints. They allow you to transform the data type (e.g. json, csv, xml) nodes, use the data to trigger a message (e.g. trigger, delay) nodes and to write custom code that uses the data received (e.g. function node).

Note that some nodes, like the inject and debug messages, have a button that allows you to actuate a node (in the case of the inject node) or to enable and disable a node (in the case of the debug node).

Flows consist of multiple nodes wired together, with output tabs linked to input tabs of the next node in the flow. Messages flow along the nodes carrying data from node to node.

Node-RED nodes consume input messages and produce output messages. Messages are JavaScript objects that contain at least a “payload” parameter, like this:

Listing 2.1 A basic Node-RED message structure

  1. msg = {
  2.   payload:”message payload”
  3. };

Nodes consume and produce messages, usually using msg.payload as the main placeholder for the data they consume and produce. However, messages can be extended to contain other parameters. For example, to set the topic of a message and add a new parameter, location, you could create a new msg object as shown in listing 2.2.

Listing 2.2 A more complex Node-RED message structure

  1. msg = {
  2.   payload:”message payload”,
  3.   topic:”error”,
  4.   location:”somewhere in space and time”
  5. };

Let’s use this knowledge to create your first flow.

Example 2.1 Building your first flow: Hello World

Let’s go ahead and start building your first flow so that you can see how simple it is to use the Node-RED UI to build and deploy a flow.

Let’s start with the easiest flow possible, a node to inject some information into the flow, wired to a debug node to see the output from the flow as a debug message. Once you have that running, you’ll build it up to the full Hello World flow.

Since this is the first time you’ll be shown how to actually build a flow, let’s start slowly and explain each step with a screen-shot. Once you’ve created your first flow and seen how easy it is, you’ll mostly be shown a single picture of the final flow rather than all the screenshots.

Let’s start with the simplest Node, the comment Node. You’ll find this in the function section of the node palette. Drag and drop a comment node onto the flow workspace as shown in Fig 2.3.


Screen Shot 2015-07-31 at 2.03.20 PM.png


Figure 2.3: Using a comment node is a great way to add visible comments into flows


Once you’ve done that, take a look at the info pane on the right (remember to switch to info if the debug tab is selected). You’ll see a little info on the node, including the node name, a unique ID, and a properties field with a description of the node. For the comment node there’s not much to say; however, more sophisticated nodes have a lot more info.

Double-click on the comment node, and you’ll see a configuration window (Fig 2.4) You can give the comment node a name and add detailed text if you like.


Screen Shot 2015-07-31 at 12.16.33 PM.png

Figure 2.4 Give the comment a name and add any info you want in the text box


Ok, now let’s add the first node that actually does something, an inject node. The inject node is used to generate input into a flow and is one of the first nodes in the node palette under input. If you drag and drop an inject node onto the flow workspace, and then look at the info tab, you’ll see the documentation for the inject node. Note that the name of the node on the workspace changes from inject to timestamp, because the default behaviour for the node is to inject a timestamp – the current time in milliseconds since January 1, 1970.


Screen Shot 2015-07-31 at 12.03.59 PM.png

Fig 2.5 The inject node allows you to insert events as messages, defaulting to a timestamp


You’ll also notice that the inject node (now named timestamp) has a blue dot top right and a grey square centre right. The blue dot indicates that the node hasn’t been deployed since it was last changed; the grey square is the output point for the node. This is where you attach ‘wires’ that route output message from the inject node to the next node in the flow.

To get a sense for the inject node and the whole flow deployment process, let’s add a debug node to see what happens, wire them together and then deploy the flow and test it.

Start by dragging a debug node from the node palette to the workspace. Again you can look at the info for the node.

Then you’ll wire the two nodes together. To do that, click on the grey output point for the inject node and, holding the mouse button down, drag towards the debug node. An orange wire appears, which you can then attach to the grey input point on the debug node.

Screen Shot 2015-07-31 at 12.40.55 PM.png

Figure 2.6 Wiring an inject node to a debug node


This is the simplest flow possible and will send the current timestamp to the debug node for display in the debug pane. Let’s try it!

Click the deploy button in the Node-RED window (top right). You’ll see a pop-up saying the flow has been successfully deployed. You will also notice that the blue dots on the nodes disappear, indicating there are no undeployed changes.

Now, before you try the flow, make sure the the debug tab is selected on the right pane. Then click on the left tab on the inject node and look at what appears in the debug pane.


Screen Shot 2015-07-31 at 12.48.44 PM.png

Figure 2.7 Debug output from your first flow – the time when you clicked the inject node.


As you can see, the inject node, when clicked, generates a timestamp (number of milliseconds since January 1, 1970), which is converted to a message and sent along the output wire, which is delivered to the debug node as an input message. The debug node’s default behaviour is to show any message it receives, which it does in the debug pane on the right.

Congratulations, you created and deployed your first flow!

Let’s now augment it a little to see what else we can do with this simple flow. Firstly, we’ll edit the inject node to deliver a text message rather than a timestamp. To do that, select the inject node in the flow and double-click it. You’ll see a configuration window like the one in Fig 2.8.


Screen Shot 2015-07-31 at 12.54.29 PM.png

Figure 2.8 Editing an inject node to send text instead of a timestamp


In the payload field, select string instead of timestamp and then type any string you like into the blank field below the payload field. As is traditional, let’s start with “Hello World – from my first NR flow!”

Once you’ve made the change, click ok to save the changes and take a look at the flow. You’ll see the blue dot has appeared on the inject node (which has also returned to being called inject rather than timestamp) to indicate that you have undeployed changes. Click the deploy button again to resolve that and then go ahead and click the tab on the inject node. If you look at the debug output you’ll see that instead of a timestamp, your text has been delivered as a message to the debug node, which displays it as usual.

Screen Shot 2015-07-31 at 1.01.40 PM.png

Figure 2.9 Sending a text message rather than a timestamp


As you can see, it’s very easy to wire up some simple nodes and get data to pass through your flow as messages. At this stage, you’ll probably be wondering a little about the messages that are flowing between nodes. The debug node can be used to examine them in more detail.

All messages in Node-RED have three default properties: the payload, which we are seeing above, a message topic, which is a user-defined string describing what the message is about (its topic, if you will) and an internal identifier. You can actually see this information if you edit the debug node configuration. Let’s do that and look at the output (Fig 2.10)


Screen Shot 2015-08-06 at 11.15.08 AM.png


Figure 2.10: setting the debug node to show the internals of a message


Select the debug node, double click and change the node output field to “Complete msg object”. If you then save this, deploy again and click the inject node, you will see in the debug pane a JSON structure that contains 3 fields: a “topic” which is currently blank, a “payload” which contains the string you set in the inject node and an internal ID field “_msgid”. Generally the internal message ID field isn’t used. However, both the topic and payload are used extensively when developing flows. You’ll see more of that later in the lecture series.

These fields are extensible, so you can define new message properties, for example, msg.location, which could be used to add the latitude and longitude values for the source of the message. Let’s take a look at that in the next example flow.

You can find the node-red description of this flow at:

Example 2.2 A second flow: weather alerts

In this example, which is similar to the one introduced in lecture 1, you’ll monitor the weather in your hometown and send yourself a tweet when the weather looks like it’s going to be good. You’ll use a weather node – openweathermap – that retrieves the weather from for the location you set it for. A simple function node will be used to check for ‘clear weather’ and a Twitter node will be used to send yourself a tweet when the weather looks good.

If you are using FRED, the openweathermap node must be installed using the Add or Remove Nodes navigation tab on FRED control panel left side of the Node-RED palette.  Click on this, and then look for the “openweathermap” node.  If you are running Node-RED on a Pi or desktop computer, nodes are managed through the Node-RED palette manager available in the Node-RED menu in the top right. Select Manage palette, then Install. From there you can search for “openweathermap” and click the Install button.

First you will need to get an API key at OpenWeatherMap. OpenWeatherMap offers a great service that provides detailed weather information for worldwide locations. Visit and follow the instructions as shown in the screenshots below.



You will need to sign up for an OpenWeatherAccount as shown below:

After signing up you will be redirected to your Home page where you will be able to access or re-generate your API Key. It should look something like this:

Now, let’s use that API key to build a weather alerts flow. Drag and drop a weather node from the left pane onto the workspace, as shown in Figure 2.11.


Figure 2.11. Drag and drop a weather node onto a blank workspace


If you select the info pane on the right, you can see a description of the openweathermap node with detailed information on how to configure and use it. Some interesting things to note:

  • It has a full JSON structure as its msg.payload, with quite a lot of weather detail, all presented as name:value pairs, e.g. wind speed and tempc
  • The node defines 3 new message properties, msg.location, msg.time and As mentioned above, you are free to add properties to messages, and the openweathermap node has added these new properties to carry extra information associated with the message.

Let’s configure the node and look at the actual data structure it generates after it has queried your local weather. Start by double-clicking on the node and fill out the form with your location. Type your city and country in the fields. Add the API key you obtained from and click on “Ok” as shown in Fig. 2.12.



Figure 2.12. Set your city and country in the configuration form, use the API key you obtained in the step above.


Then drag and drop a debug node and wire it to the openweathermap node. Click on “Deploy” to see that the payload object from the openweathermap node in the debug pane.


Listing 2.3 The message payload for the openweathermap node is a JSON structure describing weather conditions, temperature, wind, cloud cover and sunrise times.

  1. {
  2.   “weather”: “Clear”,
  3.   “detail”: “sky is clear”,
  4.   “tempk”: 295.104,
  5.   “tempc”: 21.903999999999996,
  6.   “humidity”: 53,
  7.   “maxtemp”: 295.104,
  8.   “mintemp”: 295.104,
  9.   “windspeed”: 2.22,
  10.   “winddirection”: 273.007,
  11.   “location”: “Vancouver”,
  12.   “sunrise”: 1432038196,
  13.   “sunset”: 1432094081,
  14.   “clouds”: 8,
  15.   “description”: “Vancouver weather(49.25,-123.12) is Clear (sky is clear).”
  16. }

As you can see, the node delivers quite a lot of information about your local weather, all as the usual name:value pairs. For this example, you want to use the “weather” field which you’re going to test to see if it’s reported as “Clear”. If it’s “Clear” you’ll then send a tweet.

To program that logic, you’ll use a function node. You saw one of these in lecture 1, but without the details. Let’s see how they are used by dragging one onto the flow workspace and double clicking it to open up the node editor.

Figure 2.13. Add a function node and wire it to the openweathermap node.

Now double-click on the function node and type/copy this (Note if you use copy/paste – make sure you paste as plain text, wordpress sometimes inserts odd characters)


Listing 2.4 Function node “If Clear Weather”

  1. if ( === “Clear”) {
  2.     msg.payload = “Clear skies ahead today!”
  3.     return msg;
  4. }
  5. return null;

Figure 2.14. Edit the function node adding the JavaScript shown in listing 2.4

Looking at Listing 2.4, you can see that this will parse the incoming message payload for the weather parameter and compare it to the string “Clear” (line 1). If it is equal, it will rewrite the message payload with your own string “Clear skies ahead today!” (line 2). Otherwise it will return a null message (line 5). This last bit is important because in Node-RED nodes ignore null messages.

You can do all sorts of things now, for example wire this message to an email node, or a pushbullet node. For this tutorial we will use the Twitter output node. Drag  a Twitter node onto the workspace, double-click and and fill out your Twitter account credentials as shown in Fig 2.15.

Figure 2.15. Set your Twitter account credentials into the node configuration

Once you’ve set up these credentials, Node-RED stores them and you can reuse them when you create another Twitter node in future flows.

Once you’ve wired up the flow, you can hit the deploy button and then watch your Twitter account to see the new tweet every time the weather is reported as clear.

Click on “Deploy”:

Figure 2.16. The full weather flow sending tweets if the weather report mentions clear skies.

Other useful UI features

One of the most useful features of the Node-RED UI is the ability to copy and paste nodes and even full flows using the standard cut’n’paste clipboard. Node-RED flows are simply coded as JSON strings and can be exported from a workspace, and imported into a workspace using the Node-RED pulldown menu in the top right of the Node-RED window.

Rather than building the flows in this example, or in fact anywhere in this lecture series, by hand yourself, you can simply copy them from our website and paste them into a workspace. After that all you need to do is configure them correctly, e.g. with credentials, and deploy them.

Let’s quickly show you how to do that.

Click on the link for this example which is at the end of the example below and look for the JSON code. Copy it to the clipboard using the usual CTL C or copy item in the edit menu.

Using the pulldown menu in the top right of the Node-RED window, select Import->Clipboard as shown in Fig 2.17

Screen Shot 2015-08-15 at 12.55.58 PM.png

Figure 2.17 Selecting the Import from Clipboard menu item in Node-RED


You will see a popup with an input field (Fig 2.18). Paste your clipboard into the input window and click OK.


Screen Shot 2015-08-15 at 12.56.27 PM.png

Figure 2.18 Pasting a flow into the import window of Node-RED


The new flow will appear in the current workspace and can be moved to a location and dropped. Once it’s in place, you can configure it as you would a flow you created yourself and deploy it.


You can find the node-red description of this flow at:


In this lecture you have created your first flows and seen how to wire together a set of basic nodes to achieve quite complex tasks. We’ve skipped over a few of the underlying details in order to get you going quickly, and we’ll return to those in lectures 3 and 4. However, so far you’ve seen how to use the Node-RED visual flow builder, the basic classes of input, output and processing nodes. You were given a brief overview of messages and how to use the function node to write your own JavaScript code to do simple processing on messages within a flow. In the next lecture, you’ll take a more in-depth look at the programming model of Node-RED and get a better understanding of the main programming elements and nodes and how to craft more complex flows using a variety of nodes.


About Sense Tecnic: Sense Tecnic Systems Inc have been building IoT applications and services since 2010. We provide these lectures, and FRED, cloud hosted Node-RED as a service to the community. We also offer a commercial version to our customers, as well as professional services. Learn more.

© Lea, Blackstock, Calderon

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Node-RED: Lecture 1 – A brief introduction to Node-RED

This lecture will introduce you to some examples of Node-RED, both to give you a sense of its capabilities and to introduce its basic concepts. We’ll also discuss the history of Node-RED and its origins to give you an idea of what Node-RED is good for and of what types of tasks are best left to a more traditional programming language and environments.

Node-RED is a powerful tool for building Internet of Things (IoT) applications with a focus on simplifying the ‘wiring together’ of code blocks to carry out tasks. It uses a visual programming approach that allows developers to connect predefined code blocks, known as ‘nodes’, together to perform a task. The connected nodes, usually a combination of input nodes, processing nodes and output nodes, when wired together, make up a ‘flows’.

Originally developed as an open source project at IBM in late 2013, to meet their need to quickly connect hardware and devices to web services and other software – as a sort of glue for the IoT – it has quickly evolved to be a general purpose IoT programming tool. Importantly, Node-RED has rapidly developed a significant and growing user base and an active developer community who are contributing new nodes that allow programmers to reuse Node-RED code for a wide variety of tasks.

Although Node-RED was originally designed to work with the Internet of Things, i.e. devices that interact and control the real world, as it has evolved, it has become useful for a range of applications.

This lecture will introduce you to some examples of Node-RED, both to give you a sense of its capabilities and to introduce its basic concepts. We’ll also discuss the history of Node-RED and its origins to give you an idea of what Node-RED is good for and of what types of tasks are best left to a more traditional programming language and environments.

By the end of this lecture you should have a high-level understanding of Node-RED, its programming model and some simple pre-built nodes as well as an insight into the origins and best usage of Node-RED.

Getting to know Node-RED

Let’s get started by looking at a few simple examples of the kinds of thing you can do with Node-RED. Our goal in this lecture is not to get you programming with Node-RED, but to give you a sense of its capabilities through some high level examples. If you really want to dive straight in, you can skip ahead to lecture 2 where you’ll be taken through the steps to create and run your first flow.

This first example flow will receive a message from a Twitter feed containing a hashtag and turn on and off a LED when a new tweet has arrived. For this example let’s assume that NODE-RED is running on a Raspberry PI – a common situation for many Node-RED users.

Example 1.1 Using Twitter to control a Raspberry PI

The Node-RED flow to check Twitter and to see if a hashtag #led is detected, turns on a LED connected to a Pi is shown in Fig. 1.1. As you can see, it’s quite simple; it consists of 3 nodes wired together – a twitter node on the left, a trigger node in the middle and a Raspberry Pi (gpionode) node on the right. (gpio stands for General Purpose Input/Output and is the name given to the generic Raspberry PI node that handles IO). The nodes, represented by rounded boxes, usually have an icon to represent their type, e.g. the Twitter bird icon in the first node, and a text name that can be set by the programmer.

Each of these three nodes is built into the Node-RED visual programming tool and can be dragged from a node palette onto the main workspace. They are then ‘wired’ together by joining their output tabs to the input tab of the next node in the flow (the little grey blobs on the right or left of the node boxes.) The next lecture will look at the UI in detail, but for now let’s focus on the high-level capabilities.


Figure 1.1 A simple 3 node flow that uses a Twitter tag to trigger a LED on a Raspberry PI.

The Twitter node is a built-in node in Node-RED and hides all of the complexity of using the Twitter API. It can be configured with a user’s account credentials and a variety of search strings, in our case it’s simply looking for hashtag ‘#led’.

When the Twitter node sees the tag either in a tweet to the user, or the public tweet feed, it creates a new message with the details of the tweet, which is forwards to the next node in the flow. In our example, since the Twitter node’s output is wired to the trigger node’s input, the trigger node receives a message with the details of the tweet.

The trigger node is another built-in node in Node-RED and its default behaviour is to wait for any message on its input. When it receives a message, it ‘triggers’, and sends a message on its output with the value “1” as the message body. It then waits 1 second and sends a second message with the value “0” in the message body.

Since the trigger node is connected to the gpionode, which controls the Input/Output or IO pins on the Raspberry PI, it get’s these two messages, 1 second apart, on its input.

Again, the gpionode is a built-in node in Node-RED that hides the details of setting the IO pins on the PI. When it gets a message with a value of “1” in the body, it takes the IO pin high, and when it receives a message with body of “0”, it takes the pin low. In this example, the gpionode is configured to control pin number 12, and so the node shows that in the node icon.

If you have your PI wired up with an LED connected to Pin 12, the gpionode going high for 1 second and then low will cause the LED to blink on for 1 second.

If you think about what’s happening here, it’s actually an incredibly powerful example of Node-RED and the Internet of Things (IoT). It’s connecting the ‘Twitterverse’ to a PI and an LED, but requires no programming – all the tasks are carried out by pre-built nodes in Node-RED which just need configuring. This really demonstrates the power of Node-RED when running on a simple device such as a Raspberry PI.

So just to recap. Node-RED is a flow-based programming tool with a visual editor that allows you to wire together nodes to create flows. In this case, 3 nodes create our first flow. Real-world events, e.g. a tweet with the #led hashtag, are converted to messages, which ‘flow’ along the wires between the nodes. Nodes process messages on their input and, as a result, send messages on their outputs to the next node in the flow. In this simple example, we look for a tweet with hashtag #led and use it to trigger a message to a Raspberry PI node that cause IO pin 12 to go high for 1 second, which in turns causes a LED to blink on for 1 second.

This example – and the rest in this lecture – are mostly to show you the kinds of things we can do with Node-RED. If you want to play around with the example, you should look at lecture 2 to see how to setup an account with FRED (the cloud based Node-RED service we use) and how to get started. You can find the node-red description of this flow at:

Example 1.2 Using Node-RED to alert you when you’re falling behind on your exercise schedule

Our second example shows a different usage of Node-RED. Rather than controlling a device such as a Raspberry PI, this flow combines weather info and your fitbit usage to send you an email alert when the weather is good and you’re behind on your exercise schedule. The flow is a little more complicated, using 6 nodes (see Fig 1.2), but again, by using built-in nodes you can accomplish complex tasks with little or no programming.




Figure 1.2 A simple flow to alert you when you are not exercising and the weather is good


As before, nodes are dragged from the node palette in the Node-RED UI and wired together on the flow canvas. Starting at the leftmost node, let’s begin with an openweather node which you can configure to check the weather for any major city at set intervals. When it checks, it packages up the results in a message which is forwarded to the next node, in this case, to a switch node. The switch node performs a simple test, checking the input message for a value and sending a message with a value of “1” if the test is true. In our case, it’s been configured to test the temperature value (tempc) to see if it’s 15 degrees celsius or higher.

When the switch node tests to true, or ‘switches on’, it sends a message onto the fitbit node which is the next node in the flow. The fitbit node is another powerful built-in node that you can configure to get statistics on your fitbit device from the portal. The fitbit node packages the statistics for your device into a message and forwards it to the next node in the flow.

The next node is another switch node. This one has been configured to check if the number of steps you’ve walked today is less than 1000. It does this by testing to see if the fitbit data field, summary.steps, is less than 1000. Remember, it just got these statistics in a message from the fitbit node, which in turn got them by making a call to the fitbit API on

If you have walked less than 1000 steps today, then the switch node generates a message that is passed on through the flow to a function node. The function node is a generic all-purpose node that allows you to write your own programming logic in Javascript. This node will be discussed in more detail in the next lecture. For now you can assume that this node just creates a new message with an email subject line and a text body that can be sent by email.

Once this message is created within the function node, it is sent through the flow to the final node which is an email node. This node will take the body of any incoming message and send it to the email account configured when the node is initialized. In this case, you’d set it up to send a message to yourself.

So again, a very simple flow that uses the power of Node-RED’s built-in nodes to check your local weather. And if it’s reasonably warm and you haven’t done much walking today, it sends you an email reminder.

Although the example seems quite simple, just checking the weather and your fitbit activity, you can use similar flows to check the status of home devices, of computers in your server farm, of devices in a factory, etc., and you can then use output nodes to send mail, tweets, make API calls to back-office software, control real world devices – in fact the choices are endless. Node-RED is an extremely powerful tool for wiring together inputs and outputs and offers many nodes, either built in or developed by the Node-RED community, to carry out an amazing range of tasks.


You can find the node-red description of this flow at:

Example 1.3 Building a simple web service using Node-RED’s built-in HTTP nodes

Our final example in this high-level introduction is yet another class of flows. It shows how to create a simple web service that responds to a browser sending a HTTP request by querying some real-world data, and then delivers a web page with a graphical representation of that data back to the browser – essentially Node-RED is being used to provide a web server and simple service all with a few nodes.

Sticking with the fitbit node introduced in the previous example, and this example creates a simple web service that allows you to query your fitbit stats for the day and, using a nice donut chart, display how many calories you’ve ‘burned off’ through exercise.

The flow is shown in Fig 1.3 and consists of 4 nodes. The first and final nodes are http input and output nodes that work together to listen for HTTP requests and send HTTP responses. This flow listens for a HTTP request from any source, but let’s assume a regular browser here. When it arrives, it queries your fitbit stats from, then uses a template node to build a HTTP page, and passes that to the http output node which sends back the webpage to the browser.

Figure 1.3 A simple web server example to graph fitbit data


Again, this is just to give you a sense of the power and flexibility of Node-RED without going into all the details of how this flow works. You’ll get to know more about that in later lectures. At a high level, the http input node has been configured to listen for HTTP requests on a URL, which is a combination of the hosting service that’s used in these lectures plus your login name {username} and the /fitbit string. This will be explained in more detail in lecture two.

When a HTTP request comes in, the http input node creates a message to trigger the fitbit node, which is the next node in the flow. The fitbit node gets the current statistics for the user, and then passes those statistics, as a message, to a template node. The html template node is another pre-built node in Node-RED, which, like the function node, allows you to build arbitrary code. However, rather than using JavaScript like the function node, the template node works with text such as HTML.

The HTML code in the template node is shown in Listing 1.1, so you can see what’s happening. However, there’s no need to examine it in detail if you don’t want to. Basically, the template node takes the calorie consumption data from fitbit and, using a 3rd party graphing library, creates a simple HTML file that will tell the receiving browser to use the 3rd party graphing library to display a donut chart of calorie consumption.


Listing 1.1 A simple HTML template to display a donut chart of calorie usage


  1. <!doctype html>
  2. <head>
  3.     <title>A Node RED Example</title>
  4.     <link rel=”stylesheet” href=”//”>
  5.     <script src=”//”></script>
  6.     <script src=”//”></script>
  7.     <script src=”//”></script>
  8. </head>
  9. <html>
  10.     <div id=”chart-example” style=”height: 250px;”></div>
  11.     <script>
  12.         Morris.Donut({
  13.            element: ‘chart-example’,
  14.            data: [
  15.              {label: “Activity Calories”, value: {{payload.summary.activityCalories}} },
  16.              {label: “Calories BMR”, value: {{payload.summary.caloriesBMR}} },
  17.              {label: “Calories Out”, value: {{payload.summary.caloriesOut}} }
  18.           ]
  19.        });
  20.     </script>
  21. </html>


Once the template node has generated the HTML file, it passes it as a message to the final node, which is a http response node. This node packages up the HTML as a HTTP response which it send back to the browser.

The user will then see a simple donut chart of their calorie usage for the day (see Fig 1.4)  – all built and served by a simple Node-RED flow!



Figure 1.4 A donut chart, served up by Node-RED showing calorie counts from fitbit


You can find the node-red description of this flow at:

The History of Node-RED

As the examples in the previous section have shown, Node-RED is a powerful tool for building IoT applications and services. Its genesis was triggered exactly by this need to rapidly prototype IoT applications and Node-RED was created as an open source project by the IBM Emerging Technologies group, in particular by two researchers, Nick O’Leary and Dave Conway-Jones. They created Node-RED initially as a tool for themselves as they were working on IoT projects and were “looking for a way to simplify the process of hooking together systems and sensors when building proof-of-concept technologies for customers.”

An initial version of Node-RED was released as an open source project in late 2013 and built up a small but active user and developer group during 2014. At the time of writing, Node-RED is still an emerging technology, but has already seen significant adoption by makers, experimentalists and a number of large and small companies experimenting with using it for their needs.

Today there is a vibrant user and developer community, with a core group working on the Node-RED code itself and most developers contributing nodes or flows to the flow library. You can check out the mailing list and github repositories using the links provided in Appendix A or just google them.

Since Node-RED is still a rapidly evolving technology, be aware that things can change quickly. These lectures were written against the 0.11.0 release of Node-RED and the examples will be updated as Node-RED evolves. However, it’s always wise, as with any new technology, to check compatibility if things aren’t working the way you expect.

Node-RED and the IoT

When the IBM folks created Node-RED, they were mostly focused on the Internet of Things, i.e connecting devices to processing and processing to devices. As a tool for rapid application development for the IoT, Node-RED is both powerful and flexible. Its power comes from a combination of two factors:

  • Node-RED is an example of a flow-based programming model – messages representing events flow between nodes, triggering processing that results in output. The flow-based programming model maps well to typical IoT applications which are characterised by real-world events that trigger some sort of processing which in turn results in real-world actions. Node-RED packages these events as messages which offer a simple and uniform model for events as they flow between nodes that make up the flows.
  • The set of built-in nodes is the second part of the Node-RED story. By building a set of powerful input and output nodes, each of which hides much of the complexity of interacting with the real world, Node-RED offers developers powerful building blocks to allow them to quickly put together flows that accomplish a lot, without having to worry about the programming details.


These two factors make Node-RED a powerful tool for IoT application development. When combined with the flexibility to create and use nodes such as the function node, which allows the developer to quickly write arbitrary JavaScript, plus the Node-RED community, who are constantly creating and sharing new nodes, Node-RED is likely to be one of the main tools in the IoT developers toolbox.

However, Node-RED isn’t always the best tool for IoT application development. While powerful and flexible, it isn’t always the right solution. Some situations where Node-RED may not be the first choice include:

  • Complex multi-function IoT applications. Node-RED excels at rapid application development and at acting as the glue for connecting events to actions, or sensors to actuators, if you like. However, when an application gets above a certain size, it becomes complex to visually program and manage through Node-RED. There are some features to help with this, e.g. sub-flows (see lecture 5), but eventually the UI becomes a bottleneck.
  • Flow-based programming is a programming model that is not always the most suitable for application development. In much the same way that certain programming languages excel at some tasks but not others, flow-based programming has its weaknesses. A good example are loops: Node-RED is cumbersome when handling loops.
  • Specific use cases. Flow-based programming is a general purpose model and not targeted or optimized for specific needs, for example Data Analytics or User Interface development. At the moment Node-RED has no specific support for these type of applications and no easy way to add such support. Obviously, because the underlying technology of Node-RED is JavaScript, you can leverage its capabilities for these needs. If want to use Node-RED to prototype something, one approach is to implement parts or all of your application in another language more suited to your task and use Node-RED for the overall control.


Having said that, as has been shown in this lecture and will be demonstrated in the following lectures,  Node-RED is a powerful tool for a large number of IoT applications. As it evolves it will become both more sophisticated and more usable in a wider range of circumstances.

Perhaps just as important, as you explore Node-RED in subsequent lectures, you will realise that although Node-RED’s roots are in the IoT, it is a tool that can be used to build a wide variety of applications, not just IoT applications. In fact, in these lectures you will see example of Node-RED being used for web apps, social media apps, back-office integration, IT task management, i.e. across the range of modern computing tasks.

Our hope is that by the end of this lecture course, you will see Node-RED, as we do, as a flexible and powerful tool that can be used in many situations, both for prototyping but also for production level development.


In this lecture we’ve taken you through a high-level look at Node-RED. You’ve seen how Node-RED wires together building blocks, using a visual tool to rapidly create simple flows that actually carry out sophisticated real-world tasks. You’ve also learnt a little about the origins of Node-RED as a rapid application development tool for the IoT and how, as it has evolved, it’s now being used for a variety of tasks, not just IoT programming. Finally, we’ve discussed the limitations of Node-RED and indicated some areas where other languages and tools may serve you better. In the next lecture, we’ll start on some real programming and show you how to build your own flows using the basic nodes that Node-RED provides.


About Sense Tecnic: Sense Tecnic Systems Inc have been building IoT applications and services since 2010. We provide these lectures, and FRED, cloud hosted Node-RED as a service to the community. We also offer a commercial version to our customers, as well as professional services. Learn more.

© Lea, Blackstock, Calderon

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.