Have you ever felt that, that little friction,
that feeling you're hitting a kind of ceiling
with your automation tools? Like they're amazing,
right? But sometimes they just, they can't quite
do that one specific thing you need, that custom
bit, you know, beyond the standard blocks. It
feels like there's something more powerful hidden
away, maybe a secret weapon. That secret weapon.
Yeah. That's exactly what we're digging into
today. Welcome everyone to the deep dive. I'm
actually really excited about this one. We get
to unpack something super powerful. Today, we're
taking that deep dive into the NAN code node,
which, let's be honest, for a lot of folks, it
kind of looks like this. Complex, maybe even
intimidating code thing inside a platform that's
otherwise pretty friendly. Oh, totally understandable.
It does look a bit scary from the outside. But
our mission today is simple. Pull back that curtain.
Make it clear. We want to show you how this thing
that seems like a big programming hurdle is actually
Well, it's a clear path to doing some seriously
next level automation. We're going to cover handling
tricky data, calling other services, processing
big batches of stuff, and even creating your
own files right inside NAN. It's way more doable
than you might think. OK, let's unpack that.
Because it is a common thought, I've definitely
felt it myself, that the code node is strictly
for the super hardcore programmers, the ones
who just breathe JavaScript, has that vibe, doesn't
it? It absolutely does. Makes you hesitate, maybe
click something else instead. But here's the
kicker, the really interesting part. You can
get the basics down surprisingly fast, like really
fast, a focused afternoon maybe, just to get
the core ideas. And once you do, oh man, it completely
unlocks what NEN can do. Think about other platforms
like Make or Zapier, great tools. But they just
don't offer this level of custom logic, this
deep dive into manipulating data. The code node...
pushes N8n way beyond those limits. You can do
complex data transformations, call external APIs
in real time, process huge amounts of info, generate
custom files, reports, data exports, whatever.
It turns the N8n from just an orchestrator into
a real data processing engine. That sounds like
it could really... Yeah, revolutionize how people
use it. So fundamentally, then, how does it actually
process the information? Is it looking at things
one by one or the whole lot together? That's
the beauty. It's flexible. It can handle items
individually, like processing each order separately,
or it can look at the entire batch, the whole
collection as one single unit. That flexibility.
Yeah, that sounds key for different kinds of
problems. Now, to really get a handle on this,
I imagine setting up a little sandbox, a practice
area is probably the way to go. Oh, absolutely.
Best way to learn is just jump in. FIRO blank
workflow in any in. So for our example, let's
imagine you've got some sample order data. Picture
it like a JavaScript array. Basically a list,
right? A list of product objects. What's an object?
Think of it like a digital box for each product,
laptop, mouse, whatever. And inside that box
are details like price, quantity, maybe custom
email. Now a key thing is understanding how to
handle that list. Do you process each product
one at a time? We're all together. And ENN actually
has a node called split out that does exactly
that, breaks the list into individual items.
But, and this is cool, the code node can also
do that splitting internally if you want, or
control. Exactly. So the code node basically
has these two main ways it operates. The first
is run once for each item. That's the default.
It processes each item, each product in our list,
totally on its own. The second mode is run once
for all items. This one treats everything together
as one big list, one array. When you first add
a code node, it actually gives you a little snippet
of code already. Super simple, just adds a field
called my new field to show you how it works,
how it outputs data. Okay, okay, so let's make
it concrete, a practical example. How about calculating
a line total, price times quantity for each product,
and maybe extracting just the domain name from
an email address, like gettingexample .com from
someone at example .com. Perfect examples, everyday
stuff where the code note is brilliant. So first,
you need the data for the current item. You use
input .item .json. That basically tells the code
node, get me the data for this specific item
right now. Calculating line total, easy peasy,
just price quantity, basic math. Now, the email
domain. That's where you use string manipulation.
JavaScript has tools for this, methods like substring,
index off. Think of index off as asking, where's
the at symbol? It gives you the position. Then
substring says, okay, give me everything after
that position. Together, they let you snip out
just the domain. But here's something really
important, especially when you're starting out.
The spread operator. Those three dots dot order
item. This little trick says, keep all the original
data from this item and then add my new line
total and customer domain fields. And if you
forget it, poof, all your original data disappears
and you only get the new fields. Common mistake.
I gotta admit, I still wrestle with remembering
all those specific string methods myself sometimes
when I'm actually writing the code. It's a small
thing, but... Yeah, it can trip you up. Oh, you're
definitely not alone there. It really just comes
with doing it more. But the key is knowing that
these tools exist and roughly what they do. So
you know what to look up or, you know, ask an
AI helper about. So, OK, we've done things one
by one, but what about looking at the whole picture,
the entire data set? How does it handle that?
That collection all at once right it shifts into
that bash mode viewing all the items together
as one Big array like going from single letters
to reading the whole page and this this is where
it gets seriously interesting This is where you
unlock stuff. That's frankly much harder or impossible
and other tools I'm talking about the code nodes
ability to directly call external API's this
is huge it turns in 8n in this amazing hub that
can pull in data from Well, practically anywhere
on the web that offers an API. That really does
set it apart, doesn't it? Grabbing external data
on the fly, that's a massive potential. It's
absolutely massive. Magi, you have your product
data, right? And you want to enrich it by getting,
say, barcode info from a free API like world
.openfoodfacts .org. You just use your product
ID. code for this, you'll see keywords like async
and await. These are super important for things
that take time, like waiting for an API response.
Think of async as telling your workflow, hey,
this next step might take a while. Don't just
freeze up. And await is where you actually say,
OK. Pause here and wait for this specific thing
to finish. Like waiting for a download. LANN
helps a lot here with a built -in helper. This
.helpers .http request makes calling APIs much
simpler. No need for extra libraries. You'd also
want to check the response .status basically.
Did the call work? Did the server respond okay?
And crucially, never ever put your secret API
keys directly in the code. Use NAN's credentials
feature. Always. It keeps them safe. Think of
it like a password manager for your workflows.
Whoa. Okay. Just thinking about that. Imagine
scaling that. integrating with dozens of different
services all at once, pulling in real -time data
for really complex decisions. It's not just moving
data around anymore, is it? It's like building
a live intelligence system. Exactly. Imagine
an e -commerce setup pulling real -time shipping
rates from five carriers, checking inventory
levels across three warehouses, maybe factoring
in current promotions, and then deciding the
absolute best way to fulfill an order all before
the user even finishes checkout. That's the kind
of dynamic smart stuff this unlocks. It's way
beyond simple triggers and actions. So OK, we've
looked at individual items, enriched them with
APIs. But what if you have thousands, hundreds
of thousands of items? You need the big picture,
right? Beyond individuals, how do we summarize
everything? That sounds like the perfect job
for batch processing, right? Crunching all that
data together for the aggregate view. Precisely.
That's where the run once for all items mode
is king. You just toggle a setting in the node,
and suddenly instead of input .item for one thing,
you get items, which is an array, a list, holding
all your input data. If 100 orders came in, items
has all 100. So now you can easily calculate
things like total revenue across all orders,
or total items sold, or even create a more complex
category report, having up sales per category.
A really useful pattern here is often items .json
.map. you loop through the list and pull out
just the data you need from each item. The JavaScript
produce method becomes your best friend. It's
like the ultimate tool for boiling down a list.
Want to sum up all the numbers? Count items by
category dot reduce is perfect. It takes your
whole list and processes it down to a single
summary value or object. Just one little detail
to remember. When you return data in bash mode,
the final output needs to be an array containing
just one object that holds your summary results,
like putting your final report in a single folder.
OK, so we've processed everything, got our summaries.
But what about getting that information out,
like into a format? that we can use elsewhere.
Can the code node actually create files? Yes,
it absolutely can. You can generate custom files
directly from your processed data. Now that is
seriously impressive. Making files CSVs, JSON,
maybe even a formatted HTML report right inside
the workflow. That's not just processing data,
it's producing results. It's a total game changer,
yeah. So let's say you took those stats we calculated
in batch mode and you want an HTML report. How
do you do it? First, you grab the data from the
previous node. Let's say that node was named
calculateStats. You'd use something like calculateStats
.item .json to pull its output into your current
code node, symbol reference. Then... You build
your HTML. The best way is using template literals.
Those are strings using backticks instead of
quotes. They make it super easy to embed your
data right into the HTML text, like mail merge
almost. Finally, you need to package it up. Use
buffer .from to turn your text into binary data
and then this .helpers .prepare binary data.
Think of these helpers like putting your report
in a digital envelope, labeling it with a file
name like report .html, and specifying the MIME
type like text email. What's a MIME type? It
just tells the computer what kind of file it
is, HTML text, a JPEG image, et cetera, so it
knows how to open it. The key thing for file
output is the return structure. You need a JSON
property for metadata and a binary property holding
the actual file content. OK, that covers the
core capabilities from single items right through
to making files. That's a lot. Let's shift gears
slightly. Advanced techniques, best practices.
What's maybe the number one thing people miss
when they start using this more seriously? Error
handling. Easily. Hands down. Look, APIs fail
sometimes. Networks glitch. Data might not be
what you expected. You absolutely need to wrap
code that might fail, especially those API calls
in what's called a try that catch block. If you
don't, one little hiccup can crash your whole
workflow. With try .catch, if an error happens
in the try part, the catch part runs instead.
You can log the error, maybe send a notification,
return a default value. It keeps things running
smoothly. It's crucial for reliability. Performance
tips. Definitely use batch mode. Run once for
all items. For large data sets, if you can. Fewer
executions is faster. If you are looping through
lots of API calls, add a small delay between
them. Like, wait half a second, seriously. It
stops you hitting rate limits and getting blocked
by the API provider. Happens more than you think.
And, oh, keep your code nodes focused. If one
starts doing too much, split the logic into two
or three smaller nodes, way easier to debug and
manage later, like breaking down a big project.
And speaking of debugging, how do you actually
see what's going on inside the code while it's
running? How do you peek under the hood? Good
old console .log. It's simple, but it's effective.
You can put console .log some variable anywhere
in your code. When the workflow runs, whatever
you log shows up in the NAN Developer Tools panel
or the server logs. Perfect for checking variable
values, seeing if code paths are being hit, just
understanding the flow. And one last thing, don't
shy away from using AI assistants like ChatGPT
or Claude. Seriously, give them good context.
Here's my code. Here's the input data structure.
I want to achieve X. And they can often spit
out really useful code snippets. Generative AI
just means AI that creates stuff like code. They
won't replace understanding why it works, but
man, they can speed things up. That's all really
solid advice. OK, so boiling it all down. If
there's one single most important thing for someone
listening right now, just starting with the code
node, what should they remember? What's the core
message? Start simple. Don't try to boil the
ocean on day one. Build a complexity gradually.
and just practice, tinker, experiment. So wrapping
up this deep dive, we've really explored how
getting comfortable with the N8n code node genuinely
transforms the platform. It elevates it, doesn't
it? From a straightforward automation tool to
something much more like a... a powerful, flexible
engine for data processing and complex workflow
orchestration. It's a real step up. Absolutely.
And we hit the four key pillars, right? Handling
individual items, bringing in external data via
APIs, crunching numbers in bash mode, and even
generating custom output files. It truly is that
secret weapon for tackling those more advanced
automation challenges that used to feel out of
reach. The only way to really internalize this,
though, is by doing. So we really hope you'll
open up any now, and maybe right after this,
start a blank workflow and just play around.
Try it with some data you have. Don't worry about
getting it perfect immediately. Just experiment.
Breaking things is part of learning here. Yeah,
totally. Remember, the code note isn't just another
note in the canvas. It's more like a gateway,
a gateway to, well, pretty much unlimited potential
in your workflows. Your imagination really does
become the main constraint. And as you start
experimenting, maybe think about this. What's
one repetitive data heavy task you deal with
regularly? Something tedious, maybe. How could
applying even a little bit of custom logic with
the code node to that one task fundamentally
change how you work or free up significant time?
What could that unlock for you? Go build something
awesome. Go to your own music.