Pipelines enable clean chaining of operations, but they’re not quite ready just yet
The usage of pipes to transport water, air, gas, or any other fluid substance has enabled tons of things we take for granted. Pipes allow us to transport water to our homes so that we can use things like the sink and bathtub. Our ovens and heating systems rely on pipes transporting gas. Even a straw for drinking out of a cup is a little pipe, if you think about it!
In software, pipes take the output of whatever expression exists on the left side of the pipe and uses that as the input for the right side of the pipe. And like their real-world counterparts, the output of one pipe can easily be hooked up as the input of another.
So how does a pipeline work?
Without pipelines we would normally apply our function arguments by putting them inside of parentheses — for example, we could do
myFunction(argumentToApply). Pipelines just give us another syntax to apply arguments. With a pipeline, we could do the same function like this:
argumentToApply |> myFunction.
As you can see, either way is “right” in that it produces the same result. But using the pipeline syntax allows us to dramatically decrease the amount nested parentheses we have.
If you’re working with data frequently, you might be doing a lot of operations on your data as it comes into your application in order to make it fit better to how your application uses it (I know I do!). Pipes are extremely useful for describing these sets of transformations since instead of having to read your data transformations backward (from the innermost parentheses to the outermost call) you can just read them top-to-bottom in the order that they execute.
Using pipelines like this also encourages us to create a lot of small, isolated functions where each function does only one thing. Later on, when we need to do more complex things, we can just stitch all of our atomic functions together in a clean, readable format! This helps with testing our application logic (we don’t have one massive function that does everything) as well as reusing logic later on.
What’s holding up the pipelines?
There’s a ton of exciting proposals going through TC39 right now, why are we diving into pipelines? For one, pipelines already exist in a whole host of functional languages — Elm, Reason, Elixir, even Bash! Because they already exist we can easily see how they improve code readability based on their usage in other ecosystems.
1) No strings attached: simple pipelines
As the name suggests, the simple pipes proposal is the least complex of the pipeline contenders, behaving exactly like the example we saw up above. Within this proposal, the pipeline is only responsible for taking the evaluated expression on the left and using it as the input to the function on the right.
Using an arrow function within a simple pipeline requires that you wrap it in parentheses.
Another “gotcha” is the output from the left side is passed into the right side function as a single argument. This means that if we have a function expecting two arguments we would need to wrap it in an arrow function in order to guarantee that our arguments are in the correct order.
Because of this, using simple pipes tends to promote the use of curried functions — especially if the data being passed in from the left side is the last argument to the function. If we curry our
prepend function, it’ll be easier to add to our pipeline since it now it doesn’t require wrapping an arrow function.
Since our curried
prepend function returns a new function that receives the output of
toUpper, it makes the pipeline considerably cleaner!
The last noticeable thing about the simple pipeline proposal is that there is no special treatment for
await. In this proposal,
await within a pipeline isn’t even allowed. Using it will throw an error!
2) Adding async magic: F# pipelines
The F# pipeline proposal is super close to the simple pipelines proposal. The only difference is the ability to use
await within a pipeline chain to allow for asynchronous operations. Using
await in the middle of a pipeline waits for the function on the left to resolve before running the functions later on the pipe.
The above example would desugar to something that looks like this:
3) Last but not least: smart pipes
The last main proposal for adding pipes draws its inspiration from Hack, a PHP dialect originating out of Facebook. In Hack pipelines, the output of the expression on the left side of the pipe is dropped into a token to be used by the expression on the right side of the pipe.
In Hack, the token used is
$$, but the ECMAScript proposal has been considering using something like
In addition to having a “token style” requiring the use of
# on the right side of the pipeline, smart pipelines would also allow a “bare style”. This “bare style” would be closer to the simple/F# pipes proposals. Based on which style is used, the pipe would assign the left value of the pipe differently. That’s what makes them so “smart”!
In addition, usage of
await inside of the middle of the pipeline would also be allowed. The possibility of using tokens along with the ability to write asynchronous pipelines allows this proposal to supply an extremely flexible pipe that can handle virtually any group of nested operations.
However, adding the extra sugar to the smart pipelines does complicate the syntax considerably over the simple pipelines and the F# proposal. In addition to adding a pipeline syntax (
|>), a placeholder syntax needs to be agreed upon and implemented.
Another concern with these smart pipelines is that there’s a lot of syntactic sugar and “magic” going on with how the pipe works. Adding this type of magic to the pipes could result in some confusing behavior and might even negatively impact readability in some cases.
Of course, since this proposal is still being fleshed out, these concerns are being taken into consideration and I certainly hope we end up with something that is both simple to understand and elegant to use.
As we’ve seen, pipelines aren’t close to becoming part of the ECMAScript spec — they’re only in Stage 1 and there’s a lot of differing opinions about what they should be.
What do you think about the pipeline proposal? Which is your favorite proposal? If you’ve got any questions feel free to reach out or tweet at me!
Plug: LogRocket, a DVR for web apps
LogRocket is a frontend logging tool that lets you replay problems as if they happened in your own browser. Instead of guessing why errors happen, or asking users for screenshots and log dumps, LogRocket lets you replay the session to quickly understand what went wrong. It works perfectly with any app, regardless of framework, and has plugins to log additional context from Redux, Vuex, and @ngrx/store.