Those Who Make, by hand
Those Who Make is a series about people who craft. Physical things, by hand, that don’t come out the same every time. I love watching people make things, and I doubly love hearing their passion for whatever it is they’re making. Even more enlightening, this is a very international series. It’s not all hipster shops in San Francisco, Portland, and Brooklyn; it’s everywhere.
This is delightful stuff.
[vimeo www.vimeo.com/58998157 w=500&h=250]
How coffee is made in a colorful shop in another country, shot in the “Vimeo style” (is this a thing?): that will always get me.
Feynman's mess of jiggling things
Richard Feynman, in the process of explaining rubber bands:
[youtube https://www.youtube.com/watch?v=baXv_5z7HVY&w=420&h=315]
The world is a dynamic mess of jiggling things, if you look at it right!
This simplification delights and amuses me. The great thing is its fractal truth: you can observe our lives at many levels and conclude that they are dynamic jiggling messes.
The Rite of March
INT. OFFICE: A team of enthusiastic young folk rush to get their “game changing” app ready for SXSW. A cacophony of phone calls, typing, and organizing swag.
EXT. PATIO: A team of folks that have done the SXSW ritual before look at their calendar, note it’s almost the middle of March, and shrug. They go back to drinking a tasty beverage and working at their own pace.
[youtube=www.youtube.com/watch
Twitter's optimizations
Data point: a few of the infrastructure pieces out of Twitter have been implemented in low-level, heavy metal C and they’re optimizing on individual machines instead of architecture. Today, twitter/fatcache, a memcached-on-SSDs:
To understand why network connected SSD makes sense, it is important to understand the role distributed memory plays in large-scale web architecture. In recent years, terabyte-scale, distributed, in-memory caches have become a fundamental building block of any web architecture. In-memory indexes, hash tables, key-value stores and caches are increasingly incorporated for scaling throughput and reducing latency of persistent storage systems. However, power consumption, operational complexity and single node DRAM cost make horizontally scaling this architecture challenging. The current cost of DRAM per server increases dramatically beyond approximately 150 GB, and power cost scales similarly as DRAM density increases.
It’s fascinating to observe Twitter’s architectural growth from the outside. They quickly exceeded the capacity of typical MySQL setups, then of Ruby and Rails, then memcached alone. They’ve got distributed filesystems, streaming distributed processing pipelines, and distributed databases. Now they’re optimizing down to the utilization of their hardware, taking advantage of the memory-like latencies of SSDs. When you start caring about power and the size of your index entries, you’ve reached a whole new level of Maslow’s hierarchy of scaling.
If trends continue and Twitter is a leader in how large-scale distributed systems are implemented, watch out. Twitter led many of us to Scala, ZooKeeper, and their own inventions like Storm and Finagle. Gird your programming and scaling fashion loins, because you’re about to learn a lot more about malloc
, ERRNO
, and processor architecture than you ever wanted to know!
Stella by Starlight
My latest weekend project, called "Stella by Starlight" after a Charles Mingus recording, was to build an analytics-style dashboard for looking at random metrics and events generated by a faked-out backend. I started with these rules for myself:
- Write every 30-60 minutes. If I don't keep myself honest in journaling this, I'll never get around to writing about it.
- Use Scala for the backend service. More on this in a moment.
- Learn D3.js. This is a primary goal of this exercise.
- Maybe use Coffeescript. More on this too, below.
And now, my notes from periodic progress reports. I've annotated it with GitHub commits at each step; you can also peek at the whole repo.
Get started with Scalatra
Per my motivation to use Scala, I figured I'd start with Scalatra. It integrates with Akka, which seems like a great thing, sticks pretty close to the Sinatra-style of web app construction, and seems like its probably approachable by a Scala beginner.
Strike 1 is that you need to use this giter8 tool. Luckily, it's available via Homebrew, so it's not a blocker.
Strike 2 is all the work needed to setup a Coffeescript gizmo to work with Scalatra. I've been blocked on SBT-related stuff before, and the instructions don't match the style of SBT project that was generated by giter8.
So I have a decision to make: should I plow forward with Scala and drop CoffeeScript or make a strategic retreat to the comfortable land of Ruby where there's probably a thing that will automatically compile CoffeeScript every time I hit a page?
For this weekend, I'm going to tradeoff getting better with Scala instead of CoffeeScript. The latter's domain is browsers, a domain I have chosen not to optimize myself for.
First commit: I have no idea what I'm doing.
Boilerplates
With one decision down, I need to figure out what my first real milestone is. It seems like a spike/prototype project like this requires two setup steps before real work can begin:
- Put all the project boilerplate in place. Get the backend service running and responding to requests. Decide on any front-end boilerplate I'm going to use (Twitter bootstrap, et. al.) and put it where your server will hand it off to a browser.
- Get your feedback loop working. Make a trivial change to the backend app, make sure it appears in the front-end. Make a front-end change and make sure everything changes properly.
Once I've got these two nailed down, I'm ready to actually iterate on the idea in your head.
I've got the Scalatra part of this working, and just fetched Bootstrap. Now I just need to get its boilerplate working and I can start actually working on an analytics dashboard.
Templates and cargo cults
So Scala can manipulate XML as a language-level thing. This is both terrifying and, in the case of emitting HTML inline within a Scalatra action, useful. But the limit of this is that not-quite-valid XML, but perfectly reasonable HTML, will cause your Scala program to flat-out not compile. Ergo, I decided it was time to bite the bullet and move my HTML bits into an actual template (commit).
That turned out to be pretty easy. Read the Scalate (Scalatra view templates) docs, skim the actual Scalate docs and you're mostly good to go. The only catch is that I had pre-generated HAML-style templates laying around which were causing a weird error about title
not being defined. Once I figured out I had cruft laying around and killed it dead, all was pretty good.
I cargo culted all the CSS from a Twitter Bootstrap example and ended up with something decent looking. Note to past-self: HTML and CSS are terrible, but things like Bootstrap will make it reasonably possible to put up a decent-looking app quickly without needing a designer or browser-bug expert.
The change loop for Scalatra is nice and quick when doing front-end work. The SBT feature that watches the filesystem for changes and automatically runs tasks on change is pretty handy and, IMO, a better place for that functionality than in something like Guard.
Let there be charts
Now I want to get Cubism in place. At first glance, I thought I was goinging against the grain here. Cubism has a slight tendency towards using Graphite or Cube as the metric source. However, the demo page for Cubism shows some charts using random data.
Peeking at the source showed me the way to creating a data source that isn't pulling from Graphite or Cube (commit). This saved me the effort of trying to reverse engineer the Graphite/Cube query APIs before I could make any progress at all.
This points to an important lesson of prototyping: when in doubt, steal from the example that looks like what I want to do. It's totally OK to cargo-cult things into your system at this point. Later on, I can do it with software engineering and craftsmanship. In the present, I want to make progress on exploring my idea.
Random numbers as a service
Now I want to emit some random numbers via JSON from the service. This ended up being a not-so-tough journey into actual Scala. Turns out the JSON support in Scalatra is pretty straight-forward. I had to take a side-trip through JodaTime, a library I'd heard about before but never worked with directly. Of course, that resulted in some temporary Maven confusion, but all was well in the end (commit).
I was pleased by how one can go about quickly emitting JSON from a Scalatra action. What you do is write a case class (somewhat analogous to a Ruby struct, but with more tricks up it s sleeve) for the structure you're going to convert to JSON. Then you return one or more instances of that class from your action and the library handles the rest. All of this mostly made sense when I read the examples and converted it to my own thing, so I guess the basics of Scala are starting to stick. Happy!
Better random numbers, an attempt
I wanted to generate more realistic data from the service. I figured I would port the random metric generator from the Cubism example's JavaScript to Scala. This would make it easier for me to grok the timeline windowing scheme that Cubism uses.
It ended up that porting this algorithm was a bit trickier than I thought. Oddly enough, you can paste the crux of the algorithm from JavaScript to Scala and it looks like valid Scala. However, doing so gave me compiler errors that took me a little while to work out. Basically, the algorithm expects to work with doubles, but the compiler infers integers if you specify any default value such as start = 0
. Adding type annotations to declarations resolves all of this. With that worked out, making the Scala compiler happy was a little more obvious.
It turned out that the Cubism example, as I cargo culted it, passes timestamp strings to the service. It was getting late and the first few things I tried to parse timestamps in Scala didn't work out, so I decided to call it there (embarrassingly broken commit).
How'd I do?
On the bright side, I didn't get hung up on Maven dependencies, I roughed my way through the Scala type system, and I had a pleasant experience with Scalatra and Cubism. On the downside, I didn't get to streaming events to the browser from the server and I couldn't quite get random metrics flowing from the server into the browser.
These weekend hacks are like that. I learn about things I expected to learn about and I learn about entirely different things too. I didn't expect to find myself pressing ahead with Scala, but doing so was an entirely different kind of educational fun than if I hadn't.
The nice things about these weekend hacks is that they're just that; a hack over the weekend. It's not a big project that I am responsible for afterwards. But it's still enough progress that I can write about it here and share it on GitHub. That feels productive. Learning plus productivity feels really good!
Adam’s Law of Redis
No matter how many times you tell everyone to not use KEYS
, there remains a non-empty set of people who think they can use KEYS
.
You can’t use KEYS
because it has to look at every key in the database. Even if you use a prefix pattern to narrow the scope.
Don’t use KEYS. If that means you need to redesign your schema, you have no choice but to redesign your schema.
Thoughts on (Programming) Scala
On a whim, I flew through Programming Scala this weekend. I’ve had the book for a while, and actively tried to read it before. But this time, it stuck.
All the ideas in Scala are fascinating for a language nerd. It’s the best instance I know of, so far, where ideas from object-oriented and functional programming are combined intentionally and at-scale to produce a language that developers are using on a day-to-day basis. For a language nerd like me, it’s fun to see how all those ideas play out together.
That said, there is a lot of language lawyering. Having to write a chapter on scoping and public/protected/private rules in OO seems like a demoralizing thing for the authors to tackle. And all those hybrid OO/FP ideas come at a conceptual cost; it seems like there’s a lot to know. I’ve noted before that I’m very interested to see how Scala does in the marketplace of minds. It’s a very large language, but I think it’s large in a way that is already familiar to developers. So it could end up that Scala isn’t a great beginner language, but is fine for someone who already knows one FP and one OO language.
I should note that this isn’t my first Scala rodeo. I’ve tried, at various times, to tinker and hack on little projects or simply to grok other people’s code. The blocker on these previous attempts is that I, personally, am sbt-challenged. Whenever I’ve tried to compile projects or add dependencies to my own, I end up in an sbt-shaped trough of disallusionment. Part of this is my ongoing war of attrition with Maven. Part of this is, well, I’m not sure yet. I should note that I can mostly make leiningen, also Maven-based, work. So it’s not entirely Maven’s fault.
Most interesting to me is that Scala could have the versatility of Ruby, wherein one can grow a program from a script, to a message-based program, to a hybrid OO/functional system, to a multi-machine distributed program. You can’t say this about other JVM languages like Java or Clojure. The JVM is a gift and a curse. It makes Scala and Java impractical for scripts, due to startup time. But once your program is somewhat grown-up, Hotspot and the JVM’s excellent concurrency features come in quite handy.
More specific to the book, it cleared up some ideas I’d previous found confusing:
- What’s a method call/operator overloading? It’s an object, a dot or space, and then a method/operator name.
- Implicit methods/views; if you declare methods with the
implicit
keyword and they have the right type signature, the compiler will use them to coerce objects to your own types, giving you many of the benefits of something like Ruby’s open classes - How functions, maps, and ad-hoc data structures that are typical in Ruby map to actual types in Scala; lots of things get converted to
Function
andTuple
objects by the compiler, which makes sense when you think about it in an ML-ish everything-is-strongly-typed way. - Internal DSLs feel weird, but parser combinators for external DSLs seems like it would be great.
- for-comprehensions; I guess I’ve read enough about them in Clojure now that they make sense in Scala. It’s worth noting that Scala’s for-comprehensions feel simpler than Clojure’s.
- Self-type annotations; I’ve seen this all over in Scala code and didn’t quite understand what was going on. It sure does have an odd name.
And some things are still confusing to me:
- Type bounds, variance; when will I need these?
- Linearization of object hierarchies; rules, I don’t like learning them!
- Tail-calls/trampolines; the JVM makes this a headhurt.
- Path-dependent types; not sure when I’d really need this, but it’s good to know about.
- Anything that’s a band-aid over type-erasure; again, the JVM is sometimes a headhurt.
I don’t have any projects that imminently need the things that Scala provides. Further, I think imposing Scala on a team that’s already succeeding at Ruby or Python is a stretch. You have to be in a place where you really need low, predictable latencies to accept the tradeoff of working with a much larger language.
That said, it’s totally a reasonable choice as a way to get yourself onto the JVM; if Clojure isn’t your thing, Scala probably is. Even if neither are your thing, don’t be a wuss; read some code in either language and expand your mind to reduce your headhurt.
Lessons from premature design
Lessons from Premature Abstractions Illustrated. I’ve run afoul of all three of these:
Make sure you have someone on the team or externally available that will keep the critical, outside look at the project, ready to scream and shout if things turn bad.Don’t let your technical solution influence your design decisions. It’s the tool that needs to fit the job, not the other way round.
Don’t build abstractions as long as you have no proven idea on how the levels below that abstraction will look like.
I could have used an outside, trusted voice to gently reel me in if when I went off into the unproductive weeds. Someone to ask “how will this help the team in two weeks?”, someone to point out ideas that might be great but have only achieved greatness in my head. A person who is asking questions because they want me to succeed, not because they’re trying to take me down a notch.
I have rushed into implementing the first idea in my head. Sometimes I’ve convinced myself that my first idea is the best, despite knowing I need to review it from more angles. I’ve jumped into projects with a shiny new tool and a bunch of optimism, only to cut myself on a sharp edge later on.
I’ve built systems that look fine on their own, but don’t fit into the puzzle around them. I’ve isolated myself building up that system, afraid to figure out how to fit my system into the puzzle in a useful way. I’ve used mocks and stubs to unintentionally isolate myself from the real system.
Basically, these are all really good ways to paint yourself into a corner. It seems like being in a corner with a shiny new system/tool/abstraction would be nice. Unfortunately, my experience is that once you have to make sense of that abstraction in a team, things get dicey.
It’s dangerous to run a software project on your own! Take a friend.
Reflecting on Ruby releases
Ruby 1.8 brought us a couple changes that made many kinds of metaprogramming easier, plus a whole bunch of library additions that made Ruby feel more "grown up". Without seeking external libraries, one could write Ruby to solve many problems developers face in commonplace jobs. I wasn't around for Ruby 1.6, but I've been thinking of Ruby 1.8 as a transition from "better Perl or Java" to "better Smalltalk".
Ruby 1.9 brought us features that make some functional programming idioms easier. Lambdas, i.e. anonymous functions, require less syntax and are better defined. Enumerators make it possible to use features of Enumerable, itself a very functional-esque feature, in more places. Symbol-to-proc makes it easier to pass methods around as blocks, another FP-esque practice. I might say that Ruby 1.9 is the "better MatzLisp" version of Ruby.
Ruby 2.0 is bringing us features that, on the surface, make it easier for Rails to extend the Ruby language via ActiveSupport. I think that's too shallow of a reading. The new tools in Ruby 2.0 (excepting the highly-controversial refinements) make it easier to cleanly add functionality to Ruby's core objects and library. Reducing the cost of extending the core make it possible for more libraries and applications to judiciously make high-leverage additions to the lower levels of Ruby. That seems like a pretty good thing.
I can't find a source for this, but I could have sworn I once read that all programming is language design. It was probably related to Lisp, where you're arguably directly manipulating the AST much of the time. If the changes in Ruby 2.0 can take us closer to this level of program design, where we think more about building language up to the problem domain instead of objects and mechanism, sign me up.
Design for test vs. design for API
How many design considerations are there in an almost trivial method? Let's look at two of them. Consider this code:
def publish!
self.update_attributes(created_at: Time.now)
end
If you've been studying OO design and the SOLID principles, using TDD as a practice to guide you towards those ideas, there's a missing piece here. The reference to Time
is a dependency that should be injected. In Ruby, it's really easy for us to fix that:
def publish!(time=Time.now)
self.update_attributes(created_at: time)
end
I suspect a lot of TDDers would instinctively write the above first, skipping the first version by force of habit. But, let's stop and think about what the drives us to want the second version.
The strength of the second version is that it is designed for test. If we need to test how this model behaves when it is published at night, or on a leap day, or the day before Arbor Day, injecting the time object makes that easier.
There are some other test-focused design direction this method could go. We could create our own object whose role is to hand out timestamps, which would allow us to reasonably stub out the time reference, instead of injecting it. I'll bet there are other approaches lurking out there as well.
I want to look at another set of design considerations. I could design this code for testability, which often leads me to code that follows the SOLID principles which often leads me to decoupled code that is easier to change later. To many people, that's a good thing.
However, there's another lens I can look through: API design. How does this method hold up as a piece of behavior that developers will leverage?
Strictly speaking, the TDD'd version is a more complicated API. Even adding one optional parameter to a method carries "mass". Consider documenting the parameter-less version:
Publishes the current post. The
created_at
timestamp is set to the current time. Returns thecreated_at
timestamp.
For numbers sake, it's 40 words. More importantly, it reads linearly. Now let's look at the dependency-injected version:
Publishes the current post. By default,
created_at
is set to the current time. Optionally, callers may pass in aTime
object, or any object that returns aTime
object when sent thenow
message. Thecreated_at
column is set according to thatTime
value. Returns the value of thecreated_at
timestamp.
This one is 54 words. That's not too many more, numerically, but notice that the explanation is no longer linear. There's a default, easy case where I don't care about the timestamp. Then there's a clever case where I do care about the timestamp. In real API documentation, I'd need to specify when and why I'd want to use that clever case and what it looks like.
There's some further potential trouble lurking in this API. What if a caller passes in the wrong kind of Time
object? What if sending the now
message raises an exception? Those are important parts of the API too, both from a behavior specification perspective and when considering the user experience of using this API in code and possibly troubleshooting it when things go wrong.
My point is, that optional argument is starting to look rather weighty. Adding the code is pretty trivial. The possible interactions with the optional argument and its support cost is where it gets expensive. Like many things, it's a trade-off.
I won't claim to know which of these is better. Honestly, I think it comes down to a subjective view on what's important: test design, or API design. This is where I can't make a bold-sounding prognosis. I believe that design, even of code, is about deciding what to leave out. Everyone has to decide what to leave out for themselves.
Declaring coupling
A lot of discussions on software design end up focusing on dependencies and coupling. In short, hell is dependencies and the couplings it produces. It's a tricky problem because its hard to look at some program text and see all of its dependencies; some of them require intelligence to recognize.
In Ruby, we don't have very good ways to declare a class's dependencies and no ways ways to declare its couplings. We can describe a project's dependencies with a Gemfile or a file's dependencies with requires. The trick is that these specifications often explode in complexity. Requiring Ruby's thread library brings in some thread-safe data structures like queues and condition variables. Requiring ActiveRecord brings in a world of dependencies and causes a number of behavioral changes to Ruby that some consider impolite.
In some tinkerings with Clojure this weekend, I was struck how the ns
function is more effective at both declaring dependency and coupling and in restricting the possible distress those qualities may bring. Consider this snippet from my weekend project:
(ns hrq.routes
(:use compojure.core
hrq.core)
(:require [compojure.route :as route]
[compojure.handler :as handler]
[ring.middleware.params :as params]))
I have pedantic quibbles with ns
, but I like what's happening here. This file can only use the functions in compojure.core
and hrq.core
with no namespace qualifications. This file can only use the functions from compojure.route
, compojure.handler
and ring.middleware.params
when they are qualified with the proper prefix. So now I have a very good idea of what code this particular file depends on and where I should look to find behavior that this file is subject to.
To a lesser extent, I have a good guess about what state this file depends on. If there are dynamically scoped variables (pardon me if those are the wrong Clojure/Lisp words) in the dependencies declared for this file, I would need to care about them. If those files are pure behavior (i.e. referentially transparent pure functions), I have nothing to worry about.
Clojure isn't perfect in this regard; it does allow mutations and state changes outside of functions. It's not strictly referentially transparent like Haskell is. The tradeoff is worthwhile, in my opinion. Admit some possible coupling in exchange for ease of building typical programs.
I'm not sure that Clojure is inherently superior to Ruby in this regard. It's possibly a momentary cultural advantage, a reaction by those who were burned by expansive, implicit dependencies in Ruby and other languages. That said, it's a good example of Clojure’s considered separation of concerns solving problems that are quite thorny in other languages.
Intermediate variables, organizing OO, meeting Grinders half way
I work with Dave Copeland at LivingSocial, but not on the same team. Maybe someday I’ll fix that, but for now I learn a lot from his writings. Herein, a few things worth checking out yourself.
If you ever need to read my code, you’ll eventually come to suspect I have a particular dislike for intermediate variables. You’ll come to suspect this through finding lots of uses of inject
and tap
, two Ruby methods not everyone is on good terms with. You can imagine I’d side with Dave on the subject of Tap versus intermediate variables. You’d be right, but Dave says it so well, you should read his take on the joy of tap
. He also shows how to annoy people with tap
-like constructs in other languages. If you’re into combinators, Reg Braithwaite has written about tap
in terms of Kestrels.
I’ve been learning a lot about how to think about organizing a non-trivial object-oriented system this year. Gary Bernhardt is doing some fantastic work explaining a hybrid imperative/object/functional system. If you don’t have time to dive into Gary’s entire backlog (it’s worth it to find the time), Dave covers some similar ground describing the only four types of classes in your OO system. Think of these as a post-hoc observation on how many systems seem to evolve; Record objects take root, Service objects reveal themselves (often intertwined amongst other objects), Builders are sprinkled throughout, and there are a few classes hanging out that you wish you’d made immutable. These are handy guides for thinking about and refactoring an existing design. That said, I think it would be overkill to start a design with these archetypes. Caveat: some developers will really dislike organizing a system this way; tread carefully.
I’ve written about the virtues of The Grinder. I know a lot of non-Grinders wish that the Grinder knew more about how to take the code they’ve made to work and improve it so that it is more malleable in the future. Making it Right: Technical Debt vs. Slop sets out a good mindset on how this can happen. Think before you type, write a test, make it work, and then tidy it up with future malleability in mind. From there, non-Grinders need to meet Grinders in the middle is in shrinking the feedback cycle. When tests are too much effort to write or take too long to run, Grinders fall back to their old habits. When making it right involves too many intermediate steps with nothing to show for it, Grinders move on to the next thing. When a non-Grinder learn to be less precious with our work, or a Grinder learns to take a moment to round off the sharp corners on their work, you end up with a much stronger team. Fight for it.
Why I'm down on hypermedia containers
In response to my hypermedia opinions, Mike Kelly said:
These two seem to conflict: “In my opinion, abstract container formats aren’t useful.” and “Just use JSON”. People normally talk about “generic” media types, but they don’t have to a “container” at all, they can simply add conventions for linking. Having conventions for this stuff is useful because it allows us to build tooling around it, if everyone reinvents the wheel in their own way then we can’t build re-usable code. For a similar reason, “specifying your own custom MIME types” is not a good idea – there’s also the time cost associated with doing that. If you use something like hal+json you avoid that cost, and can concentrate on establishing your API’s workflows via link relations.
The logic behind the madness goes like this: abstract containers aren’t solving a problem I currently have. Unfortunately, this means they create problems for me. In the end, I’m building an API to provide functionality, not as advocacy.
As of summer 2012, there are ideas like HAL, JSON collections, etc. and specifications of those ideas. There are very few implementations. As a service provider, I wouldn’t actually get any benefit out of using those formats. The convention, within my own API, that fields ending in _url
are links is sufficient. I’d actually end up net negative, because I’d have to explain how e.g. HAL works and support client developers seeking to understand how to work with it. Anyone building to my API would likely end up having to write their own HAL code, so they don’t benefit much either.
I’ve decided to use JSON because providing an API that returns HTML and expects users to scrape it via selectors is extremely confusing to developers. Keep in mind, not everyone is savvy to the latest development trends. To them, an API means an HTTP service that returns XML or JSON. If I were to embrace HTML as a response type, I’m again stuck with explaining a new concept to client developers.
If it’s not obvious yet, one of my main principles in adopting hypermedia is to avoid educating developers on hypermedia as much as possible. I’m in the game of providing a useful API, not a system that shows off the possibilities of hypermedia and how deeply committed I am to its theories.
Finally, I’ve chosen to craft my own content types because I need some kind of contract with client developers that tells them what kind of data they can expect to see, plus some documentation that expresses that contract in a way that is easy for humans to understand. An RFC-style specification that states what a content type MUST/MAY/SHOULD include is exactly the right kind of abstraction. It allows me to update the specification with a version identifier and specify how each revision changes in terms of what data is available. Further, I found a content type the most tractable solution for specifying the input formats supported by PUT and POST endpoints. None of the abstract containers that existed as of August 2012 fit my needs for specifying links, structure, and how to submit data via POST
/PUT
/PATCH
.
Honestly, link traversal and machine-to-machine interaction are not the pain I’m feeling. What I want is the simplest possible API that allows potential client developers to understand what they can do via our API and how to do it. Further, I want it to be possible, even if it’s not magical or easy, to change the API in the future so I’m not constricted by its current design. I feel no need to apply all of the hypermedia principles to make something useful; I can cherrypick some of the hypermedia principles and still achieves the goal of an API that is stable but not set in stone.
Hypermedia opinions
Through the Gowalla API, and now the Sifter API, I’ve worked with a couple systems one could reasonably call a hypermedia API. Since smart people are talking about them today, I feel compelled to throw in my two cents.
A URL-based API alone won’t prevent breakage, as many point out. It won’t even prevent people from figuring out how to extract IDs and continue to bang URL strings together. I don’t know why creating URL strings is the security blanket of many API client developers, but it seems you can’t take it away from a few of them.
That said, a URL-based API does make life easier for good actors. Client developers, especially the ones that can’t upgrade their deployed applications quickly, can sleep better knowing that the upstream API could change and their code won’t break. Service developers can rest easy knowing it’s not easy, but it is possible to change their URL structure if they need to.
A hypermedia API means you can’t skip on documentation. In fact, it means you probably need better documentation. You’ll need to explain what each link in the API responses means (is photos_url
the URL for my photos, or my friends’ photos?) and what kind of data they can expect that URL to return (a photo object? a collection of photo objects? a user?) As I mentioned before, a lot of client developers don’t have their minds wired for hypermedia yet, so you’ll need a lot of examples for how they should build their clients.
In my opinion, abstract container formats aren’t useful. Squeezing your application’s data into someone else’s data model is not a great place to live. That said, I do think specifying your own custom MIME types is a promising idea. You specify each of the content types in your API, RFC-style, and then provide examples of that data. Should you need to change your response formats, you update your RFC-style specification, adding a new field as a “MAY provide” type feature.
The rub of this approach is that you can end up with an explosion of response-type handling code in your application. Again, it’s not that hypermedia principles make it easy, they just make it possible.
I’ve seen some hypermedia APIs expressed as hypertext, using HTML and cleverness. I don’t see the benefit of this. Just use JSON, and maybe specify your own response types.
The shortcoming in much of the hypermedia content out there is they focus on clients reading data and hand-wave over clients that need to create or update data. I can see why; I think that machine discoverable parameters are a tarpit. Trying to tell a client what fields the server will accept, what types are accepted, and then handle all the possible error flows is verbose and riddled with edge-cases. The way I’m solving this is to specify, in the documentation, which URLs clients can expect to POST
/PUT
/etc. data to and what content-type those URLs expect to receive. I’m pretty much doing the least hypermedia-ish thing possible, but I think that’s the correct choice right now.
The great thing about the principles of hypermedia APIs and its discourse if a focus on workflows. The biggest mistake I’ve made in building APIs is in exposing a database instead of a service. No one cares what your table structure or domain model looks like. They want to fetch some data, preferably indexed in a way that is immediately useful to them, and maybe write some data back. When you look through this lens, API design starts to look a lot like UI design. It’s fun!
You should read about hypermedia APIs, if only to challenge your thinking. It helped me a lot to do just that. Check out Steve Klabnik’s Designing Hypermedia APIs; it’s not perfect, but it makes a lot of these things easier to grasp.
In the end, I think designing, building, and supporting a hypermedia API is hard. You may be better off with an RPC-over-HTTP, or ID-based REST design. The good news is you can benefit from an incremental application of the principles of hypermedia APIs; you don’t have to go all in before you get something out of it.