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Ideas for the clever mercenary who’ll never learn Lisp

This talk is dedicated to those who bring guns to knife fights.

And tactical explosives. And smoke bombs. And third parties who think your enemies are their enemies.

“Of course there are ways you could have won... There always are, in every lost battle. The world around us redounds with opportunities, explodes with opportunities, which nearly all folk ignore because it would require them to violate a habit of thought...”



This talk won't advocate a particular tool. In fact, it's worth maintaining a healthy dose of skepticism when confronted with advocacy. Particularly when the advocate isn't institutionally accountable to you.

Program in pictures: data-directed programming.

Once I had to deal with a confusing subproject. It seemed simple, but our first implementation was wrong. We soon realized that no one could easily articulate how the thing was supposed to act.

So I sat down with the Product Owner, and we used the language of basic set theory to describe it. But the spec still turned out buggy.

The next time, I started writing down a simple visual matrix and asked him to fill it in. He improved it, and so it looked something like:

Normal spec

And under different conditions:

Text-enabled spec

At first, I coded it in the usual, rather ugly way. Then I slapped my forehead. Why not translate the picture from paper to the screen?

One Lisp tradition is for your code to follow the visual metaphor. Since we worked in node.js, I transformed it into the following spec:

normalSpec = {
  Ra: {Aa: 'video', Av: 'video', Al: 'video', An: 'video'},
  Rv: {Aa: ' ',     Av: 'video', Al: ' ',     An: 'video'},
  Rl: {Aa: ' ',     Av: ' ',     Al: 'video', An: 'video'},
  Rn: {Aa: ' ',     Av: ' ',     Al: ' ',     An: 'video'},

textEnabledSpec = {
  Ra: { Aa: 'text',  Av: 'text',  Al: 'text',  An: 'video'},
  Rv: { Aa: ' ',     Av: 'video', Al: ' ',     An: 'video'},
  Rl: { Aa: ' ',     Av: ' ',     Al: 'video', An: 'video'},
  Rn: { Aa: ' ',     Av: ' ',     Al: ' ',     An: 'video'},

// Elided about 30 significant lines of lower-level "plumbing" code,
// which executes both datastructures.

This is simple to extend. And if we no longer need one of them, it's trivial to delete. Spec bugs are simpler to fix.

You can also use it to automatically generate test-cases. And it could've easily come from the network, as JSON.

This technique wasn't new to me. Once upon a time, I wrote rich-client GUIs using LispWorks, which has a wonderful GUI language like this.

A political note

Unfortunately, such simple techniques may result in "fear, uncertainty and doubt". They appear unusual to others; and easily enter the realm of politics. You may experience resistance.

To understand this, note that institutions evolve ways to defend themselves from dangerous technological choices; otherwise they'll more likely fail to accomplish their roles. Also note that programmers are trained by society to understand only a very restricted part of the programming cosmos. (To more reliably produce a software workforce.)

Combine these two factors, and you can predict political resistance to anything which deviates from that narrow range.

Monitoring graphs

For another project, we used Munin, which monitors your systems and displays colorful graphs. I wrote a little data-driven language which took counters out of Redis and graphed them:

var graph = {type: 'multigraph',
             name: 'GETs',
             roots: [{type        : 'combined',
                      title       : 'GET failures (click for more detailed graphs!)',
                      description : 'Failures GETing from cloud',
                      units       : 'fails/\${graph_period}',
                      subgraphs   : [{type     : 'plain',
                                      category : 'articles',
                                      item     : 'failure'},

                                     {type     : 'plain',
                                      category : 'comments',
                                      item     : 'failure'}]}],

             backings: [{type     : 'plain',
                         category : 'articles',
                         item     : 'success'},

                        {type     : 'plain',
                         category : 'articles',
                         item     : 'failure'},

                        {type     : 'plain',
                         category : 'comments',
                         item     : 'success'},

                        {type     : 'plain',
                         category : 'comments',
                         item     : 'failure'}]}

A machine-unreadable spec

Recently, I had to implement the OpenRTB spec, which is just this PDF which isn't human-readable. I was certainly not going to tediously hand-code this. So I just ran the PDF through some sort of pdf2txt, and then had Emacs record my keystrokes while I massaged part of it into Clojure datastructures (something like the JavaScript/JSON examples above). So I could replay that massage over the entire spec.

Now it was completely in a machine-readable and -manipulable format. About 1000 lines of neatly indented data. So I wrote some Clojure to generate a perfectly-commented JavaScript skeleton from it. (The comments came from the human-readable descriptions.)

At that point, all I had to do was customize that JavaScript skeleton, putting in the proper values.

(I could also use it to help generate some test cases and documentation browser.)

Concluding thoughts

Were these examples of data, or code? In the Lisp perspective, the line between the two is blurred — if there is a real line. Data is powerful. You can interpret in different ways. (As code, tests, etc.) If you express a language in it, as in our examples above, you can operate on code using your powerful data-handling muscles. You can store it somewhere, or pull it from the network. Many possibilities for the enterprising mercenary.

Sculpt your program while it runs: incremental development.

I once had a job where we had a legacy Python codebase. ("Legacy" means "painful" and "not written by me".) There were no unit tests; to test the program, you ran it. And waited.

The codebase's first programmer once told me that he built job security by intentionally writing spaghetti.

All this wrecks your design->edit->debug cycles. When you just want to run a tiny bit of code, it's unnecessary to have to pull in the whole rest of the program. Much nicer to write layers of small code: with things broken down into abstractions, which you compose like virtual Lego.

My solution was to write standalone modules, designed to run alongside an interactive REPL. A single keystroke executed the sourcecode I worked on, so I could grow my systems incrementally.

I believe this led to notably simpler, more composable code.

(Note that SQL and Unix have REPLs too — "interactive prompts". They allow you to interact with a live, growing system. Rather than stopping and restarting it each time.)

Explore 3rd parties

Recently, I wanted to get closely acquainted with Amazon's EC2 API for Java. With a dynamic language and my favorite IDE (Clojure and Emacs), I was able to quickly explore and visualize the API. Here is how my IDE's "inspector" printed out a Java object:

{InstanceId: ...}
Type: class
Value: {InstanceId: ...}
  instanceId:            i-nnnnnnnn
  imageId:               ami-nnnnnnnn
  state:                 {Code: 16, Name: running, }
  keyName:               Staging server
  amiLaunchIndex:        0
  productCodes:          []
  instanceType:          m1.large
  launchTime:            Mon Nov 2 20:00:00 CET 2011
  placement:             {AvailabilityZone: eu-west-1a, GroupName: , Tenancy: default, }
  kernelId:              aki-nnnnnnnn
  monitoring:            {State: disabled, }

Be deadly with one scripting language, and one editor/IDE.

Commonly, you need to deal with all sorts of "meta" stuff, which isn't directly about coding. Such as getting data from Riak and stuffing it into MySQL, munging text, generating bits of code (like print statements), etc.

Many people follow tedious steps like:

  1. Run curl to get data out of Riak.
  2. Pipe it into a file.
  3. Open it with Chrome, for readable JSON.
  4. Copy bits of it into their clipboard.
  5. Paste it into MySQLAdmin.

But why not rely on the power of a general-purpose programming language? One designed to handle data powerfully and interactively?

(Maybe the first time it takes longer, but next time you've got a reusable snippet of code which you can take anywhere.)

Which scales? During an emergency, I needed to transform gigabytes of data from a MySQL database. So I just loaded it into an Amazon cloud image, cloned it 20 times, and reimplemented a poor man's MapReduce in Python over it all.

Build a strong domain language (without heavy DSLs or macros).

"Macros" are commonly advertised as Lisp's big power. (They allow you to safely transform code which Lisp doesn't understand, into a form which Lisp does. To keep your program fast, this is generally done before runtime.)

But you don't always need this power. Macros can be less dynamic and flexible than the data+functions of our previous examples.

One way to look at those examples is to view them as building languages to express our thoughts in.

Work on simplicity.

There's an interesting movement towards simplicity in the Clojure world.

"Simplicity Matters" keynote:

For your convenience, Clojure's creator coined the term "complected". At work, you can complain, "This code is complected", meaning that there are multiple concepts intertwined.

Why does it matter? You've probably had the experience of enormous productivity when you start a project. Then some mysterious force slows your team down, eventually grinding productivity to a near-halt, regardless how "Agile" your process is.

It may have fallen victim to a force of software architecture, where your program is unable to grow. Like buildings which fell under their own weight, before innovations like arches.

Like the rather extreme legacy Python spaghetti code we discussed earlier, where to understand one part, you're forced to understand many other parts.

Clojure can be evaluated as a case-study on how pursuing simplicity is integral to good, truly agile design.

Program like a gamer.

The Smalltalk community has a feature where you can save an "image" of your running program, and run it later. Like a virtual machine, or a game you can save and reload. Some Lisp implementations support this too.

I never used this technique personally, but I have known people in finance who shipped their personal trading platforms as LispWorks images. When it was time for a software update, these apps would just connect to the server and live-patch themselves.

My understanding is that many Smalltalk programs live as images, not textual sourcecode.

(That said, I haven't used this personally, so can't knowledgeably evaluate this practice. Aside from virtual machines like VMWare, which I use all the time.)

Empower your users with some of your power.

One technique is to give users simple defaults; and a way for "power users" to decide tradeoffs for themselves.

Macros are one example of allowing users to (safely) mold the language, to better fit their needs. Such a feature assumes that you're more of an expert in your domain than some language designer who may know nothing about your field.

Another example is the (perhaps intimidating-sounding) metaobject protocol. What does it mean? Meta- means "about", so it's about objects. A protocol is rules it follows.

So, the metaobject protocol allows you to change the rules of your object system, giving you guarantees that it will operate in a certain way (which you can depend on). Because along the way, the OOP system designers made some tradeoffs for the common case, but which may be the totally wrong tradeoff for your project.

Perhaps the most common example is having objects backed by a database. I did this in Python once, with MongoDB.

A more exotic example came from Boeing, which has used classes and instances to design planes. (Veitch, Handbook of Programming languages Vol. 4) Some parts you could model as normal object variables, like the length of the passenger compartment. But other parts need to be calcuated from them, like seating capacity. It's like a large spreadsheet, where some cells depend on others.

Instead of eagerly recalculating all the time, you can use the metaobject protocol to calculate parts lazily, only as you request them.

Let your tools operate on themselves.

There's many forms of introspection. Here's an example taken from:

CL-USER> (defun add (x y) (+ x y))

CL-USER> (disassemble 'add)
;; disassembly of #<Function ADD>
;; formals: X Y

;; code start: #x737496f4:
   0: 55       pushl ebp
   1: 8b ec    movl ebp,esp
   3: 56       pushl esi
   4: 83 ec 24 subl esp,$36
   7: 83 f9 02 cmpl ecx,$2
  10: 74 02    jz 14
  12: cd 61    int $97              ; SYS::TRAP-ARGERR
  14: 80 7f cb 00 cmpb	[edi-53],$0 ; SYS::C_INTERRUPT-PENDING
  18: 74 02    jz 22
  20: cd 64    int $100             ; SYS::TRAP-SIGNAL-HIT
  22: 8b d8    movl ebx,eax
  24: 0b da    orl ebx,edx
  26: f6 c3 03 testb bl,$3
  29: 75 0e    jnz 45
  31: 8b d8    movl ebx,eax
  33: 03 da    addl ebx,edx
  35: 70 08    jo 45
  37: 8b c3    movl eax,ebx
  39: f8       clc
  40: c9       leave
  41: 8b 75 fc movl esi,[ebp-4]
  44: c3       ret
  45: 8b 5f 8f movl ebx,[edi-113]   ; EXCL::+_2OP
  48: ff 57 27 call *[edi+39]       ; SYS::TRAMP-TWO
  51: eb f3    jmp	40
  53: 90       nop
                                    ; No value

Now add optimizations to the "add" function, and see what it compiles to:

(defun add (x y)
  (declare (optimize (speed 3) (safety 0)))
  (declare (fixnum x y))
  (the fixnum (+ x y)))

CL-USER> (disassemble 'add)
;; disassembly of #<Function ADD>
;; formals: X Y

;; code start: #x7374dc34:
   0: 03 c2    addl eax,edx
   2: f8       clc
   3: 8b 75 fc movl esi,[ebp-4]
   6: c3       ret
   7: 90       nop
; No value

Piggyback on strong systems.

I've heard of Clojure dialects targeting:

  • Java
  • .NET
  • JavaScript
  • Python

Clojure programmers expect to assimilate many advantages (and disadvantages) of their host platforms. For example, Java is an enormous ecosystem; but Python seems more attractive for those commandline scripts where immediate startup time is valuable.

It is worth considering how you can turn the systems you encounter to your advantage, even if they at first appear hostile to you.