Author Archives: zo0ok

All JavaScript objects are not equally fast

One thing I like with JavaScript and NodeJS is to have JSON in the entire stack. I store JSON on disk, process JSON data server side, send JSON over HTTP, process JSON data client side, and the web GUI can easily present JSON (I work with Angular).

As a result of this, all objects are not created the same. Lets say I keep track of Entries, I have an Entry-constructor that initiates new objects with all fields (no more no less). At the same time I receive Entry-objects as JSON-data over the network.

A strategy is needed:

  1. Have mix of raw JSON-Entries and Objects that are instanceof Entry
  2. Create real Entry-objects from all JSON-data
  3. Only work with raw JSON-Entries

Note that if you don’t go with (2) you can’t use prototype, expect objects to have functions or use instanceof to identify objects.

Another perhaps not obvious aspect is that performance is not the same. When you create a JavaScript object using new the runtime actually creates a class with fast to access properties. Such object properties are faster than

  • an empty object {} with properties set afterwards
  • an object created with JSON.parse()

I wrote a program to test this. The simplified explanation is that I obtained an array of objects that I then sorted/calculated a few (6) times. For a particular computer and problem size I got these results:

3.3s       R       Produce random objects using "new"
4.4s       L       Load objects from json-file using JSON.parse()
3.0s       L2      json-file, JSON.parse(), send raw objects to constructor
3.2s       L3      load objects using require() from a js-file

I will be honests and say that the implementation of the compare-function sent to sort() matters. Some compare functions suffered more or less from different object origins. Some compare functions are more JIT-optimised and faster the second run. However, the consistent finding is that raw JSON-objects are about 50% slower than objects created with new and a constructor function.

What is not presented above is the cost of parsing and creating objects.

My conclusion from this is that unless you have very strict performance requirements you can use the raw JSON-objects you get over the network.

Below is the source code (for Node.js). Apart from the parameters R, L, L2 and L3 there is also a S(tore) parameter. It creates the json- and js-files used by the Load options. So typically run the program with the S option first, and then the other options. A typicall run looks like this:

$ node ./obj-perf.js S
Random: 492ms
Store: 1122ms

$ node ./obj-perf.js R
Random: 486ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 3350ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 3361ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 3346ms

$ node ./obj-perf.js L
Load: 376ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 4382ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 4408ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 4453ms

$ node ./obj-perf.js L2
Load: 654ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 3018ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 2974ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 2890ms

$ node ./obj-perf.js L3
Load: 1957ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 3436ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 3264ms
DISTS=110463, 110621, 110511, 110523, 110591, 110515 : 3199ms

The colums with numbers (110511) are checksums calculated between the sorts. They should be equal, otherwise they dont matter.

const nodeFs = require('fs');

function Random(seed) {
  this._seed = seed % 2147483647;
  if (this._seed <= 0) this._seed += 2147483646;
} = function () {
  return this._seed = this._seed * 16807 % 2147483647;

function Timer() {
  this.time =;

Timer.prototype.split = function() {
  var now =;
  var ret = now - this.time;
  this.time = now;
  return ret;

function Point() {
  this.a = -1;
  this.b = -1;
  this.c = -1;
  this.d = -1;
  this.e = -1;
  this.f = -1;
  this.x =  0;

function pointInit(point, rand) {
  var p;
  for ( p in point ) {
    point[p] = % 100000;

function pointLoad(json) {
  var p;
  var point = new Point();
  for ( p in point ) {
    point[p] = json[p];
  return point;

function pointCmp(a,b) {
  return pointCmpX[a.x](a,b,a.x);

function pointCmpA(a,b) {
  if ( a.a !== b.a ) return a.a - b.a;
  return pointCmpB(a,b);

function pointCmpB(a,b) {
  if ( a.b !== b.b ) return a.b - b.b;
  return pointCmpC(a,b);

function pointCmpC(a,b) {
  if ( a.c !== b.c ) return a.c - b.c;
  return pointCmpD(a,b);

function pointCmpD(a,b) {
  if ( a.d !== b.d ) return a.d - b.d;
  return pointCmpE(a,b);

function pointCmpE(a,b) {
  if ( a.e !== b.e ) return a.e - b.e;
  return pointCmpF(a,b);

function pointCmpF(a,b) {
  if ( a.f !== b.f ) return a.f - b.f;
  return pointCmpA(a,b);

var pointCmpX = [pointCmpA,pointCmpB,pointCmpC,pointCmpD,pointCmpE,pointCmpF];

function pointDist(a,b) {
  return Math.min(

function getRandom(N) {
  var i;
  var points = new Array(N);
  var rand   = new Random(14);

  for ( i=0 ; i<N ; i++ ) {
    points[i] = new Point();
    n = pointInit(points[i], rand);
  return points;

function test(points) {
  var i,j;
  var dist;
  var dists = [];

  for ( i=0 ; i<6 ; i++ ) {
    dist = 0;
    for ( j=0 ; j<points.length ; j++ ) {
      points[j].x = i;
    for ( j=1 ; j<points.length ; j++ ) {
      dist += pointDist(points[j-1],points[j]);
  return 'DISTS=' + dists.join(', ');

function main_store(N) {
  var timer = new Timer();
  points = getRandom(N);
  console.log('Random: ' + timer.split() + 'ms');
  nodeFs.writeFileSync('./points.json', JSON.stringify(points));
  nodeFs.writeFileSync('./points.js', 'exports.points=' +
                                      JSON.stringify(points) + ';');
  console.log('Store: ' + timer.split() + 'ms');

function main_test(points, timer) {
  var i, r;
  for ( i=0 ; i<3 ; i++ ) {
    r = test(points);
    console.log(r + ' : ' + timer.split() + 'ms');

function main_random(N) {
  var timer = new Timer();
  var points = getRandom(N);
  console.log('Random: ' + timer.split() + 'ms');
  main_test(points, timer);

function main_load() {
  var timer = new Timer();
  var points = JSON.parse(nodeFs.readFileSync('./points.json'));
  console.log('Load: ' + timer.split() + 'ms');
  main_test(points, timer);

function main_load2() {
  var timer = new Timer();
  var points = JSON.parse(nodeFs.readFileSync('./points.json')).map(pointLoad);
  console.log('Load: ' + timer.split() + 'ms');
  main_test(points, timer);

function main_load3() {
  var timer = new Timer();
  var points = require('./points.js').points;
  console.log('Load: ' + timer.split() + 'ms');
  main_test(points, timer);

function main() {
  var N = 300000;
  switch ( process.argv[2] ) {
  case 'R':
  case 'S':
  case 'L':
  case 'L2':
  case 'L3':
    console.log('Unknown mode=' + process.argv[2]);


Review: NUC vs Raspberry Pi

I like small, cheap, quiet computers… perhaps a little too much. For a long time I have used a Raspberry Pi V2 (QuadCore@900MHz and 1GB RAM) as a workstation. To be honest, I have not used it for web browsing, that is just too painful. But I have used it for programming and running multiple Node.js services, and a few other things.

Despite there are so many single board computers it is hard to find really good alternatives to the Raspberry Pi. And when I look into it, I find that Intel NUCs are very good options. So, I just decided to replace my RPi2 workstation with the cheapest NUC that money can currently buy: the NUC6CAY with a Celeron J3455 CPU. It sounds cheap, particularly for something server like. The interesting thing with the J3455 CPU is that it is actually Quad Core, with no hyper threading. To me it sounds amazing!

I also have an older NUC, a 54250WYKH with an i5 CPU.

Raspberry Pi V2:   ARMv7    4 Cores      900MHz                  1GB RAM
NUC                Celeron  4 Cores      1500MHz (2300 burst)    8GB RAM
NUC                i5       2 Cores (HT) 1300MHz (2600 burst)   16GB RAM

I/O is obviously superior for the NUCs (both using SSD) versus the RPI v2 having a rotating disk connected to USB. But for my purposes I think I/O and (amount of) RAM makes little difference. I think it is more about raw CPU power.

Node.js / JavaScript
When it comes to different Node.js applications, it seems the older i5 is about twice as fast as the newer Celeron (for one Core and one thread). I would say this is slightly disappointing (for the Celeron). On the other hand the Celeron is about 10x faster than the RPi V2 when it comes to Node.js code, and that is a very good reason to use a NUC rather than a Raspberry PI.

JavaScript: await async

With Node.js version 8 there is finally a truly attractive alternative to good old callbacks.

I was never a fan of promises, and implementing await-async as a library is not pretty. Now when await and async are keywords in JavaScript things change.

The below program demonstrates a simple async function doing IO: ascertainDir. It creates a directory, but if it already exists no error is thrown (if there is already a file with the same name, no error is thrown, and that is a bug but it will do for the purpose of this article).

There are four modes of the program: CALLBACK, PROMISE, AWAIT-LIB and AWAIT-NATIVE. Creating a folder (x) should work. Creating a folder in a nonexisting folder (x/x/x) should fail. Below is the output of the program and as you see the end result is the same for the different asyncronous strategies.

$ node ./await-async.js CB a
Done: a
$ node ./await-async.js CB a/a/a
Done: Error: ENOENT: no such file or directory, mkdir 'a/a/a'

$ node ./await-async.js PROMISE b
Done: b
$ node ./await-async.js PROMISE b/b/b
Done: Error: ENOENT: no such file or directory, mkdir 'b/b/b'

$ node ./await-async.js AWAIT-LIB c
Done: c
$ node ./await-async.js AWAIT-LIB c/c/c
Done: Error: ENOENT: no such file or directory, mkdir 'c/c/c'

$ node ./await-async.js AWAIT-NATIVE d
Done: d
$ node ./await-async.js AWAIT-NATIVE d/d/d
Done: Error: ENOENT: no such file or directory, mkdir 'd/d/d'

The program itself follows:

     1	var nodefs = require('fs')
     2	var async = require('asyncawait/async')
     3	var await = require('asyncawait/await')
     6	function ascertainDirCallback(path, callback) {
     7	  if ( 'string' === typeof path ) {
     8	    nodefs.mkdir(path, function(err) {
     9	      if (!err) callback(null, path)
    10	      else if ('EEXIST' === err.code) callback(null, path)
    11	      else callback(err, null)
    12	    })
    13	  } else {
    14	    callback('mkdir: invalid path argument')
    15	  }
    16	};
    19	function ascertainDirPromise(path) {
    20	  return new Promise(function(fullfill,reject) {
    21	    if ( 'string' === typeof path ) {
    22	      nodefs.mkdir(path, function(err) {
    23	        if (!err) fullfill(path)
    24	        else if ('EEXIST' === err.code) fullfill(path)
    25	        else reject(err)
    26	      })
    27	    } else {
    28	      reject('mkdir: invalid path argument')
    29	    }
    30	  });
    31	}
    34	function main() {
    35	  var method = 0
    36	  var dir    = 0
    37	  var res    = null
    39	  function usage() {
    40	    console.log('await-async.js CB/PROMISE/AWAIT-LIB/AWAIT-NATIVE directory')
    41	    process.exit(1)
    42	  }
    44	  switch ( process.argv[2] ) {
    45	  case 'CB':
    46	  case 'PROMISE':
    47	  case 'AWAIT-LIB':
    48	  case 'AWAIT-NATIVE':
    49	    method = process.argv[2]
    50	    break
    51	  default:
    52	    usage();
    53	  }
    55	  dir = process.argv[3]
    57	  if ( process.argv[4] ) usage()
    59	  switch ( method ) {
    60	  case 'CB':
    61	    ascertainDirCallback(dir, function(err, path) {
    62	      console.log('Done: ' + (err ? err : path))
    63	    })
    64	    break
    65	  case 'PROMISE':
    66	    res = ascertainDirPromise(dir)
    67	    res.then(function(path) {
    68	      console.log('Done: ' + path)
    69	    },function(err) {
    70	      console.log('Done: ' + err)
    71	    });
    72	    break
    73	  case 'AWAIT-LIB':
    74	    (async(function() {
    75	      try {
    76	        res = await(ascertainDirPromise(dir))
    77	        console.log('Done: ' + res)
    78	      } catch(e) {
    79	        console.log('Done: ' + e)
    80	      }
    81	    })());
    82	    break
    83	  case 'AWAIT-NATIVE':
    84	    (async function() {
    85	      try {
    86	        res = await ascertainDirPromise(dir)
    87	        console.log('Done: ' + res)
    88	      } catch(e) {
    89	        console.log('Done: ' + e)
    90	      }
    91	    })();
    92	    break
    93	  }
    94	}
    96	main()

Please note:

  1. The anonymous function on line 74 would not be needed if main() itself was async()
  2. The anonymous function on line 84 would not be needed if main() itself was async
  3. A function that returns a Promise() (line 19) works as a async function without the async keyword.

Callback is the old simple method of dealing with asyncrounous things in JavaScript. A major complaint has been “callback hell”: if you call several functions in sequence it can get rather messy. I can agree with that, BUT I think each asyncrounous call deserves its own error handling anyway (and with proper error handling other options tend to be equally tedious).

I dont think using a promise (66-71) is very nice. It is of course a matter of habit. One thing is that not all requests in the success-path are actually success in real life, or not all errors are errors (like in ascertainDir). Very commonly you make a http-request which itself is good, but the data you receive is not good so you want to proceed with error handling. This means that the fulfill case needs to execute the same code as the reject case, for some “successful” replies. Promises can be chained, but it typically results in ignoring proper error handling.

awaitasync library
I think the syntax of the asyncawait library is rather horrible, but it works as a proof-of-concept for the real thing.

async await native keywords
With the async/await keywords in JavaScript, suddenly asyncrounous code can be handled just like in Java or C#. Since it is familiar it is appealing! No doubt it is clean and practical. I would hesitate to mix it with Callbacks or Promises, and would rather wait until I can do a complete rewrite.

Common sources of bugs in JavaScript are people trying to return from within (callback/promises) functions, people not realising the rest of the code continues to run after the asyncrous call, or things related to variable scopes. I guess in most cases the await/async makes these things cleaner and easier, but I would expect problems where it causes unexpected effects when not properly used.

Finally, if you start using async/await keywords there is no polyfill or fallback for older browser (maybe Babel can do that for you). As usual, IE seems to lag behind, and you can forget about Node v6 (or earlier). Depending on your situation, this could be a show stopper or no issue at all.

Thinking small and big (in programming)

When programming, thinking small often allows for a quick start but after a while your project slows down as it grows with pain. Based on every such painful experience there are countless of good practices in programming for thinking big. However, thinking big is difficult and comes with overhead, and if you think too big there is a risk you will only think big, and not think about your problem and your actual code very much.

I will give a number of examples of smaller and bigger thinking (don’t get stuck reading the list).

Memory usage
Small thinking: everything fits in RAM
– smaller: everything fits in CPU cache, or CPU registries
Big thinking: using external storage, streaming, compression
– bigger: scaling out

Small thinking: single process and thread
Big thinking: multi threading, sending work to other processes
– bigger: scaling out

Thinking small: portable by standard compliance
– smaller: single platform
Thinking big: target specific tweaks, build and configuration options
– bigger: target specific dependencies (Mysql for Linux, MS SQL for windows)

Source management
Small thinking: versioned tarballs
– smaller: just a single local file
Big thinking: a git/svn repository
– bigger:several repositories, bug tracker, access rights

Small thinking: single standard compile command
– smaller: no building required in the first place
Big thinking: make
– bigger: autoconf, tools and configuration required (Babel)
– even bigger: build a build-and-config-system (like menuconfig for Linux kernel)

Small thinking: assert()
Big thinking: unit tests
– bigger: test driven development, test coverage analysis
– even bigger: continuous integration

Small thinking: command line options
– smaller: hard coded
Big thinking: configuration file
– bigger: configuration (G)UI
– even bigger: download configuration, find out configuration itself, selection of different configurations (like XML-file, JSON-file or database)

Error handling
Small thinking: crash with error message
Big thinking: log file(s), verbose levels
– bigger: error recovery, using system logs (like Windows event log)
– even bigger: monitoring, choice of different external log systems

Thinking small: single CLI or GUI
Thinking big: build a backend library or server that allows for different UIs

Dependencies (code)
Thinking small: only standard library
Thinking big: require libraries (external and own code broken out to libraries)
– bigger: optional dependencies, supporting different libraries that do the same thing
– even bigger: dependencies can be loaded dynamically during run time

Dependencies (databases, services)
Thinking small: no dependencies
Thinking big: external storage
– bigger: allow multiple clients against common storage
– even bigger: distributed, scaled out, storage

Small thinking: functions and data
– smaller: rely on global data
Big thinking: encapsulation (OO-style), immutable data (FP-style)

Small thinking: functions are specific for data
Big thinking: generic functions by templates, interfaces, generators, iterators

Small thinking: code is fast enough
Big thinking: architecture allows scaling out for more performance as required

Small thinking: manual copy-replace is good enough
Big thinking: testing, continuous integration, rollback, zero-downtime

Thinking small: automation not needed since all tasks are simple enough
Thinking big: automation makes complex tasks fast and easy

One size does not fit all
The important thing to understand is that there is no silver bullet. Each program, problem or project has its own requirements and its own sweet spot of small-vs-big. But this my change over time; even if you need to be a bit big later on, it may not help in the beginning.

Perhaps obvious; it is not meaningful to say that a full CI-environment is better than assert() (and you may argue that they are entirely different things). Having global data is not (for all problems) worse than having completely immutable state. And so on.

Your need for big varies within a project: you may need a very big and configurable build process to build something that has very small requirements when it comes to scaling out.

There are no safe default choices
You need to make qualified small-vs-big choices. If you are an experienced programmer you often don’t need to think much about it. If you work within an environment where you already master tools that are available (perhaps even mandatory) you can use them with little overhead. However, if you take your environment (perhaps just an IDE, or more) for granted and you rely on it, it may not be so easy for someone else to pick up where you left.

Just as you can fall behind others who use better tools you can grow fat and fall behind those who use fewer tools (and stay smaller).

If in doubt: start small
Going small in every aspect is often not good enough (except for very isolated problems). But it can be a good start and if (or when) it fails you will learn from your mistake. You will understand how to architect your software better and you will understand why some (big) tools and practices really exist. That is good wisdom!

Going big in every aspect is most definitely going to make you fail. You may need to do this for building systems like SAP or Windows (but such large project often do fail). If you fail with something far too big it is hard to learn from it. Chances are you never really got down to the requirements and chances are much energy was spent integrating tools and frameworks into a development and operation environment that worked at all.

Small sometimes goes a long way
There are often theoretical discussions about small-vs-big. Big often looks attractive and powerful. However, some problems are just hard regardless how you solve them, and a small solution is often more right on target.

There was a macro-kernel vs micro-kernel discussion. A micro kernel is a big solution: more encapsulation, more isolation, less global data, more dynamic loading and so on. Linux is obviously more successful than HURD (the GNU micro kernel), mostly because it actually works.

Agile and Refactoring
Agile and refactoring are about encouraging you to start small, make things that are good enough for now, and fix them later on (if ever needed). Often the problem down the road is not what you expected when you started.

Architecture, Microservices, UNIX
The UNIX principle is that everything is a program that does one thing well.
Microservices is much the same thing except it spans over several networked services.

This works. Because most of the time, for most purposes, the developers (of UNIX and Microservices) can think small. Most programs in a UNIX system, like most services in a Microservice architecture, are for most practical purposes small programs or small services.

UNIX: some programs need to be highly secure, some need an interactive UI, some need to log, some have high performance requirements, some have dynamic dependencies and some are better not written in C. This is why you should build a microservice architecture (not a monolith) and this is how you should build it (unless you are as good as Torvalds and you can land a Monolith in C – but that works thanks to very good architecture and practices – and Linux is still just the Kernel in a much bigger system).

Limited time
All software projects have limited time available. Time is spent on:

  1. Understanding requirements
  2. Producing code that correctly and efficiently matches the requirements
  3. Test and deployment
  4. Solution architecture
  5. Tools and frameworks: understanding and integration

#1 deliver value even on its own (sometimes a technical solution is not even required).
#4 and #5 only deliver value if the they save total time (by lowering #2 and #3).
#2 sometimes is just not possible without #5, then please go ahead with #5.

But if #2 takes one week if you use Notepad to code a single index.html file containing HTML+CSS+JavaScript (and this solves the requirements), then there must be a good case for spending time on #4 and #5 (going big) instead of just solving the problem (staying small).

#4 and #5 produce what I call invented problems; problems that you did not have in the first place, that are not related to your requirements but comes with your tools. The most obvious example is licencing issues. If you go multithreading and/or use an external database you suddenly have deadlocks, race conditions, transactions and semaphores to worry about: is that price worth it for what you get from the database or multithreading? Deployment (and server configuration) is absolutely necessary, often rather complicated, and delivers no value to the customer what so ever.

Always ask yourself: how hard would it be to solve this problem using the smallest reasonable set of tools?

Maintain vs Replace
Many big practices are about producing maintainable code. Often this never pays off:

  • There is no need for the code anymore
  • The code does what it needs to do, and no change is required
  • Even though the code itself is maintainable, no one understands the problem and the solution well enough to actually improve (or even change) it

When (if) the moment of change actually comes, often a fresh start is anyway the best solution. If programs are made small and they do one thing well (so it is quite easy to test that another program can replace it) replacing them is not a big deal.

This means that ugliness (global variables, lack of encapsulation, hard coded limitations, lack of proper test coverage, inability to scale, and so on) often is not a problem. On the other hand, a (big) program that is not fit for purpose (not correct and efficient) never produce much value in the fist place.

Performance (and Scaling)
Golden rule of optimization:

  1. Don’t
  2. Experts only: see (1)

This is not entirely true but most of your code is not performance critical. In computing, there are two ways you can get faster:

  1. Go small: find ways to make your code require less resources
  2. Go big: assign more resources to run your code

The truth is that modern hardware is extremely powerful. Even a Raspberry Pi V1 (with 700MHz CPU and 512MB RAM) can serve enormous amounts of network requests or crunch amazingly many numbers. If a Raspberry Pi is not enough for you, you either have

  1. very many users
  2. a very complicated/large/heavy problem
  3. coded a solution that mostly wastes resources

If you know that #1 (only) is your case, go ahead and scale out big. Be sure to know your bottlenecks and seriously consider your storage model.

If #2 is your case you need to sit down and think.

If #3 is your case, you should have stayed small from the beginning. It is probably cheaper to rewrite significant parts of your solution in C (or another language that uses minimal resources) and keeping all data in RAM, than it is to scale your code out.

Availability (and Redundancy)
You may need high availability: downtime, unexpected or not, is expensive.

The big solution is to go for redundancy: if one goes down the other takes over. This can be the right thing to do – when everything else has already been tried. Sometimes the cure is worse than the disease.

The small solution is to keep your program simple and when something unexpected happens, let it crash. This way you will quite soon (pre production) have nailed out the critical errors. And if you can not make it stable no redundancy or fault-tolerant environment will really save you.

When going big understand the cost. The road to hell is sided by good intentions. Beware of grande architectures.

10 most important X-wing upgrade cards

Starting to play X-wing Miniatures can be a challenge. There are quite many cards with quite different effects and to get best effect you want them to work well together or with your pilot. Where to start?

This list is written with a Rebel (Resistance) player in mind, but many cards are very useful for Imperial or Scum lists as well. It is also quite focused on classic A/B/X/Y-wing lists.

These are the 10 upgrade cards I recommend that you familiarize with. They are all quite straight forward to play.

10. Autothrusters
For ships that can use them Autothrusters is a very good defensive measurement (much better than Stealth Device I would say).

9. Fire Control System
This is a very common upgrade for the B-wing.

8. BB-8 (Astromech)
Especially (T-65) X-wing high skill pilots (such as Luke and Wedge) can make very good use of BB-8.

7. Primed Thrusters (Tech)
This is T-70 X-wing only, and allows you too boost when you are stresed. Boosting after a red manuever allows you to position yourself in flexible and unpredictable ways. The alternative here is the slightly more expensive Pattern Analyzer which allows you any action, but just the round you get stress.

6. Targeting Astromech (Astromech)
The T-70 X-wings have 3 great red maneuvers. With Targeting Astromech you get a free target lock, and the price of a red manuever is very low.

5. Crack Shot (Elite)
A 1pt card that basically produces one extra damage once. I shield upgrade is 4p. You may have a better use for your Elite slot, but keep Crack Shot in mind, especially for A-wings who get two Elite slots (A-Wing Test Pilot) and just roll 2 red dice.

4. R3-A2 (Astromech)
To deal stress to whoever you target is usually very good. Look for pilots who can deal with stress (Nien Numb, Braylen Stramm).

3. Twin Laser Turret (Turret)
This is one of the most feared weapons in the Galaxy. 360 degrees, range 2-3, and very likely to cause 1-2 damage. Particularly good with Horton Salm (your opponent will kill him first).

2. Veteran Instinct (Elite)
Sometimes 2 extra skill makes all the difference, and 1pt is cheap. The problem is obviously that it occupies your Elite slot. Alternatively check out Adaptability for 0pt.

1. Push The Limit (Elite)
Allows you to do two actions (and get a stress). As long as you can make a green maneuver next turn that is fantastic (and you can keep pushing the limit). This is most useful for A-wings that have 4 actions to choose from and plenty of green maneuvers. Sometimes it is great for T-70 X-wings, B-wings and the Millenium Falcon (YT-1300 + Title).

Ship specific upgrades
Some cards are almost mandatory and thus not included above:

  • Integrated Astromech (X-wing)
  • Alliance Overhaul (ARC-170)
  • Chardaan Refit (A-Wing)
  • A-Wing Test Pilot (A-Wing)
  • Millenium Falcon (YT-1300)

If you fly the above ships, make sure you understand these upgrades.

Free upgrades
Some upgrades are free (except they use an upgrade slot) or saves cost. If your ship can take them, think twice before leaving them out.

  • Trick Shot (0p Elite)
  • Adaptability (0p Elite)
  • Guidance Chip (0p Modifications – missiles/torpedoes only)
  • Extra Munitions (2p – instead of second missile/torpedo/bomb)
  • Collision Detector (0p System)

Lodash Performance Sucks!

To continue my Functional Programming Sucks series of posts I will have a closer look at reduce().

I complained with Lodash (and Underscore) for different reasons. One complaint was performance, but I just read the code and presumed it was going to be slow without measuring. Then I complained with the performance of Functional Programming in general.

I thought it would be interesting to “improve” the Functional code with Lodash functions, and to my surprise (I admit I was both wrong and surprised) I found Lodash made it faster! After reading a little more about it I discovered this is a well known fact.

So, here are four different implementations of a function that checks if the elements (numbers) in an array are ordered (cnt is incremented if the array is sorted, such was the original problem).

// Standard reduce()
    this.test = function(p) {
        if ( false !== p.reduce(function(acc,val) {
            if ( false === acc || val < acc ) return false;
            return val;
        }, -1)) cnt++;

// Lodash reduce(), and some other Lodash waste
    this.test = function(p) {
        if ( false !== LO.reduce(p,function(acc,val) {
            if ( false === acc || val < acc ) return false;
    //      if ( !LO.isNumber(acc) || val < acc ) return false;
            return val;
        }, -1)) cnt++;

// My own 4 minute to implement simpleReduce(), see below
    this.test = function(p) {
        if ( false !== simpleReduce(p,function(acc,val) {
            if ( false === acc || val < acc ) return false;
            return val;
        }, -1)) cnt++;

// A simple imperative version
    this.test = function(p) {
        var i;
        for ( i=1 ; i < p.length ; i++ ) {
            if ( p[i] < p[i-1] ) return;

// my own implementation reduce()
    function simpleReduce(array, func, initval) {
         var i;
         var v = initval;
         for ( i=0 ; i<array.length ; i++ ) {
             v = func(v, array[i]);
         return v;

The interesting thing here is that the standard library reduce() is the slowest.
However, my simpleReduce is faster than Lodash reduce().

(seconds) reduce()
Std Lib Lodash Simple Imperative
Raspberry Pi v1 (ARMv6 @ 700) 21 13 9.3 4.8
MacBook Air (Core i5 @ 1400) 0.46 0.23 0.19 0.16

The conclusion is that from a performance perspective Functional Programming sucks. Lodash sucks too, but a little bit less so than the standard library (however, if you decorate all your code with isEmpty, isString, isNumber and that crap it will get worse).

That said, the generic nature of Lodash comes at a cost. The most simpleReduce() imaginable outperforms Lodash. As I see it, this leaves Lodash in a pretty bad (or small) place:

  • Compared to the standard library it is an extra dependency with limited performance benefits
  • The generic nature of Lodash comes at both a performance cost and it allows for sloppy coding
  • A hand written reduce() outperforms Lodash and is a good excercise for anyone to write. I expect this is quite true also for other functions like take or takeRight.
  • For best performance, avoid Functional Programming (and in this case the imperative version is arguably more readable than the FP reduce() versions)

Whats up with the Standard Library???
JavaScript is a scripted language (interpreted with a JIT compiler) that has a standard library written in C++. How can anything written in JavaScript execute faster than anything in the standard library that does the same thing?

First, kudos to the JIT designers! Amazing job! Perhaps the standard library people can learn from you?

I can imagine the standard library functions are doing some tests or validations that are somehow required by the standard, and that a faster and less strict version of reduce() would possibly break existing code (although this sounds far fetched).

I can (almost not) imagine that there is a cost of going from JS to Native and back to JS: that function calls to native code comes with overhead. Like going from user space to kernel space. It sounds strange.

I have read that there are optimizations techniques applied to Lodash (like lazy evaluation), but I certainly didn’t do anything like that in my simpleReduce().

For Node.js optimizing the standard library truly would make sense. In the standard library native code of a single-threaded server application every cycle counts.

UPDATE: I tried replacing parts of the above code: 1) the lambda function that is passed to reduce(), 2) the imperative version, with native code. That is, I wrote C++ code for V8 and used it instead of JavaScript code. In both cases this was slower! Obviously there is some overhead in going between native and JavaScript JIT, and for rather small functions this overhead makes C++ “slower” than JavaScript. My idea was to write a C++ reduce() function but I think the two functions I wrote are enough to show what is happening here. Conclusion: don’t write small native C++ functions for performance, and for maximum performance it can be worth to rewrite the standard library in JavaScript (although this is insane to do)!

All FP-sucks related articles
Functional Programming Sucks)
Underscore.js sucks! Lodash sucks!
Functional Programming Sucks! (it is slow)
Lodash Performance Sucks! (this one)

Functional Programming Sucks! (it is slow)

Update 2017-07-17: Below i present numbers showing that functional code is slower than imperative code. It seems this has changed with newer versions of Node.js: functional code has not turned faster but imperative code has become slower. You can read a little more about it in the comments. I will look more into this. Keep in mind that the below findings may be more accurate for Node.js v4-6 than for v8.

Functional programming is very popular with contemporary JavaScript programmers. As I have written before, Functional programming sucks and functional libraries for JavaScript also suck.

In this post I will explain more why Functional Programming sucks. I will start with the conclusion. Read on as long as you want more details.

Functional Programming practices are bad for performance
It is very popular to feed lamda-functions to map(), reduce(), filter() and others. If you do this carelessly the performance loss is significant.

It is also popular to work with immutable data. That is, you avoid functions that change (mutate) current state (side effects) and instead you produce a new state (a pure function). This puts a lot of pressure on the garbage collector and it can destroy performance.

The Benchmark Problem
Sometimes I entertain myself solving problems on I particularly like the mathematical challenges in the Project Euler section (the Project Euler is also an independent organisation – HackerRank uses the challenges in Project Euler to create programming challenges).

This article refers to Project Euler 32. I will not go into details, but the solution is basically:

  1. Generate all permutations of the numbers 1,2,3,4,5,6,7,8,9 (there are 9! of them)
  2. For each permutation, check if it is “good” (very few are)
  3. Print the sum of the good instances

The first two steps give good benchmark problems. I have made different implementations of (1) and (2) and then compared the results.

Benchmark Results
I have three different permutation generators (all recursive functions):

  1. Pure function, immutable data (it may not be strictly pure)
  2. Function that mutates its own internal state, but not its input
  3. Function that mutates shared data (no allocation/garbace collection)

I also have three different test functions:

  1. Tests the orginal Project Euler problem
  2. Simplified test using reduce() and lamda function
  3. Simplified test implemented a standard loop

I benchmarked on two different systems using Node.js version 6. I have written elsewhere that Node.js performance on Raspberry Pi sucks.

(seconds) Project Euler Test Simplified Test
Test Function: Functional Imperative
Permutation Gen: Pure Semi Shared Shared Shared Pure
Raspberry Pi v1 (ARMv6 @ 700) 69 23 7.4 21 3.7 62
MacBook Air (Core i5 @ 1400) 0.77 0.29 0.13 0.40 0.11 0.74

Comparing columns 1-2-3 shows the performance of different generators (for Project Euler test)
Comparing columns 4-5 shows the performance of two different test functions (using fast generator)
Comparing columns 5-6 shows the performance of two different generators (for fast simple test)

This shows that the benefit of using shared/mutable data (not running the garbage collector) instead of immutable data is 5x performance on the Intel CPU and even more on the ARM. Also, the cost of using reduce() with a lamda function is more than 3x overall performance on the Intel CPU, and even more on the ARM.

For both the test function and permutation generation, making any of them functional-slow significantly slows down the entire program.

The conclusion of this is that unless you are quite sure your code will never be performance critical you should avoid functional programming practices. It is a lot easier to write imperative code than to later scale out your architecture when your code does not perform.

However, the pure immutable implementation of the permutation generator is arguably much simpler than the iterative (faster) counterpart. When you look at the code you may decide that the performance penalty is acceptable to you. When it comes to the reduce() with a lamda function, I think the imperative version is easier to read (and much faster).

Please notice that if your code consists of nice testable, replaceble parts without side effects you can optimize later on. The functional principles are more valuable at a higher level. If you define your functions in a way that they behave like nice FP functions it does not matter if they are implemented using imperative principles (for performance).

Generating Permutations
I used the following simple method for generating permutations. I start with two arrays and I send them to my permute-function:

  head = [];
  tail = [1,2,3,4];


My permute-function checks if tail is empty, and then: test/evalute head.
Otherwise it generates 4 (one for each element in tail) new sets of head and tail:

  permute( [1] , [2,3,4] )
  permute( [2] , [1,3,4] )
  permute( [3] , [1,2,4] )
  permute( [4] , [1,2,3] )

The difference in implementation is:

  • Pure version generates all the above 8 arrays as new arrays using standard array functions
  • Semi pure version generates its own 2 arrays (head and tail) and then uses a standard loop to change the values of the arrays between the (recursive) calls to permute.
  • Shared version simply creates a single head-array and 9 tail-arrays (one for each recursion step) up front. It then reuses these arrays throughout the 9! iterations. (It is not global variables, they are hidden and private to the permutation generator)

The simplified test
The simplified test checks if the array is sorted: [1,2,3,4]. Of all permutations, there is always exactly one that is sorted. It is a simple test to implement (especially with a loop).

// These functions are part of a "test-class" starting like:
function testorder1() {
    var cnt = 0;

// Functional test
    this.test = function(p) {
        if ( false !== p.reduce(function(acc,val) {
            if ( false === acc || val < acc ) return false;
            return val;
        }, -1)) cnt++;

// Iterative test (much faster)
    this.test = function(p) {
        var i;
        for ( i=1 ; i<p.length ; i++ ) {
            if ( p[i] < p[i-1] ) return;

I tried to optimise the functional reduce() version by breaking out a named function. That did not help. I also tried to let the function always return the same type (now it returns false OR a number) but that also made no difference at all.

All the code
For those who want to run this themselves or compare the permutation functions here is the entire program.

As mentioned above, the slowest (immutable data) permutation function is a lot smaller and easier to understand then the fastest (shared data) implementation.

'use strict';


function arrayToNum(p, s, e) {
    var r = 0;
    var m = 1;
    var i;
    for ( i=e-1 ; s<=i ; i-- ) {
        r += m * p[i];
        m *= 10;
    return r;

function arrayWithZeros(n) {
    var i;
    var a = new Array(n);
    for ( i=0 ; i<a.length ; i++ ) a[i] = 0;
    return a;


function permutations0(n, callback) {


function permutations1(n, callback) {
    var i;
    var numbers = [];
    for ( i=1 ; i<=n ; i++ ) numbers.push(i);

function permute1(head, tail, callback) {
    if ( 0 === tail.length ) {

    tail.forEach(function(t, i, a) {
        permute1( [t].concat(head),



function permutations2(n, callback) {
    var i;
    var numbers = [];
    for ( i=1 ; i<=n ; i++ ) numbers.push(i);

function permute2(head, tail, callback) {
    if ( 0 === tail.length ) {
    var h2 = [tail[0]].concat(head);
    var t2 = tail.slice(1);
    var i  = 0;
    var tmp;
    while (true) {
        permute2(h2, t2, callback);
        if ( i === t2.length ) return;
        tmp   = h2[0];
        h2[0] = t2[i];
        t2[i] = tmp;


function permutations3(n, callback) {
    var i;
    var head  = arrayWithZeros(n);
    var tails = new Array(n+1);

    for ( i=1 ; i<=n ; i++ ) {
        tails[i] = arrayWithZeros(i);

    for ( i=1 ; i<=n ; i++ ) {
        tails[n][i-1] = i;

    function permute3(x) {
        var j;
        var tail_this;
        var tail_next;
        var tmp;
        if ( 0 === x ) {
        tail_this = tails[x];
        tail_next = tails[x-1];

        for ( j=1 ; j<x ; j++ ) {
            tail_next[j-1] = tail_this[j];

        while ( true ) {
            head[x-1] = tail_this[j];
            if ( j === x ) return;

            tmp            = head[x-1];
            head[x-1]      = tail_next[j-1];
            tail_next[j-1] = tmp;



function testprint() {
    this.test = function(p) {

    this.done = function() {
        return 'Done';


function testorder1() {
    var cnt = 0;

    this.test = function(p) {
        if ( false !== p.reduce(function(acc,val) {
            if ( false === acc || val < acc ) return false;
            return val;
        }, -1)) cnt++;

    this.done = function() {
        return cnt;


function testorder2() {
    var cnt = 0;

    this.test = function(p) {
        var i;
        for ( i=1 ; i<p.length ; i++ ) {
            if ( p[i] < p[i-1] ) return;

    this.done = function() {
        return cnt;


function testeuler() {
    var sums = {};

    this.test = function(p) {
        var w1, w2, w;
        var m1, m2, mx;
        w =  Math.floor(p.length/2);
        w1 = 1;
        w2 = p.length - w - w1;
        while ( w1 <= w2 ) {
            m1 = arrayToNum(p,     0, w1      );
            m2 = arrayToNum(p,    w1, w1+w2   );
            mx = arrayToNum(p, w1+w2, p.length);
            if ( m1 < m2 && m1 * m2 === mx ) {
                sums['' + mx] = true;

    this.done = function() {
        var i;
        var r = 0;
        for ( i in sums ) {
            r += +i;
        return r;


function processData(input, parg, targ) {
    var r;

    var test = null;
    var perm = null;

    switch ( parg ) {
    case '0':
        perm = permutations0;
    case '1':
        perm = permutations1;
    case '2':
        perm = permutations2;
    case '3':
        perm = permutations3;

    switch ( targ ) {
    case 'E':
        test = new testeuler;
    case 'O1':
        test = new testorder1;
    case 'O2':
        test = new testorder2;
    case 'P':
        test = new testprint();

    r = perm(+input, test.test);

function main() {
    var input = '';
    var parg = '1';
    var targ = 'E';
    var i;

    for ( i=2 ; i<process.argv.length ; i++ ) {
        switch ( process.argv[i] ) {
        case '0':
        case '1':
        case '2':
        case '3':
            parg = process.argv[i];
        case 'E':
        case 'O1':
        case 'O2':
        case 'P':
            targ = process.argv[i];

    process.stdin.on('data', function (s) {
        input += s;

    process.stdin.on('end', function () {
       processData(input, parg, targ);


This is how I run the code (use a lower value than 9 to have fewer than 9! permutations)

### Project Euler Test: 3 different permutation generators ###
$ echo 9 | time node projecteuler32.js 3 E
8.95user ...
b$ echo 9 | time node projecteuler32.js 2 E
25.03user ...
$ echo 9 | time node projecteuler32.js 1 E
70.34user ...

### Simple check-order test, two different versions. Fastest permutations.
b$ echo 9 | time node projecteuler32.js 3 O1
23.71user ...
$ echo 9 | time node projecteuler32.js 3 O2
4.72user ...

(the timings here may not exactly match the above figures)

All FP-sucks related articles
Functional Programming Sucks)
Underscore.js sucks! Lodash sucks!
Functional Programming Sucks! (it is slow) (this one)
Lodash Performance Sucks!

Underscore.js sucks! Lodash sucks!

In a world of functional programming hype there are two very popular JavaScript frameworks: underscore.js and Lodash. Dont use them! It is a horrible idea. They suck just like functional programming sucks!

They make claims like:

  • Lodash: A modern JavaScript utility library delivering […] performance
  • Underscore: JavaScript library that provides a whole mess of useful functional programming helpers.

The road to hell is sided by good intentions. This is how it goes.

1. Sloppy data types
There are many good things about JavaScript. The sloppy dynamic typing is perhaps not one of them. The following are for example true:

  • ’27’ == 27
  • undefined == null
  • 0 == ”
  • ‘object’ === typeof null

Now that I consider myself an experienced programmer I find it quite convenient to not need to be explicit about data types. But I dont mix and change types! If a variable is a number from the beginning it keeps being a number. I carefully pick types: Objects, Arrays, Number, String and stick to that type (for a variable or property). Occationally – mostly for return variables – I break the rule.

Lodash and Underscore is about allowing yourself to be sloppy:

  • Dont know if its an object or an array: use map, foreach, filter, reduce and many more
  • Dont know if it is empty (or what being empty even means): use isEmpty
  • Dont know if it is String Object or a String primitive or something else: use isString

If you dont know what it is is you already have a much bigger problem than how to do something with it.
If you mix String Objects and String primitives AND other things, and you want to know if it is any kind of string you are doing something wrong.

So Step 1 with Lodash and Underscore is that you

  1. Add a depenceny
  2. Allow sloppy and inconsistent typing
  3. No one can now presume anything about your types anymore
  4. Your code is now impossible to maintain or extend without lodash or underscore

2. Performance!
My experience after many years in software development is that when an application is not well received by the users it is very often because of (bad) performance. And bad performance causes weird, hard to reproduce, bugs and instability as well.

An important type of optimization that the JIT can do relies on the runtime generating classes with strict types for your objects (it guesses and learns the types as the program runs). If you allow a property to assume values of different types you are likely to destroy this optimization.

Lets look at the little function isEmpty.

/** Underscore **/
_.isEmpty = function(obj) {
    if (obj == null) return true;
    if (isArrayLike(obj) && (_.isArray(obj) || _.isString(obj) || _.isArguments(obj))) return obj.length === 0;
    return _.keys(obj).length === 0;

/** Lodash **/
function isEmpty(value) {
    if (isArrayLike(value) &&
        (isArray(value) || isString(value) ||
         isFunction(value.splice) || isArguments(value))) {
        return !value.length;
    return !nativeKeys(value).length;

If you KNOW the datatype you can just do:

/** String **/
if ( 0 === s.length )

/** String that may be null **/
if ( null === s || 0 === s.length )

/** Array **/
if ( 0 === a.length )

/** Object **/
function objectIsEmpty(o) {
    for ( x in o ) return false;
    return true;

(you may want to check o.hasOwnProperty(x) depending on what you actually want – but if you dont know what you want using Lodash or Underscore will produce equally unexpected results as my code)

The worst thing with the Underscore and Loadash implementations are the last lines:

    return _.keys(obj).length === 0;
    return !nativeKeys(value).length;

Unless the JIT compiler and runtime is very smart those two will produce an entire array of strings on the heap – just to check if the array length is 0! Even though this in most practical cases will have an acceptable overhead it can get very expensive.

This IS the beauty of FP generic programming. Focus on WHAT, not HOW, and any little innocent check (like isEmpty) can turn horribly expensive.

However, to be honest, I took some plain functional code and replaced plain JavaScript with Lodash functions (forEach, reduce, isNumber, isEmpty and a few more) and the code actually got faster! Still much slower than imperative code, but slightly faster than not using Lodash.

3. Complexity and Optimization
Now that you have added an extra dependency, made your data objects sloppy, made your application harder for the JIT to optimize, perhaps your application is not as quick as you need it to be. If you are writing a front end you are probably quite fine. But if you are coding a Node.js backend, performance matters a lot more and waste is more unacceptable. If you are really unlucky these sloppy types give you hard to find bugs and your backend is not completely stable and reliable.

What do you do? Common practices in the business could be things like:

  • Minification/uglification
  • Scale out service architecture
  • Failover technology
  • Spend time optimizing code and modules

This is how little sloppyness, laziness and convenience when making initial decisions about your architecture later can cause you huge problems and costs.

Of course, just including lodash and using isEmpty() in a few places is not going to do much (if any) harm.
But finding lodash or underscore generally preferable to not using them is one kind of low quality thinking that causes software to be bad.

Be explicit, careful, consistent and smart about the data types you use.
Be restrictive with libraries and frameworks that introduce overhead and hide relevant details.

Use the standard library. However, you can find that for example the Array functions of Lodash outperform the standard library equivalents. That may not be true in the future (and I really wonder how it can happen at all).

All FP-sucks related articles
Functional Programming Sucks)
Underscore.js sucks! Lodash sucks! (this one)
Functional Programming Sucks! (it is slow)
Lodash Performance Sucks!

Functional Programming Sucks*

(*like everything else that is mindlessly applied the wrong way to solve the wrong problems)

Functional Programming Rocks!
You can do amazing things with Haskell. The historical importance of Lisp is huge. Math, computer science, algorithms and engineering can come together beautifully with functional programming. Writing small reusable, testable functions with no side effects is a great thing to do in any language – but more than anywhere else those virtues are emphasized in functional programming. I love first class functions!


The unfortunate JavaScript Hype
As JavaScript made map, filter and reduce part of the standard those have become the foremost frontier of functional programming. Being quite serious, many people argue that if you replace your for-loops with map, filter, reduce and forEach you have achieved something significant when it comes to readability. What do you think?

// non-functional smelly loop
redFruits = [];
for ( f in fruits ) {
  if ( 'red' === fruits[f].color ) redFruits.push(fruits[f]);

// fantastic functional alternative
greenFruits = fruits.filter(function(f) {
  return 'green' === f.color;

As a bonus with functional, you can use lamda-functions… that is functions, with no name, that can not be reused. You can even use arrow-functions if you think this is too easy a concept and you think the code gets more readable by omitting the confusing word function.

While there are a lot of balanced articles about using functional ideas in JavaScript, for a lot of people eliminating for-loops at any cost seems to be functional enough.

The argument here is that your code should be declarative, it should express intention rather than instructions. In the case above with the fruit-filter it does make some sense. But if that argument should make any sense, it requires that the programmer does not abuse map, filter, forEach or reduce to eliminate a for-loop just for the sake of it. I have seen things like:

// functional
applePrice = Object.keys(fruits).filter(function(f) {
  return 'apple' ===;

// when this would work
for ( f in fruits ) {
  if ( 'apple' === fruits[f].name ) {
    applePrice = fruits[f].price;

I have several objections with the functional version. The Object.keys thing is hardly pleasant to the eye. It is false marketing to use filter: the intention is find or search, not filter. So you still need to read the details (just as with the for-loop), you are just first fooled into thinking its a filter (and chaining is very popular, so then you have no function names and no variable names). But perhaps the worst problem is the lack of error handling. Functional code is not meant to have side effects, and error handling is exactly that. If ‘apple’ is not found you get an ugly Error. You can of course try/catch, or make temporary array variable apples and check that its length is one, but people who prefer functional style usually dont do it.

I understand your objection: people can write crappy code with any language an paradigm and just becuase I have seen bad applications of filter does not mean there is anything wrong with filter or FP. Of course. The bad thing is recommending people to use it like a silver bullet. The bad thing is that good FP is actually difficult and junior programmers will get it wrong trying to be fashionable.

Here is another example to show I am not inventing this rant.

Functional is preferable-Hype
Another favorite example of this functional hype is from rosettacode. Look at this amazing collection of implementations of a simple algorithm: the Luhn Algorithm. I suggest:

  1. Read the introduction so you get some idea
  2. Look at the C-implementation: imperative and simple. Testable and no side effects.
  3. Look at the functional implementations: C++11, Common Lisp, Haskell, JavaScript (ES5.1 version), PicoLisp, Python, Rust, Scheme

Look at Scala: there are two versions, a Functional Style (recommended) and an Imperative style. The people att IOCCC would be jealous with this shit!

I can only come to one conclusion: the emperor is naked.

I mean, if you code in PicoLisp, for fun or for a good reason, and you have to implement the Luhn algorith, please do so! But to say “recommended” about the functional Scala code or to think that the C++11 code is in anyway more reasonable than the original C-code… it makes no sense. No real systems programmers will choose Rust over C based on this. And Python – a language known for its clarity… such a sad example.

Trendy fashionable “functional programmers” suck
So, the problem here is not primarily with Function Programming itself or the code that guru coders write with Functional Programming. The problem is that people with far too little theoretical background, training and experience follow the hype and the result is ugly.

Functional Programming Sucks (at)
There are some things that Functional Programming is not very good at: State and Side Effects. This is by design. From a FP perspective (and a general perspective as well) State and Side Effects are nasty and problematic: they should be avoided and when they cant be avoided they need special attention (like Monads).

The problem here is that JavaScript is mostly a programming language for the web. A simple (modern, single page) web application:

  1. Loads data from a server
  2. Presents data to the user
  3. Lets the user update the data
  4. Sends the data back to the server

This is (almost) all about state and side effects! You may have some functions for validation, filtering, sorting and calculations on the client, and those benefit from Functional Programming ideas. But your core enterprise is about state, side effects and error handling! Functional Programming is so unsuitable for this that a simple web page just can’t be written functionally, unless you start breaking rules. And this is of course what programmers end up doing because in the end of the day they have a real application to build for real users.

The difficult thing is to break the right rules in the right way at the right place for the right reason. This is architecture – mixing concepts, and designing how your application lives, its breaths and heartbeats. Architecture is difficult, and it is not made easier relying on unsuitable silver bullets.

Functional Reactive Programming
There is one thing that is even more hyped and even more theoretic than Functional Programming, and that is Functional Reactive Programming.

But it makes sense (as I understand it). It is not about taking Functional Programming one step further but about putting it in context. You have data that changes (signals, events, behaviours, whatever). That data can be pipelined with high quality through FP logic to produce the content of your GUI or the requests to the server.

Keep the functional parts functional, and let other parts of your application just deal with I/O, GUI and state. Dividing your code into separate modules with clear responsibilites and clear interactions have always been a good idea.

My experience is that when a real world application is not well received by the users a lot of the time its because performance sucks. When performance is bad usability is also bad, and stability gets bad (especially on the server side).

Functional programming is typically bad for performance.

  • anonymous functions can often not be JIT compiled and will never be optimized
  • recursion is nice, but rarely faster than a loop
  • the chaining concept creates arrays and arrays and arrays just for throwing away, which is fun for the garbage collector, but it is not fast
  • the immutable object concept is about generating new RO copies of object instead of changing objects, which is expensive and wasteful

Perhaps worse, since functional programming and proper use of and map(), filter(), reduce() and friends is hard (easy things get difficult) not so experienced programmers end up writing implementations with unnecessary computational complexity ( O(n) turns into O(n^2) ). It is not funny – you cant afford that for most anything that goes to production.

Agile, refactoring and generic code
It is HARD to design code properly from the beginning! Good objects, classes, functions, modules, packages, namings, dependency trees and architecture dont come for free! Agile and Refactoring are about accepting that the design will not be optimal the first time, thus we should not bother too much about it, but rather fix the code when we have learnt more about the problem and our code starts getting (too) ugly.

A strong argument for FP is that it is highly generic – which is true. But until a programmer has spent much time with her domain and problem she will not know what things can and should be made generic. Making things too generic is called overengineering, and it is perhaps the worst sickness in our industry.

I usually:

  • start with one source file rather than many
  • allow myself some copy-paste until I see what code really gets repeated
  • make my code as specific as possible, unless I see an obvious generalisation or the actual need for generalisation emerges
  • dont worry too much about global variables in the beginning (after a while there will be a natural place for them or for what the represent)
  • allow quite long functions until I see what parts of them actually do something meaningful on its own
  • code quite defensively with lots of error handling (it usually pays of quite quickly)

This works for getting quick practical results. Later, during refactoring, when the code base has grown, and when I have learnt more about the domain, I can break out pieces of critical code that creates nice generic functions. But thinking FP first – no way!

All FP-sucks related articles
Functional Programming Sucks (this one)
Underscore.js sucks! Lodash sucks!
Functional Programming Sucks! (it is slow)
Lodash Performance Sucks!

Unique T-65 X-wing Titles


There is an ongoing discussion about fixes for the X-wing (in the X-wing Miniatures game). I wrote another post about possible X-wing upgrades.

I now have another idea. The game is called X-wing and the X-wing ship could be a little special. I imagine and think that many of those T-65 were built by Incom, but later during the Galactic civil war (and perhaps even later on) X-wings were built in small shops, small batches, not always with the same original components available. Over the years X-wings were worn, damaged in battles, refitted for special missions, repaired with available parts, subjected to the forces of the Galaxy and adjusted to their pilots preferences.

In the game the concept Title can refer to a ship model (BTL-A4) or a unique individual ship (Millenium Falcon). In this way, there can be many unique X-wing titles, all corresponding to a unique X-wing with its history, flaws and upgrades.

Below I have created (by the X-wing game standards) many X-wing Titles. They are all Unique and they are not to be used with the T-70 X-wing (or any other X-wing that may come). I think it is fine that the X-wing is having many titles in ways no other ships have. The X-wing was the most common starfighter on the Rebel side, yet it has very few upgrade options in the game. The ships could survive many years while I imagine the Empire scrapped TIE fighters that didn’t match specifications to ensure a consistent pilot experience.

I have no information on how the X-wings were identified. I guess they all had some kind of build number, and in the below list they have the format “X-123”. It is not a model, it is a specific ship. If you have information on how the identical ships where named/numbered feel free to let me know!

All the titles are supposed to be good for its points. The X-wing needs a little fix, and these titles should work that way. Most of them have a positive squad cost, and many have a restriction, requirement or drawback. They cannot be thrown in without thought just to make your squad better.

The purpose is to make your X-wings a little better, but perhaps more to personalize them and to make them fit your play style or your current list better. And they should be a little more challenging and interesting to fly.

Some titles have a negative cost, but not for the purpose of being able to fit 5 identical X-wings into a swarm.

Several titles have a Pilot Skill must equal to 4 requirement: they are only for the rarely used 23p Red Squadron Pilot. These Titles may seem a little better, but its because the Red Squadron Pilot (without an Elite upgrade) is in a particularly bad place (I dont think the 4PS B/Y/Z95/X-T70 generics see much play either). These PS4 only upgrades often reflect some more experimental technology that requires much training to use, thus those titles are only available to specially trained (generic) pilots (not to famous named pilots).

X-Wing ID Restriction Cost Description
X-113 1 When attacking with Primary Weapon you may roll 1 extra red die. You cannot attack next round.
X-116 1 When attacking with Primary Weapon you may roll 1 extra red die. If you do so, in Deal Damage Step, ignore all Crits.
X-122 1 When attacking with Primary Weapon, if defender cancelled any Crits with Shield tokens, deal one stress token to defender.
X-17K -1 You can not equip an Astromech. This X-wing has 3 shields.
X-08X PS=4+ 1 This X-wing has a hull value of 2. Once per round when defending you may roll one extra green die.
X-14D Targeting Astromech 1 When you Spend a Target Lock you may keep it (instead of discarding it).
X-14K Targeting Astromech 1 You may acquire Target Lock on any ship in play regardless of range.
X-16F R2 Astromech 1 When executing a 4-K-turn, you may treat it as a speed 3, 4 or 5 K-turn.
X-19A R2 Astromech 1 When executing a 1-bank, you may treat it as a 1-turn the same direction.
X-222 R5 Astromech 0 At the End Phase, you may discard your Astromech to recover 1 shield.
X-291 1 Your action bar gains the Boost action icon. You cannot equip an Astromech.
X-2B7 0 Your action bar gains the Barrel roll icon. You can not equip an Astromech.
X-30P 2 When you equip this card, place 1 ordnance token on a Torpedo Upgrade card. When you are instructed to discard an Upgrade card, you may discard 1 ordnance token on that card instead.
X-370 PS=4+ 1 You must use a T-70 X-Wing maneuver dial
X-379 PS=4+ 1 You must use an A-wing maneuver dial. Your X-wing has 2 hull.
X-414 PS=4 0 Your action bar gains the Cloak Action
X-416 PS=4 1 Your action bar gains the Cloak Action. You may equip one Elite upgrade card.
X-441 PS=4 0 If your maneuver causes you to overlap X-442 and you are touching X-442 you may perform your action as if you did not overlap. You may equip one Elite upgrade card.
X-442 PS=4 0 If your maneuver causes you to overlap X-441 and you are touching X-441 you may perform your action as if you did not overlap. You may equip one Elite upgrade card.
X-460 PS=4 1 You may equip two missile upgrade cards. Get the cheapest one for free. You cannot equip a Torpedo. You may equip one Elite upgrade card.
X-465 PS=4 0 Your pilot skill is 5. You must equip one Elite upgrade card.
X-466 PS=4 1 Your pilot skill is 6. You must equip two different Elite upgrade cards.
X-484 PS=4 0 You may equip one Crew and one Elite upgrade card.
X-491 PS=4 1 Dual Card. You may equip on Elite upgrade card. You may flip this card during the end phase.
A) Your PS=0
B) Your PS=12
X-4X0 PS=4 0 You may equip one Illicit upgrade card and one Elite upgrade card.
X-502 PS=4+ 1 If your maneuver cases you to overlap a ship or an obstacle you may choose to instead execute another slower manuever with the same bearing (including 1-turn).
X-5LX 1 When you attack, the defender does not benefit from obstacles.