Raspbian – kerberos not found

I have this very strange error on my RPi V2 with Raspbian (8.0). I suspect I will throw away the memory card and never fix it, but I will document the error for future reference.

My problem was that curl, ssh, sshd suddenly did not work. When I start the web browser I get “I/O error”. This screenshot shows (at least a symptom of) the problem.

I tried to reinstall ssh and curl:

$ apt-get install --reinstall curl

and that did not help.

Apart from this the system works ok. It shutdowns and starts properly. No I/O errors from dmesg. I doubt I will ever figure this one out. It seems to me the system is corrupt at disk level, probably an SD-card problem, and that a new install on a new SD-card is the only way forward.

Upgrading Qnap TS109 from Jessie to Stretch

I have an old Qnap TS109 NAS that has been running Debian since long. I have previously written about my upgrade to Wheezy and Jessie.

The upgrade to Strech is the same procedure, and it was fine in the end, but…

In a very late phase of the upgrade I got:

update-initramfs: Generating /boot/initrd.img-4.9.0-5-marvell
flash-kernel: installing version 4.9.0-5-marvell

The initial ramdisk is too large. This is often due to the unnecessary inclusion
of all kernel modules in the image. To fix this set MODULES=dep in one or both
/etc/initramfs-tools/conf.d/driver-policy (if it exists) and
/etc/initramfs-tools/initramfs.conf and then run 'update-initramfs -u -k 4.9.0-5-marvell'

Not enough space for initrd in MTD 'RootFS1' (need 4210887 but is actually 4194304).

Well, the MODULES=dep thing does not help, but there is a another fix. You can compress your initramfs image. The procedure is described in the end of troubleshooting TS109.

The very short procedure is (I recommend you read the real article above):

echo "COMPRESS=xz" > /etc/initramfs-tools/conf.d/compress
apt-get install xz-utils
update-initramfs -u

Apart from that little problem, Stretch is just fine on QNAP TS109.

Vue.js: loading template html files

Update 2018-05-27: A few months have passed since I wrote this post. I have used my solution/library for several real applications and it has worked very well. So everything looks exactly as it did when I posted v0.1 and that is a good thing. There are obviously improvement opportunites and probaby limitations/bugs. But for my purposes I have not encountered any problems to fix. And nobody has notified me of needed fixes.

You may want to code your Vue.js application in such way that your html templates are in separate html files, but you still do not want a build/compile step. Well, the people writing Vue dont want you do do this, but it can easily be done.

All you need is to download this single js file and include it in your Vue.js web page. All instructions and documentation required are found in the js file.

I wrote a little library that simply does what is required in a rather simple way. I will not hold you back and I will show you by example immediately:

  • A Rock-paper-scissors Vue-app, all in 1 file: link
  • A Rock-paper-scissors Vue-app, modularised with separate html/js files: link
  • Source of VueWithHtmlLoader library: link

These are the code changes needed to use VueWithHtmlLoader:

 * 1) After including "vue.js", and
 *    before including your component javascript files,
 *    include "vuewithhtmlloader.js"
 * 2) In your component javascript files
 *    replace: Vue.component(
 *       with: VueWithHtmlLoader.component(
 *    replace: template: '...'
 *       with: templateurl: 'component-template.html' (replace with your url)
 * 3) The call to "new Vue()" needs to be delayed, like:
 *    replace: var myVue = new Vue(...);
 *       with: var myVue;          
 *             function initVue() {
 *               myVue = new Vue(...);
 *             }
 *             VueWithHtmlLoader.done(initVue);

My intention is that the very simple Rock-paper-scissors-app shall work as an example.

Disclaimer: the library is just written and tested only with this application. The application is written primarily to demonstrate the library. The focus has been clarity and simplicity. Please feel free to suggest improvements to the library or the application, but keep in mind that it was never my intention to follow all best practices. The purpose of the library is to break a Vue best practice.

What the library does:

  1. It creates a global object: VueWithHtmlLoader
  2. It provides a function: VueWithHtmlLoader.component() that you shall use instead of Vue.component() (there may be unsupported/untested cases)
  3. When using VueWithHtmlLoader.component(), you can provide templateurl:’mytemplate.html’ instead of template:’whatever Vue normally supports’
  4. The Vue()-constructor must be called after all templateurls have been downloaded. To facilitate this, place the code that calls new Vue() inside a function, and pass that function to VueWithHtmlLoader.done()
  5. The library will now load all templateurls. When an html template is successfully downloaded over the network Vue.component() is called normally.
  6. When all components are initiated, new Vue() is called via the provided function

Apart from this, you can and should use the global Vue object normally for all other purposes. There may be more things that you want to happen after new Vue() has been called.

The library has no dependencies (it uses XMLHttpRequest directly).

Obviously there are people (like me) with an AngularJS (that is v1) background who are used to ng-include and like it. We see Vue as a better, smaller AngularJS for the future, but we want to keep our templates in separate files without a build step.

I also expect many developers with various backgrounds to try out Vue.js. They may also benefit from a simple way to keep templates in separate files without worrying about a build tool.

As I see it, there are different sizes of applications (and sizes of team and support around them).

  1. Small single-file applications: I think it is great that Vue supports simple single-file applications (with x-template if you want), implemented like my game above. This has a niche!
  2. Applications that clearly require modularization, but optimizing loading times is not an issue, and you want to use the the simplest tools available (keep html/js separate to allow standard editor support and not require a build step). AngularJS (v1) did this nicely. I intend Vue to do it nicely too with this library.
  3. Applications built by people or organizations that already use Webpack and such tools, or applications that are so demanding that such tools are required.

I fully respect and understand the Vue project does not want to support case 2 out of the box and that they prefer to keep the Vue framework small (and as fast as possible).

But i sense some kind of arrogance with articles like 7 Ways To Define A Component Template in Vue.js. I mean 1,2 are only useful for very small components. 3 is only useful for minimal applications that dont require modularization. 4 has very narrow use cases. 5 is insane for normal development (however, I can see cases where you want to output/generate it). And 6,7 requires a build step.

8. Put the damn HTML in an HTML-file and include it? Nowhere to be seen.

The official objection to 8 is obviously performance. I understand that pre-compiling your html instead of serving html that the client will compile is faster. But compared to everything else this overhead may be negligable. And that is what performance is all about, focusing on what is critical and keeping everything else simple. My experience is that loading data to my applications take much more time than loading the application itself.

The Illusion of Simplicity
AngularJS (v1) gave the illusion of simplicity. You just wrote JavaScript-files and (almost) HTML-files, the browser loaded everything and it just worked. I know this is just an illusion and a lot happens behind the scenes. But my experience is that this illusion works well, and it does not leak too much. Vue.js is so much simpler than AngularJS in so many ways. I think my library can keep my illusion alive.

Other options
There is thread on Stackoverflow about this and there are obviously other solutions. If you want to write .vue-files and load them there is already a library for that. For my solution I was inspired by the simple jquery example, but: 1) it is nice to not have a jquery dependency, 2) it is nice to keep the async stuff in one place, 3) the delayed call of new Vue() seems forgotten.

Feedback, limitations, bugs…
If you have suggestions for improvements or fixes of my library, please let me know! I am happy to make it better and I intend to use it for real applications.

I think this library suits some but not all (or even most) Vue.js applications. Lets not expect it to serve very complex requirements or applications that would actually benefit more of a Webpack treatment.


  • A minified version – I have not really decided on ambition/obfuscation level
  • Perhaps change loglevel if minified Vue is used? or not.
  • I had some problems with comments in html-files, but I failed to reproduce them. I think <!– comments –> should definitely be supported.

JavaScript: Sets, Objects and Arrays

JavaScript has a new (well well) fancy Set datastructure (that does not come with functions for union, intersection and the likes, but whatever). A little while ago I tested Binary Search (also not in the standard library) and I was quite impressed with the performance.

When I code JavaScript I often hesitate about using an Array or an Object. And I have not started using Set much.

I decided to make some tests. Lets say we have pseudo-random natural numbers (like 10000 of them). We then want to check if a number is among the 10000 numbers or not (if it is a member of the set). A JavaScript Set does exactly that. A JavaScript Object just requires you to do: set[314] = true and you are basically done (it gets converted to a string, though). For an Array you just push(314), sort the array, and then use binary search to see if the value is there.

Obviously, if you often add or remove value, (re)sorting the Array will be annoying and costly. But quite often this is not the case.

The test
My test consists of generating N=10000 random unique numbers (with distance 1 or 2 between them). I then insert them (in a kind of pseudo-random order) into an Array (and sorts it), into an Object, and into a Set. I measure this time as an initiation time (for each data structure).

I repeat. So now I have 2xArrays, 2xObjects, 2xSets.

This way I can test both iterating and searching with all combinations of data structures (and check that the results are the same and thus correct).

Output of a single run: 100 iterations, N=10000, on a Linux Intel i5 and Node.js 8.9.1 looks like this:

                         ====== Search Structure ======
(ms)                        Array     Object      Set
     Initiate                1338        192      282
===== Iterate =====    
        Array                 800         39       93
       Object                 853        122      170
          Set                1147         82      131

By comparing columns you can compare the cost of searching (and initiating the structure before searching it). By comparing rows you can compare the cost of iterating over the different data structures (for example, iterating over Set while searching Array took 1147ms).

These results are quite consistent on this machine.

Some findings are very clear (I guess they are quite consistent across systems):

  • Putting values in an Array, to sort it, and the search it, is much slower and makes little sense compared to using an Object (or a Set)
  • Iterating an Array is a bit faster than iterating an Object or Set, so if you are never going to search an Array is faster
  • The newer and more specialized Set offers little advantage to good old Objects

What is more unclear is why iterating over Objects is faster when searching Arrays, but iterating over Sets if faster when searching Objects or Sets. What I find is:

  • Sets seem to perform comparably to Objects on Raspberry Pi, ARMv7.
  • Sets seem to underperform more on Mac OS X

Obviusly, all this is very unclear and can vary depending on CPU-cache, Node-version, OS and other factors.

Smaller and Larger sets
These findings hold quite well for smaller N=100 and larger N=1000000. The Array, despite being O(n log n), does not get much more worse for N=1000000 than it already was for N=10000.

Conclusions and Recommendation
I think the conservative choice is to use Arrays when order is important or you know you will not look for a member based on its unique id. If members have unique IDs and are not ordered, use Object. I see no reason to use Set, especially if you target browsers (support in IE is still limited in early 2018).

The Code
Here follows the source code. Output is not quite as pretty as the table above.

var lodash = require('lodash');

function randomarray(size) {
  var a = new Array(size);
  var x = 0;
  var i, r;
  var j = 0;
  var prime = 3;

  if ( 50   < size ) prime = 31;
  if ( 500  < size ) prime = 313;
  if ( 5000 < size ) prime = 3109;

  for ( i=0 ; i<size ; i++ ) {
    r = 1 + Math.floor(2 * Math.random());
    x += r;
    a[j] = '' + x;
    j += prime;
    if ( size <= j ) j-=size;
  return a;

var times = {
  arr : {
    make : 0,
    arr  : 0,
    obj  : 0,
    set  : 0
  obj : {
    make : 0,
    arr  : 0,
    obj  : 0,
    set  : 0
  set : {
    make : 0,
    arr  : 0,
    obj  : 0,
    set  : 0

function make_array(a) {
  times.arr.make -= Date.now();
  var i;
  var r = new Array(a.length);
  for ( i=a.length-1 ; 0<=i ; i-- ) {
    r[i] = a[i];
  times.arr.make += Date.now();
  return r;

function make_object(a) {
  times.obj.make -= Date.now();
  var i;
  var r = {};
  for ( i=a.length-1 ; 0<=i ; i-- ) {
    r[a[i]] = true;
  times.obj.make += Date.now();
  return r;

function make_set(a) {
  times.set.make -= Date.now();
  var i;
  var r = new Set();
  for ( i=a.length-1 ; 0<=i ; i-- ) {
  times.set.make += Date.now();
  return r;

function make_triplet(n) {
  var r = randomarray(n);
  return {
    arr : make_array(r),
    obj : make_object(r),
    set : make_set(r)

function match_triplets(t1,t2) {
  var i;
  var m = [];
  m.push(match_array_array(t1.arr , t2.arr));
  m.push(match_array_object(t1.arr , t2.obj));
  m.push(match_array_set(t1.arr , t2.set));
  m.push(match_object_array(t1.obj , t2.arr));
  m.push(match_object_object(t1.obj , t2.obj));
  m.push(match_object_set(t1.obj , t2.set));
  m.push(match_set_array(t1.set , t2.arr));
  m.push(match_set_object(t1.set , t2.obj));
  m.push(match_set_set(t1.set , t2.set));
  for ( i=1 ; i<m.length ; i++ ) {
    if ( m[0] !== m[i] ) {
      console.log('m[0]=' + m[0] + ' != m[' + i + ']=' + m[i]);

function match_array_array(a1,a2) {
  times.arr.arr -= Date.now();
  var r = 0;
  var i, v;
  for ( i=a1.length-1 ; 0<=i ; i-- ) {
    v = a1[i];
    if ( v === a2[lodash.sortedIndex(a2,v)] ) r++;
  times.arr.arr += Date.now();
  return r;

function match_array_object(a1,o2) {
  times.arr.obj -= Date.now();
  var r = 0;
  var i;
  for ( i=a1.length-1 ; 0<=i ; i-- ) {
    if ( o2[a1[i]] ) r++;
  times.arr.obj += Date.now();
  return r;

function match_array_set(a1,s2) {
  times.arr.set -= Date.now();
  var r = 0;
  var i;
  for ( i=a1.length-1 ; 0<=i ; i-- ) {
    if ( s2.has(a1[i]) ) r++;
  times.arr.set += Date.now();
  return r;

function match_object_array(o1,a2) {
  times.obj.arr -= Date.now();
  var r = 0;
  var v;
  for ( v in o1 ) {
    if ( v === a2[lodash.sortedIndex(a2,v)] ) r++;
  times.obj.arr += Date.now();
  return r;

function match_object_object(o1,o2) {
  times.obj.obj -= Date.now();
  var r = 0;
  var v;
  for ( v in o1 ) {
    if ( o2[v] ) r++;
  times.obj.obj += Date.now();
  return r;

function match_object_set(o1,s2) {
  times.obj.set -= Date.now();
  var r = 0;
  var v;
  for ( v in o1 ) {
    if ( s2.has(v) ) r++;
  times.obj.set += Date.now();
  return r;

function match_set_array(s1,a2) {
  times.set.arr -= Date.now();
  var r = 0;
  var v;
  var iter = s1[Symbol.iterator]();
  while ( ( v = iter.next().value ) ) {
    if ( v === a2[lodash.sortedIndex(a2,v)] ) r++;
  times.set.arr += Date.now();
  return r;

function match_set_object(s1,o2) {
  times.set.obj -= Date.now();
  var r = 0;
  var v;
  var iter = s1[Symbol.iterator]();
  while ( ( v = iter.next().value ) ) {
    if ( o2[v] ) r++;
  times.set.obj += Date.now();
  return r;

function match_set_set(s1,s2) {
  times.set.set -= Date.now();
  var r = 0;
  var v;
  var iter = s1[Symbol.iterator]();
  while ( ( v = iter.next().value ) ) {
    if ( s2.has(v) ) r++;
  times.set.set += Date.now();
  return r;

function main() {
  var i;
  var t1;
  var t2;

  for ( i=0 ; i<100 ; i++ ) {
    t1 = make_triplet(10000);
    t2 = make_triplet(10000);

  console.log('TIME=' + JSON.stringify(times,null,4));


When to (not) use Web Workers?

Web Workers is a mature, simple, standardised, compatible technology for allowing multithreaded JavaScript-applications in the web browser.

I am not going to write about how to use Web Worker (check the excellent MDN article). I am going to write a little about when and why to (not) use Web Worker.

First, Web Workers are about performance. And performance is typically not the best thing to think about first when you code something.

Second, when you have performance problems and you throw more cores at the problem your best speedup is x2, x4 or xN. In 2018 it is quite common with 4 cores and that means in the optimal case you can make your program 4 times faster by using Web Workers. Unfortunately, if it was not fast enough from the beginning chances are a 4x speedup is not going to help much. And the cost of 4x speedup is 4 times more heat is produced, the battery will drain faster, and perhaps other applications will be suffering. A more efficient algorithm can often produce 10-100 times speedup without making the maintainability of the program suffer too much (and there are very many ways to make a non-optimised program faster).

Let us say we have a web application. The user clicks “Show report”, the GUI locks/blocks for 10s and then the report displays. The user might accept that the GUI locks, if just for 1-2 seconds. Or the user might accept that the report takes 10s to compute, if it shows up little by little and the program does not appear hung. The way we could deal with this in JavaScript (which is single thread and asyncronous) is to break the 10s report calculation into small pieces (say 100 pieces each taking 100ms) and after calculating each piece calling window.setTimeout which allows the UI to update (among other things) before calculating another piece of the report. Perhaps a more common and practical approach is to divide the 10s job into logical parts: fetch data, make calculations, make report, but this would not much improve the locked GUI situation since some (or all) parts still take significant (blocking) time.

If we could send the entire 10s job to a Web Worker our program GUI would be completely responsive while the report is generated. Now the key limitation of a web worker (which is also what allows it to be simple and safe):

Data is copied to the Worker before it starts, and copied from the Worker when it has completed (rather than being passed by reference).

This means that if you already have a lot of data, it might be quite expensive to copy that data to the web worker, and it might actually be cheaper to just do the job where the data already is. In the same way, since there is some overhead in calling the Web Worker, you can’t send too many too small pieces of work to it, because you will occupy yourself with sending and receiving messages rather than just doing the job right away.

This leaves us with obvious candidates for web workers (you can use Google):

  • Expensive searches (like chess moves or travelling salesman solutions)
  • Encryption (but chances are you should not do it in JavaScript in the first place, for security reasons)
  • Spell and grammar checker (I don’t know much about this).
  • Background network jobs

This is not too useful in most cases. What would be useful would be to send packages of work (arrays), like streams in a functional programming way: map(), reduce(), sort(), filter().

I decided to write some Web Worker tests based on sort(). Since I can not (easily, and there are probably good reasons) write JavaScript in WordPress I wrote a separate page with the application. Check it out now:

So, for 5 seconds I try to do the following job as many times I can, while I keep track of how much the GUI is suffering:

  1. create an array of 10001 random numbers: O(n)
  2. sort it: O(n log n)
  3. get the median (array[5000]): O(1)

The expensive part is step 2, the sort (well, I actually have not measured 1 vs 2). If the ratio of amount of work done per byte being sent is high enough then it can be worth it to send the job to a Web Worker.

If you run the tests yourself I think you shall see that the first Web Worker tests that outsource all of 1-2-3 are quite ok. But this basically means giving the web worker no data at all and when it has done a significant amount of job, receiving just a few numbers. This is more Web Worker friendly than Chess where at least the board would need to be sent.

If you then run the tests that outsource just sort() you see significantly lower throughput. How suitable sort()? Well, sorting 10k ~ 2^13 elements should require each element to be compared (accessed) about 13 times. And there is no data sent that is not needed by the Web Worker. Just as a counter example: if you send an order to get back the sum of the lines most of the order data is ignored by the Web Worker, and it just needs to access each line value once; much much less suitable than sort().

Findings from tests
I find that sort(), being O(n log n), on an array of numbers is far too fast to be outsourced to a Web Worker. You need to find a much more “dense” problem to benefit of a Web Worker.

Islands of data
If you can design your application in such way that one Web Worker maintains its own full state and just shares small selected parts occationally, that could work. The good thing is that this would also be clean encapsulation of data and separation of responsibilites. The bad thing is that you probably need to design with the Web Worker in mind quite early, and this kind of premature optimization is often a bad idea.

This could be letting a Web Worker do all your I/O. But if most data that you receive is needed in your application, and most data you send comes straight from your application, the benefit is very questionable. An if most data you receive is not needed in your application, perhaps you should not receive so much data in the first place. Even if you process your incoming data quite much: validating, integrating with current state, precalculating I would not expect it to come very close to the computational intensity of my sort().

Unfortunately, the simplicity and safety of Web Worker is unfortunately also its biggest limitation. The primary reason for using a Web Worker should be performance and even for artificial problems it is hard to get any benefit.

Note to self: never try-catch more than necessary!

A wrote a function, and then a unittest, and the unit test was good.
Then I called the function from my real project, and it failed!

I isolated the problem and thought I had found a bug in V8 (except after many years as a programmer I have I learnt it is never the compilers fault).

This was my output:

$ node bug.js 
Test good
main: err=Not JSON

This is my simplified (faulty) code:

function callSomething(callback) {
  var rawdata = '{ "a":"1" }';
  var jsondata; 

  try {
    jsondata = JSON.parse(rawdata);
  } catch (e) {
    callback('Not JSON', null);

function test() {
  callSomething(function(err,data) {
    if ( err ) console.log('Test bad: ' + err);
    console.log('Test good');

function main() {
  var result = {
    outdata : {}

  callSomething(function(err,data) {
    if ( err ) {
      console.log('main: err=' + err);
    } else {
      result.outata.json = data;
      console.log('main: json=' + JSON.stringify(result.outdata.json));


How can the test not fail when main fails?

Well, here is the correct output

$ node nodebug.js 
Test good
main: json={"a":"1"}

of the correct code main function:

function main() {
  var result = {
    outdata : {}

  callSomething(function(err,data) {
    if ( err ) {
      console.log('main: err=' + err);
    } else {
//    result.outata.json = data;
      result.outdata.json = data;
      console.log('main: json=' + JSON.stringify(result.outdata.json));

The misnamed property caused an Error which was (unintentionally) caught, causing the anonymous callback function to be called once more, this time with err set, but to the wrong error.

It would have been better to write:

function callSomething(callback) {
  var rawdata = '{ "a":"1" }';
  var jsondata; 

  try {
    jsondata = JSON.parse(rawdata);
  } catch (e) {
    callback('Not JSON', null);

and the misnamed propery error would have crashed the program in the right place.

Don’t ever try more things than necessary. And if you need to try several lines, consider making separate try for each.

Minification of real web Application

I have built and I maintain a reasonably large (AngularJS) web application and here follow a few notes on the effect of minification.

I start with the findings:

                            Uncompressed         GZIP     Minified    Min+GZIP

App 1:  Size        (kb)            1130         1130          843         841
        Transferred (kb)            1150          375          861         308
        Load time    (s)             2.8          1.6          2.7         1.7

App 2:  Size        (kb)             708          708          659         659
        Transferred (kb)             721          359          672         347
        Load time    (s)             4.0          3.5          3.1         3.5

You should always enable gzip on the server. It is faster to compress and send less data than to send the uncompressed data. The benefits of gzip are huge and there are no negative side effects.

Minification saves some bandwidth (and if unlike me you do it ahead of time, some loading time). But unless your code contains mostly comments the effects are marginal (although that might be a big saving if you use very much bandwidth or you are looking for fastest possible load times).

Also, gzip tends to be good at what minification can easily do, and while the effect of minification alone is quite significant, the effect of minification together with gzip is smaller.

Behind the figures
The figures above come from Firefox Load time over the internet.

  • App1: About 100 files are served, mostly .js (a few .html and .css)
  • App2: About 80 files are served, mostly .js (a few .html and .css)
  • App1: Angular is always pre-minified 165kb, gzipped to 67kb.
  • App2: Angular+modules is always pre-minified 298kb, gzipped to 127kb
  • App2 contains a few fonts which are neither minified nor gzipped (142kb)
  • Files served by Node.js
  • Files minified by custom Node.js code in real time
  • Files gzipped by nginx in real time
  • Not everything is initiated when Load is complete (more html-files are loaded dynamically as user navigates, and data is loaded from APIs on demand)

Implications of minification
Minification (and possibly packaging of code) has more implications than gzip. Possible negative side effects are:

  • A build process is not strictly needed for web development, but minification is often done as part of a build process, increasing complexity of development, testing and deployment.
  • Testing and development is made harder when debugging minified code (although there are tools to mitigate this).
  • More aggressive minification can have unexpected results

The minification code I run in Node.js, when I serve a file, basically just:

  • Removes all white space in the beginning and end of lines
  • Removes all comments

This nice thing about this simple minification strategy is that everything that is obviously just waste is removed at a low cost, but the code is for all practical purposes completely unchanged (even line numbers are preserved to not complicate debugging). Also, developers should feel free to write as many comments as they like in the code, yet comments should never be served in a public facing application. More powerful minification comes at higher costs, and the effects are probably mostly lost after gzip.

I guess every project and system have a sweet spot when it comes to minification and I think my simple minification strategy makes sense for my needs.

Programming paradigms rock and suck!

I wrote a few articles about why functional programming sucks (1, 2) and why some functional libraries suck (3, 4). Obviously the titles were a little click bait and I think anyone reading the articles understood that I argue that it is the mindless hype that sucks, not the (FP) paradigm itself. Anyways, writing those articles, getting some feedback on them, and programming on my projects gave me more thoughts.

Lets say we have these programming paradigms:

  • Functional Programming: Pure, testable functions, avoiding state and variables.
  • Object Oriented Programming: Encapsulating data inside objects exposing a simplified, safe interfaces
  • Imperative Programming: Explicitly instructing the computer step by step what to do and how to do it, changing state (or Procedural Programming if you like)
  • Declarative Programming: Expressing your problem as data, and something else takes that data and produce what you want
  • State Machines: Global data and distinct states: your program reacts to input, transitions between states and modifies its data

As you perhaps understand, I do not aspire to be a computer scientist. This is all very practical.

I find – and I dont know if you will find this obvious or outrageous – that all these paradigms have strengths and weaknesses, and that a reasonably sized program or system will need to use a combination of them.

Here follows some strenghts and weaknesses:

Functional Programming Strengths

  • High reusability
  • Highly testable code
  • Compact code
  • Transformation, or streaming, or raw data matches the reality of networked services

Functional Programming Weaknesses

  • Obscurity – some code can be very cryptic and hard to read
  • Over engineering – attempting to make super reusable code can complicate simple things
  • Avoiding state – sometimes you have a state and you need to deal with it (and with FP there is a risk you try to avoid reality rather than face it head on)
  • Performance – FP comes with some overhead
  • Algorithmic complexity – it can be hard to understand the algorithmic complexity, or to write an efficient implementation, and this can lead to performance problems

Object Oriented Programming Strengths

  • Encourages clear APIs
  • Encourages reusability
  • Encourages information modelling
  • Allows refactoring of implementation

Object Oriented Programming Weaknesses

  • It adds little (no) value and significant overhead to serialize/deserialize data as it is sent/received and stored/loaded
  • Inheritance is a questionable concept
  • Ideally, objects have only one-way-dependencies, but in the real world this creates difficult, artificial design problems and complexity
  • It adds code and concepts that may cost more than you practically get from it: public/private declaration, getters/setters

Imperative Programming Strengths

  • Simple and straight forward
  • Little overhead: high performance
  • Algorithms explicitly implemented: high performance
  • Works the same in many different languages

Imperative Programming Weaknesses

  • Bad testability: if you have too big procedures with too many side effects
  • Bad maintainability: if you end up writing too big, complex modules
  • You can end up writing much code, copying and pasting
  • It takes discipline

Declarative Programming Strengths

  • Can be very compact
  • Can be very easy to read (if you understand/accept the “language”)
  • Can allow for quickly adding or changing features

Declarative Programming Weaknesses

  • Performance may be bad and/or unpredictable
  • Although compact, it can be very cryptic
  • Debugging can be very hard

State Machine Strengths

  • Can deal with complex states
  • Suitable for error handling, recovery
  • Can deliver efficiency at system level

State Machine Weaknesses

  • Requires proper analysis and design upfront
  • May be hard to refactor or change
  • Complex (represents and deals with complexity rather than hides it or lets someone else take care of it)

I now imagine a simple mobile/web application (like an little online betting site). There is data storage, server side application logic, authentication, http APIs, client loading data, client side application logic, UX, and the user saving/updating data to the system.

Server Side
The server itself should be thought of as a state machine. It can be up and good, but it can also be starting or shutting down. It can have bad or missing connectivity to other services (authentication, payment, data feeds). It can be in maintenance mode or perhaps in test or debug mode. It needs to hold and renew cached data. All these things can dramatially affect how a simple API call is handled! Failing to do this in a structured way can lead to very complicated API implementions or severe performance or stability problems.

If there are adapters connecting to other systems or the storage these may very well be implemented in an Object Oriented way. They expose a simple and safe API and they hide a lot of implementation.

When it comes to server side business logic it is fed with input from the storage or cache and its output is sent over the network to clients (or the other way around). Such business logic should be designed in a Functional way: it should be clear what it does and what data it uses, and it can and should be testable.

The implementation of the business logic or the adapters may be performance critical and non trivial. Imperative programming can be used here – inside what is exposed as Functional or Object oriented code. It must not leak. But every bolt and nut in an FP function does not have to be implemented using FP principles, and every internal part of an Object need not be built on the principles of Object Orientation.

Finally, the definition of the APIs, the access rules can be Declared. Other code can execute these rules and declarations behind the scenes.

Client Side
The client also needs to be thought of as a state machine first. Is the user authenticated and logged in? Have we received all data, or are we still waiting for something? Have we received the latest updates or have there been connection problems? Does the user have edited, dirty, state that is not saved to the server? Are we having errors saving data that we are retrying? Where in the application is the user and what settings, configurations or policies applies to the user? You need to deal with all these things. If you try break it into many small modules with no mutual dependencies you will find that is very hard. Put the damn global state somewhere, and accept that it is a global state. Make it all publicly readable for anything that cares. Changing of the state is a completely different business that must only be done via specific interfaces (the entire state machine can appear like an Object).

Now Declare your user interface: the pages, buttons, colors and everything else. Write code that consumes these declarations and produce the UX. To the state machine this code may appear Object Oriented, and your UX components are probably some kind of OO objects, reusable across the pages.

Whenever you can, break stuff out into Functional, pure, testable functions.

And when it comes to getting stuff done, as long as your code is contained within Objects or Functions and they do not leak: write simple and efficient code Imperative code if that is the best.

The GUI can be described in data rather than code in a declarative way.

It is quite pointless to talk about (for example) Functional programming as better or worse than anything else. It simply depends on. And for programs/systems of non-trivial size and complexity, mixing programming paradigms are fine.

Flight-Assist Astromech

Finally, the fix for the X-wing has arrived. I have written before here and here about my thoughts on fixing the X-wing.

The Flight-Assist Astromech costs one point and lets you make a free barrel roll or boost, unless you have a target in sight and range.

I think it is a good and well balanced upgrade, with uses outside the T65 X-wing.

T65 X-wing
I think for low cost generic X-wings (Rookie Pilots at 21p) the Flight-Assist Astromech is the best upgrade (previous options were R2 and Targetting Astromech). Does it also bring the Rookie Pilot back to play? Well, I don’t compete. I think it compensates for the worst weaknesses of the X-wing in a sensible way. In the first round(s) of the game you can boost to keep up speed with T70 or other ships, and you have more options when it comes to arriving at the battle. The first round of fire it is more likely that the T65 is actually in the fight, and not behind or all enemies out of arc. In the battle you have now options to the 4-U-turn and you can re-engage quicker and more effectively.

I tried a list I call Return of the X-wing that you find here.

How about T65 aces (Luke, Wedge, Wes)? Having a high skill means your opponents fly first and you may not be allowed to use Flight-Assist. I can see situations where BB8 (however unique) would be better. No doubt Luke, Wedge and Wes are better Flight-Assist than without. The most obvious situation is a first/second round strike where a boost or barrel roll can make them just reach. I have not tried this yet.

Finally, Flight-Assist seems to be of no significant benefit to Biggs which was important.

T70 X-wing
The T70 already has boost. Targetting Astromech is great with its 3 red maneuvers while Flight-Assist is not. Also, Poe likes R5-P9 and Nien Numb likes R3-A2. I think the T70 still has many options.

Flight-Assist Astromech can specifically not be used with turret weapons. This makes it interesting to combine with BTL-A4 to produce a more dog-fighting capable Y-wing. I think this was a clever Y-wing upgrade!

I think the ARC-170 is perhaps the ship that benefits the most of Fligth-Assist. With a maneuver dial similar to the T65 and no turrets (unlike Y-wing) it can really use boost or barrel roll. But I think (after flying only once) that Flight-Assist works very well with the Auxilliary firing arc. Normally it is quite hard to make good use of the tail gun, but if you can boost or barrel roll to get your enemy behind you it is a different story. And later with a boost it is much easier to get back in the fight. I tried this with Norra in the 10th (and so far unnamed) Horton Salm squad.

I doubt Flight Assist will bring the Generic E-wings to the tables. And when it comes to Corran Horn he can really use a regenerating Astromech (R2-D2 or R5-P9). Perhaps Etahn A’baht could use it though.

I think Flight-Assist is a very good upgrade (I hope it was not too good, because while I want an upgrade for my T65s I still want a balanced game). There are old Astromechs that become even less relevant now (R2-F2, R3, R5). I think Flight-Assist helps where it was mostly needed without just making T65 more like th T70.

Y-wing: Now, on top of my list, I want new unique Y-wing pilots. There are several to choose from within Star Wars Canon universe.
Z95: The headhunter needed this flight assistance just as bad as the T65 did.

Fast binary search without Division

Arrays are simple datastructures but finding elements is not fast. However, if you have a sorted array, a favourite algorithm is Binary Search.

When it comes to JavaScript there is no binary search in the standard library. Standard functions like find and indexOf simply just don’t cut it for many purposes, being O(n).

Rosetta Code has two implementations of binary search for JavaScript. However, binary search is based on division (by 2, you split the search interval in half every iteration). In programming languages with integer math division by 2 is performed by doing a single shift, so it is a cheap operation (division is generally expensive). In JavaScript integer division by two looks like:

mid = Math.floor((lo + hi) / 2);

This is just… unacceptable. Perhaps V8 recognizes the pattern and replaces it with the right thing, I don’t care, floating point math has nothing to do with binary search and this is ugly.

Perhaps I can do better? Well, I actually can!

Instead of dividing the actual interval by 2 in every iteration you can use the powers of two: 32,16,8,4,2,1 and search with them. Lets say the array is 41 elements long. Start check element 32. If its too little try with 32+8 (skip 32+16 > 41), and if it is too large try with 16. That way you essentially do a binary search without a division.

Since I don’t want to do heap allocation for every search I calculate the powers of two before declaring the function. In the code below my maximum value is 2^31, so I can mostly search arrays of size 2^32-1. If you dont like that limit you can raise 32 to whatever you like (but at 52-53 you are running into new problems, and well before that you will run out of RAM).

function powers_init(s) {
  var i;
  var r = new Array(s);
  r[0] = 1;
  for ( i=1 ; i<s ; i++ ) {
    r[i] = 2*r[i-1];
  return r;

var powers = powers_init(32);  // [1,2,4,8,16,32,...

function binary_search_divisionless(a, value) {
  var pix = 0;
  var aval;
  var offset0 = 0;
  var offset1;

  if ( a[0] === value ) return 0;
  while ( powers[pix+1] < a.length ) pix+= 1;

  while ( 0 <= pix ) {
    offset1 = offset0 + powers[pix];
    if ( offset1 < a.length ) {
      aval = a[offset1];
      if ( value === aval ) {
        return offset1;
      } else if ( aval < value ) {
        offset0 = offset1;

  return -1;

It is perhaps not obvious why I check for a[0] before doing anything else. The function returns from the main while loop as soon as it finds what it is looking for. So a[offset0] does not contain the value in later iterations when offset>0. However, this is not guaranteed from the beginning when offset0=0, and it is not automatically being tested in the end. So I explicitly test it first.

Below follows relative performance in time for different array sizes.

Array Size:                  10      100     1000    10000   100000
Standard Library indexOf   1.65     2.02    11.89    94.00   790.00
Rosetta Code Recursive     1.32     1.48     1.84     1.81     2.02
Rosetta Code Imperative    1.18     1.08     1.41     1.43     1.45
Divisionless               1.00     1.00     1.00     1.00     1.00
   - unrolled              0.93     0.76     0.82     0.79     0.78

I am satisfied that my code is consistently faster than the alternatives. What surprised me was that binary search clearly wins even for short arrays (10). The little loop before the big loop can be unrolled for significant performance gain:

//while ( powers[pix+ 1] < a.length ) pix+= 1;
  if ( powers[pix+16] < a.length ) pix+=16;
  if ( powers[pix+ 8] < a.length ) pix+= 8;
  if ( powers[pix+ 4] < a.length ) pix+= 4;
  if ( powers[pix+ 2] < a.length ) pix+= 2;
  if ( powers[pix+ 1] < a.length ) pix+= 1;

This is also the reason why 32 was a particularly good array size for the powers of two. This is a kind of optimization I would normally not let into my code. However, if you make much use of binary search on the Node.js server side of an application, go ahead.

It is also worth noting that the following patch doubles the execution time of my code!

//  if ( offset1 < a.length ) {
//    aval = a[offset1];
    if ( undefined !== ( aval = a[offset1] ) ) {

General Compare Function
With little modification, the algorithm can be used with a compare function for arrays of objects other than numbers (or strings).

Since my algorithm only uses powers of 2, I can do division in JavaScript without using Math.floor. It is a quite easy change to eleminate the powers-array and divide by two instead. It turns out it make very little difference on performance (to do integer / 2 instead of array lookup). However, when I added a (meaningless) Math.floor() around the division, performance dropped the same as the the iterative version from Rosetta Code. So my intuition was correct to avoid it.

I checked the Lodash binary search code (sortedIndexOf) and it uses bit shift <<< to divide by two. Admittedly, the Lodash code is slightly faster than my code.

You should have a well implemented and tested binary search function available in your toolbox (admittedly, use Lodash for this). This is not the right place to lose milliseconds. For very small arrays you can argue that indexOf works equally well, but the cost of using binary search is insignificant.