Category Archives: Performance

NativeScript v1.2.0 Built in LiveSync vs the NativeScript-LiveSync Plugin

Pros of Telerik's LiveSync:

  • Works from the NativeScript Command Line
  • No extra code added to your application!
  • Possibly works on Real IOS Devices (Untested on real device, but does not currently appear to work on a IOS Simulator)

Cons of Telerik's LiveSync:

  • Not really Live. It syncs the files; but then has to restart the application from scratch, no matter what file is changed.
  • Delays while it detects any changes and then deploys the changes.
  • Delays while it is re-launching Application.
  • Loss of all application state since it reloads the app on every change.
  • If you navigated three screens deep, and make a CSS file change; you will need to re-navigate to that screen again to see it.
  • Incredibly slow LiveSync startup time. (What in the world is it doing for about a minute?)
  • Can crash the LiveSync watcher code easily (don't change any files in the tns_modules!).
  • Does not apparently detect any new files...
  • Reset of the Application even if you change a file that isn't even being used.
  • Easy to crash your application as the JavaScript and XML are not checked before being sent to the application.

Con's of Master Technology's LiveSync:

  • Until Telerik accepts the patch; you have to use the included patched runtime. (Please vote up the issue!)
  • Added coded to your project.
  • Only works on the Android platform, no IOS support.

Pro's of Master Technology's LiveSync:

  • Live, You see the app change almost exactly when your editor saves the files.
  • New files are detected and synced instantly.
  • Application state is almost always fully maintained.
  • The screen you are working on only reloads ONLY if it is the code you just changed.
  • Built in ability to detect errors in XML and JS before pushing to device to eliminate crashing the app on the device.
  • Ability to only reload application on files that are singletons or other files that you would rather have the app reloaded for.
  • Ability to restart application by touching or creating a "restart.livesync" file.


The new LiveSync code has been updated to be a seemless installation on your Box.    It now includes the modified runtimes for v1.20 of the Android runtimes.     All you have to do to install it is: tns plugin add nativescript-livesync



NativeScript - Real time development on Android

Photo (CCA):

Now if you haven't guessed recently I've really taken to NativeScript.   It is awesome tool set for development applications for your mobile phone.

However, one of its failings compared to some of the other tools is the speed of iteration.     On my machine; when I want to test a change;  I switch to a command prompt (or hit a hotkey) which then compiles the app and deploys it.     This whole process is between 20-30 seconds each time on my beefy machine.  If I made any code mistakes; then I watch it crash on the emulator, have to fix it and then wait another 20-30 seconds while it re-compiles & deploys it again.    Another, slow issue testing area is if the screen I'm changing is a little ways into the application then I have to re-navigate back to the screen and test my changes each time I redeploy the app.    So all in all, it is a slow iteration cycle.

Well, I decided to do something about it; and so if you watch my youtube video, Real Time NativeScript Development , you will see how simply including the "nativescript-updater" the screen/code updates on the emulator INSTANTLY.    And this works for any screen I'm on, no matter how far I have navigated into the program.

In addition the watcher utility will also run any changed JS or XML through a linter before transferring it to the Emulator.  This allows me to keep the majority of my stupidity from even getting to the device in the first case which will end up in a crashed callstack.

You can still intermix the normal NativeScript deploys for any reason we want.   This project has been released at and is completely trivial to include into your project.

I'm waiting on a pull request that I added in the Android-runtime ( -- until it is accepted; the neat magic only partially work.

Announcing a v8-Natives v0.0.1

What are v8-natives, you might ask?    

Well, they are the mostly undocumented javascript commands that control the v8 engine in Google Chrome, Opera and Joyent Node.js.      Some of the commands are %CollectGarbage(), %GetV8Version(), %GetOptimizationStatus() which ties with my other favorite of %OptimizeFunctionOnNextCall()


What can I do with them?

You can tell the engine to Optimize a routine, un-optimize a routine, never optimize a routine, ask it about internal data structures of an variable/object, and one of the most important items is ask if a routine is optimizable.


Why is this important?

Well, the v8 engine has several compilers built in; the lowest compiler is just a full featured javascript interpreter -- it is fast; but compared to one of the actual compilers it is so slow that molasses moves faster.     Do you want to figure out which of your code can be promoted to the faster compilers?   Do you want to see what code is a bottleneck even though at a glance it actually looks good?


So, which of these routines optimizable?

function sum1(a,b) {
try {
var c=a+b;
} catch (err) {
return -1;
return (c);

function sum2(a,b) {
return, arguments);

function sum3(a,b) {  return  sum(arguments);


Look no further:

Available on isle 15, at the deep discount of totally free; we now have all the tools you need to answer the above questions.    A fully working support library that wraps over 20 of the internal v8 native commands in a simple to use library that will not crash your script no matter if you have the v8 native support turned on or off.  Can be left in your app and deployed; and finally   supports both Node and Browsers.

Simple things like "v8.helpers.testOptimization(sum1);"  would tell you right away if the sum1 can be optimized....   Or v8.collectGarbage() will do a full GC before you run some timings on a performance critical code...   Lots of things to help your inner-performance surface.

You can get it at your local npm repository:  npm install v8-natives or check out the github page @



Node & Browser Javascript Compression Update

I wrote a post on Data Compression back in October,, discussing how I sped up a Data Compression Library that we have been using internally for all web socket traffic and how by combing techniques from different comparable libraries LZJBn.js was born.

Well fast forward several months --- I ran across another library that well professional curiosity compels me to to bench mark to see how well my cool LZJBn will trounce it.

Using 526 different sized files from real packets that we send:

Compression Decompression Compressed Size Original Size
LZJBn.js 0.503752017 0.1777535 15,890,401 37,345,189
node-lz4* 0.363441773 0.1109069 11,364,620 37,345,189

* - Node-lz4 does not compress files under a really small size; so there was 8 files comprising of a total of 182 bytes of data that was not compressed in this test on the lz4 side.  So because of this; when sending any data packets you will have to tag your packets as compressed or uncompressed.

And on even larger size using the ENWIKI file I used in the prior blog post:

Compression Decompression Compressed Size Original Size
LZJBn.js  1.87325003 0.279163174 34,332,875 50,000,896
node-lz4 0.713367233 0.207553456 27,591,715 50,000,896

Now if you look at the numbers it not only compressed and decompressed faster; but it also had a even better compression ratio.

After many tests and a very timely bug fix from the author of node-lz4 on a bug I reported; I have to shamefully say node-lz4 totally skunks my LZJBn.js module in the tests.   In addition Node-lz4 also has a native module for the node side, but those numbers aren't relevant to this test as this was purely testing the JavaScript library speeds.

So those who are wanting to implement as close to real time compression as possible using JavaScript; there is a now a new King of the Hill and sadly (for me) it is node-lz4.

Congrats Pierre; for a Job well done porting LZ4 to JavaScript -- and I know which library I will be using in the future!

For those who are interested the primary LZ4 site is here and the original author (Yann Collet) who created the lz4 compression format has a blog here:


Data Compression Revisited

Update: There is a relevant update for this in a new post.

Over a year ago; one of my co-workers bench marked several compression libraries and since then we have been using library called jslzjb by Bear.  This is on a un-released product and we currently use it almost constantly on a wide variety of devices and browsers to reduce the amount of data going over websockets.

Interestingly enough a couple months ago  Colt "mainroach" McAnlis wrote a very interesting blog "State Of Web Compression" where he did quite a few compression tests on different compression methods.  And in that blog post he referenced compressjs by Dr. C. Scott Ananian (CSA).   CompressJS is a fairly comprehensive javascript compression test library with several implementations of different javascript compression libraries (and results). So I made a note in our project tracker that someone on our team at some point in the future should check out CSA's version of LZJB vs the original that we are running since LZJB was showing up still as the fastest of the bunch on his tests.

So mid-last week; we discovered a bug caused by the compression library; if we turned it off -- it worked; if it was on; it caused issues only with apparently a couple characters.    I was tasked with the bug report and so I also took the opportunity to check out the newer rewrite of LZJB also since I was dealing in that area of the system and CSA's version might fix everything and be fairly drop in replacement.

But before we did so, we needed to see the speed increase or hit we would take.  So to make real world tests, I took Chrome. connected to my local instance of the our product; turned off compression and then promptly saved a couple "HAR with content" from the network tab->websockets and basically generated about 32megs of real transmission data doing a variety of things in our system.   Then I wrote a simple JS program to extract all the actual data packets into separate packets from the har file.  Created a couple additional files with the characters that were actually causing the problems; and then promptly added CSA's  test data which basically made over 37megs of test data across 526 different files.

From there, I wrote a very simplistic node test framework that read in every packet into memory; then ran each through a compression function (using the nano-second precision timer) and then ran it through the decompression function with the same timing.  Then just to verify compared the output buffer with the original to verify compression-decompression worked successfully and recorded the stats.  (For consistency; I load ALL the data first; run the tests on ONE compression library; and exit with the results for that library -- this should keep the memory footprint the same for every library and eliminate and gc hits beyond what the library itself causes.)

So my first attempt failed as Node reads things in as Buffers; and Bear's LZJB only works with Arrays and Strings.   So adding a quick toString() (outside of the timing) and I have my first timings; and a slew of failed files.  37,345,189 Bytes of Data; Compression was ~2.75 seconds, Decompression was ~1.29 seconds.  Not bad speed wise, but a tad over 50 of the files failed, that isn't good.

Next up was grabbing CSA's version and directly copy and pasted my test suite for it; and ran it.  Failed -- it didn't like strings; it wanted buffers or TypedArray's.   So I removed the ".toString()" and re-ran it; and got ~2.78 / ~0.63; and no failures.  I'm like not too bad a tiny hit on compression; but twice as fast at decompression.    But; I know this test isn't fair; Bear's does a String -> Array conversion that CSA's doesn't do.  And I know that String->Array converter is one of the slowest parts (from profiling it a while back).    So to make the test a bit fairer; I remove the .toString() from Bears harness; and modify the code slightly so that it will treat Buffers like Arrays.   And my new output is ~0.62 / ~1.25; but the same 50 odd files failed.

I'm like WOW; ~.62 seconds compression.  We now know the conversion hit is really killing us; so allowing it to use buffers makes it a considerably faster, nice win.  But 50 files failed; not good at all. And the decompression is still twice as slow as CSA's version.    So at this point, since I barely understand the routine and I do understand optimization, I decide I'm going to attempt to speed up something rather than "fix" something I don't fully understand.

I grab CSA's version, duplicated the "compress" routine and start messing with it.  I see a lot of what I considered "low" lying fruit; and a couple hours later my "new" version of CSA's is clocking at ~2.47 vs the original ~2.78; much better but still a far cry from the ~0.62 of Bear's compress.  Disappointing to say the least.

However, now I have a much better grasp of how the routine works; and realize that I would have to rewrite CSA's version to get any major speed up.    So I decide to go back to Bear's routine and see if I can fix it.   By looking at the original 'C' source; I can see a couple issues in the bear's conversion and correct them; and so now I am getting ALL my files passing with bears routine.   I also notice that Bears version is based on a older LZJB version so I upgrade the routine to use the newer hash (and a couple other tweaks).  Then to make a already long story much shorter; I spend the time to figure out why CSA's version of the decompress is so blasted fast and apply those techniques to Bears decompression routine.

So at the end of a couple days; the results are using the ENWIKI8 file (100,000,000 bytes):

Compression Decompression Compressed Size
Bears' Modified 3.092157 0.556772 68551699
CSAs' Original 10.96466 1.975028 67820737
CSAs - Modified 9.848296 1.975028 67820737


I then took the ENWIKI8 file and split it in basically half (so at least I could get a benchmark with Bears Original);

Compression Decompression Compressed Size Original Size
Bears' Original* 1.963242 1.904763 38204603 50,000,896
Bears' Modified 1.849097 0.280587 34332875 50,000,896
CSAs' Original 5.875302 0.992718 33966678 50,000,896
CSA's Modified 5.285134 0.992718 33966678 50,000,896


All 526 Files:

 526 Files (1k to 917k) Compression Decompression Compressed Total Size  Original Total
Bears' Original* 0.626847 1.258599 17250904 37,345,189
Bears' Modified 0.486729 0.177391 15890401 37,345,189
CSAs' Original 2.782399 0.636517 15709537 37,345,189
CSAs' Modified 2.413777 0.636517 15709537 37,345,189

* - Not technically Bears' original; this version supports Buffers and has the bug fix that allows it to compress all the files properly; no other bug fixes, enhancements or changes.

By using the New Hash; we caused our compression to be similar to CSA's (which he also uses the new hash).  In addition Bears' Modified now uses the same decompression idea as CSA; so it is now blazing fast for decompression.

So at the end of the a couple days work; we went from ~2.75 / ~1.29 down to ~0.49 / ~0.18; major win!

The funny thing is this didn't actually fix the original bug that we uncovered, it did however fix some other bugs we had patched around in our code (so now we can remove those patches).    The original bug was actually caused by Converting from a from a Array to a String on the decompression side.   On the conversion from a string to an array; we convert UCS-2/UTF-16 to UTF-8 encoded.   However, Bears code never had any converting code back from UTF-8; back into UCS-2/UTF-16 which is what JavaScript expects.   So all my Tests passed; if you read in a Buffer -> Compressed -> Decompressed -> Buffer.    But the minute you went String/Buffer -> Compress -> Decompress -> String; your data would be wrong if it had any UTF-8 encoded characters.   So by adding a UTF-8->UCS-2/UTF-16 on the other side on the toString conversion path we now have flawless (& much faster) compression and decompression.

Final Stats:

Megabytes Per Second Percentage of Compression
50m 100m 37m   50m 100m 37m
Bears 12.92679 N/A 19.80708 23.6% N/A 53.8%
Modifed 23.47808 27.4053 56.23253 31.3% 31.4% 57.4%
CSA 7.280249 7.728159 10.92311 32.1% 32.2% 57.9%
Modifed 7.96465 8.457858 12.24314 32.1% 32.2% 57.9%

So as of this moment; LZJB is still winning in speed; but it is now considerably faster than Colt's or CSA's website numbers show...  CSA still has a slightly better compression (even at the default level 1); but I would much, much rather have the extra 46 seconds over the minuet .5% file loss in size in our real data).

All these tests and numbers were done using Nodejs (10.2) -- Running a set of tests on the browser (not as comprehensive, but with several sized files) showed similar improved speed/compression results, under Chrome and Firefox.

I want to thank to Colt McAnlis for posting his article -- which led me to Dr. Ananian's compressjs and started the ball rolling on what has turned into a making a copy of LZJB running 22% faster during compression, and 86% faster during decompression with a 8% reduction is payload size!   Now our data moves faster to all of our devices, meaning the customer has gets his screens up sooner and that is why Performance Matters!

Updated Compression: LZJBn.js

Update: There is a relevant update for this in a new post.

Transforming JavaScript JSON

Colt McAnlis posted a very interesting blog post ( this evening on using Transposing to reduce the JSON data size; his post was right on the money.

We have been using a similar technique for a couple years now.  (Although, we use a different compression method over websocket as gzip is too expensive in pure JavaScript).

However, one thing that I commented on is that he went to step one, and step two gives him better results -- it actually improves the compression.

I created my own "original dataset" to show this example.   The Dataset has Spaces here in the blog and show it for formatting purposes to make it easier to read; but all my numbers are excluding spaces and returns as a raw json wouldn't have those in it.

The original Data (265 Characters):

[{Id: 1, Name: 'Nathan', Address: 'Somewhere', Country: 'USA', City:'Here', State:'OK',Zip:'55555'},
 {Id: 2, Name: 'Colt', Address: 'Elsewhere', Country: 'USA', City: 'There', State: 'CA',Zip:'44444'}
 {Id: 3, Name: 'You', Address: 'Not Sure', Country: 'USA', City: 'Where', State: 'AZ', Zip:'33333'}]

Colt's Transposing (211 Characters):
'Address':['Somewhere','Elsewhere','Not Sure'],
'Zip': ['55555','44444','33333']}

We transpose it into basically a JSON CSV (206 Characters):
 [3,'You','Note Sure','USA','Where','AZ','33333']]

Now for every additional row of data we add with this dataset you add:

Original: 48 Characters of Static unchanging field definitions. (Ouch!)
Colt's: 7 Characters
Ours: 9 Characters

So how do we end up with better compression when after a dozen or so records our raw size is actually larger than Colt's?    Well; we only use [] and comma's.   He has added additional data to his data stream in addition to the [] and commas, he has  {}, and the colons.    By having more redundancy in our stream we compress better.

Wait; there is another easy savings if you think about the data...    Why send the header row?  If you already know the layout of what you are requesting; you can entirely eliminate the header row; which would then shrink your "raw" data down another 55 characters.  Meaning we start out at a small 151 characters.

So if you are dealing with straight raw characters; Colt's method actually is smaller (after about 30 rows) .  However, If you are going to compress the stream; the additional redundancy in our transformation appears to be better suited to make smaller compressed files.

Measure everything and think about how you actually use your data might be the difference in how you send your data making all the difference in how fast your app actually responds to requests because Performance Matters.