Bob and Javascript Prototypes – A beginners guide

A rose by any other name would smell as sweet – William Shakespeare’s Romeo and Juliet

The subject of Prototypes in Javascript has always been confusing for developers. In this post I will attempt to explain the essence of a prototype in its most basic sense using a fictional developer named Bob.

Bob, a new Javascript developer wanted to build a utility object which contained a few choice arithmetic operations. Along the way he also wanted to get a basic understanding of prototypes. Like his fellow developers he looked for some sample code to build on, preferably an object which he could use as a helper or a prototype object to start with. A "hello world" object if you will. Scouring the internet, he found a simple generic object, called utilsObj which had a few methods that implemented basic arithmetic operations.

This was simple enough to analyze and understand. An object with a few methods baked in. Perfect.

//utility object - a prototype or template to build on
const utilsObj = Object.create(null); //empty object
// utility methods
utilsObj.add = function(n1, n2){
    return n1 + n2;
utilsObj.subtract = function(n1, n2){
    return n1 - n2;
utilsObj.multiply = function(n1, n2){
    return n1 * n2;
utilsObj.divide = function(n1, n2){
    return n1/n2;

Testing the methods in the console was simple. Everything worked and he was now ready to begin.

//testing methods...
utilsObj.add(10, 2); //12
utilsObj.subtract(10, 2); //8
utilsObj.multiply(10, 2); //20
utilsObj.divide(10, 2); //5

He started creating his object using the Object.create method with the prototype(helper) object, utilsObj passed in as its parameter. He then added the remainder method to the resulting object – myObj. With the remainder method in, he had all the arithmetic operations he needed.

//create a generic object
const myObj = Object.create(utilsObj);

//The remainder method
myObj.remainder = function(n1, n2){
    return n1 % n2;

He had read that by passing in the prototype object (utilsObj) during creation, would allow his object – myObj, to access methods of the prototype object. This, he needed to verify.

//remainder method added in 
myObj.remainder(10, 2); //0 

//from the prototype utilsObj
myObj.add(10, 2)); //12
myObj.subtract(10, 2)); //8
myObj.multiply(10, 2)); //20
myObj.divide(10, 2)); //5

To his amazement he found that he not only could access the remainder method that he had added, but also methods from the prototype object as if they belonged to his object – myObj!

It was as if when the add method was executed, JS looked up myObj. It did not find it and decided to check the prototype object – utilsObj. This felt more like inheritance via prototypes a.k.a. prototypal inheritance.

He doubted this simplicity. To confirm, he decided to look under the hood. What he saw surprised him…


There it was, the utilsObj object, with the four arithmetic methods. His newly created object – myObj with the new remainder method that he had added. But, myObj also contained a new property – proto which looked identical to the utilsObj, the prototype object!

Bob knew he was onto something. It seemed that when JavaScript created myObj it also added a new property, proto and pointed its value to the prototype object, utilsObj. So this is how myObj could access methods in the prototype – utilsObj!

His excitement piqued with this new found knowledge he decided to do one more experiment – use myObj as a prototype. So he created another object appObj and passed in myObj as the prototype object.

const appObj = Object.create(myObj);

First he confirmed whether he could access the remainder method of myObj which he had used as the prototype object. That worked fine.

appObj.remainder(10, 2); //0

Next he tested if methods of utilsObj could be accessed. Remember utilsObj was the prototype for myObj. They did.

//methods from utilsObj
appObj.add(10, 2); //12
appObj.subtract(10, 2); //8
appObj.multiply(10, 2); //20
appObj.divide(10, 2); //5

This started making sense to him. Could it be that one prototype object was linking to another, like a chain. To confirm he looked under the hood again.


Yes, his suspicions proved correct. It was indeed a chain. So this was the proverbial prototype chain that he had read about.

He could not believe it was all so logical and simple. He realized that "prototype" was just another name for a default or a helper object. Nothing more. Like Shakespeare’s quote – A rose by any other name would smell as sweet.

Bob’s quest complete, he meandered onto his next adventure…

Does my NodeJS application leak memory? – 5

Mark-Sweep, Mark-Compact

In the last post I talked about Scavenges, semi-spaces, new and old spaces, new space to old space promotion, garbage collection algorithms and tracing garbage collection events. In this installment I will discuss the “Mark-Sweep, Mark-Compact” garbage collector.

Mark-Sweep and Mark-Compact operate in two phases. Marking (marking all the live objects) and Sweeping, Compacting (getting rid of dead objects and reducing fragmentation if needed). Commonly referred to as mark-and-sweep collector.

Marking involves locating and processing all the live objects. An object is considered live if it is being referenced by some chain of pointers to a root object or another live object. Putting it another way, an object is live if and only if, it can be reached by following a chain of references from a set of known roots(e.g. global variables for instance)

Essentially, the marking process involves scanning, locating(discovering) and then processing(marking) the live objects. The garbage collector considers all the unmarked (unprocessed) objects as disposable or dead. Marking is done using tri color abstraction, where white, grey and black represent the marking states. In reality they are bits in a marking bitmap.

In this, white objects have not been located(or discovered) by the marker, while grey objects have been discovered but not much is known about its neighbors and finally black objects have been fully processed and are considered live. To assist in this effort a marker queue is used which tracks the marking progress.

The marking process uses the Depth First Search algorithm to scan the object graph and is best explained using an example. Consider: const a = {b: {c: [1, 2], d: [3, 4]}}; This can be written as:

const a = {};
a.b = {};
a.b.c = [1, 2];
a.b.d = [3, 4];

#1. The initial object graph. All objects are white as they have yet to be scanned. The marker queue is empty – Q = [ ].

#2. The scan starts with the root which is the variable “a”. It is marked grey and pushed to the queue. The grey indicates that “a” has been discovered(or visited, located) but has still to be fully processed. In other words its neighbors(children) if any have yet to be discovered. We now have Q = [a].


#3. Continuing with the depth search(following edge a.b) object “b” is discovered, marked grey and pushed on to the marker queue.

#4. The depth search continues and object “c” is discovered(edge b.c).  It too is marked grey and pushed to the queue – Q = [a, b, c]. At this point the search stops as there is nothing beyond “c”(c is a leaf).

#5. As the search cannot continue object “c” is popped from the queue and marked black as it now has been fully processed. In other words it has been visited and it has no neighbors.

At this point Q = [a, b].

#6. Now that object “c” has been processed, the search continues by backtracking to the parent object “b”. Continuing it identifies(discovered)  object “d” a child of “b”. Object “d” is now marked grey and pushed to the queue. We now have Q = [a, b, d].

#7. As the search cannot continue, object “d” is popped from the queue and marked black as it now has been fully processed. In other words it has been visited and it has no neighbors or children. At this point Q = [a, b].

#8. The search continues by backtracking to object “b”. As all its children(neighbors) have been processed it is considered fully processed and so it is popped and marked black. Q = [a];


#9. The search continues by backtracking to object “a”. As all its children(neighbors) have been processed it is considered fully processed and so it is popped and marked black. We now have q = []. The queue is empty as all objects have been processed. In this case all objects are live and no garbage collection will take place.

leaks-post.038Assume that in the above example at some point in the program execution, object “d” has been assigned null. So a = {b: {c: [1, 2], d: null}}; This can be written as: a.b.d = null;

This is how the marking will progress:

As “d” is detached from the tree it is never visited or processed. Object “d”, remains white after the marking process is complete and will be garbage collected.

Once marking is complete V8 triggers a sweep event when it believes memory will be required. The sweeper follows a simple process, it destroys all the unmarked (white) objects and optionally compacts the fragmented live objects.

Note that the old space could be very large in size. The default is in excess of 1 GB. It would take the collector a long time(in hundreds of milliseconds) to mark a large number of live objects. This would have an impact on performance as, this is, like I have mentioned before, stop the world gc. To avoid this, an “incremental marker” is used. In this, the live objects are marked in small increments or steps, making sure that no step takes more than 5ms giving as much time as possible to allow the application program to run and be performant. Using the same premise as marking, it would take a sweeper a long time to sweep large swaths of memory. For this, lazy sweeping is used, where only the “just” needed amount of sweeping is done based on how much memory that needs to be freed for the current requests. Rest of the sweeping is kept for a later time.

Now that the theory is out of the way and that you have understood how marking and sweeping take place let’s trace through the logs of two contrived examples. To quickly illustrate the point we will be running both programs in limited memory space by limiting the new space to 1MB and old space to 20 MB.

Example 1:

In the first example the program with every iteration, pushes a string of 1,000,000 characters into the array a.b.c. No memory is being freed. As the maximum old space is limited to a little more than 20MB, the application will eventually run out of memory.

Run with: node -trace_gc --trace_incremental_marking --max_semi_space_size=1 --max_old_space_size=20 example-1.js
A contrived example to gradually exhaust memory

"use strict";
const a = {b: {c: []}};
let i = 0;

function aString (s) {
let text = '';
for (let i = 0; i < 10000; i++) {
//string of 100 characters
text += 'Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the ';
s.push(text); //push in 1,000,000 bytes of data

//Push in 1,000,000 bytes of data till the system
//runs out of memory
setInterval(function () {
}, 500);

We will use the command:

node -trace_gc -trace_gc_verbose –trace_incremental_marking  –max_semi_space_size=1  –max_old_space_size=20 example-1.js

trace_gc – Creates the basic gc trace, discussed in the previous post
max_semi_space_size – The size of the new space. This is set at 1MB. Remember there are two semi spaces in the new space.
max_old_space_size – The max size of old space. This is set at 20MB
trace_incremental_marking – traces the progress of the incremental marker

The following is the trace with the commentary interspersed:

node -trace_gc --trace_incremental_marking --max_semi_space_size=1 --max_old_space_size=20 example-1.js

V8 initialization - we ignore this
[35841:0x103000000] 72 ms: Scavenge 2.1 (6.0) -> 2.1 (7.0) MB, 0.8 / 0.0 ms [allocation failure].
[35841:0x103000000] 73 ms: Scavenge 2.1 (7.0) -> 2.1 (8.0) MB, 0.7 / 0.0 ms [allocation failure].

Scavenge GC events. With every iteration 1,000,000 bytes are pushed and as the new space is limited to 1MB the new space will fill up pretty much immediately
promoting all data to old space. The scavenge event has been triggered due to "allocation failure" as there is no new space available for 
incoming data and no data is being deleted. In a time span of about 7 seconds the size of the objects grows from 4.0MB to 8.9MB. 
Allocation grows from 11MB to 16MB

[35841:0x103000000] 640 ms: Scavenge 4.0 (11.0) -> 3.7 (11.0) MB, 0.9 / 0.0 ms [allocation failure].
[35841:0x103000000] 643 ms: Scavenge 4.0 (11.0) -> 3.9 (12.0) MB, 1.6 / 0.0 ms [allocation failure].
[35841:0x103000000] 1666 ms: Scavenge 4.7 (12.0) -> 4.6 (12.0) MB, 1.7 / 0.0 ms [allocation failure].
[35841:0x103000000] 2173 ms: Scavenge 4.9 (12.0) -> 4.9 (12.0) MB, 2.4 / 0.0 ms [allocation failure].
[35841:0x103000000] 3181 ms: Scavenge 5.6 (12.0) -> 5.6 (13.0) MB, 2.2 / 0.0 ms [allocation failure].
[35841:0x103000000] 3183 ms: Scavenge 5.9 (13.0) -> 5.9 (13.0) MB, 2.0 / 0.0 ms [allocation failure].
[35841:0x103000000] 4191 ms: Scavenge 6.5 (13.0) -> 6.5 (14.0) MB, 2.0 / 0.0 ms [allocation failure].
[35841:0x103000000] 4698 ms: Scavenge 6.9 (14.0) -> 6.9 (14.0) MB, 1.8 / 0.0 ms [allocation failure].
[35841:0x103000000] 5709 ms: Scavenge 7.5 (14.0) -> 7.5 (15.0) MB, 1.9 / 0.0 ms [allocation failure].
[35841:0x103000000] 6213 ms: Scavenge 7.9 (15.0) -> 7.9 (15.0) MB, 2.3 / 0.0 ms [allocation failure].
[35841:0x103000000] 6718 ms: Scavenge 8.5 (15.0) -> 8.5 (16.0) MB, 2.0 / 0.0 ms [allocation failure].
[35841:0x103000000] 7223 ms: Scavenge 8.9 (16.0) -> 8.9 (16.0) MB, 2.4 / 0.0 ms [allocation failure].

V8 needs to free memory for allocation. All the promoted data is now accumulating in the old space. V8 triggers incremental marking for a possible sweep GC event.
[IncrementalMarking] Start (GC epilogue)
[IncrementalMarking] Start marking
[IncrementalMarking] Running

The scavenge event happens in the new space while marking happens in the old space. So these two events can happen concurrently.
[IncrementalMarking] Scavenge during marking.
The inflow of data from the new space is being promoted to the old space at a steady pace and the incremental marker decides to speed up it rate of marking to keep up with the inflow

[35841:0x103000000] Increasing marking speed to 3 due to high promotion rate
In this scavenge event the marker marked memory in 9 steps which took a total of 1.2 ms or 0.13ms per step. Note that this is since last gc event which was at 7223ms.

[35841:0x103000000] 8236 ms: Scavenge 9.5 (16.0) -> 9.5 (17.0) MB, 2.1 / 0.0 ms (+ 1.2 ms in 9 steps since last GC) [allocation failure].
Marking and Scavenging continues...
[IncrementalMarking] Scavenge during marking.
[35841:0x103000000] 8743 ms: Scavenge 9.9 (17.0) -> 9.9 (17.0) MB, 2.2 / 0.0 ms (+ 1.1 ms in 6 steps since last GC) [allocation failure].
[IncrementalMarking] Scavenge during marking.
[35841:0x103000000] 9251 ms: Scavenge 10.5 (17.0) -> 10.5 (18.0) MB, 2.1 / 0.0 ms (+ 2.1 ms in 10 steps since last GC) [allocation failure].
[IncrementalMarking] Scavenge during marking.
[35841:0x103000000] 9755 ms: Scavenge 10.8 (18.0) -> 10.8 (18.0) MB, 2.4 / 0.0 ms (+ 1.5 ms in 6 steps since last GC) [allocation failure].
[IncrementalMarking] Scavenge during marking.
[35841:0x103000000] 10766 ms: Scavenge 11.5 (18.0) -> 11.5 (19.0) MB, 2.2 / 0.0 ms (+ 1.9 ms in 10 steps since last GC) [allocation failure].
[IncrementalMarking] Scavenge during marking.
[35841:0x103000000] 11274 ms: Scavenge 11.8 (19.0) -> 11.8 (19.0) MB, 2.1 / 0.0 ms (+ 1.2 ms in 6 steps since last GC) [allocation failure].
[IncrementalMarking] Scavenge during marking.
[35841:0x103000000] 12287 ms: Scavenge 12.4 (19.0) -> 12.4 (20.0) MB, 2.2 / 0.0 ms (+ 2.1 ms in 10 steps since last GC) [allocation failure].

[IncrementalMarking] requesting finalization of incremental marking.
[IncrementalMarking] (Incremental marking task: finalize incremental marking).
[IncrementalMarking] Finalize incrementally round 0, spent 0 ms, marking progress 0.

In the marking phase objects can be in three possible states
White - The GC has not found this one yet
Grey - The GC has found this one but has not looked at its neighboring objects. In other words it is yet to confirm whether this object is being referenced by any of its neighboring objects
Black - GC has found this object and has looked at all its neighbors. These have been fully processed.

[IncrementalMarking] Black allocation started
[IncrementalMarking] Stopping.
[IncrementalMarking] Black allocation finished

Old space is filling up. Based on heuristics, V8 understands that a scavenge will not work so triggers more of the mark-sweep events. Hence the reason - [scavenge might not succeed]
In this marking event notice that it is from the start of the marking event which started after the 7223ms scavenge event and not from the last GC event as was the case previously. In this case it also specifies that the longest step was 0.4ms.

[35841:0x103000000] 12800 ms: Mark-sweep 12.8 (20.0) -> 12.6 (20.0) MB, 6.2 / 0.0 ms (+ 13.7 ms in 69 steps since start of marking, biggest step 0.4 ms) [allocation failure] [scavenge might not succeed].

Object size is now at 16.9 MB. Remember that the max old space is 20MB so we are close to running out. Notice also that the duration of each Mark-sweep event has gone up. From 2.4ms at 7223ms to 17.8ms at 18455ms.

[35841:0x103000000] 13313 ms: Mark-sweep 13.2 (20.0) -> 13.2 (21.0) MB, 11.8 / 0.0 ms [allocation failure] [scavenge might not succeed].
[35841:0x103000000] 13830 ms: Mark-sweep 13.6 (21.0) -> 13.6 (21.0) MB, 12.2 / 0.0 ms [allocation failure] [scavenge might not succeed].
[35841:0x103000000] 14849 ms: Mark-sweep 14.2 (21.0) -> 14.0 (22.0) MB, 12.1 / 0.0 ms [allocation failure] [scavenge might not succeed].
[35841:0x103000000] 15366 ms: Mark-sweep 14.3 (22.0) -> 14.3 (22.0) MB, 12.3 / 0.0 ms [allocation failure] [scavenge might not succeed].
[35841:0x103000000] 15883 ms: Mark-sweep 14.9 (22.0) -> 14.9 (23.0) MB, 12.4 / 0.0 ms [allocation failure] [scavenge might not succeed].
[35841:0x103000000] 16399 ms: Mark-sweep 15.3 (23.0) -> 15.3 (24.0) MB, 11.7 / 0.0 ms [allocation failure] [scavenge might not succeed].
[35841:0x103000000] 17417 ms: Mark-sweep 15.9 (24.0) -> 15.9 (24.0) MB, 12.2 / 0.0 ms [allocation failure] [scavenge might not succeed].
[35841:0x103000000] 17933 ms: Mark-sweep 16.3 (24.0) -> 16.3 (24.0) MB, 12.9 / 0.0 ms [allocation failure] [scavenge might not succeed].
[35841:0x103000000] 18455 ms: Mark-sweep 16.9 (24.0) -> 16.9 (24.0) MB, 17.8 / 0.0 ms [allocation failure] [scavenge might not succeed].
last resort and the system runs out of space

16399 ms: Mark-sweep 15.3 (23.0) -> 15.3 (24.0) MB, 11.7 / 0.0 ms [allocation failure] [scavenge might not succeed].
17417 ms: Mark-sweep 15.9 (24.0) -> 15.9 (24.0) MB, 12.2 / 0.0 ms [allocation failure] [scavenge might not succeed].
17933 ms: Mark-sweep 16.3 (24.0) -> 16.3 (24.0) MB, 12.9 / 0.0 ms [allocation failure] [scavenge might not succeed].
18455 ms: Mark-sweep 16.9 (24.0) -> 16.9 (24.0) MB, 17.8 / 0.0 ms [allocation failure] [scavenge might not succeed].

Cannot get stack trace in GC.
FATAL ERROR: MarkCompactCollector: semi-space copy, fallback in old gen Allocation failed - JavaScript heap out of memory

To summarize:

  1. First came the scavenge event. No data is being freed so all data is promoted to the old space.
  2. Data keeps being promoted at a high rate which pushes V8 to trigger the incremental marker to prepare for a possible sweep event.
  3. As no data is being freed, all data objects are live the mark-sweep event has no effect.
  4. Eventually the system runs out of memory.

This was an extreme example to show the various GC events. Let us now consider a more saner example. In this example we actually free up memory to see how the sweeping action works. For every tenth iteration the array ‘c’ is emptied.

Example - 2
Run with: node -trace_gc --trace_incremental_marking --max_semi_space_size=1 --max_old_space_size=20 example-2.js
reset when 10,000,000 bytes have been accumulated

"use strict";

const a = {b: {c: []}};

let i = 0;

function aString (s) {
let text = '';
for (let i = 0; i < 10000; i++) {

//string of 100 characters
text += 'Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the ';

setInterval(function () {

//reset when 10,000,000 bytes have been pushed into the array
if (i === 10) {
i = 0;
a.b.c = [];


}, 500);

We will use the same command line parameters as before:

node -trace_gc -trace_gc_verbose –trace_incremental_marking  –max_semi_space_size=1  –max_old_space_size=20 example-2.js

In the trace below I have removed some of the traces to improve clarity.

node -trace_gc --trace_incremental_marking --max_semi_space_size=1 --max_old_space_size=20 example-2.js

V8 initialization - we ignore this
[40225:0x103000000] 20 ms: Scavenge 2.1 (6.0) -> 2.1 (7.0) MB, 0.7 / 0.0 ms [allocation failure].
[40225:0x103000000] 21 ms: Scavenge 2.1 (7.0) -> 2.1 (8.0) MB, 0.7 / 0.0 ms [allocation failure].

Scavenge events. All data promoted as no space is available in new space
[40225:0x103000000] 562 ms: Scavenge 4.0 (11.0) -> 3.6 (11.0) MB, 0.8 / 0.0 ms [allocation failure].
----more scavenges--------
[40225:0x103000000] 7144 ms: Scavenge 8.8 (16.0) -> 8.8 (16.0) MB, 2.2 / 0.0 ms [allocation failure].

Marking started in preparation for a sweep GC event.
[IncrementalMarking] Start (GC epilogue)
[IncrementalMarking] Start marking
[IncrementalMarking] Running

Marking and scavenges are interspersed
[IncrementalMarking] Scavenge during marking.

All data promoted, marking speed increased to keep up promoted data inflow
[40225:0x103000000] Increasing marking speed to 3 due to high promotion rate
Marking and Scavenge happen concurrently.
[40225:0x103000000] 8155 ms: Scavenge 9.4 (16.0) -> 9.4 (17.0) MB, 2.3 / 0.0 ms (+ 1.0 ms in 8 steps since last GC) [allocation failure].

----more marking and scavenges--------
[40225:0x103000000] 10690 ms: Scavenge 11.4 (18.0) -> 11.4 (19.0) MB, 2.3 / 0.0 ms (+ 1.6 ms in 9 steps since last GC) [allocation failure].
[IncrementalMarking] requesting finalization of incremental marking.
[IncrementalMarking] Black allocation finished

Marking complete and a sweep triggered. Size of objects reduced from 11.7MB to 7.7MB in 2.9 ms. V8 does lazy sweeping, that is,
enough sweeping(cleanup) as it can in the given amount of time so as not to hold up the application.
[40225:0x103000000] 11197 ms: Mark-sweep 11.7 (19.0) -> 7.7 (19.0) MB, 2.9 / 0.0 ms (+ 8.2 ms in 46 steps since start of marking,
biggest step 0.4 ms) [GC interrupt] [GC in old space requested].
Back to scavenges, only promotions. Note the increase in object size
[40225:0x103000000] 11702 ms: Scavenge 8.3 (19.0) -> 8.3 (19.0) MB, 1.6 / 0.0 ms [allocation failure].

----more scavenges--------
[40225:0x103000000] 17247 ms: Scavenge 12.3 (19.0) -> 12.3 (20.0) MB, 1.5 / 0.0 ms [allocation failure].
Second mark-sweep event is triggered. Size of objects reduced from 12.6MB to 5.0MB by lazy sweeping in 5.9 ms.
Time increased compared to last sweep as more memory is swept. In a real live application 5.9ms is very high.
During this time the application has paused.
[40225:0x103000000] 17758 ms: Mark-sweep 12.6 (20.0) -> 5.0 (18.0) MB, 5.9 / 0.0 ms [allocation failure] [scavenge might not succeed].

Back to scavenges. Size of objects increased from 5.6MB to 11.5MB
[40225:0x103000000] 18263 ms: Scavenge 5.6 (18.0) -> 5.6 (18.0) MB, 1.6 / 0.0 ms [allocation failure].

----more scavenges here-----
[40225:0x103000000] 26335 ms: Scavenge 11.5 (19.0) -> 11.5 (19.0) MB, 1.9 / 0.0 ms [allocation failure].
Marking started in preparation for another mark-sweep GC
[IncrementalMarking] Start (idle)
[IncrementalMarking] Start sweeping.
[IncrementalMarking] Start marking
[IncrementalMarking] Running

Compared to the previous marking cycle above the reason for speeding up marking is different - "because of low space left"
[40225:0x103000000] Speed up marking because of low space left
[40225:0x103000000] Marking speed increased to 3
[IncrementalMarking] Scavenge during marking.

GC's desperation continues, progressively increasing the marking speed
[40225:0x103000000] Speed up marking because of low space left
[40225:0x103000000] Marking speed increased to 6
----more marking--------
[IncrementalMarking] (GC interrupt: finalize incremental marking).
[IncrementalMarking] Finalize incrementally round 0, spent 0 ms, marking progress 1.
[IncrementalMarking] Complete (normal).
[40225:0x103000000] Speed up marking because of low space left
[40225:0x103000000] Postponing speeding up marking until marking starts
[IncrementalMarking] Stopping.

Making done. mark-sweep is triggered, lazy sweeping now… object size 12.3 => 4.7 in 3.0ms
[40225:0x103000000] 27352 ms: Mark-sweep 12.3 (19.0) -> 4.7 (18.0) MB, 3.0 / 0.0 ms (+ 4.2 ms in 8 steps since start of marking,
biggest step 1.4 ms) [GC interrupt] [GC in old space requested].

Cycle continues
[40225:0x103000000] 27860 ms: Scavenge 5.2 (18.0) -> 5.2 (19.0) MB, 2.7 / 0.0 ms [allocation failure].
[40225:0x103000000] 28364 ms: Scavenge 5.7 (19.0) -> 5.7 (19.0) MB, 1.9 / 0.0 ms [allocation failure].

By now the typical GC cycle must be apparent in this case.

  1. V8 initialization
  2. Starts with Scavenge in the new space. Data gets promoted to old space
  3. As the promoted data increases, V8 starts up the incremental marker
  4. Marking in the old space is intersped with the Scavenge events
  5. Marking completes and triggers the sweeper(mark-sweep)
  6. The Lazy sweeper runs for a small time duration to free up memory.  

The idea is not to dig too deep into these traces but to get an overview of how your app is performing with one important thing in mind. During a GC event the application is blocked. It cannot process any request till the GC is over.

If in the above example we were to reset every 100,000 bytes there would few, if any, mark-sweep events as everything would be done in the new space. No data would be promoted. As scavenges are fast the pauses would be very short. By the same token you can also set the size of the new space to allow for more scavenges. Given this knowledge you now have a way to check if your app is leaking and/or non performant. In my next post I will provide more realistic examples.

Does my NodeJS application leak memory? – 4

The heap, garbage collection and scavenges

In the last post I discussed the heap, dead and live objects and the object graph. Recapping…

Most of the allocations required by an application happen on the heap. The stack only contains SMIs (immediate 31-bit integers). This could be data or pointer to data on the heap. As functions execute, V8 cleans up the dead memory regions that have been abandoned by the stack. This cleanup event(A.K.A garbage collection) is triggered at intervals determined by V8. One such point is when V8 detects that it will soon run out of heap memory. The V8 subsystem that performs garbage collection is called the garbage collector. V8, like most JavaScript engines has a built in “garbage collector”.

V8 uses an extremely simple technique for garbage collection. In this it scans the stack for any references(handles) which are active(live) and considers the referenced objects live. The rest of the heap is considered garbage(dead) and is marked to be reclaimed for re-use.

One of the problems we face is that V8 is single threaded. When garbage collection happens the rest of the program stops. This is why it is called “stop the world” garbage collector. This has performance implications because if the execution of the program is held up for long it may result in sluggish behavior. From a UI standpoint this may result in browser rendering to be less than 60 fps resulting in the user experience being less than optimal. More information on this sluggishness in browser rendering, also called “Jank” can be found here.

Collection types and cycles

If you think about it, most of the allocations we request are localized to the executing function. In comparison the allocations that require to be alive across multiple function calls(e.g. globals) are fewer. For this reason V8 uses a “Generational” garbage collection system where the lifetime of the objects determine their placement in the heap. In this the heap is divided into two major lifetime sections or generations. The new and the old generation(or space). The new space holds objects that are short lived while the old space holds objects that are intended to be around for a longer period of time.

This is how it works. Most objects(if they are not too big) start their life in the new space(young generation). When memory is required, V8 quickly scans the new space for any live objects and considers the rest of the objects as dead and re-usable. This quick scan and cleanup is called a scavenge(minor collection cycle) and usually lasts less than a millisecond.

If an object survives two scavenge cycles it is considered “old” and moved(promoted) to the old space. At some point when V8 determines that it may need more memory in the old space it triggers a major garbage collection cycle(mark-sweep and mark compact) and removes dead objects from the old space.

One consequence of garbage collection is memory fragmentation. Dead and live objects are intermixed in the heap. If there are many of these pockets of free memory(space occupied by dead objects it would make allocation of new memory slow as objects would have to be split  up for storage. To fix this the garbage collector moves objects around and lays them out in a contiguous memory space. This process is called “memory compaction”.

Spaces in the Heap

To understand both the minor(scavenge) and major(mark-sweep and mark-compact) cycle, it is helpful to visualize how the heap is divided in to spaces and their functions. The heap is divided into multiple sections or spaces. The spaces that are relevant to us are shown in Figure 4.1. below which provides a pictorial representation.

Spaces in the heap

Briefly, here is what each space does:

  • New space: Short lived objects allocated here. Scavenge works in this space
  • Old Space = Old data space + Old pointer space: Promoted objects from new space and other raw data objects and pointers respectively. Mark-sweep and mark-compact work in this space.
  • Code space: Executable instructions
  • Other miscellaneous spaces: These include the Cell space, property cell space and map space and they contain specialized data and pointers. No garbage collection takes place here.

Within  these spaces we will concern ourselves only with the new space and the old space. Let’s look at scavenges and the new space first.


Consider this simple example(Figure 4.2) in which a string of 100,000 bytes  in length is created and stored in the local variable text. The setInterval timer runs the aString function every 200ms till the program is aborted .

Figure 4.2

It is clear that there should not be any memory growth, because every time the function returns the variable “text” goes out of scope and is discarded. The program does not have any practical application but works well for learning about scavenges. Running this piece of code with the trace_gc option in the terminal we get the output in figure 4.3.


The dissected trace(the one in bold) is shown below in figure 4.4.


Trace Dissected

This is the simplest of the gc traces that V8 offers. It gives an excellent overview of memory allocation. This is also the first trace I run to check on how memory allocation is doing.  The explanations given in the figure for the dissected trace are self explanatory.

Analyzing the above trace:

  • It is a scavenge event, triggered as a result of allocation failure. In other words V8 ran out of memory in the new space and decided to reclaim dead space.
  • Each gc event is less then 1.0 ms. This is ok. If this time tends to increase then there is a possibility of a leak. It means that V8 is finding it hard to gc.
  • The difference in size of before and after objects which is about 1MB remains fairly constant.
  • The before and after overall memory size(41.1MB) remains fairly constant. This indicates that all of the dead space is being cleaned up. If it did not, then this would be another indication of a leak. In real applications it is not this ideal and some variation should be expected.

Continuing with the analysis let us assume that the new space is 1MB in size and that it is only used for text. Visualizing at a very simplistic level:

  1. For a 1MB sized new space the start and end of new space in the heap would be between 0 and 1048575 bytes
  2. Initially the pointer is at 0th byte.
  3. Allocation is requested for 100,000 bytes
  4. The pointer moves 100,000 bytes from 0 to 99999 bytes. The pointer is at the 100000th  position waiting for the next allocation.
  5. After the 10th allocation the pointer is at 1000000th position
  6. For the 11th allocation there is no memory left so a GC is triggered
  7. When allocation is requested for the text variable (11th allocation) V8 finds that there is no memory left in the new space.
  8. V8 reaches out to the stack and finds that there are no live objects. This means all of the new space contains dead objects and can be re-used.

Please keep in mind that V8 uses a variety of techniques to trigger a GC. Also, it tends to manipulate the size of new space depending on current conditions. This is the reason why the trigger points are never the same. It is the trend that is important and not the exactness of the values.

V8’s scavenge is based on Cheney’s algorithm. In this, the new space is divided into two semi-spaces, the to-space and from-space. There are five steps  that the garbage collector goes through to GC.

  1. Memory allocation starts in the to-space
  2. Once the to-space is out of memory a GC event is triggered
  3. To and from spaces are swapped out. At this point to-space is empty and from-space is full.
  4. Live objects are copied from from-space to to-space, laying them next to each other in contiguous memory locations. This also compacts the memory. from-space now only contains dead objects while to-space contains only live objects.

Note: Live objects are in blue, while the dead objects are in gray.

from-space is now fully reclaimed and can be reused and the cycle continues. For a pictorial representation see figure 4.5 below.


Cheyney's Alogorithm

We used a simple example to displayed the V8 GC trace and further dissected it to see how scavenges work. No special tools were used. As such, you can start using it with your existing projects. I will talk about Mark sweep and mark compact next time.


  1. A tour of V8: Garbage Collection (An excellent series of blog posts on GC by Jay Conrod)
  2. An excellent collection of references on V8 performance by Thorsten Lorenz

Does my NodeJS application leak memory? – 3

The heap, objects dead or live?

In the last post I discussed the stack. To quickly recap, the stack is LIFO which is fast and managed automatically. It is small in size and stores only local variables(immediate small integers). Everything else is stored on the heap.

The heap, compared to the stack is bigger in size, more freeform in nature and stores reference types such as objects and strings. Variables that have to span function calls, including globals and variables captured by closures are also stored here.

The heap is dynamically allocated by the OS. It is self managed, that is, the running program(in this case V8)  makes requests from the OS for allocation and de-allocation. It is divided into multiple sections or spaces. The spaces that are relevant to us are the New Space, Old space, Code space, Map Space, Large object space. More on this when I talk about garbage collection. However for now it is extremely important to understand how the stack and the heap interact when a function or an application is executing.

A heap can be imagined as a network of interconnected objects. Consider the example we used in the previous post drawn a little differently.

Figure 3.1

In the above figure(3.1):

  1. aSmallInt is a SMI (immediate 31-bit integer) stored on the stack.
  2. aFloat is a number object so a reference(a handle in V8 speak) is stored on the stack with the number object on the heap.
  3. anObject is a literal object. Reference to this object is stored on the stack while the object itself is stored on the heap. The object is split up into three other string objects one each for a country code.

We can draw this in a different way:

Figure 3.2

This is a very simplistic rendering of an object graph. The local handles (or references) point to the objects on the heap. The fact that the heap is a network of interconnected objects becomes clear with an object graph.

As long as the parameters of the function test exists on the stack, its handles(references) exist and so do the objects on the heap. The objects in the heap are called live objects. Once the function ends  (returns) both the handles go out of scope and the objects in the heap are now considered dead. V8 can now reclaim the memory area used up by these objects. Understanding  dead and live objects is important for visualizing how the code affects garbage collection.

Objects, Dead or Live?

An object is considered live if it is being referenced by some chain of pointers to a root object or another live object. I will discuss two examples to illustrate this.

Simple Example

Consider the following simple snippet. The function  getCountryCode returns the countryCode if one is found or otherwise an empty string:

var countryCodes = {no: &amp;amp;amp;amp;quot;+47&amp;amp;amp;amp;quot;, us: &amp;amp;amp;amp;quot;+1&amp;amp;amp;amp;quot;, uk: &amp;amp;amp;amp;quot;+44&amp;amp;amp;amp;quot;};
var cc = '';

function getCountryCode(countryAbbreviation) {
    var ret = null;

    if (countryAbbreviation in countryCodes) {
       ret = countryCodes[countryAbbreviation];

    return ret;

cc = getCountryCode('no');


In this code snippet:

  • The literal object countryCodes is global.
  • cc, the variable which will hold the results is initialized with an empty string and is a global.
  • countryAbbreviation is a local variable and a string.
  • ret, the return variable is also a local variable and a string.

1. Before getCountryCode is run the graph looks like in the following figure(3.4).  Note that both the global variables, cc and countryCodes are stored in the heap pointed to by global handles.


Figure 3.4


2. Before the functions ends, that is before getCountryCode returns, here is how the object graph looks (figure 3.5). Now there are two local variables. Both are string objects and stored on the heap pointed to by local handles.

Figure 3.5



3. Once the function ends(returns) the graph looks like in figure 3.6. As the function has ended, both ret and countryAbbreviation string objects have lost their references and now can be cleaned up by the garbage collector as these objects are dead. On the other hand both the global variables cc and countryCodes remain alive and the garbage collector will not touch them.

Figure 3.6


In summary:

  1. On entering the module, countryCodes object and cc which are global variables are allocated space on the heap. Both the globals are considered as root objects. The stack is empty at this point.
  2. On entering getCountryCode, ret and countryAbbreviation, both local variables (not small integers) are stored on the heap with the references on the stack. Both the local variables are considered root objects. They are also live objects as they can be accessed within the function scope. In other words they are live for the entire execution of the function.
  3. When the function returns, the global variable cc contains the value of 47, the country code for Norway.
  4. Both ret and countryAbbreviation go out of scope, are popped from the stack and discarded.
  5. The references to the heap for ret and countryAbbreviation are removed. Both ret and countryAbbreviation in the heap are considered dead.
  6. Both globals are still live as they can be used again till the program terminates.


Example with a Closure

Now consider the same example but in a form of a closure in which processCountryCodes returns a reference to the inner function getCountryCode. Please note that the variable getCountryCode to which the function is assigned is redundant and used here for clarity. I could have easily returned the function itself directly.

var cc, code;

function processCountryCodes() {
    var countryCodes = {no: &amp;amp;amp;amp;quot;+47&amp;amp;amp;amp;quot;, usa: &amp;amp;amp;amp;quot;+1&amp;amp;amp;amp;quot;, uk: &amp;amp;amp;amp;quot;+44&amp;amp;amp;amp;quot;};
    var getCountryCode = function (countryAbbreviation) {
        var ret = null;

        if (countryAbbreviation in countryCodes) {
            ret = countryCodes[countryAbbreviation];
        return ret;

    return getCountryCode;

cc = processCountryCodes();
code = cc('no');

1. Before the function processCountryCodes is run the object graph looks like in the figure(3.8) below. Both global variables are allocated space on the heap. They are referenced via global handles. Simple enough.

Figure 3.8
Figure 3.8


2. Before the function processCountryCodes returns the object graph looks like in the following figure 3.9:

Figure 3.9
Figure 3.9

The inner function getCountryCode is a closure and closes over the object countryCodes. In other words before processCountryCodes returns it needs to save a reference to the countryCodes object. This is because once processCountryCodes returns, the stack will be wiped clean. To retain the reference V8 automatically creates an internal object called the “Context object” which is an instance of the JSFunction class and adds the countryCodes object to it as a property. For the same reason V8 also allocates space for the inner function in the heap. Please remember that V8 creates the context object when it enters the outer function processCountryCodes.

3. Before the function processCountryCodes returns the object graph looks like in the figure 3.10. The global variable cc now holds a reference to the inner function getCountryCode.

Figure 3.10
Figure 3.10


4. On execution of the inner function the graph looks like in the figure 3.11 The global variable cc now holds a reference to the inner function getCountryCode. Global variable code now contains the result “+47”.

Figure 3.11
Figure 3.11


Please note that I have not included the objects for the inner function because that is the same as the earlier example.

In summary:

  1. On entering the module, cc and code which are globals are allocated space on the heap. The stack is empty at this point. cc and code are now considered live root objects.
  2. On entering processCountryCodes V8 builds a special “Context” object on the heap and adds the countryCodes object as a property to it.
  3. Before processCountryCodes returns, the local variable now holds a reference to the inner function which is allocated space on the heap
  4. Once processCountryCodes returns, global variable cc holds the reference to the inner function.
  5. Global variable holds the result “+47” once the inner function is executed via the global variable cc.

The key point here to note is that even though both processCountryCodes and getCountryCode have been executed the heap structure remains intact. The reason is that both the global variables will keep holding references to the objects in the heap till the program terminates.

I hope that you now have the necessary tools to visualize your code in terms of an object graph. In the next post I will talk about Garbage collection as it relates to the heap and build on the information in this post.


Does my NodeJS application leak memory? – 2

Memory Management, Introducing the Stack


Catching up from last time, a memory leak happens when regions of memory cannot be reclaimed for reuse. The process of reclaiming memory is the job of the garbage collector which is a part of the V8 engine. A garbage collection cycle or an event is triggered when the memory requested by an application is not available. Goes without saying that garbage collection(GC) and memory management work hand in hand. Even though the inner workings of memory management are a core part of V8 and the OS(Linux, Windows…) and do not affect day to day programming, it is important to get a certain degree of understanding to appreciate how it affects leaks and performance issues.

In this post I will talk a little bit of the overall memory organization and attempt to explain how the stack works in a very simplistic way.

Memory Organization

Memory is organized into multiple sections or segments, each section with a specific purpose. The two segments that are relevant to us are the stack and the heap. The stack is used to track and manage executing functions. In this functions store their arguments,  local variables, and housekeeping data on the stack. The heap is used for storing everything else that the stack does not store such as objects and large data.

In V8 the stack only stores immediate small integers(Smi). These are 31 bit signed integers which are only stored on the stack and never on the heap. The stack also stores references (called handles in V8 speak) to data stored on the heap.

The following figure(2.1) makes it clear:


In the figure above:

  1. The local variable aSmallInt is a 31 bit signed integer and is hence placed directly on the stack.
  2. Variable aFloat and anObject are placed on the heap with references stored on the stack.

The Stack

A stack is simple LIFO (Last In First Out) data structure akin to a stack of pancakes or bills. The stack grows down, in other words, it starts off at the highest address and grows down in memory addresses. Every executing function pushes its local variables and arguments on the stack along with  housekeeping data such as the return address.

The stack has the following important properties:

  • It is small in size (984K Bytes for 64 bit systems, 492K bytes for 32 bit), very fast and extremely efficient in managing its resources.
  • It is statically allocated and de-allocated, that is, no special request has to be made for allocation. Stacks are managed automatically.
  • A stack is sub divided into stack frames. A stack frame is the area in the stack allotted to each function. Each stack frame houses the calling function’s arguments, local variables and housekeeping data.
  • A typical stack stores value types such as integers, floats and booleans. However in V8 the stack only stores immediate small integers(31bit).

For the stack to manage itself it maintains two pointers. The stack pointer and the base pointer. The stack pointer(SP) as the name suggests points to the last value pushed to the stack. In other words it is always pointing to the top of the stack. The base pointer (a.k.a the frame pointer) points to the start of the frame.

The best way to understand the operation and simplicity of the stack is to go through an example. Consider a simple piece of code in which a function add is called and the result put the variable ret.

Step – 1: The state of the stack at the start is:

  • The base pointer is just starting up so contains null.
  • The stack pointer is pointing to its latest push.
  • Each address slot in the stack is 4 bytes(32 bit) away from its immediate neighbor.
  • The highest address is at the bottom. The stack pointer moves up, towards lower memory addresses.


Step – 2: Before add is called space is reserved for the return variable ret and arguments n and m and set to values 2 and 3.


  • The stack pointer moved up after every push.
  • Each memory address is offset by 4 bytes from the base pointer.


Step – 3: Function add is now called. The return address of add is saved(pushed). This is the address where the function will return with the results.


  • I have used the term ‘return address’ instead of the actual memory address in hex. This is for pure simplicity.
  • The pointer which points to the next instruction to be executed is called the instruction pointer(eip).


Step – 4: Before starting to execute add the address of the old frame is first saved. The base pointer now moves to the new frame.


  • The housekeeping data consists of return address and the frame address.
  • Return address takes back the program where the program left off.
  • The frame address(value of base pointer) allows the stack to connect one frame with another.


Step – 5: On entering function add, space for variable r is reserved.


  • The stack frame consists of  the arguments(2 and 3), the address of the previous frame, the return address and the local variable r.


Step – 6: Addition of n and m is performed and the result of 5 is put into the variable r and now its time to return. This means items need to be popped off the stack. In other words unwind the stack.


Step – 7: The result of 5 is popped off the stack and held in a temporary register(eax). The stack pointer moves down.


  • The temporary register is located in the CPU and is named “eax”.



Step – 8: The saved address of the previous frame is popped off and the base pointer moves back to the previous frame. The return address is popped off and control is returned to module. The result of 5 is put into the variable ret.


Step – 9: The frame is now popped off, stack is empty and the program terminates.


In reality the stack is not empty at this pointer. It has frames for the module which is calling  program. However we do not need to go into that kind of detail.


The important lesson to learn here is that the stack is merciless in terms of cleaning up after itself. It does not need any garbage collection process. However, it does hold the key to the garbage collection process. Namely any references(handles) to objects on the heap. To understand that connection we need to look at the heap next which I will explain in my next post.



For this post I have relied heavily on two excellent posts by Gustavo Duarte:

Other well written posts that I have referenced:

Does my NodeJS application leak memory? – 1

A Gentle Introduction

A journey of a thousand miles begins with a single step – Lao Tzu

One of the key requirements for writing software for emergency management is consistent performance. Every time user feedback and/or testing reveals performance issues, the question that always comes to my mind is whether the application is leaking memory or if is it something else. The last thing an emergency software developer wants to hear is that the alerts did not get to the recipients in time or that emergency personnel experienced sluggish performance.

Simply put, a memory leak is when an application can no longer efficiently manage its memory resources resulting either in a crash or sluggish performance. Putting it differently, a memory leak occurs when freed memory is not reclaimed for reuse. Depending on the size and frequency of leak an application could either crash or behave erratically. While all platforms and programming languages are susceptible to memory leaks, I will be restricting my scope to NodeJS/V8.

Memory leaks in the front end (or client) code can go unnoticed for a long time or may never be detected. This is because the user could shut down the browser or refresh the page before a potential memory leak causes a problem. This is not the case when you are writing backend server real time applications for messaging, authentication and Internet of Things. A server issue affects all its clients not just one user. It is not practical to restart the server every time there is an issue. 

The go to response of a developer is to scour the internet and typically end up examining heap dumps or watching the growth of memory. If one has not understood the theory behind memory leaks and garbage collection this exercise usually results in frustration. For example, just pure memory growth for some period of time does not necessarily mean that there is a leak.

I went through the same frustrations  like most of my fellow developers and decided that I had to find a simpler way to check for leaks. This was very important to me as all my work relates to developing NodeJS backend server apps.

One of the things we tend to ignore is the power of the logs. Both in terms of analyzing them and of implementing them in our code. Depending on the type of log, the vitals of the server app can be provided in realtime. This can provide warning of an upcoming problem, be it leak or a security threat.

There are a ton of log analysis and management tools, including monitoring tools available, but that creates a learning curve for a specific product which may not be available in the near future. Many also do not fit the budget of a small startup. Many free open source tools tend to have difficulty in keeping up with the evolution of NodeJS/V8.

In my quest to find something that was not dependent of any external product or that required code modification, I decided to stick with understanding the garbage collection trace events of V8/NodeJS. My journey took me through analyzing the V8 source code, thousands of lines of garbage collection traces, including a quick detour into the world of Linux internals.

In the series of posts that follow I will describe my journey on how I use the V8 logs to look for leaks starting with some basic information on memory management

Done is better than Perfect

Earlier in the week I attended a NodeJS meet-up at Hacker Dojo. Outside the event room was a sign “Done is better than perfect”.

In May of 2015  the Amtrak Northeast Regional train from Washington, D.C. bound for New York City derailed resulting in 8 deaths. Investigations revealed that this particular route did not have the advanced safety technology (“positive train control” (PTC)) meant to prevent high-speed derailments. Under current law, the rail industry must adopt the technology by the end of this year.

Last month, 6 Irish students died in Berkeley when a balcony collapsed. One of the surviving students will never walk again. Mayor Tom Bates said “investigators believe the wood was not sealed properly at the time of construction and was damaged by moisture as a result. He said that appears to be the prime cause of Tuesday’s tragedy.”

The story is always the same.  Investigation after a horrific accident reveals that though the service was completed (done) it was not done “right”. Good practices and established processes were not followed. Just enough was done to pass an inspection or get a permit.

The Shinkansen (A.K.A Japan’s bullet train) was introduced in 1964. Over its 50-year history, carrying nearly 10 billion passengers, and traveling at speeds ranging from 120mph to 180mph, there have been no passenger fatalities due to derailments or collisions, despite frequent earthquakes and typhoons. As a young engineer on training it was a treat for me to travel on the Shinkansen from Osaka to Tokyo. I was amazed at the smoothness of the ride and marveled at the engineering feat that made it so. The Shinkansen was not built to perfection, it was just built right!

In the case of Bookout v. Toyota, Toyota was found guilty of a UA (unattended acceleration) death. It was due to a software bug which was a result of not following the established development processes. Michael Dunn in this EDN article titled “Toyota’s killer firmware: Bad design and its consequences”  provides an exhaustive explanation of the consequences of winging it. When the feds reached a settlement of $1.2B Eric Holder, the U.S. Attorney General said “When car owners get behind the wheel, they have a right to expect that their vehicle is safe. If any part of the automobile turns out to have safety issues, the car company has a duty to be upfront about them, to fix them quickly, and to immediately tell the truth about the problem and its scope. Toyota violated that basic compact.”

This same concept applies to the software we write. When a user uses our software they have a basic expectation that it will function as promised. That it will function under load, in different network conditions and if it involves critical systems it will have built in redundancies.

In the everyday world where software apps govern our daily lives, the phrase “Done is better than Perfect” has a very simple definition. Build small, build well, ship often. “Build well” means do it right, follow good development and test processes, and aim for proper working functionality.

Dave Rooney in his post puts it well, “Done” and “perfect” refer to the scope of the functionality, not its quality.”

Mark Zuckerberg in his letter to his shareholders about the “hacker culture” before the IPO wrote: “…Hackers try to build the best services over the long term by quickly releasing and learning from smaller iterations rather than trying to get everything right all at once. To support this, we have built a testing framework that at any given time can try out thousands of versions of Facebook. We have the words “Done is better than perfect” painted on our walls to remind ourselves to always keep shipping.” It was just about doing smaller iterations but every iteration done right. The iteration should work like the user expects.

Aileron in their post put it aptly,  “A corollary to “good is better than perfect” is “done is better than perfect”. This does not mean doing a mediocre job or producing a poor-quality product. Being done and finishing a task gives every business person an outcome. This result will give them important success or failure metrics to decide what their next step will be.”

Done right is better than perfect!

Life is what we make it

Yesterday, after a gap of about 30 years, RT called me. RT and I were classmates, pursuing an undergraduate degree in Industrial Electronics. After a few pleasantries he told me that he clearly remembered how I had helped him learn the Darlington pair.

RT suffers from Multiple sclerosis. His vision is failing rapidly and needs assistance to carry out even the most basic of tasks  we take for granted. His condition seems to worsen by the day.

About six weeks back I had the opportunity to attend a company team building exercise in Grand Lake, Colorado. A picturesque gateway to the Rocky Mountain National Park.

The walk from the hotel to the city center, about a mile was painful to say the least. A few days before the trip I had hurt my knee during a run. All through the walk the majestic rockies and the serene Grand Lake bore witness to my complaints.

Even with a limp I did get a nice lunch and sat by the lake, sipping coffee trying to exercise my knee, hoping to reduce the pain. The lake was partially frozen and the afternoon sun  glistened off its icy cover. The expanse of the quiet lake would have put anyone at ease. Not me.

The walk back was even more painful, because part of the walk was uphill. With an ice pack on my knee I sat on the couch in my hotel room blankly staring at the snow covered peaks while my face wrenched in pain.

In my self involved world that I had woven for myself I had totally forgotten that I was fortunate enough to lay at the bottom of a crucible of natural beauty which I chose to ignore.

When RT talked with me he sounded in much better spirits than I was then. He sounded happy, fulfilled and  buoyant. He was enjoying talking with me. Never in this short conversation did he hint about his illness. Something I need to heed and learn…

“Live long and prosper my friend..”





Internet of Things and the power of Text Messaging

Text messaging or SMS(Short Message Service) is well known for alerts and notifications. Be it in a crisis or a marketing campaign. It is actually the most powerful medium for quick and effective dissemination of messages.

This is for good reason. In 2013, every 97 out of 100 citizens used a mobile phone. That is about  7 billion users. Compare this to the fact that 40 out 100 citizens are internet users.

SMS does have its shortcomings such as unreliable delivery or spoofing but it still remains the most ubiquitous of communication technologies which fed the frenzy of a USD 100 Billion global market in 2014.

Impressive statistics aside, SMS comes with its inherent power of simplicity and availability. It is native to every mobile phone, smart or otherwise. It works from day one of the service, and everyone knows how to use it. 7 Billion of them!

But, we have a new phenomenon growing and that is the Internet of Things(IoT). Essentially IoT consists of sensors(they see and hear), actuators(they do things) managed by a controller. A control system with internet thrown into the mix.

Experts estimate Billions of devices(sensors and actuators) will be connected. The most popular device in this fray is the mobile phone. Use of SMS now takes IoT to another level. Every mobile phone with SMS becomes a sensor and an actuator.

This is akin to using SMS for online banking authentication. A authentication code is received via SMS(sensor) and the user enters the code when prompted(actuator).

Using SMS allows IoT to reach users around the world and not only in developed cities and countries. A rice farmer in a developing part of the world would appreciate control over his crops via IoT if he could use a technology like SMS. Expecting him to watch dashboards or install apps on smart phones is not feasible.

The world is catching up to this concept, I think. No wonder the Global (application to person) A2P SMS market is expected to reach USD 70.32 Billion in 2020.

Companies like Nexmo, Twilio, Tropo and others are doing a fantastic job of bringing SMS to the doorstep of every app developer.

At the end everyone should be able to benefit from IoT, all 7 billion of them. SMS is one way.

In this regard posts by Tropo (Controlling the Internet of Things with Tropo ) and Mosaic NetworX(SMS and the Internet of Things: Text Messaging Between Connected Devices) are worth a read.


Node.js and the tick

One of the popular searches on the internet about getting into programming with NodeJS is the keyword “tick”, presumably from all the talk about non-blocking I/O related to NodeJS.

Coming from an E.E. background and having studied around Microprocessors such as 8080, Z80, 8086 and 68000, I look at the concept of “tick” with fond familiarity.

The activities we see in our daily lives such as a school are governed by a time schedule, typically synchronized by the school bell. The ringing of the bell signifies start of a class, recess, lunch or end of a class session. In other words, the school bell synchronizes the various “events” that take place in a school. This allows the school to carry out multiple activities with hundreds of students in a methodical manner.

In case of a C.P.U or a Microprocessor it is the “clock cycle” that keeps its activities, such as reading from the memory, in sync and making sure that there are no collisions on the “bus”. In this case each clock cycle is called a “tick”.  A C.P.U with a system clock of 1GHz would translate to 1000,000,000 clock cycles or “ticks”.  It can choreograph hundreds of thousands of operations effortlessly .

A C.P.U. also maintains a table of instructions(machine code) and their respective “ticks”. For example an “add” operation might need 5 clock cycles or ticks. This allows a C.P.U to sequentially and accurately synchronize the execution of instructions and its other activities without losing a “tick”.

So, how does all this relate to NodeJS? 

It is common knowledge that NodeJS is a set of libraries that sits on top of two subsystems. Google Chrome’s V8 JavaScript engine and Libuv.

It is “Libuv” that provides NodeJS the power of a multi platform “evented” model that it is known for. It includes an event loop which processes events or activities that occur from time to time. This event loop like the C.P.U. synchronizes its affairs using an internal clock. To put it simply, it picks an event for handling at every “tick” or “ticks”.

The event loop does not process or handle the event itself but delegates it to other subsystems of the operating system that it is running on. This allows it to process(or delegate) many events in the very small number of ticks.

The clock being highly accurate and regulated, the event loop and thereby NodeJS can flawlessly  provide the efficient “evented” I/O that we have all grown to love.