Everyone deserves a second chance

I was watching one of the episodes on a TV series which I loved called Outsourced last week. In that episode there is a scenario where the manager (Tod) finds out that one of his employees is struggling to meet the sales target and the top management instructs the manager to get rid of one employee to keep up with the revenues. But what does “Tod” the manager do. He did confront the employee on his lack of performance and asked him what the cause of it was. Then along with the employee, he helps this guy to reach his targets by helping him resolve his personal issue. Now that I believe is the quintessence of a great manager.

This got me thinking, often times, even in the software industry, I have seen people who struggle being cornered and ignored. Most of the times it is not due to the lack of competence on their part. If you give them a chance to express themselves, I have often seen that it was due to a personal matter, financial crisis, insufficient information during on-boarding and many other reasons for the employee to under-perform. The saying goes “Put yourself in their shoes”. It would be appropriate to put yourself in their shoes and walk in it as well to get a feel of what the employee is going through. In my opinion, employees should not only be measured by their personal excellence, but should also be measured by how they support their team mates to succeed as well. A team will only be successful if they work as one cohesive unit. In daily stand-ups if I see someone struggling on a task for more than two consecutive days I would approach them offline and ask how I can help them out. The fact that you ask them and are there for them in their difficult time brings out a sense of belonging to them.

Johanna Rothman in her book “Behind closed doors” gives an overview of how we can deal with such employees. She calls it the “Get well plan” where the manager along with the employee comes up with a plan for the employee to succeed. You cannot simply fire a person without giving him/her a second chance to redeem themselves. I believe this approach is a fabulous idea where you can set a fixed set of goals within a fixed time frame to help out an employee who is struggling in your team.

So the next time you see someone in your team struggling, talk to them, get to know what their problem is and help them out. You will have pride (humble of course) of knowing that you were able to make a significant impact on the life of someone.

Being an architect and handling some of the managerial aspects

From mid last year, I have been playing a junior architect role, transitioning out of the technical lead role I was playing. It has been an interesting ride so far with many obstacles and many more to come I’m sure which makes it even more interesting. Taking the plunge to the new role required a change in the mindset. As a technical lead, I was predominantly focused on the technical aspects related to the product/project I was working on whereby the quality aspects of the code was the primary concern. With the switch to an architect, though most of those responsibilities still remain, there was an additional aspect which I had overlooked during the initial period.  In the capricious environment we usually work in, things do get overlooked which if left unattended will hinder the performance of your team and in turn affect the organization you work for.

Though I considered it to be a project management aspect, I came to figure out that this aspect was a responsibility of mine with this new role. So what is this overlooked aspect you ask? The management of the people aspect. Although you as an architect will not be involved with other project management aspects such as budgeting, schedule planning (though you need to assist on this one), goal setting, emitting CO2 with managerial meetings with no agenda (no pun intended of course), you do have the most important aspect of it all to manage, which is the people aspect. Though technical leadership and direction is your primary focus, you cannot overlook the fact that your team and the team morale is the determinant success factor of your team.

As an architect, you are now directly responsible of the career progression of your team. If you have a few under performers in your team, it does not mean you should corner them out and report them to management. On the contrary, you have to understand the individual’s issues, the reasons for under-performing and setting up a plan for that individual to focus on making the wrongs right. Most of the time, the under performers have a reason for their current predicament and understanding that will enable you to help that person out of the difficult situation. This is the time the team as a whole should drive the required productivity of the team until the under-performers do come up to the required standard. Constant feedback will help the people to understand their weakness and improve on it.

The catch here is that sometimes you figure out some people just do not take constructive feedback well. You cannot take this personally as this is the idiosyncratic behavior of some people. You can help someone as much as they want to be helped.

Learning to give feedback was another skill I had to obtain with this new role. Giving feedback is not the hardest part, but giving it in a way where the receiver does not go into a defensive mode is the harder part. Again, though I see this as a project manager’s responsibility, I believe as an architect, you are still responsible for the growth of your team and should give them feedback in order for them to grow. Feedback will include both the positive and the negative. “Good job with the XYZ module” is not a positive feedback. “Good job” works only if you are Hancock (you have to watch the movie to get that one). You have to be very specific regarding the accomplishment of the individual. “The new automation build script you setup for the team helped us to give regular internal releases whereby we were able to get feedback early on the features the team was working on”. Now that is a very specific positive feedback attributing what the person did and aligning that to the company’s goals of reducing operations costs and turnaround time.

Even though technical leadership still remains as the primary focus for you as an architect, you should not forget the most important people factor which will help you succeed as a team and move forward adding value to the organization you work for.

In the next post I will focus on the changes I had to make especially in the thought pattern transitioning from being a glorified geek.

Thank you for reading people, and as always your feedback is much appreciated and do please leave by a comment if you have anything to say, the good, the bad and the ugly.

Cassandra anyone?

Been some time after my last post. Blogging has not been an option for the past few weeks with other commitments going on. But you cannot stop doing what you love the most. So here I am back again fervently nudging away on the keyboard. I was analyzing the use of Cassandra for one of the projects I am working on. This post will focus on the getting started aspect with related to Cassandra and mainly in terms of Windows because fortunately or unfortunately that is the operating system I am stuck with at the moment (no pun intended).

The latest Cassandra distribution can be downloaded at here. 

I wanted to have the ability to run Cassandra as a Windows service. Here are the steps on how to get that done;

• Download Apache commons daemon from here
• Extract the Cassandra distribution to a location of your choice
• Go into the “bin” directory of the Cassandra distribution you just extracted
• Create a folder called “daemon” inside the “bin’ directory
• Extract the Apache commons daemon distribution to a location of your choice
• Copy the “prunsrv.exe” related to your architecture (32bit/64bit) to the “daemon” directory created inside the “bin” directory in your Cassandra distribution extract
• Go into your “bin” directory via the command line and execute the following command;

 cassandra.bat install

 That’s it. Now you have Cassandra installed as a windows service which you can set to start automatically if that is what you prefer.

By default, authentication and authorization is disabled on Cassandra. Enabling this is a breeze with the Cassandra YAML configuration file. Go to the “conf” directory of your Cassandra distribution where you will find a file named as “cassandra.yaml”. Open up this with your favourite text editor (Notepad++ is awesome. Just saying). Make the following changes to the already existing configuration;

• authenticator: org.apache.cassandra.auth.PasswordAuthenticator
• authorizer: org.apache.cassandra.auth.CassandraAuthorizer

Now that you have enabled authentication and authorization, you can go in and login to Cassandra. The super user in Cassandra has the following credentials which you must use to log in;

• User name : cassandra
• Password : cassandra

To log in, let us go back to the “bin” directory of the Cassandra distribution where you will find a batch file called “cqlsh”. Bring up your command line again and execute the following command;

cqlsh -u cassandra -p cassandra

Now that you are in, if required you can go and change your super user password. One of the advantages of Cassandra for me is how similar the syntax is to relational databases. You can get up and running in a very short amount of time as the commands are very similar to traditional SQL. Cassandra has its own query language called CQL.

Next up, I was looking for a convenient GUI to interact with Cassandra. Browsing around, the most user friendly interface I found was DBeaver which you can download from here.

On the next article, I will focus on functionality with respect to Cassandra and how your thinking pattern should change when working with Cassandra as it will not be the same as how you design a traditional database schema.

Performance monitoring and profiling – Part 1

Performance monitoring and profiling are two different aspects. The former takes a more of a proactive measure whereas the latter is a reactive approach. In my experience, performance is an afterthought in most cases rather than being built into the software development life cycle. I see the same thing in most cases when it comes to security as well but let us not go there today as that lends itself to a different post.

Performance monitoring

In order to monitor your application, you first need to understand what performance aspects the end users of your application expects. This would include throughput, response time, uptime etc. After you collate these information, the next step would be to create scripts for load testing your application. Coming primarily from a Java background, I normally use JMeter which comes with a user friends UI to create load testing depending on your use case be it database, Web Service, JMS etc.

So you have your load testing scripts and it is running fine. What next?

Well now that you have the load scripts in place, the next step would be note down what parameters you should monitor. Generally you would start out monitoring the CPU and Memory usage while your load test is in progress.

When you monitor CPU, you would be looking at the CPU usage as the application is being load tested. If there is high CPU usage, you need to further drill down to find out what issue might cause this. If there is low CPU usage, then probably you can try and increase the load to see what the optimum load that uses efficient CPU.

If you are on a Windows machine, by default you will open the task manager and go into the Performance tab in order to find out what your CPU is up to. A snippet of mine is as follows;

I’m running on a Core i5 with 8GB of RAM. In the upper right hand corner you can see the CPU usage history. The four boxes here represent the four CPUs. You will get the same value if you run Runtime.getRuntime().availableProcessors() within a Java program. 

By default it only shows the total CPU time. But you can easily add the kernel CPU time by clicking onn View-> Show Kernal Times. You will then be presented with the following graphs with a red line;

The space between the red line and the green line is the user CPU time. So what is the difference between the user and the kernel CPU time you might ask.

User CPU Time : The amount of time the CPU spends on running your application code.

Kernel CPU Time : The amount of time the CPU spends on operating system related activities. For example if your application does a lot of reads/writes to the disk, you will see high kernel CPU as it requires the operating system related functionality to be invoked.

But what if you needed further information on what your CPU is doing at any given moment of time? PerfMon to the rescue. Open up a Run tab and type in perfmon and you will be presented with the following screen;

You can add any counter which you are interested in monitoring by right clicking on the graph area and selecting the Add Counters menu option. If you want to monitor the user CPU and kernel CPU, select the Performance object and under that you will find the User time and Privileged time (kernel CPU time) counters.

Another key indicator to watch out for when monitoring CPU is the CPU schedule run queue. This is where all light weight processes needed to run are queued up waiting for CPU. As a rule of thumb, if your run queue depth is more than four times the available virtual processors on your machine, then you need to investigate your application further more to see what could be done. In the era of high memory machines, people often overlook the fact of using and writing efficient algorithms and data structures as there is plenty of memory available on high end servers. But the issue here is that many a times CPU is limited. If your algorithms/data structures do not scale well with added load, you will end up over loading your CPU with the only alternative being is to scale your application into different servers. So if you are having issues with the scheduler run queue depth, it is always best to see the possibility of writing certain sections of your application code in a more efficient manner so as to utilize your CPU better. The run queue depth can also be monitored using Perfmon by selecting Add Counters->System-> Processor Queue Length.

Now that we have covered some of the important factors to look into in terms of CPU when monitoring your application, in the next post we will look at what aspects we need to consider when monitoring the memory usage.

Please do leave by your comments and suggestions which is as always much appreciated as I love learning from the experience of others which I deem as invaluable.

A little bit on the JVM and JIT

As you might be aware, the JVM(Java Virtusal Machine) is what makes it possible for Java to adhere to the write-once-run-anywhere paradigm. At its core, the JVM consists of the following components;

• Heap
• Stack
• PermGen and Method Area
• JIT Compiler
• Code cache

The heap is where memory is allocated for every new operator you use during the application code development stage. Stack will store the local variables that you will assign within the scope of a method. One thing to note is that the variables defined within the scope of a method will be removed after the completion of the method. If for example, a String is assigned within the scope of a method, and its scope is guaranteed to be of local scope, then this will be stored in the stack which would otherwise be assigned within the heap.
The PermGen space will store class and method level data as well as static variables that are defined in your application. The method area is actual an area within the PermGen space where it will store all method,field, constant pool level details of your application.

The JIT compiler and the code cache go hand in hand. The JVM at its core interprets the Java Byte Code into assembly code at runtime. Interpreting can be a slow process because the code needs to be converted from byte code to machine code at runtime every time a portion of your application code is executed. This is where the JIT compiler comes into action, with its super awesome compilation of methods which it then stores in the code cache.

The JIT compiler analyzes the application code at runtime to understand which methods can be categorized as hot methods. Hot in this context meaning code fragments that are accessed more frequently. At a very high level, what the JIT compiler does is that it will have a counter for each method executed in order to understand the frequency of its usage. When the counter reaches a defined threshold value, the method then becomes eligible to be compiled by the JIT compiler to its respective assemble code which will then be stored within the code cache. What happens is that now, whenever the JIT compiler comes across calls to those methods which were compiled and stored within the code cache, it will not try to interpret them yet again but will use the already compiled assembly code available within the code cache. This gives your application a performance boost because using the compiled code is much faster than interpreting it during runtime.

When talking about the JIT compiler, there are mainly two flavors of it which we are mostly oblivious of due to the fact of the lack of documentation around them. The two types are;

• Client
• Server

The default compiler used will defer according to the machine architecture and the JVM version (32bit or 64bit) that you are running on. Let us briefly see what each one does.

The client compiler starts compiling your byte code to assembly code at the application startup time. What this indirectly means is that your application will have a much improved startup time. But the main disadvantage this brings along with it is that your code cache will run out of memory faster. Most optimizations can be made only after your application has run for a brief period of time. But since the client compiler already took up the code cache space, you will not have space to store the assembly code for these optimizations. This is where the server cache excels.

Unlike the client compiler, the server compiler will not start compiling at the start of your application. It will allow the application code to run for some time (which is often referred to as the warm-up period) after which it will start compiling the byte code to assembly code which it will then store within the code cache.
In my next post I will discuss how we can actually mix and match the client and server compilation and also introduce you to a few more JVM flags that we seldom come across but are vital for increasing the performance of your application.

Finding the Equilibrium index of an array

I wanted to do a brain teaser today so i took up an algorithmic question to give a shot at. Now i know this question already has many answer on the internet if you do a quick search. But i wanted to try it out with the solution that came to my mind. The problem statement is as follows;

Equilibrium index of an array is an index such that the sum of elements at lower indexes is equal to the sum of elements at higher indexes. For example, in an array A:

A[0] = -7, A[1] = 1, A[2] = 5, A[3] = 2, A[4] = -4, A[5] = 3, A[6]=0

3 is an equilibrium index, because:

A[0] + A[1] + A[2] = A[4] + A[5] + A[6]

6 is also an equilibrium index, because sum of zero elements is zero, i.e., A[0] + A[1] + A[2] + A[3] + A[4] + A[5]=0

7 is not an equilibrium index, because it is not a valid index of array A.

Write a function int equilibrium(int[] arr); that given a sequence arr[] of size n, returns an equilibrium index (if any) or -1 if no equilibrium indexes exist.

The solution i came up with is as follows;

 
public static int solution(int[] A) {

  if (A == null || A.length < 3)
   throw new RuntimeException("Cannot find equilirbium");
  int pointer = 1;
  int lowerIndCount = A[0];
  int upperIndCount = 0;

  for (int i = 2; i < A.length; i++) {
   upperIndCount += A[i];
   if (lowerIndCount < 0) {
    if (upperIndCount > lowerIndCount && i != A.length - 1)
     continue;
    if (upperIndCount == lowerIndCount && i == A.length - 1)
     break;
    lowerIndCount += A[pointer];
    upperIndCount = 0;
    ++pointer;
    i = pointer;
   } else {
    if (upperIndCount > lowerIndCount) {
     lowerIndCount += A[pointer];
     upperIndCount = 0;
     ++pointer;
     i = pointer;
    }
   }
  }

  if (upperIndCount == lowerIndCount)
   return pointer;

  return -1;
 }

If this is the optimum or not im not sure. Im also not sure if this will work on very large data sets. But for the data sets i tried(both negative and positive) it worked fine. And also this has O(N) complexity as I am iterating through the array only once.

Enter the Node-Hood

So today we will explore the node neighborhood in order to see what awesome goodies lie in the node-land. Ok I am no expert artist, but I remember well when I jot down something I learn into a diagram and utter those famous words “A picture speaks a thousand words”. And of course I love to learn by adding some humor to it which again makes me remember things better. So this is my lame attempt in doing just that to introduce the world of Node to anyone who might be interested.

If you are a node pro, I will save you the time and ask you to close this tab right now… Ah so you did not close it, great, then come on in. So let us see what we will find today on node-land. Following is a picture I just drew in order to emphasize what we will focus on today.

So this is the node home as of now for me. You are greeted with a door mat which leads to the site where you can get more information on the same. And as always you can see just right to the door, there is a CD( who uses CDs nowadays, I know) which states the famous command you will encounter many a times when you are working with node.

npm install <module_name>

Add a –save to the end of the command, and it will save the module you just installed in your package.json file. More on that in the future when we discuss web development with express.

The today’s special bored in the house highlights some core modules that you will find when working with node. They include;

•  fs
• crypto
• process
• http

This is not the whole list and you will indeed find many more as we go along with the journey through nodejs.

So we have a friend inside our nodehood who is asking for a cool beer. His best friend as you can see is approaching the Node Fridge to get him one. In node land, you can ask for external elements if they are willing to expose their functionality. And this is done through the use of exports. Let us see more closely what is happening inside the Node Fridge;

 
var  drink = function(name){
 return "Cool "+name;
}

module.exports.drink = drink;

So the Node Fridge is exposing a drink function which returns any drink that is passed into it with an added coolness to the drink. Of course there is no point in the fridge having the cold drinks if no one is going to be drinking it, and hence we use a mode.exports at the end to expose this coolness to the whole world.

So his friend has to open up and drink this up. Since he requested this first from his friend, he added the following;

 
var beer = request('./nodefridge');

console.log("Chilling with a ”+beer.drink("beer");

As he is making the request for the drink from the same house itself, he can do as it has been highlighted in the above code. But if he was asking this from the backyard of the house, then he will need to request for the same as follows;

 
var beer = request('./backyard/nodefridge');

The assumption here is that the nodefridge.js is residing in the backyard directory. You can download this simple example from here and run it using node main.js.

So today we will cover how file streams work in the node world. The main point here is that node file streams work with the use of Event Emitters which is a way of notifying results as and when an event occurs. It is slightly different from callbacks where you can get partial results even if an error occurs midway through. It acts as more of a Pulish/Subscribe model as opposed to the Request/Reply  model in callbacks.

Before diving into how streaming works in Node, let us see how they actual work within this new found Event emitter paradigm. Again a little comic strip will better help you understand the interaction between the Readable Streams and Writtable Streams. And no surprises with their names where RS Dude is representing Readable Streams and WS Dude will be representing Writable Streams. Let us see how this turns out;

The story is self explanatory I believe. But if you want to go all technical, here is it in technical terms.

·         The read stream will pipe itself to the writable stream where by the writable stream will it’s write method internally.

·         As it keeps on writing, the write() method can send false which means it cannot accept any more data for the moment. At this moment, the readable stream will call it’s pause() method.

·         After the writable stream is ready again, it will call it’s drain() method and in turn the readable stream will call it’s resume() method and the data will again start flowing.

·         When the end of the stream is reached by the readable stream, it will call it’s end() method and the writable stream will call it’s end() method as well and close the stream.

I have included a sample on using readable streams which is available in the same download I mentioned earlier. You can run the examples as follows;

node readable.js

node wriestream.js

Note that before you run this, please run npm install request as this example requires this module.