Java Deep

Pure Java, what else

Try and Catch in Golang

Golang as opposed to Java does not have exceptions, try/catch/finally blocks. It has strict error handling, functions called panic and recover and a statement named defer. It is a totally different approach. Is it better or is the Java approach the superior? (Sorry that I keep comparing it to Java. I am coming from Java world.)

When we handle exceptional cases in Java we enclose the commands into a ‘try’ block denoting that something may happen that we want to handle later in a ‘catch’ block. Then we have the ‘finally’ block that contains all the things that are to be executed no matter what. The problem with this approach is that it separates the commands that belong to the same concern. We want to deal with some file. So we open a file and later, no matter what, we want to close it. When the programmer writes the finally block the file opening is far away somewhere at the start of the method. To remember all the things that we have to do to clean up the actions at the start of the method you have to scroll up to the start of the method where the ‘try’ block starts.

Okay! I know that your method is too long if you have to scroll back. Your methods follow clean code principles and are not longer than ten lines each including JavaDoc. Even though the issue is still there. It is formulated according to order the execution is expected and not according to the order the logic dictates. The logic says the following: if I open a file, I will want to close it. If I allocate some resource I will want to release it. It is better keeping the concerns together. We are not programing in assembly where you write the mnemonics in the strict order of execution. We define the algorithmic solution in a high level language and the compiler will generate the assembly. Real work has to be done by the brain, mechanical work is for the CPU. These days we have CPUs.

Golang has the command ‘defer’ for the purpose. You open a file and you mention on the next line that you will want it to be closed some time calling the function you provide. This is the much better approach, which the developers of the Java language also know hence introducing the interface ‘closeable’ and try-with-resources statement.

Still programmers coming from the Java world begin introduced to Go are longing for exception handling. If you really want you can mimic it in Go. It will not be the same and I do not really get the point why to ruin something that is good to something old and mediocre, but you can write

		Try: func() {
			fmt.Println("I tried")
		Catch: func(e Exception) {
			fmt.Printf("Caught %v\n", e)
		Finally: func() {

Homework: find out the sample code that is before these lines (Go constructs) that make this possible. Solution is here:

package main

import (

type Block struct {
	Try     func()
	Catch   func(Exception)
	Finally func()

type Exception interface{}

func Throw(up Exception) {

func (tcf Block) Do() {
	if tcf.Finally != nil {

		defer tcf.Finally()
	if tcf.Catch != nil {
		defer func() {
			if r := recover(); r != nil {

func main() {
	fmt.Println("We started")
		Try: func() {
			fmt.Println("I tried")
		Catch: func(e Exception) {
			fmt.Printf("Caught %v\n", e)
		Finally: func() {
	fmt.Println("We went on")

See also a recent similar solution at from Tim Henderson

Do not (only) meet the budget

In a previous article I wrote

The actual decision (of a software architect) should lead to a solution that meets availability, performance, reliability, scalability, manageability and cost criteria. (Btw: the first six criteria should be met, the last one should be at least met and minimized, but that is a different story.)

Many times the criteria are met and there is no intention to minimize the cost. “There is a budget and we have to fit into it.” This is the approach teams follow. There are several reasons for it. But the reasons do not necessarily mean that the approach is ok. I may even accept the argument that this behaviour is unavoidable in large organizations.

Why is this approach bad?

When you sell something you want to sell it the highest price you can. Cost represents a minimum for the selling price: if the reachable price is lower than the cost to reproduce then production will stop. Budget is also a limiting factor. You go to the food shop and you buy the bread that meets your likes and is the cheapest among those if you have the money. If your money is limited you will select one that you can pay for. Why would one buy the more expensive if all other criteria they need are met?

(Do not start to talk about Apple products: feeling you are rich is also a need.)

The same is true for development projects even if the customer buying the development is in the same company. Your customer is either “business” if you are an in-house team or sales in case your company develops software for customes. In the latter case the “customer customers” are the customer of the company. The customer of the development team is sales. Same company. Customer provides the budget that you have to fit in but

nobody ever complained that a project was under budget.

I am not talking about lower budget commitment. All projects have risks and all risks have financial impact. One way of risk mitigation is to have contingency in the budget. But again: that is the budget and not the actual spending. Still teams, departments tend to fill their budget. Why do they do that? There can be several reasons and sure I will not be able list all possible.

Reasons to spend more


The more a department spends the more important it looks. I have seen such company. The company was a large expanding company heavily investing into assets, building infrastructure for years (like telecom infrastructure, roads or railways, I won’t tell you which one) and the one who was building the more miles the better performer was. The measurement was the spent money and this culture remained when it was IT spending. Weird.


In some companies budget not spent will lessen the financing possibilities of the department for the future. If you, as a software architect can save up from the original estimates it means your estimation was not good. That will be taken into account next time you estimate. Thus the department rather wastes (usually towards end of the year) the budget left over than giving it back to treasury. It is like fixing a bug creating another. (How about unit testing departments?)


Some companies underfinance maintenance, developer trainings, education, equipment or some other IT related activities and departments tend to amend the situation from other sources: overbudgeting projects. Things certainly will happen. If you want to have something you do not pay for you will not get it. If you got it you paid for it just you do not know where, when and how much. Developers, architect, project managers may even not realize that they follow this practice in some cases. I have heard a few times the argument: “We select the more expensive technology X for the project because this way we can learn it.” This is also crossfinancing. You use the project to finance the developer’s education.

All these are bad practices. I do not need to explain the first example. The second is also quite obvious. The last one, cross financing is not that obvious.

Why cross financing is bad

When you select the more expensive technology (it may be more expensive because it has license fees, or needs more development, learning) you decide to invest the company money into something. As an architect it is not your responsibility. You may suggest that to management but you should not decide even if you are in the position that you can (unless you are also the management in a small company, but in that case you decide with your management hat on). It is only management who can decide how to invest the money. Investing into people is always a good investment even if they leave your company later. (But what if you do not invest and they stay?) But as a matter of fact management may see even more lucrative investment possibilities at that very moment.

Don’t feel bad

Do I suggest that you rush to management now and ask them to lessen your budget? Do I think that you, as a software architect should feel bad if you fill the budget and cross finance?

Not at all. I said that crossfinancing is bad practice but I did not say that it is YOUR bad practice. It is a bad practice implemented in a company. The larger the organization is the more difficult it is to change it. And it is not your task, and more importantly you may not have the capabilities to do that.

The only thing you have to: know that you follow a practice that is not optimal, so to say. If there is any chance to improve it just a little bit, go for it. Everybody making one small step will save the world.

Architects Don’t Decide

As pointed out in the article A Little Architecture from Robert C. Martin the job of the architect is not

…to lead a team and make all the important decisions about databases and frameworks and web-servers and all that stuff.

It is to make

decisions that a Software Architect makes are the ones that allow you to NOT make the decisions about the database, and the webserver, and the frameworks.

As the article pointed out juniors have many times different view about the tasks that a senior does than the senior who does it. It is not only the subject of the decision however. This is a bit more. Juniors see the position of an architect as a position of power. He has the right and the power to make decisions. Having the power is always good. You long to have the position of power, don’t you. Up to certain age, maturity.

When you get into the position of being an architect you realize that the power is not a privilege. It is a reponsibility. The system the team develops, the architecture, the network, hardware, the operation is not a play field to satisfy your curiosity. It is a professional environment that works based on profit and lost, budget and costs, money, money, money. The decision the architect makes should not be biased by the actual person’s interest. This is, by the way, a very common mistake. “Let’s use NoSQL because that is so fancy! We have to use big data!”

The actual decision should lead to a solution that meets availability, performance, reliability, scalability, manageability and cost criteria. (Btw: the first six critera should be met, the last one should be at least met and minimized, but that is a different story.)

The actual decisions on the architecture and solutions (finally when unavoidable also about framework and database and other stuff) depends on many things. License structure, license cost, company culture, available developers, deadline/time to market, architecture already in place to name a few. If you consider all the constraints you have to meet you will not have too many choices. There will be compromises and finally you will end up with the only one selection that fits. That one is going to be your decision.

Did you really make a decision?

Code Naked

  • I can not merge your pull request on our Git.
  • Why?
  • There is only one file modified.
  • That is because we modified only one Java class.
  • That is exactly the problem. I can not see in the pull request the modified JUnit test.
  • I could not modify the unit test because there was no any.
  • I see. And that is a problem of the past. The present problem is that still there is no unit test.
  • But we have no time to write unit test for the whole program. There are zillion of lines of code developed during the last few hundred years. They have no unit tests and they just work.
  • I acknowledge that. That is the life when you maintain legacy code. But why did not you write unit test for the change you just created?
  • There are zillion of lines already…
  • Yes, those that “work”. But your change is new. How do you know that it works?
  • We just agreed that we do not start to write unit tests now. We do ad-hoc testing.
  • Okay. Then let’s just get back to the basics. Why do we have unit tests? What is the primary reason?
  • To have working and tested code.
  • Im my opinion this is more like documenation. Unit test is a live documentation that is more likely to be maintained than any other documenation. You just created a few lines of code without documentation.
  • I get your point, but we just do not have time for that.
  • Let’s look at the following situation. It is summer and hot. You wake up late and want to rush to fetch the bus. You take the shower and drink your coffe and run to the bus. Right?
  • Right.
  • You do not even dress. Run out naked.
  • No! No! I dress!
  • But you have no time. You are in a rush. If you have time to dress yourself why do you let your code run to production without unit tests naked?
  • I get the point. But you can survive without unit tests. If you run on the street naked you will not survive.
  • Why is that? The summer is hot, you will not catch cold.
  • Yeah, but police would catch you.
  • So this is a kind of society issue, is it?
  • Yes, it is.
  • And the coding society is not that matured yet, it lets you run naked without unit tests.
  • I assume… yes.

we still have a long way to go ahead of us…

Comparing Golang with Java

First of all I would like to make a disclaimer. I am not an expert in Go. I started to study it a few weeks ago, thus the statements here are kind of first impressions. I may be wrong in some of the subjective areas of this article. Perhaps I will write some time a review of this one later. But until then, here it is and if you are a Java programmer, you are welcome to see my feelings and experiences and the same time you are more than welcome to comment and correct me if I am wrong in some statements.

Golang is impressive

As opposed to Java, Go is compiled to machine code and is executed directly. Much like C. Because this is not a VM machine it is very much different from Java. It is object oriented and the same time functional to some extent thus it is not only a new C with some automated garbage collection. It is somewhere between C and C++ if we think the world of programming languages is on a single line, which it is not. Using a Java programmer’s eyes some things are so much different that learning them is challenging and may give a deeper understanding on programming language structures and how objects, classes and all these things are … even in Java.

I mean that if you understand how OO is implemented in Go, you may understand also some of the reasons why Java has them different.

In short if you are impatient: do not let yourself freak out by the seemingly weird structure of the language. Learn it and it will add to your knowledge and understanding even if you do not have a project to be developed in Go.

GC and not GC

Memory management is a crucial point in programming languages. Assembly lets you do all things. ​Or rather it requires you to do it all. In case of C there are some support functions in the standard library but it is still up to you to free all memory that you have allocated before calling malloc. Automated memory management starts somewhere along C++, Python, Swift and Java. Golang is also in this category.

Python and Swift use reference counting. When there is a reference to an object the object itself holds a counter that counts the number of references that point to it. There are no pointers or references backward, but when a new reference gets the value and starts to reference an object the counter increases and when a reference becomes null/nil whatever or references another object the counter goes down. So it is known when the counter is zero, there are no references to the object and it can be discarded. The problem with this approach is that an object still may be unreachable while the counter is positive. There can be object circles referencing each other and when the last object in this circle is released from the static, local and otherwise reachable references then the circle starts to float in the memory like bubbles in water: the counters are all positive but the objects are unreachable. The Swift tutorial has a very good explanation of this behavior and how to avoid it. But the point is still there: you have to care about memory management somewhat.

In case of Java, other JVM languages (including the JVM implementation of Python) the memory is managed by the JVM. There is a full blown garbage collection that runs from time to time in one or more threads, parallel with the working threads or sometimes stopping those (a.k.a. stop the world) marking the unreachable objects, sweeping them and compacting the presumably scattered memory. All you have to worry about is performance if at all.

Golang is also in this category with a small little, tiny exception. It does not have references. It has pointers. The difference is crucial. It can be integrated with external C code and for performance reasons there is nothing like a reference registry in the run-time. The actual pointers are not known to the execution system. The memory allocated can still be analyzed to gather reachability information and the unused “objects” can still be marked and swept off, but memory can not be moved around to do the compacting. This was not obvious for me some time from the documentation and as I understood the pointer handling I was seeking for the magic that Golang wizards implemented to do the compacting. I was sorry to learn, they simply did not. There is no magic.

Golang has a garbage collection but this is not a full GC as in Java, there is no memory compaction. It is not necessarily bad. It can go a long way running servers very long time and still not having the memory fragmented. Some of the JVM garbage collectors also skip the compacting steps to decrease GC pause when cleaning old generations and do the compacting only as a last resort. This last resort step in Go is missing and it may cause some problem is rare cases. You are not likely to face the problem while learning the language.

Local variables

Local variables (and sometimes objects in newer versions) are stored on the stack in Java language. So are in C, C++ and in other languages where call-stack as such is implemented. Golang related to local variables is no exception, except…

Except that you can simply return a pointer to a local variable from a function. That was a fatal mistake in C. In case of Go the compiler recognizes that the allocated “object” (I will explain later why I use quotes) is escaping the method and allocates it accordingly so that survives the return of the function and the pointer will not point to an already abandoned memory location where there is no reliable data.

So this is absolutely legal to write:

package main

import (

type Record struct {
	i int

func returnLocalVariableAddress() *Record {
	return &Record{1}

func main() {
	r := returnLocalVariableAddress()
	fmt.Printf("%d", r.i)


What is more you can write functions inside functions and you can return functions just like in a functional language (Go is a kind of functional language) and the local variables around it serve as variables in a closure.

package main

import (

func CounterFactory(j int) func() int {
	i := j
	return func() int {
		return i

func main() {
	r := CounterFactory(13)
	fmt.Printf("%d\n", r())
	fmt.Printf("%d\n", r())
	fmt.Printf("%d\n", r())

Function return values

Functions can return not only one single value, but multiple values. This seems to be a bad practice if not used properly. Python does that. Perl does that. It can be of good use. It is mainly used to return a value and a ‘nil’ or error code. This way the old habit of encoding the error into the returned type (usually returning -1 as error code and some non-negative value in case there is some meaningful return value as in C std library calls) is replaced with something much more readable.

Multiple values on the sides of an assignment is not only to functions. To swap two values you can write:

  a,b = b,a

Object Orientation

With closures and functions being first class citizens Go is at least object oriented as JavaScript. But it is actually more than that. Go lang has interfaces and structs. But they are not really classes. They are value types. They are passed by value and wherever they are stored in memory the data there is only the pure data and no object header or anything like that. structs in Go are very much like they are in C. They can contain fields, but they can not extend each other and they can not contain methods. Object orientation is approached a bit different.

Instead of stuffing the methods into the class definition you can specify when you define the method itself which struct it applies to. Structs can also contain other structs and in case there is no name for the field you can reference it by the type of it, which becomes its name implicitly. Or you can just reference a field or method as they belonged to the top struct.

For example:

package main

import (

type A struct {
	a int

func (a *A) Printa() {
	fmt.Printf("%d\n", a.a)

type B struct {
	n string

func main() {
	b := B{}
	b.A.a = 5
	fmt.Printf("%d\n", b.a)

This is almost or a kind of inheritance.

When you specify the struct on which the method can be invoked you can specify the struct itself or a pointer to it. If the method is applied to the struct then the method will access a copy of the caller struct (this struct is passed by value). If the method is applied to a pointer to the struct then the pointer will be passed (passed by reference kind of). In the latter case the method can also modify the struct (in this sense the structs are not value types, since value types are immutable). Either can be used to fulfill the requirement of an interface. In case of the example above Printa is applied to a pointer to the struct A. Go says that A is the receiver of the method.

Go syntax is also a bit lenient about structs and pointers to it. In C you can have a struct and you can write b.a to access the field of the struct. In case of a pointer to the structure in C you have to write b->a to access the same field. In case of a pointer b.a is a syntax error. Go says that writing b->a is pointless (you can interpret this literally). Why litter the code with -> operators when the dot operator can be overloaded. Field access in case of struct and, well field access through pointers. Very logical.

Because the pointer is as good as the struct itself (to some extent) you can write:

package main

import (

type A struct {
	a int

func (a *A) Printa() {
	if a == nil {
		fmt.Println("a is nil")
	} else {
		fmt.Printf("%d\n", a.a)

func main() {
	var a *A = nil

Yes, this is the point as a true hearted Java programmer you should not freak out. We did call a method on a nil pointer! How can that happen?

Type in in the variable and not the object

This is why I was using quotes writing “object”. When Go stores a struct it is a piece of memory. It does not have an object header (though it may, since it is a matter of implementation and not the language definition, but it reasonably does not). It is the variable that holds the type of the value. If the variable type is a struct then it is known already at compile time. If this is an interface then the variable will point to the value and the same time it will also reference the actual type it is having the value for.

If the variable a is an interface and not a pointer to a struct you can not do the same: you get runtime error. (Addition: As theo pointed out in his comment this is because the pointer variable does not have the type and Go runtime does not know which implementation of the polimorphic method to call. However you can have an interface variable being nil and still holding the reference to a specific type as theo shows in the example.)

Implementing interfaces

Interfaces are very simple in Go, and the same time very complex, or at least different from what they are in Java. Interfaces declare a bunch of functions that structs should implement if they want to be compliant with the interface. The inheritance is done the same way as in case of structs. The strange thing is that you need not specify in case of a struct that it implements an interface if it does. After all it is really not the struct that implements the interface, but rather the set of functions that use the struct or a pointer to the struct as receiver. If all the functions are implemented then the struct does implement the interface. If some of them are missing then the implementation is not complete.

Why do we need the ‘implements’ keyword in Java and not in Go? Go does not need it because it is fully compiled and there is nothing like a classloader that loads separately compiled code during run-time. If a struct is supposed to implement an interface but it does not then this will be discovered during compile time without explicitly classifying that the struct does implement the interface. You can overcome this and cause a run-time error if you use reflection (that Go has) but the ‘implements’ declaration would not help that anyway.

Go is compact

Go code is compact and not forgiving. In other languages there are characters that are simply useless. We got used to them during the last 40 years since C was invented and all other languages followed the syntax, but it does not necessarily mean that it is the best way to follow. After all we all know since C that the ‘trailing else’ problem is best addressed using the { and } around the code branches in the ‘if’ statement. (May be Perl was the first mainstream C-like syntax language that requested that.) However if we must have the braces there is no point to enclose the condition between parentheses. As you could see in the code above:

	if a == nil {
		fmt.Println("a is nil")
	} else {
		fmt.Printf("%d\n", a.a)

there is no need and Go does not even allow it. You may also notice that there are no semicolons. You can use them, but you need not. Inserting them is a preprocessing step on the source code and it is very effective. Most of the time they are clutter anyway.

You can use ‘:=’ to declare a new variable and assign a value of it. On the right hand side the expression defines the type usually, so there is no need to write ‘var x typeOfX = expression‘. On the other hand if you import a package, assign a variable that you do not use afterwards: it is a bug. Since it can be detected during compile time it is a code error, compilation fails. Very smart. (Sometimes annoying when I import a package that I intend to use, and before referencing it I save the code and IntelliJ intelligently removes the import, just to help me.)

Threads and queues

Threads and queues are built into the language. They are called goroutines and channels. To start a goroutine you only have to write go functioncall() and the function will be started in a different thread. Although there are methods/functions in the standard Go library to lock “objects” the native multi-thread programming is using channels. Channel is a built-in type in Go that is a fixed size FIFO channel of any other type. You can push a value into a channel and a goroutine can pull it off. If the channel is full pushing blocks and in case the channel is empty the pull is blocking.

There are errors, no exceptions. Panic!

Go does have exception handling but this is not supposed to be used like in Java. Exception is called ‘panic’ and this is really to be used when there is some real panic in the code. In Java term it is similar to some throwable that ends with ‘…Error’. When there is some exceptional case, some error that can be handled this state is returned by the system call and the application functions are expected to follow similar pattern. For example

package main

import (

func main() {
	f, err := os.Open("filename.ext")
	if err != nil {
	defer f.Close()

the function ‘Open’ returns the file handler and nil, or nil and the error code. If you execute it on the Go Playground (click on the link above) you get the error displayed.

This is not really fitting the practice we got used to when programming in Java. It is easy to miss some error condition and write

package main

import (

func main() {
	f , _ := os.Open("filename.ext")
	defer f.Close()

that just ignores the error. It is also cumbersome to check the possibility of error at each and every system or application call that may return error when we are interested in a longer chain of commands if any of those produced error and we do not really care which one.

No finally, defer instead

Closely coupled with the exception handling is the feature that Java implements with the try/catch/finally feature. In Java you can have code that is executed in a finally code no matter what. Go provides the keyword ‘defer’ that lets you specify a function call that will be invoked before the method returns even if there is/was a panic. This is a solution to the problem that gives you less options to abuse. You can not write arbitrary code to be executed deferred only a function call. In Java you can even have a return statement in the finally block or see a mess trying to handle the situation when the code to be executed in the finally block may also throw exception. Go is prone to that. I like that.

Other things…

that also may seem weird at first are like

  • public functions and variables are capitalized, there are no keywords like ‘public’, ‘private’
  • source code of libraries are to be imported into the source of the project (I am not sure I understood that properly)
  • lack of generics
  • code generation support built into the language in forms of comment directives (this is really a wtf)

In general Go is an interesting language. It is not a replacement for Java even on a language level. They are not supposed to serve the same type of tasks. Java is enterprise development language, Go is a system programming language. Go, just as well as Java, is continuously developing so we may see some change in that in the future.


I was working five years in telecom industry where service availability was God. We had to reach five nines, that is 99.999% percent of the time the service had to work.

Just a simple calculation 99% availability means that the service may be out of service 3 and half day per year. 99.9% means an outage period close to 9 hours. 99.99% means barely an hour and 99.999% five minutes. Is it okay if the phone is out of service at most 5 minutes a year? Certainly. How about your online banking interface? How about a pace maker?

How many 9s do we need?

The point is that you should not aim five nines (or any other availability value) if you do not know why you need that. The bottom line is, as always money. To get to a level of availability has a cost label attached. The more nines you need in your uptime number the more cost you have. The latter nines costs exponentially more. On the other side you have the income, the reputation and all other things that count as money value and are adversely affected by downtime.

Telecom invests a lot of money to get to five nine. Does it pay off? If we only count the lost revenue during the downtime (people can not call and thus do not pay for the talk) then it does not. If we count the reputation, lost customers churning to competition then maybe. If we consider emergency calls: definitely.

Telecom has its values established long time ago and the five nines came from common knowledge distilled during a long time. When you design a service for the enterprise or for the public you do not have that. You have to assess what is worth doing.

Ten years ago I was studying economy. A friend was also studying there and at one of the micmac (mixed micro and macro economy subject) exam he barely passed, though he was usually performing excellent. Being shocked by the unexpected low performance the professor asked him about it. He said: “I was not only studying but also following in practice what you taught us. Invest no more than what is needed to reach the goals. Any more invenstment is wasted.”

Assume for now that we assessed the costs and benefits and we decided we need X nines.

Is more always better?

But how about accidental reaching more uptime than needed? Operation wasted money, invested too much into availability. This is certainly not good. It may also happen that by time small investments (e.g.: continous improvement of personnel, new technologies) lead to better uptime. If it costs nothing the higher uptime is extra gift. But if there is a significant difference between the aimed availability and the actual, the investment was definitely oversized. What will prevent operation to aim higher than what financially feasible is?

It is a difficult organizational question. This is not simply put money in on one end of the tube and get availability on the other. You can invest in many different things that lead to higher availability. You can educate your support people. You can buy higher quality, larger MTBF harware. You can invest in software quality. Some of these have lower cost others cost a lot. If operation reaches in practice higher availability than needed then they wasted money. But it is hard if not impossible to tell which money.

Measure the people performance on system uptime?

Should you measure your operation people on uptime? Certainly. That is a core competency they have: provide the aimed uptime. If the operation does not get the required uptime there will be consequences. You have to assess the reasons, design changes in the system, in the policies, in management hierarchy, but certainly something is to be changed. If there is no change the availability remains low.

If there are no consequences of underperformance on people certainly there will be on the company.

Should you incentive the higher than aimed uptime? Obviously no. May be not that obviously, but certainly: no. If you incentive then they will be motivated reach the goal. If they are motivated to reach something the company does not need they will spend company money to reach that. If you incentive anyone for getting X they will get X. Simple feedback loop. Simple. Ok. But if you do not incentive higher than needed availability then …

should you punish higher availability?

If people get punished for higher availability, operation will still be motivated to spend extra money to get higher availability and ruin it to the required level by means that they can control. (Simply switch off service “illegally” or claiming maintenance.) Even if you do not punish them, they are still motivated to spend extra money just to be safe. We get to the mix of incentives and punishments controlling quality and budget and companies sometime end up with extremely complex motivational schemes.

This dilemma is nothing special. This is a very general management topic. You can not measure what you really want to achieve in your organization. In case of operation you can not measure that oepration is spending the money the most possible wise way and deliver services reliably the level business need. You can not measure it simply because it can not be defined what most wise way is.

What we can do is measure something that is somehow related to what we really need. We start to measure X. The management problem is that if you measure X people will deliver X. So what is the solution? Measurement and culture. We know what the measurements are, we aim the direct goals we are measured on but perform the strategic, tactic and everyday tasks keeping an eye on company goals. That is the culture part. Without culture we are only robots, and robots don’t do good work.


Random Ideas about Code Style

Some of the sentences of this article are ironic. Others are to be taken serious. It is up to the reader to separate them. Start with these sentences.

How long should a method be in Java?

This is a question I ask many times during interviews. There is no one best answer. There are programming styles and different styles are just different and many can be ok. I absolutely accept somebody saying that a method should be as short as possible, but I can also accept 20 to 30 lines methods. Above 30 lines I would be a bit reluctant.

“When I wrote this code only God and I understood it. Now only God does.”
Quote from unknown programmer. Last quarter of the XX. century.

The most important thing is that the code is readable. When you write the code you understand it. At least you think you understand what you wanted to code. What you actually coded may be a different story. And here comes the importance of readability as opposed to writeablity.

When you refactor a code containing some long method and you split up the method into many small methods you actually create a tree structure from a linear code. Instead of having one line after the other you create small methods and move the actual commands into those. After that the small methods are invoked from a higher level. Why does it make the code more readable?

First of all, because each method will have a name. That is what methods have and in Java we love camel cased talking names.

private void pureFactoryServiceImplementationIncomnigDtoInvoker(IncomingDto incomingDto){

But why is it any better than inlining the code and using comments?

// pure factory service implementation incoming dto invoker

Probably that is because you have to type pureFactoryServiceImplementationIncomnigDtoInvoker twice? I know you will not type it twice. You will copy paste it or use some IDE auto-complete feature and for that reason the type replacing ‘Incoming’ to ‘Incomnig’ does not really matter.

When you split up the code into small methods the names are a form of comment.

Very much like what we do in unit tests using JUnit 4.0 or later. Old versions had to start the test methods with the literal test... but that was not a good idea. It was discovered long time ago. (I just wonder when Go will get there.) These days Groovy (and especially spock) lets us use whole sentences with spaces and new lines as method names in unit tests. But those method names fortunately should not be typed twice. They are listed and invoked by Junit via reflection and thus they really are what they really are: documentation.

So then the question still is: Why is tree structure better than linear?

Probably that is how our brains work. We look at a method and see that there are two-three method calls in that. There can be a simple branch or loop structure in it, perhaps one nested to the other but not much deeper than that. It is simple and if method names are selected well (I mean really in a good, meaningful and talking way), they are easy to understand, easy to read.

The we can, using the navigational aid of the IDE go to the methods and we can concentrate on the limited context of the method we are looking at. There is a rough rule:

You should be able to understand what a method does in 15 seconds.

If you stare at the method longer and you still have no idea what the method does it means it is too complex. Some people are better apprehending the structure of the code, others are challanged in that. I am in the latter group, so when I review code I many times prefer smaller and simpler methods. I refuse the code to be merged or I refactor it myself depending on the role, the actual task I perform. Juniors I work with think that I am strict and picky. The truth is I am slow. The complexity of the code should be compatible with the weakest chain: any one of the team (including imaginable future maintainers of the next coming 20 years till the code is finally deleted from production) should understand and maintain the code easily.

Many times looking at git history I see refactoring ping-pong. For example the method

Result getFrom(SomeInput someInput){
  Result result = null;
  if( someInput != null ){
    result = someInput.get();
  return result;

is refactored to

Result getFrom(SomeInput someInput){
  final Result result;
  if( someInput == null ){
    result = null;
    result = someInput.get();
  return result;

and later the other way around.

One is shorter, while the other one is more declarative. Is the repetitive refactoring back and forth a problem? Most probably is, but not for sure. If it happens only a few times and by different people then this is not something to worry about too much. When the code gets refactored the developer feels the code more attached to him/herself. A more “it is my code” feeling, which is important. Even though a good developer is not afraid to touch and modify any code. (what could happen? test fail? so what? test? what test?) Note that not all developers are good developers. But what is a good developer after all? It is relative. There are better developers and there are not so good. If you see only good developers who are better than you, then probably you are lucky. Or not.

Implementing an annotation interface

Using annotation is every day task for a Java developer. If nothing else simple @Override annotation should ring the bell. Creating annotations is a bit more complex. Using the “home made” annotations during run-time via reflection or creating a compile time invoked annotation processor is again one level of complexity. But we rarely “implement” an annotation interface. Somebody secretly, behind the scenes certainly does for us.

When we have an annotation

public @interface AnnoWithDefMethod {
    String value() default "default value string";

then a class annotated with this annotation

@AnnoWithDefMethod("my default value")
public class AnnotatedClass {

and finally we when get the annotation during runtime executing

AnnoWithDefMethod awdm = AnnotatedClass.class.getAnnotation(AnnoWithDefMethod.class);

then what do we get into the variable awdm? It is an object. Objects are instances of classes, not interfaces. Which means that somebody under the hood of the Java runtime has “implemented” the annotation interface. We can even print out features of the object:

        for (Method m : awdm.getClass().getDeclaredMethods()) {

to get a result something like

my default value
class com.sun.proxy.$Proxy1
interface AnnoWithDefMethod

So we do not need to implement an annotation interface but we can if we wanted. But why would we want that? So far I have met one situation where that was the solution: configuring guice dependency injection.

Guice is the DI container of Google. The configuration of the binding is given as Java code in a declarative manner as it is described on the documentation page. You can bind a type to an implementation simply declaring


so that all TransactionLog instance injected will be of DatabaseTransactionLog. If you want to have different imlpementation injected to different fields in your code you should some way signal it to Guice, for example creating an annotation, putting the annotation on the field, or on the constructor argument and declare the


This requires PayPal to be an annotation interface and you are required to write an new annotation interface acompaniing each CreditCardProcessor implementation or even more so that you can signal and separate the implementation type in the binding configuration. This may be an overkill, just having too many annotation classes.

Instead of that you can also use names. You can annotate the injection target with the annotation @Named("CheckoutPorcessing") and configure the binding


This is a tehnique that is well known and widely used in DI containers. You specify the type (interface), you create the implementations and finally you define the binding type using names. There is no problem with this, except that it is hard to notice when you type porcessing instead of processing. Such a mistake remains hidden until the binding (run-time) fails. You can not simply use a final static String to hold the actual value because it can not be used as the annotation parameter. You could use such a constant field in the binding definition but it is still duplication.

The idea is to use something else instead of String. Something that is checked by the compiler. The obvious choice is to use a class. To implement that the code can be created learning from the code of NamedImpl, which is a class implementing the annotation interface. The code is something like this (Note: Klass is the annotation interface not listed here.):

class KlassImpl implements Klass {
    Class<? extends Annotation> annotationType() {
        return Klass.class
    static Klass klass(Class value){
        return new KlassImpl(value: value)
    public boolean equals(Object o) {
        if(!(o instanceof Klass)) {
            return false;
        Klass other = (Klass)o;
        return this.value.equals(other.value());
    public int hashCode() {
        return 127 * "value".hashCode() ^ value.hashCode();
     Class value
    Class value() {
        return value

The actual binding will look something like

  public RealBillingService(@Klass(CheckoutProcessing.class) CreditCardProcessor processor,
      TransactionLog transactionLog) {

In this case any typo is likely to be discovered by the compiler. What happens actually behind the scenes, and why were we requested to implement the annotation interface?

When the binding is configured we provide an object. Calling Klass.klass(CheckoutProcessing.class) will create an instance of KlassImpl and when Guice tries to decide if the actual binding configuration is valid to bind CheckoutCreditCardProcessor to the CreditCardProcessor argument in the constructor of RealBillingService it simply calls the method equals() on the annotation object. If the instance created by the Java runtime (remember that Java runtime creates an instance that had a name like class com.sun.proxy.$Proxy1) and the instance we provided are equal then the binding configuration is used otherwise some other binding has to match.

There is another catch. It is not enough to implement equals(). You may (and if you are a Java programmer (and you are why else you read this article (you are certainly not a lisp programmer)) you also should) remember that if you override equals() you have to override also hashCode(). And actually you should provide an implementation that does the same calculation as the class created by the Java runtime. The reason for this is that the comparison may not directly be performed by the application. It may (and it does) happen that Guice is looking up the annotation objects from a Map. In that case the hash code is used to identify the bucket in which the comparing object has to be and the method equals() is used afterwards to check the identity. If the method hashCode() returns different number in case of the Java runtime created and out objects they will not even match up. equals() would return true, but it is never invoked for them because the object is not found in the map.

The actual algorithm for the method hashCode is described on the documentation of the interface java.lang.annotation. I have seen this documentation before but understood the reason why the algorithm is defined when I first used Guice and implemented a similar annotation interface implementing class.

The final thing is that the class also has to implement annotationType(). Why? If I ever figure that out I will write about that.


Java compile in Java

In a previous post I wrote about how to generate a proxy during run-time and we got as far as having Java source code generated. However to use the class it has to be compiled and the generated byte code to be loaded into memory. That is “compile” time. Luckily since Java 1.6 we have access the Java compiler during run time and we can, thus mix up compile time into run time. Though that may lead a plethora of awful things generally resulting unmaintainable self modifying code in this very special case it may be useful: we can compile our run-time generated proxy.

Java compiler API

The Java compiler reads source files and generates class files. (Assembling them to JAR, WAR, EAR and other packages is the responsibility of a different tool.) The source files and class files do not necessarily need to be real operating system files residing in a magnetic disk, SSD or memory drive. After all Java is usually good about abstraction when it comes to the run-time API and this is the case now. These files are some “abstract” files you have to provide access to via an API that can be disk files but the same time they can be almost anything else. It would generally be a waste of resources to save the source code to disk just to let the compiler running in the same process to read it back and to do the same with the class files when they are ready.

The Java compiler as an API available in the run-time requires that you provide some simple API (or SPI of you like the term) to access the source code and also to send the generated byte code. In case we have the code in memory we can have the following code (from this file):

public Class<?> compile(String sourceCode, String canonicalClassName)
			throws Exception {
		JavaCompiler compiler = ToolProvider.getSystemJavaCompiler();
		List<JavaSourceFromString> sources = new LinkedList<>();
		String className = calculateSimpleClassName(canonicalClassName);
		sources.add(new JavaSourceFromString(className, sourceCode));

		StringWriter sw = new StringWriter();
		MemoryJavaFileManager fm = new MemoryJavaFileManager(
				compiler.getStandardFileManager(null, null, null));
		JavaCompiler.CompilationTask task = compiler.getTask(sw, fm, null,
				null, null, sources);

		Boolean compilationWasSuccessful =;
		if (compilationWasSuccessful) {
			ByteClassLoader byteClassLoader = new ByteClassLoader(new URL[0],
					classLoader, classesByteArraysMap(fm));

			Class<?> klass = byteClassLoader.loadClass(canonicalClassName);
			return klass;
		} else {
			compilerErrorOutput = sw.toString();
			return null;

This code is part of the opensource project Java Source Code Compiler (jscc) and it is in the file

The compiler instance is available through the ToolProvider and to create a compilation task we have to invoke getTask(). The code write the errors into a string via a string writer. The file manager (fm) is implemented in the same package and it simply stored the files as byte arrays in a map, where the keys are the “file names”. This is where the class loader will get the bytes later when the class(es) are loaded. The code does not provide any diagnistic listener (see the documentation of the java compiler in the RT), compiler options or classes to be processed by annotation processors. These are all nulls. The last argument is the list of source codes to compile. We compile only one single class in this tool, but since the compiler API is general and expects an iterable source we provide a list. Since there is another level of abstraction this list contains JavaSourceFromStrings.

To start the compilation the created task has to be “call”ed and if the compilation was successful the class is loaded from the generated byte array or arrays. Note that in case there is a nested or inner class inside the top level class we compile then the compiler will create several classes. This is the reason we have to maintain a whole map for the classes and not a single byte array even though we compile only one source class. If the compilation was not successful then the error output is stored in a field and can be queried.

The use of the class is very simple and you can find samples in the unit tests:

	private String loadJavaSource(String name) throws IOException {
		InputStream is = this.getClass().getResourceAsStream(name);
		byte[] buf = new byte[3000];
		int len =;
		return new String(buf, 0, len, "utf-8");
	public void given_PerfectSourceCodeWithSubClasses_when_CallingCompiler_then_ProperClassIsReturned()
			throws Exception {
		final String source = loadJavaSource("");
		Compiler compiler = new Compiler();
		Class<?> newClass = compiler.compile(source, "com.javax0.jscc.Test3");
		Object object = newClass.newInstance();
		Method f = newClass.getMethod("method");
		int i = (int) f.invoke(object, null);
		Assert.assertEquals(1, i);

Note that the classes you create this way are only available to your code during run-time. You can create immutable versions of your objects for example. If you want to have classes that are available during compile time you should use annotation processor like scriapt.

Optimize the client for the server’s sake

The Story

Once upon a time there was an application that was running on some server and the client functionality was implemented in HTML/CSS and JavaScript. The application was serving trillion (not literally) of users all hanging on the end of some phone lines talking to customers who were usually impatient and needed fast resolution to their problems. Typical call center application where speed is key.

Users were dissatisfied by the speed of the service.

No surprise. They usually are.

The application was delivering static resources for the client and JSON encoded data via REST interface. The underlying data structure was using relational database managed from Java using JOOQ. All good technologies were applied to make the service as fast as possible, still the performance was not accepted by the users. Users claimed that the system was slow, unusable, annoying, dead as fish frozen in a lake (yes, actually that was one of the expression we got in the ticketing system). We were aware that “unusable” was some exaggeration: after all there were thousands of queries running through the system daily. But “slow” and “annoying” are not measurable terms not to mention “dead fish”. First thing first: we had to measure!


To address the issue we injected some JavaScript that was measuring the actual performance and it was also reporting the client measured response times to a separate server via some very simple and very fast REST service. We paid attention not to put extra load on the original servers not to make the situation even worse. The result showed that some of the results arrived to the client within 1sec, most of them in 2sec but there was actually a significant tail of the Poisson distribution with some responses as long as 15sec. We also had the measurement on the server side and the results were similar. On the server side we measured approximately 10% more transactions that were lost for the measurement on the client and the Poisson tail on the server contained responses up to 90sec. We did not pay attention to these differences until a bit later.

Meeting the requirements may not be enough.

The actual measurements showed that the response times were in-line with the requirement so we created a report showing all good and shiny hoping that this will settle the story. We presented the results to the management and we almost got fired. They were not interested in measurements and response time milisecs. All they cared was user satisfaction. (Btw: At this point I understood why the name “user acceptance test” is not “customer acceptance test”.) We were blatantly directed not to mess with some useless measurements but go and stand by some of the users and experience direct eyes how slow the system was. It was a kind of shock. Standing by a user and “feeling” the system speed was not considered to be an engineering approach. But having nothing else in hand we did. And it worked!


We could see that some of the users were impatient. They clicked on a button and after a second when nothing happened they clicked on it again. It meant that the browser was sending a request to the server but before the response arrived the communication was cancelled on the client side and the request was sent again. Processing started from zero by the second button press but the wait time for the user accumulated.


To help the patience of the users we introduced some hour glass effect on the JavaScript level that signalled to the user that they have pressed a button and that the button press was handled by the application. Also the hour glass was moving “entertaining” the users and we also hid the button (and the whole filled in form) behind a semitransparent DIV layer actually preventing double submit. We did not have high expectations. Afterall it did not make the system faster. The users loved the new feature. First of all they felt that we care. They had been complaining and now we were doing something for them. Interestingly they also felt the system faster because of the rotating hour glass on the screen. End of story? Almost.


After a week or so we executed the measurement again. It was not a big effort since all the tooling was already there. What we experienced was that the 10% difference between the number of transactions measured on the client and on the server practically vanished. Probably these were the transactions when the user pressed the button second time. It was a full processing run on the server side, but was not reported by the client since the transaction as well as the measurement on the client side was cancelled. These got eliminated with the improved user interface that also decreased the load on the server by 10%. Which finally resulted slightly faster response times.

Usual disclaimers apply.


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