About String Concatenation in Java or “don’t fear the +”

When it comes to string concatenation in Java many people have almost religious views about performance and style. Sadly, there are some misconceptions and misinformation especially about the performance bits. Many people think that concatenating many strings using + means expensive string copying each time and is thus slow as hell which is mostly wrong.

Justin Lee has a nice writeup of the most prominent concatenation options. But imho he misses out some things and his benchmark is a bit oversimplified although it does tell a true story. I assume that he followed at least the basic rules for performance measurement as his results suggest.

Now I want to try to clarify some points I think he missed and I find important:

  • Concatenation using + in one statement is actually compiled to the use of StringBuilder (at least for Sun Java6 compilers, where I checked it in the debugger, try it yourself!). So it’s no surprise that there is no difference between these two options in Justin’s benchmark.
  • It should be clear that the format variants have some overhead because they actually do more than just concatenate strings. There is at least some string parsing and copying involved so that these methods should be used for the cases where for example parameter reordering (think I18N) is needed or readability suffers using normal concatenation.
  • You have to pay attention when using + concatenation over the course of multiple statements because it then involves string copying. Consider the following code: Critical String Concatenation Here it really does make a difference which option you choose. The StringBuilder will perform far better for higher loop counts. We had a real world issue back some time with that when we used the Simple web framework for serving directory listing of several thousand files. The HTML-code was generated using a concatenatePlus()-style method and took like 40(!) seconds. After changing the code to the StringBuilder variant the page was served in sub-second time.

Whether you use + or StringBuilder is mostly a matter of taste and readability in many cases. When your string concatenation gets more complex you should really consider using StringBuilder as it is the safe bet.

Lightweight dependency management

Managing project dependencies without maven or ant ivy, using a custom ant task to ensure classpath orthogonality.

Java’s classpath is a powerful concept – when used appropriate. As your project grows larger in terms of code and people, it gets harder to ensure that your classpath is correct. A great danger arises from JAR files containing different versions of the same resource. You might end up running different code than you think, leading to strange effects. If you build your classpath using wildcards, you can’t even control the order your JAR files are loaded.

Managing dependencies

To avoid the issues mentioned above, you need to manage your project dependencies. It’s a common practice to implement the build process of the project using maven or ant ivy. Both tools provide dependency mangement by declaration. But at a high cost. Especially maven has received some malice lately, criticizing its steep learning curve and complexity.

Scratching the biggest itch

We decided to try a different approach to dependency management, tackling only our biggest concern: The duplication of classpath resources. We take care of the scope of a third-party library, put required JARs in the repository (to us, third party binary artifacts are part of the project source) and update manually. The one thing we cannot assure manually is that every resource is unique. Sometimes, the same class is included in different JARs, as it seems to be common practice among java web frameworks.

Ant to the rescue

Thus, I wrote a custom ant task that, given the classpath, checks for duplicate entries. If it finds one, it lists the culprits and optionally aborts the build process. Included in our continuous integration system, it gets run every time somebody performs a change. You can’t forget to delete an old version of a library or check in the same library twice without breaking the build now.

Our ClasspathCollisionCheckTask

I provide this task here, without any warranty. The source code is included in the JAR alongside the classes, if you want to know what it does exactly.

Assuming you already know how to use custom tasks within an ant build script, here’s only a short usage description.

Import the custom task:

<taskdef
    name="check.collision"
    classname="com.schneide.internal.anttask.ClasspathCollisionCheckTask"
    classpath="${customtasks.library.directory}/schneidetasks.jar"
/>

Next, use it on your classpath:

<check.collision verbose="true" failOnCollision="true">
    <path>
        <fileset dir="${classpath.library.directory}">
            <include name="**/*.jar"/>
        </fileset>
        <fileset dir="${internal.library.directory}">
            <include name="**/*.jar"/>
        </fileset>
    </path>
</check.collision>

The task scans the whole path you give it and reports any collision it detects. You will see the warnings in your build log.

If the failOnCollision parameter is set to true (optional, defaults to false), the build will abort after a collision. If you want to have debug information, set the verbose parameter to true (optional, defaults to false).

Conclusion

If you manage your project dependencies manually, you might find our custom ant task useful. If you use maven or ant ivy, you already have this functionality in your build process.

Feedback

I’m very interested in hearing your opinion on the task or about your way of handling dependencies. Leave us a comment.

Industry Standard C++

The other day I was browsing through the C++ API code of a third-party library. I was not much surprised to see stuff like

#define MAX(a, b) ( (a) >= (b) ? (a) : (b))
#define MIN(a, b) ( (a) <= (b) ? (a) : (b))

because despite the fact that std::min, std::max together with the rest of the C++ standard library is around for quite a while now, you still come across old fashioned code like above frequently. But things got worse:

#define FALSE 0
#define TRUE 1

and later:

...
bool someVariable = TRUE;

As if they learned only half the story about the bool type in C++. But there was more to come:

class ListItem
{
   ListItem* next;
   ListItem* previous;
   ...
};

class List : private ListItem
{
...
};

Yes, that’s right, the API guys created their own linked-list implementation. And a pretty weird one, too, mixing templates with void* pointers to hold the contents. Now, why on earth would you do that when you could just use std::list or std::vector? Makes you wonder about the quality of the rest of the code. Especially with C++ where there are so many little pitfalls and details which can burn you. Hey, if you have no clue about the very basics of a language, leave it alone!

Unfortunately, the above example is not exceptional in industry software. It seems that the C++ world these days is actually split into two worlds. In one, people like Andrei Alexandrescu write great books about Modern C++ design, Scott Meyers gives talks about Effective C++ and the boost guys introduce the next library using even more creative operator overloading that in the spirit library (which is pretty cool stuff, btw).

In the other world, you could easily call it industry reality, people barely know the STL, don’t use templates at all, or fall for misleading and dangerous c++ features like the throw() clause in method signatures. Or they ban certain c++ features because they are supposedly not easy to understand for the new guy on the project or are less readable in general. Take for example the Google C++ Style Guide. They don’t even allow exceptions, or the use of std::auto_ptr. Their take on the boost library is that “some of the libraries encourage … an excessively “functional” style of programming”. What exactly is bad about piece of functional programming used as the right tool in the right place? And what communicates ownership issues better than e.g. returning a heap allocated object using a std::auto_ptr?

The no-exceptions rule is also only partly understandable. Sure enough, exceptions increase code complexity in C++ more than in other languages (read Items 18 and 19 of Herb Sutter’s Exceptional C++ as an eye-opener. Or look here). But IMHO their advantages still outweigh their downsides.

With the upcoming new C++0X standard my guess is that the situation will not get any better, to put it mildly. Most likely, things like type inference with the new auto keyword will sell big because they save typing effort. Same thing with the long overdue feature of constructor delegation. But why would people who find functional programming less readable start to use lambda functions? As little known as the explicit keyword is now, how many people will know about or actually use the new “= delete” keyword, let alone “= default“? Maybe I’m a little too pessimistic here but I will certainly put a mark in my calender on the day I encounter the first concept definition in some piece of industry C++ software.

Update: Concepts have been removed from C++0X so that mark in my calender will not come any time soon…

A DSL for deploying grails apps

Everytime I deploy my grails app I do the same steps over and over again:

  • get the latest build from our Hudson CI
  • extract the war file from the CI archive
  • scp the war to a gateway server
  • scp the war to the target server
  • run stop.sh to shutdown the jetty
  • run update.sh to update the web app in the jetty webapps dir
  • run start.sh to start the jetty

Reading the Productive Programmer I thought: “This should be automated”. Looking at the Rails world I found a tool named Capistrano which looked like a script library for deploying Rails apps. Using builders in groovy and JSch for SSH/scp I wrote a small script to do the tedious work using a self defined DSL for deploying grails apps:

Grapes grapes = new Grapes()
def script = grapes.script {
    set gateway: "gateway-server"
    set username: "schneide"
    set password: "************"
    set project: "my_ci_project"
    set ciType: "hudson"
    set target: "deploy_target.com"
    set ci_server: "hudson-schneide"
    set files: ["webapp.war"]

    task("deploy") {
        grab from: "ci"
        scp to: "target"
        ssh "stop.sh"
        ssh "update.sh"
        ssh "start.sh"
    }
}

script.tasks.deploy.execute()

This is far from being finished but a starting point and I think about open sourcing it. What do you think: may it help you? What are your experiences with deploying grails apps?

Analyzing Java Heap problems Part 2: Using Eclipse MAT

In part one we saw how to obtain the data to analyze, the heap dumps. Now we are looking into a nice plugin for the Eclipse IDE for analyzing the dumps.  Compared to the basic tools described in the previous article Memory Analyzer Tool (MAT) offers better usability, performance and some high level analysis and report tools.Eclipse MAT Overview After you open a hprof heap dump with MAT it will generate index files for faster access to all the data you are interested in and show an overview with nice charts.  From here you have access to other views and features:

  • The histogram is somewhat similar to what jHat offers.mat-pathtogcroot It allows you to browse, sort and filter the object instances in memory and shows you instance count and the shallow heap (memory used only by this object instance) and retained heap (memory used by this object instance including referenced objects). From the context menu you can choose “Merge Shortest Paths to GC roots” to see the reference chain of an object all the way up to the classloader. Here we can see that the JDateChooser registers itself at the MenuSelectionManager as a listener which can cause serious memory leaks as described in another post about Java memory handling.
  • The dominator tree allows you to quickly identify the biggest objects and what they reference. Again, using the context menu on an item in the list offers many options to dive deeper into the analysis.
  • The object inspector gives you detailed information about the selected objects like shallow and retained size, its fields and the class loader by whom it was loaded.
  • The leak suspects report tries to give you some high level hints about possible causes of memory problems of your application.
  • MAT Component ReportThe component report provides some very interesting statistics about Strings and collection usage which might be worth looking at if you are not hunting down leaks but trying to reduce overall memory usage. You can even get performance hints when many overfull HashMaps are detected or there are many empty collections which could be better lazily created.

I personally am using the histogram and the dominator tree the most because I am a technical guy and like to hunt down the problems in the code. Nevertheless the reports may show use other valuable aspects which you did not think of before. The MAT team are expanding the tools nicely on that side so that the benefit of these reports is ever increasing.

It is very likely that when you analyze large heap dumps you may need to increase the Java heap size for Eclipse by using the -vmargs -Xmx<memory size> parameter. That way you are able to analyze big heaps > 500M relatively fast and comfortable. For some live demo take a look at a webinar by some of SAPs Eclipse MAT committers.

Batteries not included

Your feature isn’t ready-to-use until you provide the necessary requirements alongside, too.

When I bought a label printing device lately, it came bundled with a label tape roll. That suggested instant usage – no need to think of additional parts upfront. Only tousb-battery-little find out that, you’ve guessed it already, batteries weren’t included. A bunch of standardized parts missing (Murphy’s law applied) and the whole ready-to-use package was rendered useless. The time and effort it took me to get the batteries was the same as to get the label tape I really wanted to use instead of the bundled one.

This is a common pattern not only with device manufacturers, but with software developers, too.

Instant feature – just add effort

Frequently, a software comes “nearly” ready-to-use. All you have to do to make it run is

  • upgrade to the latest graphics drivers
  • install some database system (we won’t tell you how as it’s not our business)
  • create some file or directory manually
  • login with administrator rights once (or worse: always) to gather write access to the registry or configuration file
  • review and change the complete configuration prior to first usage

The last point is a personal pet peeve of mine.

It all boils down to the question if a software or a feature is really ready-to-use. Most of the work you have to do manually is tedious or highly error-prone. Why not add support for this apparently crucial steps to the software in the first place?

It works instantly – with my setup

A common mistake made by developers is to forget about the history of a feature emerging in the development labs. The history includes all the little requirements (a writeable folder here, an existing database table there) that will naturally be present on the developer’s machine when she finishes work, because fulfilling them was part of the development process.

If the same developer was forced to recreate the feature on a fresh machine, she would notice all these steps with ease and probably automate or support (e.g. documentate) them, least to save herself the work of wading through it a third time.

But given that most developers regard a feature “finished”, “done” or “resolved” when the code was accepted by the repository (and hopefully the continuous integration system), the aching of the users wont reach them.

This is a case of lacking feedback.

Feel the pain – publicly

To close this open feedback loop, we established a habit of “adopting” features and bringing them to the user in person to overcome the problem of “nearly done”. If you can’t make your own feature run on the client’s machine within a few seconds, is it really that usable and “ready”? The unavoidable presence of the whole process – from the first feature request to the installed and proven-to-work software acts as a deterrent to fall for the “works on my machine” style of programming. It creates a strong relationship between the user, a feature and the developer as a side-effect.

We’ve seen quite a few junior developers experiencing a light bulb moment (and heavy sweating) in front of the customer. This is the hot-wired feedback loop working. In most cases, the situation (a feature requiring non-trivial effort to be run) will not repeat ever.

Batteries are part of the product

If your product (e.g. software) isn’t usable because some standard part (e.g. a folder) is missing, make sure you add these parts to the delivery package. It is a very pleasant experience for the user to just unwrap a software and use it right away. It shows that you’ve been cared for.

Observer/Listener structures in C++ with boost’s smart pointers

Whenever you are developing sufficiently large complex programs in languages like C++ or Java you have to deal with memory issues. This holds true especially when your program is supposed to run 24/7 or close to that. Because these kinds of issues can be hard to get right Java has this nice little helper, the garbage collector. But as Java solves all memory problems, or maybe not? points out, you can still easily shoot yourself in foot or even blow your whole leg away.  One of the problems stated there is that memory leaks can easily occur due to incorrect listener relations. Whenever a listener is not removed properly, which is either a large object itself or has references to such objects,  it’s only a matter of time until your program dies with “OutOfMemoryError” as its last words.  One of the proposed solutions is to use Java weak pointers for listener management.  Let’s see how this translates to C++.

Observer/listener management in C++ is often done using pointers to listener objects. Pointers are pretty weak by default. They can be :

  • null
  • pointing to a valid object
  • pointing to an invalid memory address

In listener relationships especially the latter can be a problem. For example, simple listener management could look like this:

   class SimpleListenerManagement
   {
   public:
      void addListener(MyListener* listener);
      void removeListener(MyListener* listener);
      void notifyListeners();
   private:
      std::list<MyListener*> listeners_;
   };

   void SimpleListenerManagement::notifyListeners()
   {
      // call notify on all listeners
      for (std::list<MyListener*>::iterator iter = listeners_.begin();
          iter != listeners_.end();
          ++iter)
      {
         (*iter)->notify(); // may be a bad idea!
      }
   }

In notifyListeners(), the pointer is used trusting that it still points to a valid object. But if it doesn’t, for instance because the object was deleted but the client forgot to removed it from the listener management, well, too bad.

Obviously, the situation would be much better if we didn’t use raw pointers but some kind of wrapper objects instead.  A first improvement would be to use boost::shared_ptr in the listener management:

   typedef boost::shared_ptr<MyListener> MyListenerPtr;

   class SimpleListenerManagement
   {
   public:
      void addListener(MyListenerPtr listener);
      void removeListener(MyListenerPtr listener);
      void notifyListeners();
   private:
      std::list<MyListenerPtr> listeners_;
   };

Provided that the given MyListenerPtr instance was created correctly by the client we can be sure now that all listeners exist when we call notify() on them.  Seems much better now. But wait! Using boost::shared_ptr, we now hold  strong references in our listeners list and are therefore kind of in the same situation as described in the post mentioned above. If the client forgets to remove its MyListenerPtr instance it never gets deleted and may be in a invalid state next time notify() is called.

A solution that works well in most cases is to use boost::weak_ptr to hold the listeners. If you see boost::shared_ptr on a level with normal Java references, boost::weak_ptrs are roughly the same as Java’ s weak references. Our listener management class would then look like this:

   typedef boost::shared_ptr<MyListener> MyListenerPtr;
   typedef boost::weak_ptr<MyListener> MyListenerWeakPtr;

   class SimpleListenerManagement
   {
   public:
      void addListener(MyListenerPtr listener);
      void removeListener(MyListenerPtr listener);
      void notifyListeners();
   private:
      std::list<MyListenerWeakPtr> listeners_; // using weak_ptr
   };

Note that addListener and removeListener still use MyListenerPtr as parameter. This ensures that the client provides valid listener objects.  The interesting stuff happens in notifyListeners():

   void SimpleListenerManagement::notifyListeners()
   {
      std::list<MyListenerWeakPtr>::iterator iter = listeners_.begin();
      while(iter != listeners_.end())
      {
         if ((*iter).expired())
         {
            iter = listeners_.erase(iter);
         }
         else
         {
            MyListenerPtr listener = (*iter).lock(); // create a shared_ptr from the weak_ptr
            listener->notify();
            ++iter;
         }
      }
   }

Each weak_ptr can now be checked if its object still exists before using it. If the weak_ptr is expired, it can simply be removed from the listeners list. With this implementation the removeListener method becomes optional and can as well be omitted. The client only has to make sure that the shared_ptr holding the listener gets deleted somehow.

JTable index madness

A coworker of mine recently stumbled upon a strange looking JTable:
A broken down JTable

This reminded me of an effect I have seen several times. Digging through the source code of the JTable we found an unusual handling of TableEvents:

    public void tableChanged(TableModelEvent e) {
        if (e == null || e.getFirstRow() == TableModelEvent.HEADER_ROW) {
            // The whole thing changed
            clearSelectionAndLeadAnchor();

            rowModel = null;

            if (getAutoCreateColumnsFromModel()) {
		// This will effect invalidation of the JTable and JTableHeader.
                createDefaultColumnsFromModel();
		return;
	    }

	    resizeAndRepaint();
            return;
        }
...

The hidden problem here is that the value of TableModelEvent.HEADER_ROW is -1. So sending a TableEvent to the table with a obviously wrong index causes the table to reset discarding all renderers, column sizes, etc. And this is regardless of the type of the event (INSERT, UPDATE and DELETE). Yes, it is a bug in our implementation of the table model but instead of throwing an exception like IndexOutOfBounds it causes another event which resets the table. Not an easy bug to hunt down…

Spelling the feedback: The LED bar

Our fully automated project ecosystem provides us with feedback of very different type and granularity. We felt it was impossible to render every single notable event into its own extreme feedback device (XFD). Instead, we implemented an universal feedback source: the LED bar.

ledbar-alone

You know the LED bar already from a shop window of your town. It tells you about the latest special bargain, the opening hours of the shop or just something you didn’t want to know. But you’ve read it, because it is flashing and moving. You just can’t pass that shop window without noticing the text on the LED bar.

Our LED bar sells details to us. The most important issues are already handled by the ONOZ Lamp and the Audio feedback, as both are very intrusive. The LED bar is responsible to spell the news, rather than to tell it.

A very comforting news might be “All projects sane”, which happen to be our regular state. You might be told that you rendered “project X BROKEN”, but you already know this, as the ONOZ Lamp lit up and you were the one to check in directly before. It’s better to be informed that “project X sane” was the build’s outcome. After a while, the text returns to the regular state or blanks out.

Setting up the LED bar

We aren’t the only ones out there with a LED bar on the wall. Dirk Ziegelmeier for example installed his at the same time, but blogged much earlier about it. He even gives you detailed information about the communication protocol used by the device and a C# implementation for it. The lack of protocol documentation was a bugger for us, too. We reverse engineered it independently and confirm his information. We wrote a complete Java API for the device (in our case a LSB-100R), which we might open source on request. Just drop us a note if you are interested.

Basically, we wrote an IRC bot that understands commands given to it and transforms it into API calls. The API then deals with the low-level transformation and the device handshake. This way, software modules that want to display text on the LED bar from anywhere on the internal net only need to talk on IRC.

The idea of connecting an IRC channel and the led bar isn’t unique to us, either. The F-Secure Linux Team blogged about their setup, which is disturbingly equal to ours. Kudos to you guys for being cool, too.

Effects of the LED bar

The LED bar is the perfect place to indicate project news. Its non-intrusive if you hold back those “funny” displaying effects but versatile enough to provide more than simple binary (on/off) information. Its the central place to look up to if you want to know what’s the news.

We even found out that our company logo (created by Hannafaktur) is scalable down to 7×7 pixels, which exactly fits the LED bar in height:

logo_on_led

Try this with your company’s logo!


Read more about our Extreme Feedback Devices:

Analyzing Java Heap problems Part 1: Basic actions and tools

You think that your shiny Java app has some memory issues but how do you find out if that is true and what is taking up all that memory? Knowing the potential problems is fine. Nevertheless you still have to find out your actual problems. There are several instruments available to help you analyse your Java application regarding its memory usage. I will tell you about increasing your maximum heap (most of you surely know  about that), looking at the memory of a running app, making heap dumps (on demand or on OutOfMemoryException) and analyzing the dumps.

Increasing maximum heap

The Java VM has a setting that defines the maximum amount of heap memory available to your application. It defaults to 64MB which is enough for many programs. If you have a larger application you should try to start it with that value increased by passing the -Xmx<size>m parameter to the VM at startup. <size> is the value in MBytes so just fiddle around with that. If your app is leaking memory that won’t help you for long so you have to find out *if* it leaks.

Looking at memory usage of a running application

You can use jconsole for a quick look at your applications resource usage. jconsole is part of the Sun JDK since Java 6. You can connect the jconsole to any running java applications on your computer or even reachable over network and offering the Java Management Extensions (JMX) over TCP. Non-leaking programs should have a memory graph like this:

You can see, that the memory fluctuates over time because of the garbage collection cycles. But overall it does not grow. Next we will look at an application that leaks memory:

Above we see that the garbage collector (GC) tries its best but the used memory is growing over time. If we see such behaviour we probably need a heap dump to analyze the issue further.

Making a heapdump

Basically you have two nice ways to get a heap dump of your application which you can look into at a later time:

  1. Use jmap (which is also part of the Sun JDK 6) to dump the heap of a running application to a file using a command line like jmap -dump:format=b,file=myheap.hprof <pid>
  2. Tell the VM to make a heap dump when an OutOfMemoryException occurs by adding -XX:+HeapDumpOnOutOfMemoryError to the VM parameters at startup. With another switch you can specify the path for the dumps: -XX:HeapDumpPath=jmxdata .

After you have obtained a dump of your application you certainly want to have a look at it and find the issues. You can start with Sun’s jhat which is also part of current JDKs. After supplying jhat the hprof-file you can point your browser to the integrated webserver of jhat and browse the heap looking for the objects that take up your memory.

That way you can get an idea of what objects lived in memory when the heap dump was made and how they were referenced.

Conclusion

We have seen many ways to perform memory diagnostics using only free tools which are part of the JDK from Sun. They are all nice but have their limitations. Especially jhat has problems with usability and performance when you examine larger heap dumps with it.

Next time I will show you how to use the Eclipse plugin MAT for analysis of heap dumps obtained in one of the above ways. So stay tuned!