While we have managed Jboss and WebSphere deployments using Meister and Mojo and we frequently manage resources for different target server environments through build, here is a company that focuses on both of those things.

I posted a comment about the reluctance of script writers to give up their scripting on their blog here.

It is just plain fun to run parallel workflows and builds and watch the activities and build steps light up the workflow monitor in real time like a Christmas tree. See this flash demo to see what I mean.

As customers go to machines with more and more cores, fewer machines are needed in the application lifecycle infrastructure, particularly for builds and code retrievals - the most resource intensive functions. This is helping to simplify the infrastructure, reduce maintenance and administration and drive down costs.

Several of our customers are running around 5000 builds and non-build workflows per month on two machines. The primary reason for two machines, in fact, is for disaster recovery, and the goal is to run both machines at less than half capacity so that in the event that one machine (or datacenter) fails, all the current capacity can be run as a contingency on the one machine that’s left.

Thread control is very simple with Meister and Mojo. Both use the omsubmit dependency manager program to handle this. Meister’s om program translates build events into workflow steps using omsubmit. The OMSUBMIT_MAX_USER_PROC value sets the maximum allowed number of threads.

You might think that if you are running dual, quad-core build machines that you should set the max threads at 8. However, Meister posts build operations to one thread and the associated logging operation to another. Compile operations notoriously use a lot of memory and CPU resources, but the logging operation posts to a server and waits for the operation to complete. There is really no disadvantage to setting the max threads higher than 16 in this case, so go ahead and do it.

As a non-build workflow example, I worked on JBoss deployments to 48 Linux machines. The workflow was parallelized into 48 activities each of which deployed to a single machine in parallel. The deployment activity was largely a remote execute operation that extracted archives on the remote machine. The extraction took about a second for a medium sized application. Again, this is a waiting situation where machine resources are essentially idle while the thread is in use, so use more threads. The machine was a dual, dual-core build machine and we set OMSUBMIT_MAX_USER_PROC to 50.

Watching the workflow monitor as the deployment ran, we could see roughly half of the machines light up (meaning actively running) at any one time and the entire deployment process synchronized all 48 machines in a little over two seconds.

So, don’t simply match your machine’s CPU threading capabilities - overclock! Aim high for max threads and try to determine where your performance is optimized. I’d love to provide you with some metrics as a function of thread count, but usually once something is working it’s on to the next project. I barely have enough time to blog!

One of OpenMake Software’s product strategies is to keep things simple. Build management is one of the most complex operations in all of the IT world, and one of our key benefits is to simplify, organize and automate the build process for development, testing and production.

We’ve seen a trend among our customers to simplify their build management infrastructure by going to fewer build machines with more CPU cores. Builds in particular use relatively more CPU resources than other resources as code is interpreted and compiled in memory and then finally written to disk. By reducing the total number of machines, rack space, procurement, administration and other IT overhead costs are reduced at great cost savings per machine eliminated.

Recently, I was at one of the big chip makers where they used dual quad-core CPU Linux machines for their development and builds. They had two machines and were able to control access to allow separate areas for development, testing and release builds in keeping with best practices. Having all the horsepower of 8 CPU cores on a single machine kept them from needing more machines.

Another customer does 6000 builds per month with Meister on just two build machines.

IBM, when selling BuildForge, likes to talk about big build server farms, because their tool does remote execution on multiple machines, as does Meister. However, BuildForge does not do builds at all. It can remotely execute your existing build scripts, but there is little real value add to that. BuildForge is also famously expensive. What happens over the next few years to the high investment in multi-machine remote execution software as the number of machines declines, perhaps dramatically?

A similar argument can be for Electric Cloud’s Electric Accelerator product. It’s possible in some cases, for C/C++ builds to gain an edge by pushing a compile operation to another machine, and then bringing it back. You would only do this to gain access to additional CPU resources. In the past, you might have 8 build machines that Electric Accelerator would farm operations out to. Now, you can pull all those operations into a single machine and there is no need for that functionality. Also, you are stuck with converting your GNU makefiles into other GNU makefiles.

Meister is optimized for multi-core CPU build machines and offers multi-threaded capability to both build events and non-build workflow events. You know where your build is and there are fewer dependencies on network resources. Both BuildForge and Electric Accelerator add additional overhead to build administration to coordinate across multiple machines - a dying practice, that no organization wants to invest in. Meister is the best bet for a future with fewer build machines with more horsepower.

I’ve recently been learning the Ruby on Rails framework for web development. It’s become a quite popular framework for getting database connected websites up and running relatively quickly. One way it is easier to get a site started than with other frameworks is because of the Convention over Configuration mantra that it lives by. Instead of requiring loads of configuration files to build a basic site (that can grow and become quite complex by the way) it has a feature called scaffolding which automatically builds your Model, View and Controller classes based on tables it finds in the database. It can do this by making assumptions based on standard conventions about interacting with a database from a website and naming and using classes in a standard way.

Although I am still a rookie when it comes to understanding the many facets of Ruby on Rails, I have really been trying to emulate the Convention over Configuration way of doing things in my various build/release projects at customer sites. One problem I inevitably encounter in most organizations is that the development of build and release methodologies has been left to the various development teams and not been thought about holistically using a centrally managed approach - this leads to little to no standards and Convention over Configuration is chucked aside. Not only is this inefficient from an organizational standpoint - why reinvent the wheel over and over again for each team when they are essentially tackling the same sets of problems, but it also makes for a nightmarish audit trail that could get you into trouble.

One reason this happens so frequently is that the managers that are supposed to be in charge of standards for building and releasing applications are often not privy to the kind of technical requirements that the various development units have when it comes to putting together and delivering their applications. And when developers try to explain the requirements, the standards people may get lost because they can’t possibly understand the nuts and bolts of every application.

To pull off real centralized management of builds and deployments, the standards people need to take a deep breath and rethink their objectives - start looking for the commonalities, not the differences between applications. In doing this, they will find that that problem that that developer told you was so unique and must be solved a certain way is probably very similar to the problem the other developer told you about last week - or just look on the web and see how many thousands of external developers have this same “unique” problem. It turns out that most applications can be constructed in the same type of way. Just because the source files are different between applications doesn’t mean that the paths they take to their target executable, dll, Jar, War or Ear file is very different at all. And when those paths are essentially the same - create a reusable process that the various teams can share. Use Convention over Configuration as your guide - standardize and centralize the common processes and externalize the technical specifications using highly modularized control files.

Here’s a simple task for you to try. This assumes all of your application teams have their code checked into a central repository - if not, you have bigger problems than standardizing builds and releases and should address those first. Look at your various technologies, whether it’s .Net, Java or some other and try to identify where the code tree’s start under the root of the project. You’d be amazed at how many teams check their .Net solutions into different levels of a code tree for no good reason, or Java teams that have their their source packages buried some place in the code tree. Next, look for the common root starting point for all these application types and try to come up with a simple standard based on this information. Finally, notify those that are not following that standard that you would like to move their code up and over to this new location (its usually up and not down) - it should actually be pretty easy to do. After this has been done, you can now have all build and deploy scripts use a standard root variable to find dependencies (think something like SOURCE_ROOT).

It always amazes me how many teams don’t standardize simple things like code tree start points in their source projects - it equally amazes me how much mileage you can get just out of making simple path standardization adjustments. After you’ve worked on the source tree, try doing the same with your common libraries. This isn’t rocket science - just remember, Convention over Configuration makes everything easier.

Adam

Finding the blog Enterprise Maven made me decide to go back to the basics, today. This blog is from 2006, but the best practices of production control ignored here go back decades. I’d like to point out that Oleg Gusakov, the author, wrote the blog in a very good spirit and seems like a nice guy. He just seems to be a bit naive about what’s been happening with software development in the enterprise.

In the first section, he assumes that the only enterprise build and deploy solution is one that is customized, while OpenMake Meister has been serving that role now for 12 years. He does correctly conclude that all the enterprises in the world should not be independently investing in the same type of build and deploy solution. It is a costly investment and this functionality should be productized. That’s exactly why we did it and why that is still one of our chief selling points.

He is right that developing a product that should be commoditized is a drain on the business. However, the converse, having a commercial product provide the functionality at a greatly reduced cost compared with one homegrown, provides a competitive advantage over those companies who don’t have such a product.

Through the middle of the article, again, I think Oleg is unaware of the heavy horse SCM products out there that provide a lot of the expected functionality. Tools like CA Harvest, Serena Dimensions and others are very complex and sophisticated n-tier products. They nevertheless do not provide build support, so by combining an enterprise file control tool with an enterprise build and workflow tool, Meister, you canvas the required functionality.

Lastly, regarding the enterprise development lifecycle, he is right it is an oversimplification. I like his phrase that he hopes to “grow the meat.” At OM Services, we have “fully grown meat” and the enterprise lifecycle documents that we develop with our customers and clients are typically 50-80 pages in length. Here is where I review the generally accepted best practices, going back to the seventies with mainframe development. (NO, distributed platforms are not somehow different in the high level process!)

1. It all starts with production control. Developers do not have access to production due to a fundamental conflict of interest. Maintaining business continuity trumps developers’ ease of delivery to production.
2. Since someone else puts the code into production (or operates a tool which does so), this is the basis for separation of roles and responsibilities in the enterprise software development lifecycle.
3. To ensure integrity of the production environment, the production build must be done by a group representing the business, not development. Developers do not do production builds.
4. Working backwards, if you want your test environment to be as close as possible to production, you lock this down and prevent access to developers. This is usually the QA testing environment.
5. Again, to avoid a conflict of interest, the QA testers should be working for the business, not the application development team.
6. And it follows that the build for the QA environment is done by the business.
7. The developers job from this perspective is delivering source code to the business, which wants retain the source code and the ability to use it (meaning they can build it).
8. The process of developers transferring source code to the QA build people was called “throwing it over the wall”. Now, the heavy horse SCM tools and Meister workflow make it easy to do this and allows variations of iterative development involving the QA environment.

Any type of continuous integration or agile development practice typically happens before the QA environment. Any develop methodology for the enterprise must take into account the fundamental conflict of interest between software change delivery and business continuity or ignore it and remain entirely in front of QA.

If you are a developer, you can think of this as a loss of privilege, or you can be elated that other people are doing the dirty work for you and you can focus on the art and science of engineering business solutions. If you are really depressed, maybe you should be on the other side of the wall!

We’ve heard from a number of companies that they are having problems with their WebSphere Eclipse headless builds and they are looking to us for a solution.

A headless build is an Eclipse function (and therefore an IBM WSAD/RAD IDE function) that allows you to execute Eclipse and some of it’s plug-ins at the command line. The GUI does not launch, but the main machinery does, and it makes use of the workspace metadata.

In theory this allows you to do the same build at the command line that was done in the GUI. It’s a nice theory that we subscribe to ourselves.

In practice, these companies do not feel they are successful for a number of reasons:

1. If it works it is slow. A slow build is almost as bad as a broken build.
2. Unexplained crashes in the middle of the build due to workspace corruption.
3. Crashes may leave the .lock file in the .metadata folder of the workspace. This prevents Eclipse from executing in the same location until it is cleaned up.
4. It’s a black-box build with a large number of operation swept

Meister takes a different approach and records the files and project types and then applies a traditional build with a mixture of Ant tasks and command line calls. This eliminates the need to even install RAD 7 on the build server, but you still need the runtime libraries.

I’ve worked with the IBM WebSphere EJBDeploy plug-ins and integrated Meister to actually call an Eclipse headless build via the WSAD/RAD ejbdeploy.bat file. As a rule, we always stick with the vendor-recommended approach, so we don’t have a choice. However, we don’t do the compiling and archive this way - just the EJBDeploy step.

By compiling and archiving the Java parts in a traditional way, we at least minimize the risk of a broken headless build and maximize the speed. And, yes the ejbdeploy step is usually the longest step in the build. The fact that IBM supplies the ejbdeploy.bat file at all, which mimics a command-line compiler, means we’re not the only ones taking this approach.

How do you set up an automated build for EJB client JAR’s from the IBM Rational Software Delivery 7 development environment for WebSphere 6?

This question came up recently in my work for a major insurance company. When one extends the EJB client class, that is all a developer has to do as far as RAD 7 is concerned. When the developer deploys the JAR to the server, RAD 7 quietly generates stub source Java classes, compiles them and includes them in the JAR file.

An automated build in this context means that all the code the developer created in RAD 7 and checked into version control, is checked out of version control without RAD 7 and built exactly the way the developer intended. This is what OpenMake Meister is for.

One developer I was working with was concerned with how to generate those same source files in the automated build, which in his case was using OpenMake. He was familiar with how OpenMake uses the ejbdeploy command for building EAR’s with EJB server-side code and expected some equivalent for the EJB client.

Mercifully RAD 7 actually leaves the generated source files behind in the Eclipse project, in the standard source location. This means that we get the source code for free and there is really no need to regenerate it. All one has to do is check in the generated source to version control along with the developer coded source and build a normal JAR file in the automated build.

For the developer, this means:

1. Check in all the Java source code in the project. This is easy – better than picking and choosing which Java source to check in as the developer thought he had to do. This would be ultra-high risk for making a mistake and breaking the team’s automated build.
2. Assign the “Default Java JAR” configuration mapping for the EJB client project using the OpenMake Target Generator Eclipse plug-in.

A lesson to learn from this is that not all technologies or technology variants will have an impact to the build process. The developer was considering an idealist approach to reproduce every minute step of RAD 7, but the best solution was something practical and simple. Build management is part art and part dirty science. Having a “generate” step for the EJB client Java classes in the automated build only introduces an additional point of possible failure, and we build-meisters know we don’t need any more of those!

If you are a hard-core open source programmer, you probably use Git for project code change management instead of Subversion (I chose those words carefully). There is a lot of passion from Git advocates and, while it is not a very mature solution, it has a lot of momentum to push it forward. Merely being conceived of and written by Linus Torvalds and being used on a few large open source projects, such as the very Linux kernel itself, is enough to garner wide support.

A great place to learn about Git is Sam Vilain’s Tutorial. He goes into a lot of detail on the benefits and how-to’s of using Git. Some of the highlights include repository space savings of over 90% and local-to-repository sync times dropping from hours in Subversion to minutes with Git. The real power of Git is in the highly distributed repositories and the ease and control of moving and accepting changes between repositories. For an open source project with a large number of developers it seems Git will really shine. Git has fine control over branching, merging and accepting or not accepting project changes according to various criteria.

A popular way to use Git is to have Git pull from a public Subversion or CVS repository with convenient integration with those tools to a local Git repository and work from there. Friends working on the same project can easily pass changes between each other with Git and later commit back to the centralized CVS or Subversion repository. GitHub provides a simple Git repository hosting service. Doing a lot of Java work with JBoss and WebSphere, I am naturally interested in an Eclipse plug-in for Git and indeed one exists. It looks like a newborn infant, but I will check it out.

I also have a Perl open source project that is currently pretty anemic, but I hope to revitalize it soon. I really hate the fact that I’m locked into using Subversion on SourceForge and I never came to like Subversion. I’m eager to explore moving the project to GitHub, even though I’ll probably be the only committer for awhile. Since I’m a hardcore software management person and robust Perl developer, I think Git might be my tool. I’ll let you know.

Here comes buildr: yet another Java build tool. Hopefully I, or one of my other cohorts will check this out in detail soon. But, with my experience working with all manner of build tools, with 100 companies and many more development teams, I can already make a few observations.

First of all, why another build tool for Java? I am occasionally told that Maven or Ant is a perfect tool, but clearly the people behind buildr don’t think so. The choice of JRuby as the vehicle for delivering this tool, I think is probably a good one. JRuby is a scripting language in the same vain as Perl, which is used by Meister.

Doing software builds is an ugly business involving lots of file and operating system interaction. This is not where Java shines, but scripting languages can. As long as operating systems are written in C and not Java, C-like tools will be better and faster at interacting with them. Plain Ruby itself is C-based, and JRuby no doubt inherits C-like operating traits. Calling out to a Java compiler from Perl or JRuby, though it has its own JVM, does not represent a significant overhead compared with the file system operations and the compilation/translation itself.

Both Maven and Ant are relatively difficult to extend compared most other build tools, and I’ll be buildr beats them here. If you have all the Maven plug-ins and Ant tasks you need, then good for you. If not, then you have to start developing in Java and it becomes too much of an investment to sink into a build system. It is much cheaper to extend in JRuby or Perl. My frequently cited example is the XMLBeans compile step in Meister, written in Perl, which is only 40 lines of real code. The Maven plug-in is 60 pages of Java code and no one can tell me really what it is doing (I asked on all the forums). Less code is usually more transparent, which is also good for build audits.

I am a little disappointed to see them try to placate the Maven and Ant users by promising it is a drop-in replacement for Maven and they have all the Ant tasks covered. Both tools have their drawbacks and I don’t want to see another tool with the same deficiencies. They should have the cajones (or coñejos) to apply all their resources to what they think is a better tool (with its own unique benefits and deficiencies). I imagine offering Ant task equivalents is pretty easy because of the ease of coding in JRuby compared with Java.

They also don’t mention who is supposed to use the tool. Is it for individuals, small development teams, the enterprise? Maven falls short because it is only appropriate for development teams and not for stable, controlled, enterprise builds. Ant is not even tool, but a means to create some tools for small teams. I don’t think Meister will fear buildr either.

Well, since buildr is only in incubation status with Apache, I’m not sure how much time I’ll be able to spend on it, but I am curious and I’ll let you know if I find out more.

First, let me say how nice it is to have the Mojo workflow engine that allows us to manage the compliance checks, deploy to multiple machines in parallel and validate deployment. This makes our lives a lot easier and provides clear benefits for deployment via the parallelization, dependency management, scalability, logging and reporting. Underneath the covers, and for those of you who don’t have the luxury to use this almost-free product, there are some important low-level tools that are critical to the development, testing and operation of the Mojo JBoss deployment system on Linux.

With the most important listed first, they are:

1. JBoss support
2. The Perl executable (5.6-5.10) and base language
3. Perl’s Test::Simple or Test::More modules
4. Perl’s Test::Harness module
5. The JBoss twiddle.sh script or command equivalent
6. Perl’s XML::Twig
7. Perl’s Archive::Zip
8. vi
9. ssh
10. xterm

JBoss support wins hands down due to the number of bugs and critically important undocumented features. On a scale of 1 to 10 where 10 is the best documentation, I give JBoss about a 3 or 4. Googling doesn’t even help that much for deployment issues.

You may be surprised at the prominence of Perl, but if you think about what you are really doing and what the best tool for the job is, it makes sense. You are really moving an archive (a ZIP format file), copying XML files, creating directories, changing permissions, extracting the archive to the file system perhaps. Where did I mention Java? Nowhere. The twiddle.sh command comes in handy if you get the secret commands from JBoss support that tell you if the application you deployed has actually started correctly. Notice that this is a shell script suggesting we’re not the first to use non-Java tools to manage deployment.

Particularly on the testing side, I can’t think of a viable alternative to Perl testing. We need to test that we created this directory, changed that permission, updated that file timestamp, etc. We have about 300 test cases encoded in Perl that are run with every change to the deployment system. It takes about 20 seconds to write and run a simple test case in Perl.

Lessons? Use JBoss support early and often and use Perl.

As a follow up to my article on automating XML updates, I’d like to report that I did use Excel and Perl’s XML::Twig to successfully generate XML descriptors for my web service consumer, and it was a lot easier than I thought. I’m using XFire 1.2.6 web services stack running under JBoss and using MyEclipse IDE 5.0. I’m happy to say I went from blank spreadsheet and no plan to generated XML files from spreadsheet values in one and half hours. The implementation is of course expandable and reusable. This implementation should work for WebSphere and .NET as well.

I needed to create different configurations for my web application so that the service request went to different endpoints for different environments. The endpoint is at an enterprise service bus (ESB) and there is a different ESB for each environment. I need to have my ‘dev’ instance of the consumer hit the ‘dev’ instance of the ESB, the ‘qa’ instance of my web app hit the ‘qa’ instance of the ESB, etc. We’ve set up Meister to pick up the correct XML file for the target environment for the build of the WAR.

I started by setting up the spreadsheet as follows. I had an unnecessary column for Host indicating JBoss, but I hope to include WebSphere and maybe .NET as well some day. My web app actually connects to two services a.k.a. providers, so there is a column there. And, next is the configuration label for my web app with the name corresponding to the environment it is designed for. So, the first three columns of the spreadsheet look like:

Then I needed a way to indicate the resource that would change. Right now I only have XML files, but I chose to stick with a generic URL for that. Unlike Maven or Ant generators, we start with an XML file that actually works and has been tested - not some hacked up parameterized version that takes additional effort to create. The fourth column of the spreadsheet looks like the following (with repeated entries omitted):

Next, I needed a way to specify a target location to change within the XML file. Now, I know I’m going to use XPath, but I’ll want this to one day work for properties files as well, so I came up with a URL-like thing called a Universal Datum Locator (UDL) which pre-pends the method of locating the datum to change on to a method-specific locator. It could be a property name, an XPath or a Perl regex, for example. In this case it is XPath and then the last column contains the replacement value for the datum indicated by the UDL. XPath is also very intuitive and easier to construct than it may look.

The value for the UDL looks like:

xpath://beans/bean[@factory-bean='xfireProxyFactory']/ constructor-arg[@index='1']/value

So the fifth column contains the UDL’s, which in my case is always the same XPath expression. The final column of the spreadsheet contains the replacement value of the datum indicated by the UDL:

My nifty Perl script is only about 80 lines of real code and because XML::Twig is nearly the best thing in the world, I pass the entire XPath in as a hash key to modify the source XML file:

my $twig = XML::Twig->new(

pretty_print => ‘indented’,

twig_handlers => {

“$xpath” => sub {

$_->set_text($new_datum);

}

}

);

Here, “$xpath” is directly from the “UDL” column of the spreadsheet with only the ‘xpath://’ stripped off and “$new_datum” is directly from the “Value” column. That’s a pretty useful one line subroutine if you ask me. I had the new XML files each generated into a different folder (dev/,int/, etc). Then, I checked them into version control (CA Harvest) and built each of them with Meister. If you want the full code, let me know and I’ll post it somewhere.

I did find working with the Excel 2003 XML Spreadsheet format a tiny bit awkward. You have to keep track of the column and row indices, but not bad other than that. I see Microsoft Word 2007 allows you to save as an XML document directly, but you apparently have to define bindings. I’ll have to check that out.

JBoss checks for certain watch files when handling deploying or undeploying an application. The watch files are certain key files germane to the object you are deploying. For an EAR, the watch file is the application.xml and the optional jboss-app.xml files. For a web application archive, the watch files are the web.xml and jboss-web.xml files. For single-file XML resources, such as datasources, the watch file is the XML file itself. In this article, I am dealing with archives that are deployed in unextracted (unzipped) form.

The first check is made for the existence or non-existence of a watch file. If a previously unknown watch file is found, the appropriate deployer is started and the file modification timestamp is stored in memory. If a known watch file is found to be missing, the appropriate undeployer is launched.

If a known watch file is found on a subsequent pass of checking watch files, its timestamp is checked against the time that was stored in memory by the deploy process. If the deployed watch file is newer, the appropriate deployer is launched which apparently first dumps the associated resources and then reloads the object as if it were newly found.

This leaves a hole that can lead to the horrifying result of having files deployed to the server, but not having the changes reflected in the running application.

The issue has to do with completely replacing a running application with a new version. You might first delete the application completely from the runtime area leaving the server to undeploy it. Then you replace the object with a new version of itself. The window of time between checks of the watch files is finite and I’ve found it is possible to remove and replace the archive within that window so that the JBoss server does not detect that the watch file was missing and so it is not unloaded from memory. The server does check the watch file timestamps, but if you have changed files other than the watch files and have not updated the timestamps of the watch files themselves, the server will happily ignore the new version of the archive while running the old one.

If you use this deployment strategy, then this issue is essentially a random process, and a deployment failure due to this reason happened in our case on only a few percent of all deployments. When you are running a few hundred deployments a week, or it happens for a production deployment it becomes a big problem – especially when people don’t know what the problem is. A simple resolution is to always update the timestamps of the watch files when changing anything for a deployed application. This will take care of everything but possibly compiled JSP’s. (Possibly more on that later.)

This also points to a “restart” mechanism for JBoss – simply ‘touch’ the watch files of a running application to change their timestamps to the current time. This will trigger the dump-and-reload on the next watch file check. This can be useful when the application has not changed, but an associated XML resource has.

When you work with a locking-type version control tool like CA Harvest, your Meister build project will appear in your Eclipse workspace as read-only when you check out an existing workspace. I’ve been using Eclipse for WebSphere development (WebSphere Studio Application Developer) and for JBoss via MyEclipse IDE. If you want to regenerate your Java targets, you first have to check out the Meister build project so that the files are writable.

Since this can lock the targets exclusively and prevent others from updating the target, you may not want to check out the build project, but you may still want to develop freely and update your local targets for Meister to build it. For this situation I recommend creating a separate build project that you may never check in to version control. It will be writable and it allows you great freedom for a maximally agile development environment. The ‘official’ build project may reference all the built archives in the workspace, but having your own local build project can allow you to focus for a unit build. For example, my workspace may contain an EAR project, a WAR project and one or more JAR projects. If I am principally working only on one of the JAR projects, my local build project can reference only that one JAR project.

When it’s time to release your JAR code updates to the system build and test environments, synchronize your workspace and check out the VC build project. Generate your targets, do a local system build and then check everything in. Your team system build will work fine!

I wanted to share a specific benefit I enjoyed while using Meister for Java development. As part of my role to help develop an automated JBoss build and deploy system, I ended up taking on a developer role for a web services security project for both JBoss and WebSphere. While the project involved about 1000 lines of Perl, it also got me writing simple web services and consumers for JBoss and WebSphere and building them using Meister and its Eclipse plug-in.

Believe it or not, I am still using WebSphere Studio Application Developer 5.1. While my specific tale involves that IDE, it is equally applicable to MyEclipse and Rational Application Developer set of Eclipse IDE’s. In my environment, CA Harvest is the version control/SCM tool and Meister is the build tool. After code is checked in from my desktop using the CA Harvest eclipse plug-in, the code is replicated out to a Linux server, where Meister performs the official system build that is sanctioned for deployment to the application server. There is also a Meister Eclipse plug-in that scans the WSAD workspace for build targets and dependencies. Meister stores this information in one XML file per build target and those files are also checked in to CA Harvest right along side the source code.

Working intensely within the WSAD Eclipse environment as the project manager cracked the whip, I worked with a consumer application and updated it according to the changes in the service WSDL and service endpoint URL’s. One thing I learned is that if one of the parameters for the consumer is tweaked, don’t bother tweaking the XML or generated code, just regenerate the whole client. WSAD will even check out the files before if they need to be. So everything looked good on my desktop with the service and consumer deployed to two separate WebSphere servers on ports 9080 and 9081. Now to get it into the enterprise ‘dev’ environment…

Using the ‘Generate Target Definitions’ feature of the Meister plug-in I updated the Meister build target XML definition files and checked in all my code. I then promoted the code in CA Harvest which automatically kicked off a ‘dev’ build in the Linux environment. I got an error back from Meister saying ‘jdmpview.jar’ doesn’t exist.

Since I knew my consumer app and its elementary nature, I knew that jdmpview.jar wasn’t one of my JAR’s and it must be one of WebSphere’s. Given that 200 other Java apps use the same build environment with the same standards, I probably didn’t use some new feature of WebSphere that no one else is using. Therefore, it must a problem on my local desktop with the version of JVM I was using.

Sure enough, the consumer app was using the base_v51 WebSphere runtime instead of the ee_v51. (I did inherit the initial version of the app from someone else!) And, oddly enough, there is an extra JAR in the base that is missing in the more fully featured Enterprise Edition. Meister correctly forced the runtime environment to be EE for the Linux build, overriding the developer selection. I switched the runtime in the Java build path properties, regenerated the Meister target definitions, checked them in and promoted them to a successful ‘dev’ build. Regenerating the target definitions had the effective of switching out the list of JAR files in the library path from the base_v51 set to the ee_v51 set. The whole thing including one bad and one good build took about 4 minutes.

The great benefit for me was the balance between developer and SCM functions. We could have applied more controls at the desktop level, but from my perspective, I prefer an Agile environment with more freedom even if it means occasionally hanging myself with my own rope. In this scenario I let the tools dot the I’s and cross the T’s and it took no more time than say, waiting for Outlook over VPN.

In developing Java applications for multiple server environments (e.g. dev, test and prod) there is a common pain-point of having to manage deployment descriptor or configuration files specific to each server. For example, you may have an XML log4j configuration file with some parameters different for different server environments. You may want to turn on debug messaging for the development server, but turn it off for production. At the same time, the Java source code will (eventually) be the same in production as it was in development. A similar situation applies for .NET application development.

Like many build management tasks, managing these environment-specific files is generally left to either manual or some type of scripting. This is really something that needs to have a high level of automation applied. Particularly in larger environments, much like scripted build management solutions, existing tactics fall short. This situation is in a far worse state than even the compile part of build management. It is not enough to simply have a script that can spit out some files. One of the biggest problems is information management and the fact that parameter values in the configuration files may be determined by different teams! How do a production engineering team and an application developer both feed inputs into the same XML file?

I’ve worked on this problem for several years and with a number of companies. The critical functionality can be broken down into two different items – information management and a processing engine. In an effort come up with something better, I’ve done a review of what’s out there and here is what I came up with:

• Ant ‘filter‘ task: As with many Ant tasks, this works great if you are an individual with a few items that need updating. It is a nightmare if you are working in a multi-team enterprise with multiple server environments. The main problem is that you have to constantly take working copies of XML files and insert a token for Ant to later re-replace. This leads to a management nightmare to synchronize parameterized copies of XML files with their working copies from the desktop environment. The advantage is that it works for any file type so you can use it for properties files as well as XML files.
• OOPS Consultancy Ant ‘xmltask‘: This is a good engine for specifying and performing changes to the XML and has a full feature set. In fact, we use this in some of the Meister build services. The problem is that it is only for Ant and therefore you have all the reuse, standardization and hard coding issues. Xmltask can provide part of the solution we are looking for, but we still have an information management problem to deal with.
• Maven: Maven has what is essentially the Ant filter task. The specifications are abstracted in the pom files, which is better than Ant, but it encourages templating of configuration files leading to all the problems associated with that (synchronizing templates with working files, testing templates, etc.)
• XML:DB XUpdate: This is a working draft of a specification to encode XML update instructions into an XML document. There is a Java implementation of XUpdate listed on the site called ‘Lexus’, but I couldn’t find anything on it. Since the build management task requires us to generate XML files, I’m not keen on generating XML files using xupdate tags that will allow me to generate other XML files.
• Perl XML::Twig: This has worked wonderfully for me on a back-end web services security effort and I could not be more happy with such a precise, elegant and brief XML library, which includes XPath. This is not a solution for Java or .NET developers, but it could serve as an engine to mimic xmltask or implement the XUpdate specification.
• Excel. Yes, I’ve seen Excel used effectively as the information management front-end to updating the XML. It is a convenient format to share among teams, it is centralized source of information, it can be checked into version control and it can be saved as an XML file itself for processing by another engine. In a large environment, you may have 5 or more server environments, lots of different components to configure, so you could have literally hundreds of parameters to manage. Excel gives you a nicely transparent way to view those values.

I’ve found the multi-threaded capabilities of Mojo and Meister workflows to be very valuable for builds and deployment. The chief benefit I’ve received is in saving time as you might expect. I’ve been working with a workflow that deploys a Java application to up to 24 servers. Let’s ignore the sequential part of the workflow and examine the time difference of running parallel deployments versus one where each of the 24 machines is updated in sequence. The deployment process takes about 5 seconds per machine. Sequentially, that’s 24 x 5 seconds, or 2 minutes. In parallel, well it’s not quite 5 seconds, but closer to about 20 seconds because of limitations of the Linux machine it is running on. Still, that’s a tremendous 100 second savings.

In addition to using the parallel workflow to cater to impatience and improve productivity, I want the Java application to hit all of the servers in the cluster close to the same time. In this particular strategy, only 3 machines out of the 24 are in the cluster. The rest are to support dynamic resource allocation and disaster recovery. Running the deploys in parallel allows me to hit all machines, and therefore all the machines in a cluster at close to the same time without having to figure out some ordering so that the cluster servers are hit first and then the rest. This ends up saving a lot of coding, testing and possibly debugging. Great stuff.

There are times in build management that you need to encrypt something – often a password. In the last blog, I gave an overview of the encryption process. Now, I’ll show how you can accomplish something.

Besides just having an encryption algorithm, there are a number of important details to be minded: key, block management algorithm, initialization vector, binary-to-text encoding. Here is what I ended up doing. The encrypted text ended up in the text field of an element in XML and it was successfully decrypted on the other end in pure Java.

First, you need your basic cipher We’ll use the Rijndael algorithm specified by AES. I used a 128-bit key generated with help from the Crypt::Random module:

use Crypt::Rijndael;

my $base_cipher = Crypt::Rijndael->new(
 $key,
 Crypt::Rijndael::MODE_CBC( )
);

Next you use this cipher within a block algorithm:

use Crypt::CBC;  #--Cipher block chaining

my $block_cipher = Crypt::CBC->new(
  -cipher => $base_cipher,
  -header => 'none',
  -iv    =>    $iv,
  -padding => 'space'
);

Even though the initialization vector, $iv, does not need to be secret, I enjoyed making it “randomy” with the ultra-cool Data::Random module. Also note that the padding strategy, adding spaces, is not binary safe so it works for encrypting text, but not for binary format files. Now you just encrypt:

my $encrypted_raw_binary =
               $block_cipher->encrypt( $plain_text );

use MIME::Base64;

my $encrypted_text_string  = encode_base64(
  $encrypted_raw_binary,
  ''
);

#-- empty 2nd arg means "don't break up long lines"

The last step is necessary to give you something you can easily manipulate as a string to read and write into files.

So that’s it. To decrypt you just do the reverse.

I wanted to pass along what I learned about a new area for me: encryption. I’m working on a build management project for securing Java web services and I’ve enjoyed learning about encryption methods. There are a couple of key concepts to learn and Wikipedia has some informative and entertaining pages. I recommend the “The Code Book” by Simon Singh for a great history of the subject.

One concept strange to newbies is that the encryption algorithm should be widely known and public. The key (**ahem**) is that the encryption key remains secret. If there is a problem discovered with the algorithm, you want to be the first to know. The commonly used algorithms are so robust that there is little advantage to be gained by understanding how they work, as long as the encryption key remains secret.

The U.S. government held a competition for an encryption algorithm to be the Advanced Encryption Standard (AES). The algorithm chosen for this is called Rijndael and it replaced the Data Encryption Algorithm (DEA) of the Data Encryption Standard (DES). The triple form of DEA is still commonly used and is incorrectly but widely known as Triple DES. So yes, the encryption algorithm for the U.S. government’s most top secret data is widely known.

AES specifies not only that Rijndael be used, but that it be used with a 128-bit key. Rijndael also encrypts only 16 bytes. What?! Yes, so basically you have to chop up your message into 16 byte blocks and encrypt each one separately.

If you were encrypting a long message or a lot of messages, you would be encrypting similar words over and over and a lot of your 16 byte blocks might look similar or even identical. This makes you susceptible to a form of frequency analysis attack (see “The Code Book”). So, another algorithm is tacked on to obfuscate the 16 byte blocks after encryption. A commonly used block algorithm, Cipher Block Chaining (CBC), makes the text of a block depend on the encrypted text of the preceding block as well as its own encrypted value. The first block in the message is seeded with an initialization vector (starter value of 16 bytes of text) that interestingly does NOT need to be secret. That doesn’t quite make sense to me, but I trust the experts.

If your message is not exactly a multiple of 16 bytes, you will have to pad it with something that you agree on with the decrypter. The padding characters have implications for what is “binary safe” so be careful. (See Crypt::CBC for a great rundown of commonly used padding techniques.)

The last thing you need to know is that when you encrypt your blocks and obfuscate with a good block algorithm, you end up with raw binary data. I certainly don’t recommend it for XML. This encrypted data, however, is commonly encoded using the MIME format Base 64. This, from the early use.net days, converts raw binary into alphanumeric characters plus ‘-’, ‘=’ and ‘/’. And, yes, that’s 65 characters. You will also need to decide if you will break up the lines with a carriage return after so many characters or not.

So, to get your encrypted value into XML, you can 1) choose a known encryption algorithm, 2) generate a key, 3) use a block management algorithm, 4) decide how to pad your last block, 5) generate an initialization vector (for CBC), and 6) convert it to Base 64 for suitability for text files. To decrypt, do the reverse. Enjoy!

OK, this topic might be a snoozer, but if we’re going to do build management for our RDBMS (Oracle, SQL Server, etc.) in a revolutionary new way, we need to compare what’s going on with database source code changes and builds and compare that with what we already know.

We said that when we make a runtime change to a database, we are only applying changes on top of what we already have, but in J2EE for Java, we replace the entire running application with another instance of the entire application. This is not an incremental deployment in any sense.

If we compare with the case for C/C++, our source code change might result in replacing one of the application’s executables with a new one. OK, this is a more incremental, but maybe I changed one C source file, resulting in one object file change. I still have to rebuild a new executable with possibly many additional unchanged object files.

For both Java and C, traditional build management technologies allow for incremental builds. That means, if I only change a subset of the source code, a build can be done that re-compiles the minimum number of files, taking into account the full impact of each file change. (Many applications have lost the ability to do incremental builds, but Meister can get it back.) So, for Java and C, there should be the ability to do an incremental build, but when that build step is complete, the runtime environment remains unchanged. A separate deployment step needs to happen which is less incremental to some degree.

For the database changes, there is only a single step combining both build and “deployment” and it is always incremental. When you do the build, the runtime environment is changed immediately. So, as I mentioned earlier, there are differences in build management for RDBMS’s and operating system/JVM applications. Again, let that not deter us from bringing those changes under the umbrella of a common build management system.

I’ll also be attending Ignite - Chicago. This is not the music festival, but a series of geeky 5 minute talks with about 20 slides. This is just my style, the length of the talk matching the length of my patience. My talk is entitled “Controlling Java with Perl”, and I’ll be covering many of the same topics related to build management I cover in my blog. I see O’Reilly Publishing’s name on the web page, so maybe they have something to do with it. Already there are 98 people planning to attend. Like the Windy City Hackathon, if you are planning to attend, please drop me a line – I’ll buy you a beer.