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!

I’ve been considering the management of our services code under Git. It seems that the support of the distributed development model fits perfectly with sharing and developing code, mostly Perl, among multiple sites (consultants and/or customers). It allows us to keep a primary repository under our own control, but it also allows an on-site consultant to clone a repository and either enhance or customize or both while on site. After the consultant leaves, the customer would be able to choose to receive updates from our on-line repository on GitHub, for example, or not. They could also contribute enhancements, or not, and we can decide if we want to accept any changes they pushed, or not, or futz around with them first.

A consultant could make both enhancements and customizations and as long as they are in separate commits, we can cherry-pick the enhancement commits into our master branch. Pretty cool stuff.

Some of our customers have strict controls over what executables they allow to be installed on their machines, and they may not allow the Git executable client. However, one can clone a repository onto a USB drive and make modifications to the work tree there. This would appear no different than editing files outside of version control. After the edits are done, the USB key can be returned to a machine with a Git client, the changes added and then committed to the repository on the USB key. Those changes in turn could then be pushed to the on-line repository. So, a sort of open source development could be done without violating the customer’s security policies.

With the Web 2.0 evolution, information flow between people has changed from a ‘push’ paradigm (I send you an email) to a pull paradigm (I follow you on Twitter). How could this possibly relate to code management such as branching, merging and history? Well, Git’s distributed repository model and how one obtains code updates from “friend” repositories is similar to Twitter and how you obtain status updates on the people you choose to follow. Instead of communicating micro-blog entries or status updates, Git is communicating source code branch updates.

Also like how Facebook or Twitter allows you to specify a person’s name in lieu of the communication protocol identifier (email address or web page), Git uses aliases for long repository locations so you have a more direct, natural language and human feel to what you are doing: “git fetch linus” will pull changes from Linus’ repository, which you have only had to define once.

Here is a scenario where Steve and I are working on a part of the Linux file system to provide information useful for build management and dependency tracking, which Meister and other tools can take advantage of. Steve started by cloning the master Linux repository and started working away making changes. Steve asked me to work on another part of this project, so I cloned his repository, allowing me to pick up all his changes. I am now automatically following (Git calls it remote-tracking) Steve’s “master” branch of his repository since I started my repository by cloning his. The “master” branch is a.k.a. the “trunk” code stream. I can pick up his updates periodically with:

Now, I may also want to get updates directly from the master Linux repository, but it has a complicated URL that I won’t remember and only want to look up once. So, as a one-time command I do:

Forever after:

The “fetch” command doesn’t put the master Linux changes directly into my workspace, but off to the side for me to examine first (very nice). If I want, I can accept the changes into my local work tree. To tell me which repositories I am following (which friends), I do:

“origin/master” is my own trunk. I could also get the full repository information associated with the short names, but as long as it works, I don’t want to know what it is. For me, this type of friendly and fluid interaction with repositories is one of the major advantages over CVS and Subversion.

I’m now getting about 100 spam comments a week, and so I have to require registration in order to leave comments. A few of you have asked questions to me and this will ensure I can get back to you as well.

So, until I find a new spam filter plug-in for Word Press (and figure out how to use Word Press) I’ll have to require registration.

Thanks,

Sean

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.

I’ve been using and experimenting with Facebook, LinkedIn, All About Wine, Ancestry.com and Plaxo online social networking. I am confident I will be a user of online social networks as long as I can type, and hopefully longer. Here are the things I like best about using them:

Enables Greater Social Interaction

This is especially true with people that you would not socialize with frequently such as people you don’t see in the office every day. My social interaction with these people went from ‘never’ to ’sometimes’. This includes those of my friends and relatives who are a great physical distance away. As people move, my laptops explode, and email clients change, I lose track of people’s email addresses, so I miss out on sending a holiday greeting or the latest round of baby pictures.

Between Facebook and LinkedIn, I pretty much have all of my high school and college friends somewhere and that can be beneficial professionally as well as socially.

Saves Time

Some of my friends say that Facebook uses up too much of their time, but I find that Facebook in particular saves me a lot of time. I can click on anyone’s face in the Entourage application and send them a quick note. Or, I can take advantage of Facebook’s slick auto-complete where I only need to type one or two characters to uniquely identify someone. Time savings would be #1 on my list if it were not for the fact that this greater facilitation leads me to slightly more interaction. I do not consider the greater interaction to be a distraction, but it in fact slightly improves my overall quality of life.

LinkedIn also saves me time professionally. Forwarding someone’s LinkedIn profile is much faster than attaching a resume document and writing an introductory email.

Facilitates Special Interests

Facebook also helps me share my interest in less popular topics with like-minded enthusiasts. I can easily send soccer highlight videos or punk rock news to those few friends of mine who are interested. While I could create an email list to do the same, Facebook makes it far simpler and much faster to manage these types of lists. Or, I can post something, and those who spot the post on their mini-feed and are interested can click on it to learn more.

I suppose baby pictures fall into this category. Facebook in particular has awesome picture sharing functionality. The ability to ‘tag’ people in photos and have Facebook collect them is priceless. For example, I get can all the pictures taken by many different people where I am tagged and Facebook will collect them for me into a single slide show.

The first rule for Bash/C/Korn shell scripts in a Perl program environment is to re-write them all in Perl. If your Perl environment has any sophistication, you will have common code, standardized logging (perhaps with Log::Log4perl), testing with Test::More, etc. and your shell scripts just can’t keep pace.

If you share the environment with any non-Perl applications, however, you will still have to deal with the environment profile(s). I also have some legacy shell scripts that we can’t justify converting to Perl unless they have another reason to change. (Don’t change tested code in my house ~~ head bobble + finger wave ~~, nuh-uh!)

There are two ways I know of that you can extend the benefits of your Perl implementation towards your legacy and profile shell scripts. The first is through Bahut’s excellent tip on embedding POD documentation in shell script. This solves my problem of generating HTML documentation from POD in Perl scripts and having upsetting holes where the shell scripts are. I also have some controls for the Perl scripts that run podchecker before committing to version control, which fails if no documentation is found. Now, I can extend this control to the shell scripts.

The second Perl tool you can extend is the testing functionality. I’ve found the functionality in Test::More to be useful for validating that the changes to the shell environment profiles are correct and do not introduce defects. Profiles can be notoriously tricky to change when they get fat and you have variables depending on other variables. Mostly the profiles in my case are used to set environment variables that control the version control and build system, and these can be easily validated in a test script called profiles.t via checks like:

ok( $ENV{CODE_ROOT} eq ‘/opt/code’, “CODE_ROOT set to ‘/opt/code’”);

You then just rattle off tests for all the variables that are set and you have a great way to validate that everything will still work after the profile change. For a legacy script, you may not be able to have a crack at the internals, but you can at least check the return code and maybe some external effect it has somewhere, such as a file timestamp change.

eval { `legacy_script.sh`};

ok( !$?, “legacy_script”); #– $? is zero if script executes successfully

Profile.t and any other test scripts used to test legacy shell code can be bundled with all the other Perl tests via Test::Harness for a single test suite that really tests everything shell and Perl.

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.

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 worked out a good system putting Meister build management metadata under version control. Why put it under version control when you can do a backup, you may ask? Well, there are times when you may want to develop a reusable workflow or update a Java build method script and test in an isolated test environment. For this you would use a separate test instance of the Meister server. Putting at least some of these files under control will help ensure that you move the known version of your tested server metadata file into your production environment.

There are a few other challenges to be aware of. You may want projects and dependency directories to be able to be created an edited in production while you simultaneously modify a workflow, for example. You don’t want to overwrite a dependency directory definition in production with an older one you are migrating from the test environment.

I’ve got a few other challenges as I’m using CA Harvest for version control and its excellent control over changes extends version locking to the file system by removing write access to files that are not locked by someone for change. Meister requires that most metadata files be writable, so if you check in files to CA Harvest directly, you will leave Meister metadata files read-only and that will cause problems.

So, here are a few tips from my now slick Meister metadata version control system:

• Use the …/meister/kbserver/tomcat/webapps/openmake.ear/openmake.war/ directory as the root of the file tree under version control. Don’t version anything outside of that tree, but do version everything under it for a simple boundary to your project.
• Before checking in/committing changes, copy files from the Meister runtime directory to an alternate workspace or temporary directory tree. Check in/commit from there, not the Meister runtime directory. This ensures that you don’t write into the runtime environment, possibly corrupting something.
• Before taking updates from version control, check them out to an alternate workspace, reference directory or other temporary directory. This is again to avoid corrupting the runtime environment. For CA Harvest, you can use the automated reference directories to copy from. Be careful not to preserve the read-only attribute when you copy from there. I used File::Copy::Recursive and its option not to preserve permissions for a simple, streamlined copy.
• Manage the check in’s and deploys (copy’s) according to the major subdivisions of the Meister metadata. My check in and deploy commands work by taking a directory relative to the openmake.war/ directory as an argument.
• Use the awesome Perl SCM project by yours truly to reduce your coding by 90% if you are using CA Harvest. CA Harvest also requires a number of other steps, such as creating a change package, locking files you plan to change to the package before checking them in, and breaking up the locking and check in commands into manageable chunks. The Perl SCM project helps with all that.
• If you can manage your changes incrementally, all the better. OpenMake Software’s HarRefresh and CA Harvest’s HRefresh manage reference directories with incremental changes. This gives you a workspace or reference directory with only the changes relative to a baseline, not the full baseline. This means if you changed only one workflow, then it is easy to copy just the one changed file to the production environment, instead of the whole baseline with only one changed file. You will have a very simple and transparent production change procedure with this – very nice.

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!

The majority of the time database changes (regardless of whether you are using Oracle, SQLServer, Sybase, DB2, MySQL or something else) coincide with application changes in a typical business application. For example, an application change request indicates that the Java application needs to start using a new column in an existing database. So, in order for the new version of the application to function correctly, you need to alter the existing database table to add the new column. There are some fundamental differences in how the two changes are manifested.

For the Java application, deployed to WebSphere, JBoss or other app server, let’s say you started out with a single Java class, foo.java, and this application change requires you to add a second class, bar.java. Typically, I would recompile both classes and bundle them together in an archive (a ZIP file) and ship the archive out to the runtime environment. This has the effect of replacing foo.java, whether or not it has changed along with adding bar.java. For the database change, however, you can only apply the ALTER statement to add the column to the table. You do not re-issue a command to recreate that table. Even if you issued commands to drop the table and recreate it, before applying the ALTER, you would lose all the data in the table (unless you dumped the data before hand and re-imported it).

Following J2EE standards for Java development, the whole application is completely replaced every single time it is changed, while for the database, only the changes needed are applied to the existing configuration. In other language, for Java, the existing runtime configuration is completely replaced, while for the database only a configuration delta is applied.

So, there are some significant differences between Java and database changes, but that doesn’t stop you from managing database changes effectively.

In many ways, databases are runtime systems comparable to other programmable environments such as operating systems and application servers based on Java virtual machines. All environments typically have their own specialty engineering support teams in larger companies and their own preferred programming languages for making changes: Java for application server environments such as WebSphere and JBoss, C/C++/.NET for the Linux, UNIX and Windows operating systems; and, PL/SQL/DDL for database changes.

While there are all sorts of tools that allow directly changing databases through an IDE (Interactive Development Environment), and even versioning changes, one trend has become clear in corporate environments over the last decade. That is the desire to manage database changes within their existing change and configuration management infrastructure. Going forward in this blog, I’ll address some of the challenges both organizationally and technically with doing this. The good news is that, yes it can be and is being done successfully. I will also address how Meister contributes to database change control and integrates database changes with the organization’s overall build management practice.

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.

I’ll be attending the Windy City Hackathon in Chicago on Saturday, December 15. The Hackathon is for Perl programmers to get together and help each other solve problems, learn new techniques and get to know one another. The full Hackathon lasts from Friday the 14^th through Sunday, the 16^th. This is yet another great benefit of the Perl community (YAGBPC). If you didn’t get the parenthetical humor, then you are not close enough to the Perl community and you need to attend.

If you will be attending, drop me a line – I’d love to meet you.