Development Team Best Practices


I wanted to put together some thoughts on high level best practices for your dev shop.  These practices will skew towards larger dev shops inside of big corporations but can also be applied to 1-2 man teams.  These thoughts are inspired by a combination of The Pragmatic Programmer and The Joel Test.  I read both of these sources early in my career and they have continuously provided inspiration on what kinds of practices highly effective dev team should follow.  Futhermore I’ve found it surprising how many shops I’ve worked with that break these practices.  Following these practices will increase your dev team’s effectiveness, making for happy developers and manager.  Breaking these practices will result in a slower development velocity and frustrated developers and managers.

  • Streamline your local build process.  

Do you think your build process is pretty fast?  It could be faster.  If it takes more than a few seconds to redeploy your code you lose your train of thought – and there is a cost to putting that train back on the rails.  As someone who has spent a lot of time doing Enterprise Java development, it often amazes me what developers will put up with in their build processes.   Here are a couple of tips to decrease build time:

    • Your environment probably supports some sort of hot swap method where you don’t need to do a full build after every change.  Java’s JVM Hot Swap feature allows this for some cases.  I’ve also heard very good things about JRebel.
    • If you have a medium to large sized application, you probably don’t need to rebuild the whole thing all of the time.  Make sure you’re not rebuilding more components than necessary.  Here are some kinds of things I’ve seen automatically included in a full build that could be excluded from a “fast” version of your build: code generated from wsdl/xsd, database migrations, unit test suite execution, minification/obsfucation of js/css.  In Java, the war/ear step will package all of your files into a war/ear.  When you deploy that war/ear to your application server it will unpackage all of the files.  You can eliminate a step by just copying your war/ear directory directly into your application server.
  • Use a debugger.  

Do you test your application by inserting new log statements and redeploying?  Stop it!  One of the best ways to cut down on redeploy time is to not have to redeploy.  Every single commercially used language supports debugging.  Here are a few links to get you started using a debugger with your language

  • Automate your QA builds and minimize QA downtime

Your QA build process should be quick and painless.  I would shoot for a combination of automated QA builds (2 or more times per day) and one-click, on-demand deployments.  The outage to a QA environment needs to be small – 5 minutes or less – to keep the deployments painless enough to not disrupt the workflow of the QA staff.

Doing frequent, automatic QA builds does a couple of great things for your IT organization- it sets a precedent that your team has the ability to quickly turn around fixes.  It also minimizes the impact of bugs in your QA process;  After all, if your team finds a bug, it’s not a huge deal since the bug might be resolved in a few hours.

Contrast this approach with an organization I recently worked with that only did two QA builds per week because their build process took so painfully long to complete.  Every bug became a news headline.  The management team would often choose not to fix bugs because of the huge turnaround time and the risk of additional lost time in case a bug fix was not successful.

  • Treat database schema updates like source control

 SQL is code, and your database structure is the product of that code.  In the same way that every developer working on a project needs to be able to run the software on their local system, those developers also need to be able to run their own copies of the database.  Database changes should be part of your normal build process, and like your code, your database schema needs a version so you know which changes have been applied.  This helps keep your development, test, and production environments all in a sane state.

It’s easy to fall into the trap where a database is very difficult to create so you end up running one development database for all of your developers to share.  The problem comes when you need to support multiple work streams at the same time – this means you need to simultaneously run multiple versions of the schema at the same time.  You can work around this problem by adding more schemas and more hardware, but the overhead involved may make it really difficult for developers to do the kind of risk taking and rapid iteration required to reach a high velocity.

I recommend using flyway or liquibase to manage database changes for most technology stacks.  Rails has built in support with Active Record Migrations.

 That’s all for now.  Please send feedback in the comments if this post has helped provide value for your team, or even if it seems too rudimentary.  I’d be happy to do a deeper dive on any one of these topics if there is interest.

Full Text Search for the enterprise with Oracle Text

You work in software and your stack includes an Oracle database.  One day the business approaches you and says ‘I want a search page for our product/order/customer data.  Make it work like Google’.  You think to yourself, “If I could make a search page work like Google I would work at Google”!!!  Fear not, developer.  This problem has been solved many times in the past.  In this blog post I’m going to show you how to approach this problem, and show you a shortcut in case your environment’s stack includes an Oracle database.

Approaching Full Text Search

The problem you’re solving has a name and that name is Full Text Search.  The problem is that your Relational database, while presumably well normalized, is not good at searching for single words across huge data sets.  You need a different kind of database which is optimized for full text search.  A Search database will physically store the data differently so that it can quickly look up your search terms and return some metadata associated with those terms.  In your RDMBS, records are identified by keys.  In your Search index, they keys are the search terms.

There are several well known full text search solutions.  The bare minimum list you should probably know about is Solr/LuceneSphinx, and ElasticSearch.  These are all great full text search solutions, but they all require a lot of overhead to operate.  New servers, new software to install, new syntaxes to learn, admin consoles, and new interfaces or libraries to build into your front end application.

Oracle Stack Solution: Oracle Text

One drawback of each of the aforementioned search solutions is that you will likely want to run it on a dedicated machine (or VM).  If you work in an Oracle shop it likely means that you work in an enterprise where provisioning hardware (even virtual hardware) can be annoyingly difficult and time-consuming process.   I this environment, Oracle Text jumps out as a really nice solution.  Oracle Text is a full text search solution that is built in to all modern version of Oracle’s database.  This means that you don’t have to request a new machine, and request for new software to be installed on that machine in each of your QA and Production environments (or request for root access to do it yourself).  With Oracle Text you just run some DDL to create the index and start using it!*  The only hardware issue you should consider is the amount of disk in use on your Oracle database.

Here’s a simple example of how to take advantage of an Oracle Text search index.  Let’s assume that I have a database with products and reviews (a product has many reviews) and I want to be able to return search results for both at once.

The most straight-forward way to start is to gather all of the data you want to index into a single VARCHAR2 column named SEARCH_TEXT on our PRODUCT table.  If you need to index more than 4000 characters, use a CLOB.

alter table PRODUCTS add SEARCH_TEXT varchar2(4000);

Now we need to populate that column with the search data we want to index from the PRODUCTS and REVIEWS tables  We are going to fetch the data into the search text column as a big space delimited string.  The below query is called a correlated update, and is specific to Oracle.   You can accomplish the same thing with a procedure but I find this more concise.

 select ||' '|| P.description ||' '||R.title ||' '||R.review_text
 where P.ID = R.PRODUCT_ID
 ) where = P.ID;

Next we create Oracle Text index on that column.  The important part is the ctxsys.context at the end of this statement.  Context is one of the three types of text indexes that oracle offers, but the best one for blocks of structured text.

 indextype is ctxsys.context;

It is worth noting that you can configure the index to use a separate tablespace so that you can control where on the disk your index lives.  See the docs for more info.

Next we we run a command to ‘sync‘ the index.  This actually indexes the data for the first time.  Run it again after you’ve inserted or updated data to update the index.  In fact, you should  plan on running this command periodically as part of a dbms_scheduler or whatever your enterprise’s favorite scheduler is.


Now we can run a full text search query and see some results.  A statement like this will return all product records which have the word ‘paper’ in the title, description, or reviews. yay!  It’s pretty awesome that we can run searches on this index in our existing RDBMS and apply whatever filters, sorts, and joins we want without having to call out to another system.

select * from PRODUCTS where contains(SEARCH_TEXT, 'paper') > 0;

Finally, we create a job to periodically ‘optimize‘ the index.  According to the docs your index gets fragmented and slower over time and this will fix it up.  I’ve had luck with running this nightly but YMMV.

ctx_ddl.optimize_index(PRODUCT_REVIEW_SEARCH_IDX, 'FULL');

After you’ve got your index up and running you can get some useful info and stats out of it with the CTX_REPORTS package.  Among other things it will tell you how fragmented your index is, and what words are the most frequently indexed.

I’ve really just scratched the surface to show you how to get a text index up and running fast.  Oracle has a ton of options to tune the index, and search features like fuzzy searching, stemming, and wildcards.

*Ok, maybe you should still consult a DBA first if you have access to one.



Performance Optimization: Doing Science

I couldn’t hold a candle to Brian Green on such topics as Quantum Entaglement, Higgs Boson, or Grand Unified Theory (despite obtaining a B.A. in Physics), however I can apply the scientific method to improving the performance of your software.

The Scientific Method (

In this article I will explain a basic, but often overlooked foundation for improving the performance of any software application.

Much of software development is an art, but performance tuning is a science.  I’ve seen a lot of good developers waste time significant amounts of time on performance with little to show for it, or just as bad, improve performance without knowing exactly which change had the desired effect.

Do you remember talking about the Scientific Method from your high school science class?  The diagram on the right is a refresher.  The scientific method is the repeatable process on which all scientific exploration is based.  It gives scientists across the world a common language and framework to compare the process and outcomes of experiments.

The scientific process provides a few of important points that can be applied to software performance optimization:

  1. Repeatable process – use the same process for every performance enhancement you make
  2. Only modify one variable at a time – Do not make multiple tweaks at the same time.
  3. Record the results of each optimization.  Track what you did and how much it helped.
Performance Optimization Method (

This sounds simple right?  It is.  The tough part for software developers is to never break these rules during a round of optimizations.  To the right I’ve also included a more detailed diagram of what the scientific process looks like when applied to performance optimization.  Let’s call it the Performance Optimization Method.

But I know what I’m doing!  Why shouldn’t I make multiple tweaks at once?

Lets say you do make two changes at once.  You optimize two queries and drop the page load time from 3s to .1s.  Do you know how much relative impact the changes had?  Did each change reduce the cost by the same amount (50%/50%)?  Did one query account for most of the cost (75%/25%)?  Or did one of the changes not even have any impact (100%/0%)?  What if the two changes were somehow interdependent?  For the most part these questions are impossible to answer unless you use a repeatable process and only modify one variable at a time.  There are exceptions *(there are always exceptions.  If you have a good profiling tool that tells you exactly what two different method calls cost and you are absolutely sure they are not somehow related then you could cut a corner and make multiple changes at once.  If the results do not turn out as expected you still need to go back and make the changes one at a time).  By the way, I hope you are testing against a volume of data you expect in production.

Don’t forget to record the result of each optimization.  This way you can throw your results into a table, and with a little explanation about the process and results you turn it into a report and send it to management so they can see how you’re spending their budget (and how good you are at science).  Having these sorts of metrics reports also makes it easy for stakeholders to justify the time spent on performance optimization activities.

The law of diminishing returns applies to performance enhancements.  At some point you will have picked all of the low-hanging fruit and enhancements start to get progressively more expensive.  Stakeholders need insight into how this is progressing on your project so they can make decisions on how much more to spend on performance.  Metrics reports should provide sufficient detail for stakeholders to make those decisions.

Ultimately you will end up with a faster application and a clear story of how you got there.  Isn’t science fun?

Configuring Jetty, Maven, and Eclipse together with Hot Swap

For over a year I’ve been developing a Java webapp in Hibernate with maven and Jetty.  Recently I’ve figure out how to make them all play nice with each other.  For too long I had to restart my application server, which takes upwards of 45 seconds, for any code changes to make it to my development server.  This tutorial will show you how to setup Jetty in embedded mode, and using Eclipse, attach a debugger to enable True Hot Swap of code onto your Jetty server.

Environment Information:

JDK 1.5+
Eclipse 3.4.0
maven 2.0.10
m2eclipse 0.9.7 (maven plugin for eclipse)
Jetty 6.1.10

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