How do you get your database schema changes live, on to your production system? As your team of developers and DBAs are working on the changes to the database to support your business-critical applications, how do these updates wend their way through from dev environments, possibly to QA, hopefully through pre-production and eventually to production in a controlled, reliable and repeatable way? In this article, I describe a model we use to try and understand the different stages that customers go through as their database change management processes mature, from the very basic and manual, through to advanced continuous delivery practices. I also provide a simple chart that will help you determine “How mature is our database change management process?”
This process of managing changes to the database – which all of us who have worked in application/database development have had to deal with in one form or another – is sometimes known as Database Change Management (even if we’ve never used the term ourselves). And it’s a difficult process, often painfully so.
Some developers take the approach of “I’ve no idea how my changes get live – I just write the stored procedures and add columns to the tables. It’s someone else’s problem to get this stuff live. I think we’ve got a DBA somewhere who deals with it – I don’t know, I’ve never met him/her”. I know I used to work that way. I worked that way because I assumed that making the updates to production was a trivial task – how hard can it be? Pause the application for half an hour in the middle of the night, copy over the changes to the app and the database, and switch it back on again? Voila!
But somehow it never seemed that easy. And it certainly was never that easy for database changes. Why? Because you can’t just overwrite the old database with the new version. Databases have a state – more specifically 4Tb of critical data built up over the last 12 years of running your business, and if your quick hotfix happened to accidentally delete that 4Tb of data, then you’re “Looking for a new role” pretty quickly after the failed release.
There are a lot of other reasons why a managed database change management process is important for organisations, besides job security, not least:
- Frequency of releases. Many business managers are feeling the pressure to get functionality out to their users sooner, quicker and more reliably. The new book (which I highly recommend) Lean Enterprise by Jez Humble, Barry O’Reilly and Joanne Molesky provides a great discussion on how many enterprises are having to move towards a leaner, more frequent release cycle to maintain their competitive advantage. It’s no longer acceptable to release once per year, leaving your customers waiting all year for changes they desperately need (and expect)
- Auditing and compliance. SOX, HIPAA and other compliance frameworks have demanded that companies implement proper processes for managing changes to their databases, whether managing schema changes, making sure that the data itself is being looked after correctly or other mechanisms that provide an audit trail of changes.
We’ve found, at Red Gate that we have a very wide range of customers using every possible form of database change management imaginable. Everything from “Nothing – I just fix the schema on production from my laptop when things go wrong, and write it down in my notebook” to “A full Continuous Delivery process – any change made by a dev gets checked in and recorded, fully tested (including performance tests) before a (tested) release is made available to our Release Management system, ready for live deployment!”. And everything in between of course.
Because of the vast number of customers using so many different approaches we found ourselves struggling to keep on top of what everyone was doing – struggling to identify patterns in customers’ behavior. This is useful for us, because we want to try and fit the products we have to different needs – different products are relevant to different customers and we waste everyone’s time (most notably, our customers’) if we’re suggesting products that aren’t appropriate for them. If someone visited a sports store, looking to embark on a new fitness program, and the store assistant suggested the latest $10,000 multi-gym, complete with multiple weights mechanisms, dumb-bells, pull-up bars and so on, then he’s likely to lose that customer. All he needed was a pair of running shoes!
To solve this issue – in an attempt to simplify how we understand our customers and our offerings – we built a model. This is a an attempt at trying to classify our customers in to some sort of model or “Customer Maturity Framework” as we rather grandly term it, which somehow simplifies our understanding of what our customers are doing. The great statistician, George Box (amongst other things, the “Box” in the Box-Jenkins time series model) gave us the famous quote:
“Essentially all models are wrong, but some are useful”
We’ve taken this quote to heart – we know it’s a gross over-simplification of the real world of how users work with complex legacy and new database developments. Almost nobody precisely fits in to one of our categories. But we hope it’s useful and interesting.
There are actually a number of similar models that exist for more general application delivery. We’ve found these from ThoughtWorks/Forrester, from InfoQ and others, and initially we tried just taking these models and replacing the word “application” for “database”. However, we hit a problem. From talking to our customers we know that users are far less further down the road of mature database change management than they are for application development. As a simple example, no application developer, who wants to keep his/her job would develop an application for an organisation without source controlling that code. Sure, he/she might not be using an advanced Gitflow branching methodology but they’ll certainly be making sure their code gets managed in a repo somewhere with all the benefits of history, auditing and so on. But this certainly isn’t the case (yet) for the database – a very large segment of the people we speak to have no source control set up for their databases whatsoever, even at the most basic level (for example, keeping change scripts in a source control system somewhere). By the way, if this is you, Red Gate has a great whitepaper here, on the barriers people face getting a source control process implemented at their organisations.
This difference in maturity is the same as you move in to areas such as continuous integration (common amongst app developers, relatively rare for database developers) and automated release management (becoming increasingly common amongst app developers, growing for the database).
So, when we created the model we started from scratch and biased the levels of maturity towards what we actually see amongst our customers. But, what are these stages? And what level are you? The table below describes our definitions for four levels of maturity – Baseline, Beginner, Intermediate and Advanced. As I say, this is a model – you won’t fit any of these categories perfectly, but hopefully one will ring true more than others.
Does this model reflect your team at all? Where are you on this journey? We’d be very interested in knowing how you get on. We’re doing a lot of work at the moment, at Red Gate, trying to help people progress through to continuous delivery for the database as part of DLM. For example, if you’re currently not source controlling your database, then this is a natural next step. If you are already source controlling your database, what about the next steps – continuous integration and automated release management?
To help understand these issues, there’s a DLM library here on Simple-Talk to help you get up and running with the tools you need to progress. We’re also offering training workshops. All feedback is welcome and it would be great to hear where you find yourself on this journey!