Continuous integration versus delayed integration

-

A vigorous area of debate in the development and architecture community exists around the value of Continuous Integration. To give context, when a software development team gets to a certain amount of concurrent work that involves multiple teams making changes in the same codebase, there are two main ways to handle this:

  • Each individual stream of work take a copy of the code and start working on their own
  • Each team coordinate all work streams within the same codebase

In my experience, the former method seems like a great idea, especially when folks start worrying about "what happens when project A impacts project B's timeline?". Everybody just works on your own feature and at the end of each team's work stream, just merge all the individual team's efforts back together in a shared location. The great advantage here is that if Feature A is pulled from the delivery, Feature B can be deployed independently. What is frequently overlooked is the fact that eventually, Features A and B still need to be mixed together. The longer we wait to do this, the greater the risk of integration problems and the less time we have to resolve them.

On the other hand, having everybody pushing work into the same development stream immediately introduces some problems that need to be resolved at build time. It's immediately necessary to have a process to allow reconciling competing Features and disable features should it not be complete before delivery. The major downside continuous (or at least frequent) integration is that individual developers and Feature teams are a bit slower day to day as they uncover unanticipated problems due to competing requirements in the various work streams, but the upside is that these are discovered early in the process instead of at a very late stage.

In short, delayed integration doesn't actually save any time in the long run as the will eventually still manifest themselves, just later in the process when you have less time to fix them. To all folks doing multi-stream development, seriously investigate limiting your work to "one branch per environment", and work out how to independently configure the environments to know which features they should enable.

Comments

Post a Comment

Popular posts from this blog

Please use ANSI-92 SQL Join Syntax

-
Image
Way back in 1992, a standard was published for SQL. In it, the syntax for specifying joins in SQL Database was finally standardized. This was a good thing because (for folks who've been doing this a while), trying to decode the vagaries of how various Databases handled outer joins could drive you bonkers yielding different results depending on the parse order of things in the from and where clauses. Unfortunately, some DBMS vendors took a while to adopt this standard (Oracle didn't support it until 2001), but at this point if you're using a major RDBMS and NOT using it, you're probably mired in a tradition or habit that bears changing. As an example of what I'm describing, here are some examples of what I mean. Old School (no standard syntax + implementation and evaluation of predicates varied by vendor [and query]): select * from customer, order where customer.customer = order.customerid ANSI way (agreed to standard, predicate evaluation has some rules ...
Read more

The difference between Scalability, Performance, Efficiency, and Concurrency explained

-
I thought I'd give some explanation of the difference between scalability, performance, Efficiency, and concurrency. To make things more accessible (and because I'm watching a cooking show on TV) I'm going to use a baking metaphor to help explain the differences. In our case, the system in question is baking cookies for consumers. Scalability, is how well something responds to adding more resources. If we can double our capacity by doubling the number of resources, we've got linear scalability. In our baking example, if we add more ingredients, we can scale to a larger number of cookies. To contrast with our other metrics, this doesn't actually increase the performance of creating a single cookie (they still take 10 minutes to bake), and the efficiency is the same (we just have more capacity). We also cannot bake more cookies in a fixed time period, but over a larger time period we can produce more cookies. Performance is how fast something can get done, u...
Read more

the myth of asynchronous JDBC

-
Image
I keep seeing people (especially in the scala/typesafe world) posting about async jdbc libraries. STOP IT! Under the current APIs, async JDBC belongs in a realm with Unicorns, Tiger Squirrels, and 8' spiders. While you might be able to move the blocking operations and queue requests and keep your "main" worker threads from blocking, jdbc is synchronous. At some point, somewhere, there's going to be a thread blocking waiting for a response. It's frustrating to see so many folks hyping this and muddying the waters. Unless you write your own client for a dbms and have a dbms that can multiplex calls over a single connection (or using some other strategy to enable this capability) db access is going to block. It's not impossible to make the calls completely async, but nobody's built it yet. Yes, I know ajdbc is taking a stab at this capability, but even IT uses a thread pool for the blocking calls (be default). Someday we'll have async databa...
Read more