Disruptive changes fuel innovation, innovation creates disruptive changes

-

As a developer who works extensively both ruby and java, I'm amazed at the turmoil in the ruby/rails space relative to java.  In the last few years, ruby and rails have had numerous massively headache inspiring incompatible changes that cause developers who used to know what they're doing to realize they're doing it the "old fashioned" way.

A particularly entertaining example from recent history is the handling of scopes in active record.  Not only has this changed from Rails 2.x to Rails 3.0, it's getting further changed in Rails 3.1 (in an incompatible way). I will agree that these are good changes, but they are certainly an effect of not spending time to think things through the first time and cause problems for newbies and old hats alike.

Compared with java  based projects (anyone still remember moving from hibernate 2.x to hibernate 3.0?) this rate of change is mind numbing and can be pretty frustrating. But, compared with the amount of innovation in java, ruby and rails are so far ahead of the game for high productivity/startup type application that java isn't really even a competitor.

I think the disruptive change in the ruby/rails space is what is actually part of what fuels the innovation, so while it's pretty shocking and can be frustrating, I think it can also be a competitive advantage. Moreover, the innovation fuels change, which fuels more innovation... so it's a self sustaining effect. In general, the more chaotic and random things are, the more opportunities there are for new game-changing innovation. The more game-changing innovation that happens, the more chaotic and random things are...

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 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

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