This page contains my thoughts on various technical topics. Some could probably be expanded into whole blog posts. We'll see if I ever get there.
Before I start giving opinions on programming languages, I want to start by stating that a programming language is simply a tool. In the hands of a master, almost any language can be used effectively. While some languages are more appropriate for certain scenarios, there are always larger issues, such as the understanding and competenence of those surrounding you.
PHP was one of the first programming languages I used on personal projects (as opposed to classroom work). At the time I learned and used it, I only really knew C++. And, coming from C++, PHP was just easier to use. And, when you are a student and want to get things done fast, the choice is easy.
In my PHP days, I wrote a mountain of blog posts about PHP. I was excited about PHP 5 over PHP 4 because it meant increasing the liklihood of better organized code through OO. At the time (and I believe it is still the case), many PHP programs were glorified shell scripts. They include other files haphazzardly. They don't have a unified API or library design. They just felt hacked up. I always tried to be a "good" PHP programmer by utilizing classes, implementing tests through PHPUnit, making source code readable, etc.
After learning other scripting languages (like Perl and Python), I quickly abandoned PHP in favor of them. They offer everything PHP does (and more) and they do it better. About the only thing PHP has/had going for it was its ubuiquity. You could run a PHP program nearly everywhere. But in the corporate world, where I had my own servers, I could do anything I wanted.
My gripes about PHP will likely echo others':
I first ran into Perl when I was very young and my father thought I might enjoy learning how to program. He gave me a print out of a free book on how to learn Perl. My mind could not grok it at the time, so I moved on.
My next encounter with Perl was at an internship in 2006 at Tellme Networks, where it was used extensively. I learned beginner Perl during the internship. And, when I started full-time in early 2007, I picked it up slowly over the course of a year. Of course, any time someone says they learn Perl, what they really mean is "they learned how to use Perl in the way that others at the organization use Perl." I was fortunate that "Tellme Perl" was very readable: lots of modules (as opposed to scripts), lots of tests, usage of Perl::Critic, and strict and warnings were used everywhere. Best of all, there were a lot of great Perl programmers and they loved imparting their knowledge via code reviews and demonstrations.
My favorite aspect of Perl is CPAN. Hands down the best library collection of any programming language. I hate reinventing the wheel. And with CPAN, you often don't need to. Excellent resource. Other likes of Perl include:
Obviously I have technical gripes about Perl:
Lua is my favorite embeddable programming language. I fancy the language because it is small, extremely fast (LuaJIT can rival C/C++), and is minimal.
I often find myself using Lua in C or C++ programs to accomplish tasks that I don't want to code in C or C++. I can write things in a higher-level language without dealing with the complexities of C/C++. Or, if I want the ability to run user-supplied code, Lua is often where I turn to facilitate that. I can create more than 100,000 Lua contexts per second on a modern CPU and each context consumes just a few kB of memory, so it scales very well. And, each context is locked down by default and there are hooks to limit allocations and CPU usage, so it is pretty easy to keep in check, even when running user-supplied code.
Lua isn't perfect (no language is). My gripes typically echo that of the larger community. But, for what it is, it is amazing and I love working with it.
Python is currently my favorite dynamic/scripting language. You can do almost everything in Python. The syntax is highly readable. It has a great set of libraries available. The community is active. Performance is decent. The source code and manual is peppered with Monty Python references. There's very little about Python that I don't like.
Where do I begin with Cassandra? I dated Cassandra for a few months at Xobni, where she served as the sole data storage layer for Xobni Cloud, a cloud-based service for storing email, contact, and personal interaction data. She seemed to get the job done, but it was really rough.
When I first started, Xobni was running Cassandra 0.6.2 or 0.6.3 in EC2 utilizing the ephemeral (built-in) storage on instances (as opposed to EBS). Every time any sort of workload was incurred, disk I/O would spike through the roof and we'd see I/O queue and wait times reach alarming values. Furthermore, processes would OOM frequently.
Over time, we made application improvements to reduce I/O, learned how to tune the JVM, applied Cassandra settings that were appropriate for us, etc. But, every time we made an improvement, Cassandra hit us with a new curveball that left us scratching our heads. It felt as if we were constantly "leveling up" to achieve the title of Cassandra Zen Master, only to have Cassandra fight back every step of the way.
My latest experience with Cassandra was with 0.6.13. If someone were to ask, I would probably recommend not using it. The reasons are explained in the following paragraphs.
My chief complaint against Cassandra stems from complications due to memory management. Cassandra blows through a JVM heap like you wouldn't believe. I once witnessed Cassandra allocating around 4GB/s! At that rate, you will incur many stop-the-world garbage collections (at least with the Sun/Oracle JVM). This can cause peer nodes to mark the paused node as down, which disrupts cluster efficiency. And, if two nodes in the same replication range pause at the same time (at least with replication factor 3), there goes your availability.
While we're speaking of memory management, I don't care for Cassandra's treatment of memory within a garbage-collected language/environment (Java/JVM). There are many parts of Cassandra that iterate over massive amounts of data (via file handles), read the data to an in-memory buffer, do something with it, and disregard the buffer, leaving it up to the JVM to GC it. This leads to rapid heap exhaustion and frequent garbage collections. It would be much better if Cassandra just allocated a single large buffer and reused it. Yes, you incur the memory allocation overhead, but you've probably already done that anyway by defining a fixed JVM heap size. And, I'll trade a little hardware resources for a more consistent system almost always.