Recently some of my fellow Perconians and I have noticed a bit of an uptick in customer cases featuring a particular MySQL error message...
I have been actively using Java Management Extensions (JMX), particularly within web applications, in order to monitor application internals and sometimes tune some parameters at runtime.
While there are a good number of high performance messaging systems available for Java, most avoid quoting benchmarks which include durable messaging and serialization/deserialization of messages.
One of the core benefits of a service oriented approach is the promise of greatly enhanced scalability and redundancy. But to realise these benefits we have to write our services to be ‘scalable’. What does this mean?
Modern web applications provide a lot of possibilities for bandwidth optimizations. I’m not going into too much details here but rather I’d like to quickly show how you can test your application under potentially poor network conditions.
MVB Tim Spann cooks up a list of useful Spring & Hibernate links and resources!
I turn on debugging to trace what’s going on. All looks good but one email bounces back after a day and some hotmail and gmails just vanish.
I am working on a Spring MVC app that demonstrates all of the different MongoDB Java APIs.
The benchmark is available on GitHub, together with article source. I encourage you to run it on your setup.
This video is presented by Steve Souders, who works on web performance and open source initiatives at Google.
Join Richard Campbell as he opens up his web performance tuning toolkit and walks you through ten different techniques for improving web performance, rating each by difficulty, risk and reward. You will learn about a variety of techniques for reducing payload size, latency, server and client compute times.
Today I've found a small nifty trick that may become helpful when exploiting SQL injection vulnerabilities for MySQL. Namely, you can abuse MySQL string typecasting.
As I mentioned in my previous post I’ve been playing around with numpy and I wanted to get the values of a collection of different indices in a 2D array.
In this keynote presentation from HTML5DevConf, Steve Souders, Google's Head Performance Engineer, clarifies the problem of caching, and explores various solutions to make your web app lightning fast.
Brian Ford presents from JSConf2012 in Scottsdale Arizona, "Is Node.js Better?"
I have found that the biggest obstacle I face when adopting a new language, technology, or framework is using something I don't fully understand how to implement myself.
What PaaS performance metrics are you using to measure the success of your Cloud platform initiative? Adopting a few PaaS performance metrics can help you avoid the high-expectations, low-benefit trap that befalls many IT transformation initiatives.
I recently wrote about an implementation of the Bellman Ford shortest path algorithm. I came across a suggestion that the algorithm would run much more quickly if we used vectorization with numpy rather than nested for loops.
I’ve been playing around with numpy this evening in an attempt to improve the performance of a Travelling Salesman Problem implementation and I wanted to get every value in a specific column of a 2D array.
So you would do the painful, slow job of writing C++, even with Rational Rose UML to help guide you, it was painful. Now Java, Node.Js and the rest have a ton of free libraries for everything.
Now I am aware of the HTTPtunnelers and other tools out there that could be used for this, but to find one that works according to our demands and to find out how it works, we decided it would easier/quicker to create a Filter and use that for our needs.
For months I have been busy implementing a web-service-proxy by using the Mule ESB CE. Although it took some work to get it setup nicely it is working well now. In this post I will show you the result