Last month, my colleagues Frederick Ryckbosch and Samuel Vandamme faced each other for a fun and geeky OpenShift Commons webinar on proactive performance management of Red Hat OpenShift. Samuel came armed with a set of challenging questions and Frederi…Read more
This is the second blog in a series of three, in which I expand on some of the points raised in Oâ€™Reilly Mediaâ€™s DevOps for Media & Entertainment report. The first post covered the two essential aspects of DevOps that are often overlooked: communication and empathy. Today, we dive into a more technical topic â€“ monitoring.
Monitoring is essential. It tells you if your service is up, down, fast, slow, and functioning as designed. When something inevitably breaks, a monitoring tool can notify you via alerts and help diagnose the problem. An effective monitoring strategy can allow organizations to reap significant benefits including:Read more
A big part of an IT manager’s job is asking, “What if…?” As cloud services become more popular, disaster plans are updated to include contingencies for the moment when the company’s cloud-hosted data resources become unavailable.
Two recent service failures highlight the damage that can result when a cloud platform experiences an outage:Read more
In the previous post, I talked about the issues we had with wanting to run untrusted code and wanting to use Jurassic to do so. The problem is that when the IL code is generated, it is then JITed and run on the machine directly, we have no control over what it is doing. And that is a pretty scary thing to have. A simple mistake causing an infinite loop is going to take down a thread, which requires us to abort it, which means that we are now in a funny state. I donâ€™t like funny states in production systems.
So we were stuck, and then I realized that Jurrasic is actually generating IL for the scripts. If it generates the IL, maybe we can put something in the IL? Indeed we can, here is the relevant PR for Jurrasic code. Here is the most important piece of code here:Read more
RxJava is missing a factory to create an infinite stream of natural numbers. Such a stream is useful, e.g. when you want to assign unique sequence numbers to a possibly infinite stream of events by zipping both of them:
In manufacturing, getting to an ROI is pretty cut and dry. What are the cost of the materials, how many products will that yield, and what can we sell it for? Calculating the value of a collaborative culture and a strong peer review practice isnâ€™t quite as intuitive.
When bugs slip through the cracks, it isnâ€™t just expensive; itâ€™s a risk to your entire business. The stakes are high, to say the least. Organizations clearly save money if their development teams can identify and fix bugs before they reach the customerâ€™s hands. We can all agree to that, but how much do they save? Do you have to wait for a disaster to get an understanding of the cost of inadequate reviewing? Before getting to an explicit value, letâ€™s first consider the scope.Read more
Todayâ€™s world has recently taken up an increased focus on machine learning â€” and with data scientists/data miners/predictive modellers/*whatever new job term may emerge* operating at the cutting-edge of technology, it cannot be forgotten that machine learning needs to be implemented in such a way to aid in the solution of real business problems.
In day-to-day machine learning (ML) and the quest to deploy the knowledge gained, we typically encounter these three main problems (but note that these are not the only ones).Read more
Data science is one of the hottest areas in computing today. Most people learn data science using either Python or R. Both are excellent languages for crunching and analyzing data.
But many Java developers feel left behind. There are great Java librari…
If you ever used Mule 3, then there are probably two things about error handling you already know:
- Itâ€™s really Java exception handling.
- Itâ€™s a â€œtrial and errorâ€� experience.
In this post, Iâ€™ll explain the major changes introduced in around error handling, including easier routing and the introduction of our new try scope.Read more
Even the smallest IoT device lives in a complex environment, which may not be fully understood at the time of development. In fact, we have already seen the security problems associated with devices being connected to the Internet for the first time. In a previous post, we discussed the benefits of service orientation for design, development, and testing. In this post, we’ll take service-based testing and service virtualization to the next step â€“ virtual labs. Building a realistic physical testing lab environment is difficult, and even when complete, it becomes the main bottleneck in system testing. Virtual labs remove this bottleneck while providing new benefits to service-based IoT device testing.
Many IoT Devices Are Not Ready for Primetime
A recent study found that 80 percent of IoT apps are not being tested for security flaws. The Barr Group found that 56% of embedded device developers donâ€™t review source code for security vulnerabilities and 37% donâ€™t have a written coding standard. These are not encouraging statistics, and itâ€™s clear that IoT device manufacturers need to take quality, safety, and security more seriously. Test automation is one important step in order to make sure testing is being done more rigorously, consistently, and thoroughly. Testing, especially for security vulnerabilities, is often seen as too costly and complex, and is therefore rushed or overlooked altogether. But itâ€™s an expensive mistake to let your customers (and attackers) test your IoT device security for you.Read more