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SKP's Java/Java EE Gotchas: Clash of the Titans, C++ vs. Java!

DZone

As a Software Engineer, the mind is trained to seek optimizations in every aspect of development and ooze out every bit of available CPU Resource to deliver a performing application. Recently, I spent some time checking on the Performance (not a very detailed study) of the various programming languages. Ahem, Slow!

Java 214
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Find vulnerabilities in your code—don’t wait for someone to exploit them

Dynatrace

With the increasing amount of sensitive information stored and processed, it’s essential to ensure that systems are secure and protected against potential threats. The deep insights into application code provided by OneAgent® help track potentially vulnerable data flow within an application.

Code 191
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Optimizing Java XPath CPU and memory overhead by 98%

Dynatrace

The system saw up to 800 application requests per second – far more than anticipated. More worrisome was a spike in CPU usage, resulting in severe service disruption as backend processing systems crashed due to the spike in load. Therefore, it was unsurprising to see a huge spike in traffic for Family Visa enrollment via Metrash.

Java 211
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What is infrastructure as code? Discover the basics, benefits, and best practices

Dynatrace

The IT world is rife with jargon — and “as code” is no exception. “As code” means simplifying complex and time-consuming tasks by automating some, or all, of their processes. Today, the composable nature of code enables skilled IT teams to create and customize automated solutions capable of improving efficiency.

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Java Or Scala Performance – Which One Is Better?

Alex Podelko

For years, the debate has raged on regarding which programming language is better, Java or Scala. While some argue that just because Java is older it is better, others believe Scala is better for a variety of reasons. In essence, Java is classified as an object oriented programming language. The Size and Quality of the Code.

Java 100
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Progressive delivery at cloud scale: Optimizing CPU intensive code with Dynatrace

Dynatrace

Our Cluster Performance Engineering Team in collaboration with our Autonomous Cloud Enablement (ACE) and development teams quickly identified the root cause and fixed the problem in no time! And the code-level root cause information is what makes troubleshooting easy for developers. Step 3: Identifying root-cause in code.

Code 238
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Kubernetes Observability: Code Profiling With Flame Graphs

Percona

It shows which code paths are more busy on the CPU in given samples. An example of a flame graph can be found below: Each box is a function in the stack, and wider boxes mean more time the system was busy on CPU on these functions. Flame graphs are a graphical representation of function calls.

Code 106