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Java Parallel GC Tuning

DZone

Parallel garbage collector (Parallel GC) is one of the oldest Garbage Collection algorithms introduced in JVM to leverage the processing power of modern multi-core systems. In this article, we will delve into the realm of Parallel GC tuning specifically.

Tuning 243
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Transform mainframe applications into z/OS Java services with end-to-end transaction visibility and anomaly detection (Preview)

Dynatrace

Business, finance, and administrative applications written in the common business-oriented language (COBOL) have run tirelessly on IBM Z systems since the early 1960s. COBOL applications must be processed on expensive general processors, while Java applications are eligible to run on IBM Z specialty processors such as zIIPs.

Java 174
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Mastering MongoDB® Timeout Settings

Scalegrid

MongoDB’s Java and Ruby drivers have this parameter set to 10 seconds by default – if there is no established connection or a new link within that time frame then it gives up on trying. x+ in Java). We have provided some sample code below to illustrate the timeout configuration in Java and Ruby.

Java 130
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Dynatrace Application Security detects and blocks attacks automatically in real-time

Dynatrace

Compared to intrusion detection systems (IDS/IPS), WAFs are focused on the application traffic. For most enterprises, using a RASP solution would mean running multiple agents on their production systems, potentially creating risk due to incompatibilities. WAFs protect the network perimeter and monitor, filter, or block HTTP traffic.

Traffic 228
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A guide to Autonomous Performance Optimization

Dynatrace

If you want to see a more hands-on approach, I encourage you to watch the recording as Stefano did a live demo of Akamas’s integration with Dynatrace, showing how to minimize the footprint of a Java application with automated JVM tuning. Automation is the key to optimizing our systems. via Cloud APIs or plain simple SSH).

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

which is difficult when troubleshooting distributed systems. Troubleshooting a session in Edgar When we started building Edgar four years ago, there were very few open-source distributed tracing systems that satisfied our needs. We chose Open-Zipkin because it had better integrations with our Spring Boot based Java runtime environment.

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Kubernetes Observability: Code Profiling With Flame Graphs

Percona

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. Kubernetes limitations In Linux, by default, performance system events can’t be collected by unprivileged users.

Code 111