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GC easy – Universal Java GC Log Analyser

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GCeasy Team

GCeasy is a Universal GC Log Analyzer. Its the Industry's first machine learning guided Garbage collection log analysis tool. GCeasy has in-built intelligence to auto-detect problems in the JVM & Android GC logs and recommend solutions to it.

Let’s crash the applications! – W-JAX 2021

The JAX Online Conference serves Java and software architecture enthusiasts, featuring global speakers. This year’s highlight is a session by an architect on “Let’s crash the applications!” exploring issues like memory leaks and deadlocks through code snippets. Understanding these triggers may ease diagnosis and resolution of such problems.

7 habits of highly effective Performance Troubleshooters! – All Day Devops Conference

The 6th Annual All Day DevOps conference took place on October 28, 2021, featuring architect Ram Lakshmanan's webinar on the "7 habits of highly effective Performance Troubleshooters." The presentation covered problem forecasting, immediate response strategies, root cause identification, and preventive measures for future issues.

Detect proactively whether application’s memory is under-allocated

The post discusses the consequences of under-allocating memory in applications, such as degraded response times and OutOfMemoryError occurrences. It emphasizes proactively monitoring Garbage Collection behavior through logs to identify memory allocation issues. Analyzing patterns in GC logs can help distinguish between high object creation due to traffic spikes and potential memory leaks.

Garbage Collection Patterns to predict outages

The author analyzes various Garbage Collection (GC) patterns observed in applications using GCeasy. Key patterns include healthy saw-tooth behavior, heavy caching, memory leaks, and consecutive full GCs, each indicating different performance issues. Understanding these patterns helps diagnose application health and optimize memory usage to prevent errors like OutOfMemory.

Is today’s microservice more bloated than yesterday’s monolith?

The author reflects on the evolution from monolithic to microservice architecture over 20 years. Initially, a monolithic application efficiently managed critical services on modest memory. In contrast, modern microservices often require significantly larger memory allocations, leading to concerns about resource consumption, response times, and overall complexity, despite their development advantages.

Java performance Talk in QAInsights Clubhouse

Naveen Kumar Namachivayam invited Ram Lakshmanan, the architect of yCrash, to speak in the Performance Engineers Clubhouse. The discussion covered fundamental aspects of garbage collection, memory significance, and GC throughput, enriching the understanding of performance engineering topics. The session is available for viewing on YouTube.

OutOfMemoryError: Kill process or sacrifice child – causes & solutions

The 'Kill Process or sacrifice child' OutOfMemoryError occurs when processes exceed available RAM, prompting the Operating System to terminate certain processes to safeguard others. This often happens in containerized environments. Solutions include increasing RAM, minimizing unnecessary processes, and optimizing memory usage with tools like yCrash.

“I don’t have to worry about Garbage collection” – Is it true?

Garbage Collection (GC) is automated in modern programming platforms, but developers should pay close attention to it. Inefficient GC can lead to poor customer experiences, significant financial waste, and missed performance improvements. By optimizing GC, application response times can improve dramatically without code changes, proving its critical role in performance monitoring and capacity planning.

How many millions of dollars enterprises waste due to Garbage collection?

Enterprises are unknowingly wasting millions due to ineffective garbage collection. This issue stems from automatic garbage collection in languages like Java, which pauses applications to clear unreferenced objects, leading to significant lost processing time. Effects include reduced throughput and financial losses, signaling a need for better garbage collection tuning and efficient coding practices.

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