Garbage Collection: Unsung Hero

In this post, we embark on a journey to unravel the pivotal role of Garbage Collection analysis and explore seven critical points that underscore its significance.

Garbage Collection is a facet often disregarded and underestimated, yet beneath its surface lies the potential for profound impacts on your organization that reach far beyond the realm of application performance. In this post, we embark on a journey to unravel the pivotal role of Garbage Collection analysis and explore seven critical points that underscore its significance.

Improve Application Response Time Without Code Changes

Automatic garbage collection (GC) is a critical memory management process, but it introduces pauses in applications. These pauses occur when GC scans and reclaims memory occupied by objects that are no longer in use. Depending on various factors, these pauses can range from milliseconds to several seconds or even minutes. During these pauses, no application transactions are processed, causing customer requests to be stranded.

However, there’s a solution. By fine-tuning the GC behavior, you can significantly reduce GC pause times. This reduction ultimately leads to a decrease in the overall application’s response time, delivering a smoother user experience. A real-world case study from one of the world’s largest automobile manufacturers demonstrates the impact of GC tuning without making a single line of code change. They were able to reduce their response time by 50% just by tuning their GC settings without a single line of code change.

Efficient Cloud Cost Reduction

In the world of cloud computing, enterprises often unknowingly spend millions of dollars on inefficient garbage collection practices. A high GC Throughput percentage, such as 98%, may initially seem impressive, like achieving an ‘A grade’ score. However, this seemingly minor difference carries substantial financial consequences.

Imagine a mid-sized company operating 1,000 AWS t2.2x.large 32G RHEL on-demand EC2 instances in the US West (North California) region. The cost of each EC2 instance is $0.5716 per hour. Let’s assume that their application’s GC throughput is 98%. Now, let’s break down the financial impact of this assumption:

  • With a 98% GC Throughput, each instance loses approximately 28.8 minutes daily due to garbage collection. In a day, there are 1,440 minutes (equivalent to 24 hours x 60 minutes). Thus, 2% of 1,440 minutes equals 28.8 minutes.
  • Over the course of a year, this adds up to 175.2 hours per instance. (i.e. 28.8 minutes x 365 days)
  • For a fleet of 1,000 AWS EC2 instances, this translates to approximately $100.14K in wasted resources annually (calculated as 1,000 EC2 instances x 175.2 hours x $0.5716 per hour) due to garbage collection delays.

This calculation vividly illustrates how seemingly insignificant pauses in GC activity can amass substantial costs for enterprises. It emphasizes the critical importance of optimizing garbage collection processes to achieve significant cost savings.

Trimming Software Licensing Cost

In today’s landscape, many of our applications run on commercial vendor software solutions like Dell Boomi, ServiceNow, Workday, and others. While these vendor software solutions are indispensable, their licensing costs can be exorbitant. What’s often overlooked is that the efficiency of our code and configurations within these vendor software platforms directly impacts software licensing costs.

This is where proper Garbage Collection (GC) analysis comes into play. It provides insights into whether there is an overallocation or underutilization of resources within these vendor software environments. Surprisingly, overallocation often remains hidden until we scrutinize GC behavior.

By leveraging GC analysis, enterprises gain the visibility needed to identify overallocation and reconfigure resources accordingly. This optimization not only enhances application performance but also results in significant cost savings by reducing the licensing footprint of these vendor software solutions. The impact on the bottom line can be substantial.

Forecast Memory Problems in Production

Garbage collection logs hold the key to vital predictive micrometrics that can transform how you manage your application’s availability and performance. Among these micrometrics, one stands out: ‘GC Throughput.’ But what is GC Throughput? Imagine your application’s GC throughput is at 98% — it means that your application spends 98% of its time efficiently processing customer activity, with the remaining 2% allocated to GC activity.

The significance becomes apparent when your application faces a memory problem. Several minutes before a memory issue becomes noticeable, the GC throughput will begin to degrade. This degradation serves as an early warning, enabling you to take preventive action before memory problems impact your production environment.

Troubleshooting tools like yCrash closely monitor ‘GC throughput’ to predict and forecast memory problems, ensuring your application remains robust and reliable. 

Unearthing Memory Issues

One of the primary reasons for production outages is encountering an OutOfMemoryError. In fact, there are nine different types of OutOfMemoryErrors:

1.     java.lang.OutOfMemoryError: Java heap space

2.     java.lang.OutOfMemoryError: PermGen space

3.     java.lang.OutOfMemoryError: GC overhead limit exceeded

4.     java.lang.OutOfMemoryError: Requested array size exceeds VM limit

5.     java.lang.OutOfMemoryError: Unable to create new native thread 

6.     java.lang.OutOfMemoryError: Metaspace

7.     java.lang.OutOfMemoryError: unable to create new native thread

8.     java.lang.OutOfMemoryError: Direct buffer memory

9.     java.lang.OutOfMemoryError: Compressed class space

GC analysis provides valuable insights into the root cause of these errors and helps in effectively triaging the problem. By understanding the specific OutOfMemoryError type and its associated details, developers can take targeted actions to debug and resolve memory-related issues, minimizing the risk of production outages.

Spotting Performance Bottlenecks During Development

In the modern software development landscape, the “Shift Left” approach has become a key initiative for many organizations. Its goal is to identify and address production-related issues during the development phase itself. Garbage Collection (GC) analysis enables this proactive approach by helping to isolate performance bottlenecks early in the development cycle.

One of the vital metrics obtained through GC analysis is the ‘Object Creation Rate.’ This metric signifies the average rate at which objects are created by your application. Here’s why it matters: If your application, which previously generated data at a rate of 100MB/sec, suddenly starts creating 150MB/sec without a corresponding increase in traffic volume, it’s a red flag indicating potential problems within the application. This increased object creation rate can lead to heightened GC activity, higher CPU consumption, and degraded response times.

Moreover, this metric can be integrated into your Continuous Integration/Continuous Deployment (CI/CD) pipeline to gauge the quality of code commits. For instance, if your previous code commit resulted in an object creation rate of 50MB/sec and a subsequent commit increases it to 75MB/sec for the same traffic volume, it signifies an inefficient code change.

To streamline this process, you can leverage the GCeasy REST API. This integration allows you to capture critical data and insights directly within your CI/CD pipeline, ensuring that performance issues are identified and addressed early in the development lifecycle.

Efficient Capacity Planning

Effective capacity planning is vital for ensuring that your application can meet its performance and resource requirements. It involves understanding your application’s demands for memory, CPU, network resources, and storage. In this context, analyzing garbage collection behavior emerges as a powerful tool for capacity planning, particularly when it comes to assessing memory requirements.

When you delve into garbage collection behavior analysis, you gain insights into crucial micro-metrics such as the average object creation rate and average object reclamation rate. These micro-metrics provide a detailed view of how your application utilizes memory resources. By leveraging this data, you can perform precise and effective capacity planning for your application.

This approach allows you to allocate resources optimally, prevent resource shortages or overprovisioning, and ensure that your application runs smoothly and efficiently. Garbage Collection analysis, with its focus on memory usage patterns, becomes an integral part of the capacity planning process, enabling you to align your infrastructure resources with your application’s actual needs.

How To Do Garbage Collection Analysis

While there are monitoring tools and JMX MBeans that offer real-time Garbage Collection metrics, they often lack the depth needed for thorough analysis. To gain a complete understanding of Garbage Collection behavior, turn to GC logs. Once you have GC logs, select a free GC log analysis tool that suits your needs.

With your chosen GC log analysis tool, examine Garbage Collection behavior in the logs, looking for patterns and performance issues. Pay attention to key metrics, and based on your analysis, optimize your application to reduce GC pauses and enhance performance. Adjust GC settings, allocate memory efficiently, and monitor the impact of your changes over time. 


In the fast-paced world of software development and application performance optimization, Garbage Collection (GC) analysis is often the unsung hero. While it may be considered an underdog, it’s high time for this perception to change. GC analysis wields the power to enhance performance, reduce costs, and empower proactive decision-making. From improving application response times to early issue detection and precise capacity planning, GC analysis stands as a pivotal ally in optimizing applications and resources.

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