Stories with Tag System Monitoring

• Show HN: Appstat – Process Monitor for Windows

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  Posted: 2025-03-04 15:24:32

  Verbose tl;dr

  Appstat is a free, open-source process monitor for Windows presented as a modern alternative to existing tools. It offers a clean and responsive UI, focusing on real-time performance monitoring with detailed metrics like CPU usage, memory consumption, I/O operations, and network activity. Appstat aims to provide a comprehensive view of system resource utilization by individual processes, enabling users to quickly identify performance bottlenecks and troubleshoot issues. It boasts features like customizable columns, sorting, filtering, process tree views, and historical data charting for deeper analysis.

  Pragmar has introduced Appstat, a new process monitoring tool specifically designed for Windows. Appstat aims to provide a comprehensive and detailed view of all running processes, offering more information and a more user-friendly experience than the built-in Windows Task Manager. It presents this information in a clean, modern interface that focuses on clarity and ease of navigation.

  The tool goes beyond simply listing processes, providing in-depth metrics such as CPU usage, memory consumption, disk I/O operations (both read and write), and network activity. Users can see not only the overall resource usage of each process but also historical data, allowing them to track trends and identify potential performance bottlenecks or unusual activity over time. This historical tracking feature facilitates diagnosing intermittent issues or understanding resource usage patterns.

  Appstat offers several features designed to enhance user workflow. A powerful search function allows users to quickly locate specific processes by name or PID. Users can also group processes, simplifying the analysis of related applications or services. Furthermore, the tool allows users to easily terminate processes directly from the interface, providing a convenient way to manage running applications.

  Targeting developers, system administrators, and power users who need a more powerful and insightful process monitoring solution, Appstat is positioned as a valuable tool for troubleshooting performance issues, identifying resource-intensive applications, and gaining a deeper understanding of system behavior. While it provides comprehensive data, the interface is designed to be accessible and intuitive, making it suitable for users with varying levels of technical expertise.

  • Windows
  • Process Monitor
  • System Monitoring
  • performance monitoring
  • Appstat
  • Software
  • utility
  • Debugging
  • Troubleshooting
  • resource usage
  • Real-Time Monitoring

  Summary of Comments ( 4 )
  https://news.ycombinator.com/item?id=43255855

  Verbose tl;dr

  HN users generally praised Appstat as a useful tool. Several pointed out its similarity to existing tools like Sysinternals Process Monitor (Procmon) while highlighting Appstat's simpler interface and easier setup as advantages. Some appreciated its focus on security-relevant events. Others suggested potential improvements, such as adding filtering capabilities, including command line arguments, and enhancing the UI with features like column sorting. A few users mentioned alternative tools they preferred, including Procmon and ETW Explorer. The developer actively responded to comments, addressing questions and acknowledging suggestions for future development.

  The Hacker News post for "Show HN: Appstat – Process Monitor for Windows" generated a moderate amount of discussion, with several commenters offering their perspectives and experiences.

  A significant portion of the conversation revolves around comparing Appstat to existing process monitoring tools, particularly Process Monitor (Procmon) from Sysinternals. Several users praise Procmon as a powerful and comprehensive tool, questioning whether Appstat offers enough unique features to justify its existence. One commenter points out the steep learning curve associated with Procmon, highlighting the need for simpler alternatives, particularly for less experienced users. They suggest Appstat could potentially fill this niche.

  The author of Appstat actively participates in the thread, responding to queries and providing clarification about its features. They emphasize the tool's focus on providing a more user-friendly interface compared to Procmon, acknowledging that Procmon offers greater depth in terms of data collection. They also discuss Appstat's specific use cases, such as troubleshooting application crashes and identifying performance bottlenecks. This direct engagement from the creator allows for a deeper understanding of the tool's purpose and intended audience.

  One commenter expresses skepticism about the cross-platform capabilities of Appstat, specifically questioning its performance on Linux. The author responds by acknowledging the current limitations of the Linux version and outlining future development plans. This exchange provides valuable insight into the current state of the project and its roadmap.

  Further discussion centers on the potential licensing model for Appstat. One commenter inquires about plans for open-sourcing the project. The author responds by stating that they are currently considering various options, including a potential dual-licensing approach. This suggests the possibility of both a free community edition and a paid commercial version.

  The overall sentiment within the comments section appears to be cautiously optimistic. While several users acknowledge the value of existing tools like Procmon, there's also a recognition that there's room for alternative solutions with different design philosophies and target user groups. The author's active participation and responsiveness to feedback contribute positively to the discussion, leaving a favorable impression of the project and its potential.

• 3,200% CPU Utilization

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  Posted: 2025-02-28 17:01:43

  Verbose tl;dr

  The author experienced extraordinarily high CPU utilization (3200%) on their Linux system, far exceeding the expected maximum for their 8-core processor. After extensive troubleshooting, including analyzing process lists, checking for kernel issues, and verifying hardware performance, the culprit was identified as a bug in the docker stats command itself. The command was incorrectly multiplying the CPU utilization by the number of CPUs, leading to the inflated and misleading percentage. Once the issue was pinpointed, the author switched to a more reliable monitoring tool, htop, which accurately reported normal CPU usage. This highlighted the importance of verifying monitoring tool accuracy when encountering unusual system behavior.

  This blog post details a fascinating journey of troubleshooting perplexing CPU utilization on a Linux server. The author, Joseph Mate, begins by describing the initial observation of an astonishing 3200% CPU usage, a figure far exceeding the expected capacity of the server's 8-core processor. This anomalous reading prompted an investigation into the underlying cause.

  The initial suspicion fell upon a potential runaway process consuming excessive resources. However, standard tools like top and htop failed to identify any single culprit responsible for such a dramatic spike in CPU usage. Each process appeared to be consuming a reasonable amount of resources individually.

  Further investigation using more granular performance monitoring tools like perf began to reveal a more nuanced picture. perf pointed towards a high volume of system calls related to timekeeping functions, specifically gettimeofday and clock_gettime. This suggested that an excessive number of these calls were being made, potentially contributing to the inflated CPU utilization figures.

  The author then meticulously analyzed the codebase of the running application, a Rust-based program. Despite the absence of any obvious loops or excessive calls to time functions within the application's logic, the investigation persisted. Suspicion then shifted towards potential interactions with external libraries or dependencies.

  Through rigorous profiling and tracing, the root cause was finally unearthed. It was discovered that the application's logging library, specifically the tracing crate, was inadvertently configured to capture timestamps with nanosecond precision for every single log event. This extremely high-resolution timekeeping, while seemingly innocuous, resulted in a substantial overhead due to the sheer volume of logging operations performed by the application. Each call to capture a timestamp with nanosecond precision involved multiple system calls to the underlying timekeeping functions, ultimately accounting for the observed surge in CPU utilization.

  By modifying the logging configuration to use less granular timestamps (millisecond precision), the author observed a dramatic reduction in CPU load, bringing the utilization back down to expected levels. The post concludes by highlighting the importance of careful consideration of logging configurations, especially concerning the precision of timestamps, as seemingly minor details can have a profound impact on overall system performance, particularly in high-throughput applications. The case serves as a cautionary tale about the potential performance pitfalls associated with overly aggressive logging practices.

  • CPU Utilization
  • High CPU
  • performance
  • Troubleshooting
  • System Monitoring
  • Linux
  • htop
  • Stress Testing
  • CPU Stress
  • docker
  • Containers
  • resource limits
  • Kernel
  • system administration

  Summary of Comments ( 117 )
  https://news.ycombinator.com/item?id=43207831

  Verbose tl;dr

  Hacker News users discussed the plausibility and implications of 3200% CPU utilization, referencing the original author's use of Web Workers and the browser's ability to utilize multiple threads. Some questioned if this was a true representation of CPU usage or simply a misinterpretation of metrics, suggesting that the number reflects total CPU time consumed across all cores rather than a percentage exceeding 100%. Others pointed out that using performance.now() instead of Date.now() for benchmarks is crucial for accuracy, especially with Web Workers, and speculated on the specific workload and hardware involved. The unusual percentage sparked conversation about the potential for misleading performance measurements and the nuances of interpreting CPU utilization in multi-threaded environments like browsers. Several commenters highlighted the difference between wall-clock time and CPU time, emphasizing that the former is often the more relevant metric for user experience.

  The Hacker News post "3,200% CPU Utilization" generated a fair number of comments discussing the linked blog post about achieving extremely high CPU utilization with a custom-built prime number generator. The discussion revolves primarily around the nuances of CPU utilization reporting, the efficiency of the prime-finding algorithm, and the relevance of the benchmark itself.

  Several commenters pointed out that exceeding 100% CPU utilization is expected on multi-core systems. One commenter explained that on a 32-core system, 3200% utilization represents all cores running at 100%, which isn't unusual or inherently problematic. This clarifies that the title, while attention-grabbing, might be misinterpreted by those unfamiliar with this aspect of system monitoring.

  A significant portion of the discussion focuses on the efficiency of the prime-finding algorithm used in the benchmark. Some commenters questioned whether the algorithm is genuinely optimized, suggesting potential improvements and alternative approaches. One comment proposed using a segmented Sieve of Eratosthenes for improved performance, arguing that the demonstrated approach might not be the most efficient way to generate primes. This sparked a back-and-forth about the practical benefits of different sieving methods and the optimal approach for maximizing CPU usage.

  Several commenters questioned the value and relevance of the benchmark itself. Some argued that achieving high CPU utilization is not inherently useful and doesn't necessarily reflect real-world performance gains. They pointed out that without a comparative benchmark against existing prime-finding algorithms, the 3200% figure is essentially meaningless in terms of performance evaluation. This led to a discussion about the purpose of such benchmarks and whether they accurately represent practical application scenarios.

  The practicality of using Go for CPU-bound tasks also emerged as a discussion point. Commenters debated the suitability of Go's garbage collection and runtime characteristics for performance-critical computations. One user questioned the choice of Go, given its known performance limitations compared to languages like C or C++ for such computationally intensive tasks.

  Finally, some commenters offered suggestions for further optimizing the code and the benchmark itself. These include utilizing SIMD instructions, optimizing memory access patterns, and comparing the performance against established libraries like primesieve. This feedback highlights the collaborative nature of Hacker News, where users contribute ideas and expertise to refine and improve projects.

• Agent-Less System Monitoring with Elixir Broadway

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  Posted: 2025-02-18 14:53:44

  Verbose tl;dr

  This blog post demonstrates how to build an agent-less system monitoring tool using Elixir and Broadway. It leverages SSH to remotely execute commands on target machines, collecting metrics like CPU usage, memory consumption, and disk space. Broadway manages the concurrent execution of these commands across multiple hosts, providing scalability and fault tolerance. The collected data is then processed and displayed, offering a centralized overview of system performance. The author highlights the benefits of this approach, including simplified deployment (no agent installation required) and the inherent robustness of Elixir and its ecosystem. This method offers a lightweight yet powerful solution for monitoring server infrastructure.

  This blog post explores building a system monitoring solution using Elixir and Broadway, specifically focusing on an agent-less approach. The author argues that traditional agent-based monitoring, while offering granular data collection, introduces overhead and complexity through agent deployment and maintenance. Agent-less monitoring, leveraging protocols like SSH, offers a simplified alternative by querying systems directly without requiring resident software.

  The post begins by outlining the conceptual architecture of their solution. It details how Broadway, a concurrent and fault-tolerant processing library in Elixir, acts as the central processing engine. It receives monitoring tasks, distributes them to designated workers, and manages the results. Crucially, the chosen agent-less method utilizes SSH to execute commands remotely on target systems. The post emphasizes Broadway's robustness in handling potentially unreliable network operations inherent in SSH-based communication.

  The author then delves into the implementation specifics. They demonstrate setting up a Broadway pipeline configured to process monitoring tasks. These tasks are structured as messages containing the target hostname and the command to execute. The implementation leverages Erlang's SSH application to establish connections and execute commands remotely. A critical component highlighted is the error handling mechanism built around Broadway's retry and failure handling capabilities. This ensures resilience against transient network issues or temporary unavailability of target systems. The retrieved monitoring data is then processed and formatted, ready for storage or visualization.

  A key advantage emphasized is the flexibility afforded by this approach. The system can be readily extended to support various monitoring commands and metrics. Adding new systems to monitor only requires configuring the necessary connection details, without deploying any agents. The post also touches upon the scalability of the solution. Broadway's concurrent processing model allows for parallel execution of monitoring tasks, improving efficiency and reducing overall monitoring time. The author acknowledges potential security considerations associated with managing SSH credentials and advocates for secure storage and access control mechanisms.

  Finally, the post concludes by reiterating the benefits of the agent-less approach, highlighting its simplicity, scalability, and reduced overhead. It positions this approach as a compelling alternative to traditional agent-based solutions, especially in scenarios where agent deployment is impractical or undesirable. The author suggests potential future enhancements, such as integrating with different data visualization tools and exploring alternative agent-less protocols.

  • Elixir
  • Broadway
  • System Monitoring
  • Agentless Monitoring
  • Observability
  • DevOps
  • distributed systems
  • Erlang
  • OTP
  • concurrency
  • fault tolerance
  • scalability
  • performance monitoring
  • Real-Time Monitoring

  Summary of Comments ( 16 )
  https://news.ycombinator.com/item?id=43090167

  Verbose tl;dr

  Hacker News users discussed the practicality and benefits of the agentless approach to system monitoring described in the linked blog post. Several commenters appreciated the simplicity and reduced overhead of not needing to install agents on monitored machines. Some raised concerns about potential security implications of running commands remotely via SSH and the potential performance bottlenecks of doing so. Others questioned the scalability of this method, particularly for large numbers of monitored systems. The discussion also touched on alternative approaches like using message queues and the potential benefits of Elixir's concurrency features for this type of monitoring system. A compelling comment suggested exploring the use of OSquery for efficient data gathering, which prompted further discussion on its pros and cons. Finally, some commenters expressed interest in the author's open-sourcing of their project.

  The Hacker News post titled "Agent-Less System Monitoring with Elixir Broadway" sparked a small but focused discussion with 5 comments. No single comment overwhelmingly dominated the conversation, but several offered interesting perspectives on the article's topic.

  One commenter questioned the term "agent-less," pointing out that while the system described doesn't require installing dedicated agent software on monitored machines, it still relies on SSH access, which functionally acts like an agent. They argued that this approach trades one set of tradeoffs (agent installation and maintenance) for another (managing SSH keys and potential security concerns).

  Another comment focused on the choice of Erlang/Elixir for this type of task. They acknowledged the platform's strengths in concurrency and distributed systems but expressed concern about the operational overhead and debugging complexity compared to simpler scripting solutions, especially for smaller deployments. They suggested that the benefits of Elixir might become more pronounced with larger and more complex monitoring setups.

  A third commenter praised the article's clear explanation and the elegant approach to building a robust monitoring system with Broadway. They highlighted the benefits of leveraging Elixir's OTP framework for handling failures and ensuring reliability.

  The remaining comments were shorter and less substantive. One simply expressed appreciation for the article, while another briefly mentioned using a similar approach with a different technology.

  Overall, the comments section, while brief, provided some thoughtful critiques and perspectives on the advantages and disadvantages of the proposed agent-less monitoring approach using Elixir and Broadway. The discussion centered on the practical implications of SSH as an "agent" substitute and the suitability of Elixir/OTP for this kind of task in different scales of deployment.