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Stories with Tag Observability

Exploring a Language Runtime with Bpftrace

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Posted: 2025-05-28 16:46:55

This blog post demonstrates how to use bpftrace, a powerful tracing tool, to gain insights into the inner workings of a language runtime, specifically focusing on Golang's garbage collector. The author uses practical examples to show how bpftrace can track garbage collection cycles, measure their duration, and identify the functions triggering them. This allows developers to profile performance, diagnose memory issues, and understand the runtime's behavior without modifying the application's code. The post highlights bpftrace's flexibility by also showcasing its use in tracking goroutine creation and destruction, providing a comprehensive view of the Go runtime's dynamics.

This blog post by Marc Gaudet details the author's exploration of using bpftrace, a powerful tracing tool for Linux systems, to gain insights into the inner workings of a language runtime, specifically focusing on the Crystal programming language. The author's motivation stemmed from curiosity about Crystal's memory allocation patterns and performance characteristics. He wanted to understand how frequently the garbage collector was being invoked and how much memory was being allocated and deallocated over time.

The post begins by highlighting the inherent difficulty of directly observing a language runtime's internal operations due to its complexity and highly optimized nature. Traditional profiling tools often lack the granularity required for this task, leading the author to choose bpftrace. Bpftrace's ability to attach to specific events within the kernel and user-space programs makes it ideally suited for this type of investigation.

The author then walks through the process of crafting custom bpftrace scripts to monitor memory allocation and garbage collection within the Crystal runtime. He explains the specific events he traced, such as malloc, free, and Crystal's GC-related functions, providing the actual bpftrace code snippets used. These scripts leverage kernel tracepoints and user-level static tracing (USDT) probes that Crystal provides, allowing for precise tracking of memory operations and GC cycles. The author demonstrates how to aggregate and display the collected data in real-time using histograms and counters, providing immediate feedback on runtime behavior.

The first script focuses on tracking memory allocation and deallocation sizes, providing insights into the distribution of allocated memory blocks. The second script tracks the frequency and duration of garbage collection cycles, providing an understanding of the GC's impact on overall performance. He discusses the limitations encountered during this exploration, such as the need for USDT probes within the runtime itself to gain visibility into higher-level language constructs.

The post concludes by emphasizing the power and versatility of bpftrace as a tool for runtime analysis. The author highlights the relative ease with which detailed information can be extracted from even complex systems like language runtimes, facilitating performance optimization and deeper understanding of internal behavior. The exploration ultimately provides valuable insights into Crystal’s memory management behavior, demonstrating the practicality of bpftrace for runtime introspection. While the specific examples focus on Crystal, the post underscores the broader applicability of bpftrace for exploring other languages and runtime environments as well.
Summary of Comments ( 0 )
https://news.ycombinator.com/item?id=44117937

Hacker News users discussed the challenges and benefits of using bpftrace for profiling language runtimes. Some commenters pointed out the limitations of bpftrace regarding stack traces and the difficulty in correlating events across threads. Others praised its low overhead and ease of use for quick investigations, even suggesting specific improvements like adding USDT probes to the runtime for better visibility. One commenter highlighted the complexity of dealing with optimized code and just-in-time compilation, while another suggested alternative tools like perf and DTrace for more complex analyses. Several users expressed interest in seeing more examples and tutorials of bpftrace applied to language runtimes. Finally, a few commenters discussed the specific example in the article, focusing on garbage collection and its impact on performance analysis.

The Hacker News post titled "Exploring a Language Runtime with Bpftrace" (https://news.ycombinator.com/item?id=44117937) has a modest number of comments, generating a discussion around the use of bpftrace for profiling and understanding runtime behavior.

One commenter highlights the effectiveness of bpftrace for quickly identifying performance bottlenecks, specifically referencing its use in tracking garbage collection pauses. They express appreciation for bpftrace's accessibility and ease of use compared to more complex profiling tools.

Another commenter points out the potential of combining bpftrace with other tools like perf for a more comprehensive analysis. They suggest using perf to get a general overview and then leveraging bpftrace's targeted tracing capabilities to delve deeper into specific areas of interest.

A subsequent commenter mentions the challenges of applying bpftrace to complex, multi-threaded applications, where tracing can become overwhelming and difficult to interpret. They acknowledge the power of the tool but emphasize the need for careful consideration of the tracing strategy.

Further discussion revolves around the advantages and limitations of bpftrace compared to traditional debugging and profiling techniques. One user specifically mentions using bpftrace for production debugging, highlighting its low overhead and ability to provide insights without significantly impacting performance. They contrast this with more invasive methods that might require stopping or restarting the application.

The conversation also touches upon the learning curve associated with bpftrace. While some users find it relatively straightforward, others note the need to invest time in understanding its syntax and capabilities to effectively utilize its features. The discussion also hints at the evolving nature of bpftrace and its growing community, suggesting that resources and support are becoming more readily available.

Finally, a comment focuses on the specific application of bpftrace within the context of the linked article, discussing its utility in exploring the inner workings of language runtimes. They commend the article for demonstrating practical use cases and providing valuable insights into the behavior of managed languages.
Show HN: rtcollector - A modular, RedisTimeSeries-native observability agent

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Posted: 2025-05-22 19:53:37

rtcollector is an open-source observability agent designed specifically for RedisTimeSeries. Its modular architecture allows users to collect metrics from various sources using plugins, and directly ingest them into RedisTimeSeries. It aims to be a lightweight and efficient solution, leveraging the speed and capabilities of RedisTimeSeries for metric storage and analysis. The project supports collecting metrics from system resources, Prometheus exporters, and custom applications, offering a flexible way to consolidate and monitor time series data.

The project rtcollector, hosted on GitHub, introduces a novel approach to observability by leveraging RedisTimeSeries as its primary backend. It's designed as a modular agent, offering flexibility and extensibility for collecting, processing, and storing various metrics. The core functionality revolves around ingesting metrics from diverse sources, transforming them as needed, and persisting them within RedisTimeSeries for efficient querying and analysis.

This agent distinguishes itself by being specifically tailored for RedisTimeSeries, allowing it to capitalize on the database's inherent capabilities for handling time-series data. Instead of relying on traditional monitoring systems or complex agents, rtcollector streamlines the process by directly integrating with RedisTimeSeries. This potentially simplifies deployment and reduces the overhead associated with managing multiple components.

The modular architecture of rtcollector facilitates customization and adaptation to different monitoring needs. Users can select and configure specific modules, referred to as "collectors," to gather metrics from various sources, such as system performance counters, application logs, or external APIs. This modularity promotes a focused approach, where only the necessary components are activated, thereby minimizing resource consumption and improving overall efficiency.

Furthermore, rtcollector incorporates processing capabilities, enabling users to transform and enrich collected metrics before storage. This might include aggregations, calculations, or applying custom logic to derive more meaningful insights from raw data. By performing these transformations within the agent, rtcollector optimizes the data flow and reduces the burden on downstream analysis tools.

The project emphasizes simplicity and ease of use, aiming to provide a straightforward method for collecting and storing time-series data within RedisTimeSeries. Its native integration with the database, combined with the modular design and processing features, makes it a potentially valuable tool for building observability solutions. The project leverages the Go programming language, contributing to its performance and portability.
- Redis
- time series
- RedisTimeSeries
- Observability
- Monitoring
- Metrics
- Agent
- Data Collection
- Open Source
- Go
- Golang
- Modular
- rtcollector
Summary of Comments ( 0 )
https://news.ycombinator.com/item?id=44066120

Hacker News users discussed rtcollector's niche appeal, questioning its advantages over existing solutions like Prometheus. Some commenters appreciated its simplicity and ease of use, especially for smaller projects or those already invested in RedisTimeSeries. Concerns were raised about the potential performance implications of using Lua scripting within Redis, and the lack of features like service discovery. The project's modularity and potential for customization were seen as positives, though some doubted the necessity of a dedicated agent for this purpose. Overall, the reaction was mixed, with some interest but also skepticism about its broader applicability and long-term viability.

The Hacker News post titled "Show HN: rtcollector - A modular, RedisTimeSeries-native observability agent" generated several comments discussing various aspects of the project.

One commenter questioned the necessity of another agent, expressing skepticism about the value proposition over established solutions like Prometheus. They specifically mentioned Prometheus' service discovery, alerting, and visualization capabilities, wondering how rtcollector compares in these areas.

Another commenter pointed out the potential benefit of using RedisTimeSeries for reduced cardinality issues compared to other time-series databases like Prometheus. They highlighted how high cardinality can lead to performance problems and increased storage costs, suggesting that rtcollector, by leveraging RedisTimeSeries, might offer a solution to these challenges.

A subsequent comment built upon this by noting that the project's modular design could be appealing. The ability to collect metrics from various sources and consolidate them within RedisTimeSeries was seen as a potential strength. However, the same commenter also echoed the earlier sentiment about the need for a clear comparison with existing solutions to better understand rtcollector's niche.

Another user expressed interest in the project specifically for its RedisTimeSeries integration, mentioning their existing use of Redis and the desire to avoid adding another dependency like Prometheus. They saw rtcollector as a potentially convenient way to leverage their current infrastructure for metrics collection.

One comment touched upon the potential advantages of a push-based system like rtcollector compared to Prometheus' pull-based approach. They suggested that push-based systems can be more efficient in certain scenarios, although they didn't elaborate further on the specific use cases where this advantage would be most pronounced.

Finally, a commenter raised the point that many existing Redis monitoring tools offer similar functionality. They questioned the uniqueness of rtcollector and suggested that the project author should clarify what distinguishes it from these existing tools. This reinforced the recurring theme of needing a clearer differentiation from the established landscape of monitoring solutions.
Monitoring Node.js: Key Metrics You Should Track

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Posted: 2025-05-19 11:06:59

This post emphasizes the importance of monitoring Node.js applications for optimal performance and reliability. It outlines key metrics to track, categorized into resource utilization (CPU, memory, event loop, garbage collection), HTTP requests (latency, throughput, error rate), and system health (disk I/O, network). By monitoring these metrics, developers can identify bottlenecks, prevent outages, and improve overall application performance. The post also highlights the importance of correlating different metrics to understand their interdependencies and gain deeper insights into application behavior. Effective monitoring strategies, combined with proper alerting, enable proactive issue resolution and efficient resource management.

This blog post from Last9, titled "Monitoring Node.js: Key Metrics You Should Track," provides a comprehensive guide for developers seeking to effectively monitor their Node.js applications and ensure optimal performance and stability. The post emphasizes the importance of proactive monitoring to identify and address potential issues before they impact users. It categorizes key metrics into four primary areas: resource utilization, event loop, garbage collection, and HTTP metrics.

Within resource utilization, the post highlights the crucial role of monitoring CPU usage, breaking it down into user, system, and idle time. It underscores that consistently high CPU usage can indicate performance bottlenecks and suggests profiling tools to pinpoint the root cause. Memory usage is also explored, including heap usage and memory leaks. The blog stresses the importance of tracking memory leaks, which can lead to application crashes, and recommends heap snapshots and memory profiling tools for diagnosis. Furthermore, it mentions the significance of monitoring I/O operations, including disk reads and writes, and network activity, as these can significantly impact application performance, especially in I/O-bound applications.

The event loop section delves into the heart of Node.js's asynchronous nature. It explains how the event loop processes events and tasks, and why monitoring its health is critical. The post introduces key metrics like event loop delay and tick time. Excessive delays or long tick times can signify that the application is struggling to keep up with incoming requests, leading to performance degradation. It provides guidance on tools and techniques to measure and analyze event loop performance.

Garbage collection is another crucial aspect discussed in the post. It explains how Node.js's garbage collector manages memory allocation and deallocation. Monitoring garbage collection activity, including metrics like garbage collection frequency, pause times, and heap size before and after garbage collection, can provide valuable insights into memory management efficiency. Excessively frequent or long garbage collection cycles can indicate memory leaks or inefficient memory usage, negatively affecting application performance. The post recommends analyzing these metrics to optimize memory management and minimize performance impact.

Finally, the post covers HTTP metrics, essential for understanding application performance from a user's perspective. It emphasizes the importance of tracking metrics such as request throughput, response times (including percentiles like p95 and p99), and error rates. Understanding these metrics allows developers to identify performance bottlenecks, optimize API endpoints, and improve overall user experience. The post also highlights the value of tracking status codes, particularly the frequency of 5xx errors, which indicate server-side issues, and 4xx errors, pointing to client-side problems. By monitoring these HTTP metrics, developers gain valuable insights into the health and performance of their applications from the user's perspective. The post concludes by reiterating the importance of continuous monitoring and utilizing appropriate tools and techniques for effectively managing and optimizing Node.js applications.
Summary of Comments ( 2 )
https://news.ycombinator.com/item?id=44028483

HN users generally found the article a decent introduction to Node.js monitoring, though some considered it superficial. Several commenters emphasized the importance of distributed tracing and application performance monitoring (APM) tools for more comprehensive insights beyond basic metrics. Specific tools like Clinic.js and PM2 were recommended. Some users discussed the challenges of monitoring asynchronous operations and the value of understanding event loop delays and garbage collection activity. One commenter pointed out the critical role of business metrics, arguing that technical metrics are only useful insofar as they impact business outcomes. Another user highlighted the increasing complexity of modern monitoring, noting the shift from simple dashboards to more sophisticated analyses involving machine learning.

The Hacker News post "Monitoring Node.js: Key Metrics You Should Track" linking to a Last9 blog post has generated several comments discussing various aspects of Node.js monitoring.

Several commenters discuss the importance of event loop latency as a crucial metric. One commenter highlights that Node.js performance is intrinsically tied to how quickly it can process the event loop, making latency a direct indicator of potential bottlenecks. They emphasize that high event loop latency translates directly into slow response times for users. Another commenter builds on this, mentioning that while garbage collection can contribute to latency, it's essential to differentiate between GC pauses and other sources like slow database queries or external API calls. They suggest tools and techniques to pinpoint the root cause of latency spikes.

Another thread within the comments focuses on the practical application of monitoring tools. One commenter shares their experience using specific open-source tools for monitoring Node.js applications and mentions the challenges of effectively correlating different metrics to identify and diagnose performance issues. Another commenter advocates for a more holistic approach, suggesting combining system-level metrics (CPU, memory) with application-specific metrics (request latency, error rates) for a comprehensive understanding of performance. They underscore the need to define clear alerting thresholds based on service-level objectives (SLOs) to avoid alert fatigue.

Several commenters emphasize the importance of profiling to understand CPU usage within a Node.js application. They point out that simply tracking overall CPU utilization isn't enough; you need to know which functions are consuming the most CPU cycles. One commenter suggests using specific profiling tools and flame graphs to visualize CPU usage and identify performance hotspots.

The discussion also touches upon garbage collection and its impact on performance. Commenters acknowledge that GC activity can introduce pauses in the event loop, leading to latency spikes. They recommend monitoring GC activity and tuning GC settings to minimize its impact. One commenter cautions against prematurely optimizing GC without proper analysis, suggesting that it's often more effective to focus on optimizing application code first.

Beyond these core themes, individual comments mention other valuable considerations: the importance of asynchronous programming in Node.js, the benefits of using logging and tracing for debugging and performance analysis, and the need for robust error handling mechanisms. One commenter even shares a personal anecdote about a challenging performance issue they encountered and how they resolved it. Another commenter mentions the importance of monitoring external dependencies like databases and caches, as their performance can significantly impact the overall performance of a Node.js application.
Monitoring my Minecraft server with OpenTelemetry and Prometheus

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Posted: 2025-05-08 11:03:14

This blog post details how the author used OpenTelemetry and Prometheus to monitor their Minecraft server's performance. They instrumented the server using a custom Minecraft plugin leveraging the OpenTelemetry Java agent, collecting metrics like online players, TPS (ticks per second), memory usage, and chunk loading times. This data was then sent to a Prometheus instance for storage and visualization, enabling the author to identify performance bottlenecks and optimize their server configuration for a smoother gameplay experience. The post highlights the flexibility and power of OpenTelemetry for monitoring even unconventional applications like game servers.

This blog post details the author's journey in setting up comprehensive monitoring for their personal Minecraft server using OpenTelemetry and Prometheus. Motivated by a desire to understand server performance characteristics and potential issues like lag, they opted for a more robust solution than simply relying on in-game commands or third-party plugins. The post focuses on collecting metrics, rather than logs or traces.

The chosen approach leverages the Minecraft server's built-in JMX (Java Management Extensions) interface. JMX exposes various internal performance statistics, providing a rich data source for monitoring. The author employed the OpenTelemetry Java instrumentation agent to capture these JMX metrics. This agent acts as a bridge, collecting data from the Minecraft server's JMX interface and converting it into OpenTelemetry's standardized format. Specifically, they configured the agent with a dedicated configuration file to target relevant JMX MBeans (Managed Beans) within the Minecraft server, ensuring only the desired metrics are gathered. This included metrics like CPU usage, memory allocation (heap and non-heap), garbage collection activity, the number of loaded chunks, the number of entities (players, mobs, items), and the server's tick time, a crucial indicator of performance.

The collected metrics are then exported to a Prometheus instance. Prometheus, a popular open-source monitoring system, scrapes the OpenTelemetry Collector, which acts as an intermediary, receiving and processing the metrics from the agent before forwarding them to Prometheus. This architecture allows for greater flexibility and decoupling between the Minecraft server and the monitoring backend.

Finally, the author uses Grafana to visualize the collected data. Grafana, a powerful visualization tool, allows for creating dashboards that display the metrics in an easily understandable format. The blog post includes examples of the created Grafana dashboards, showcasing how they provide insights into the server's performance. These dashboards enable the author to observe trends, identify potential bottlenecks, and proactively address performance issues before they impact gameplay. The author concludes by highlighting the benefits of their setup, emphasizing the gained visibility into the Minecraft server's inner workings and the potential for further customization and exploration of OpenTelemetry’s tracing and logging capabilities. They also briefly mention using Docker to containerize the entire monitoring stack.
Summary of Comments ( 53 )
https://news.ycombinator.com/item?id=43925005

HN commenters generally praised the author's approach to monitoring their Minecraft server using OpenTelemetry and Prometheus, finding it clever and a good practical application of the technologies. Some pointed out alternative tools like Spark or Grafana's Minecraft exporter, suggesting they might be simpler for this specific use case. Others discussed the potential performance overhead of using OpenTelemetry, with one commenter mentioning noticeable lag when instrumenting a busy Bukkit server. The conversation also touched on the broader benefits of learning OpenTelemetry for professional software development.

The Hacker News post "Monitoring my Minecraft server with OpenTelemetry and Prometheus" generated several comments discussing various aspects of the project and its implications.

Several commenters expressed appreciation for the project and its novelty. One user found the project "pretty cool" and highlighted the potential for using OpenTelemetry in gaming, suggesting it could be a "fun way to get familiar with the tools." Another echoed this sentiment, stating that applying observability tools to Minecraft was "a pretty clever idea." This user also appreciated the detailed explanation provided in the blog post.

A significant thread of discussion revolved around the practicality and usefulness of such extensive monitoring for a Minecraft server. Some questioned the necessity of such detailed metrics, especially for a personal server. One commenter jokingly remarked, "I just yell at the kids to get off when it's laggy," highlighting a more common, less technical approach to managing server performance. Another user pointed out that simpler tools like top or vmstat might suffice for most personal server needs.

However, others defended the project, arguing that it served as a valuable learning exercise and a demonstration of OpenTelemetry's versatility. One commenter suggested that even if overkill for a personal server, the project showcased how these tools could be applied to more complex gaming environments or other applications. They also highlighted the benefit of having detailed performance data for troubleshooting and optimization.

Another commenter discussed the potential for using eBPF for even deeper performance insights, suggesting it could be a valuable addition to the project. They specifically mentioned using eBPF to trace network activity and identify performance bottlenecks.

Some users also discussed alternative monitoring tools and approaches. One commenter mentioned using Prometheus to monitor their Factorio server, demonstrating that the concept of applying these tools to games isn't entirely unique. Another user suggested using Grafana for visualization, which was already implemented by the original poster.

Finally, one commenter shared a similar experience monitoring a Valheim server, further emphasizing the growing trend of applying observability tools to gaming environments. They also highlighted the challenges of getting these tools to work correctly and the satisfaction of successfully implementing them.

Overall, the comments reflect a mixture of amusement, curiosity, and genuine interest in the project. While some questioned its practicality for a personal Minecraft server, many recognized its value as a learning experience, a demonstration of OpenTelemetry's capabilities, and a potential starting point for more complex monitoring solutions in gaming and other domains.
Observability 2.0 and the Database for It

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Posted: 2025-04-25 02:39:00

GreptimeDB positions itself as the purpose-built database for "Observability 2.0," a shift towards unified observability that integrates metrics, logs, and traces. Traditional monitoring solutions struggle with the scale and complexity of this unified data, leading to siloed insights and slow query performance. GreptimeDB addresses this by offering a high-performance, cloud-native database designed specifically for time-series data, allowing for efficient querying and analysis across all observability data types. This enables faster troubleshooting, more proactive anomaly detection, and ultimately, a deeper understanding of system behavior. It leverages a columnar storage engine inspired by Apache Arrow and features PromQL-compatibility, enabling seamless integration with existing Prometheus deployments.

The blog post "Observability 2.0 and the Database for It" on Greptime's website argues that the current approach to observability, reliant on separate systems for metrics, logs, and traces, is fragmented and inadequate for the complexities of modern cloud-native environments. This fragmentation, dubbed "Observability 1.0," results in siloed data, difficult correlation, and ultimately, hinders comprehensive system understanding. The post proposes "Observability 2.0" as a solution, emphasizing a unified data platform capable of seamlessly integrating and analyzing these diverse data types.

GreptimeDB is presented as the purpose-built database designed to power this next generation of observability. It boasts a unique architecture optimized for handling the high volume, high velocity, and varied structure of observability data. Specifically, it employs a columnar storage format for efficient querying and aggregation, combined with a distributed, cloud-native design for scalability and resilience. The database leverages Apache Arrow for memory management and data transfer, promoting interoperability and performance. Additionally, PromQL and SQL support are provided for familiar query interfaces and flexible data exploration.

The blog post highlights several key advantages of adopting GreptimeDB and embracing Observability 2.0. These include improved query performance, enabling faster troubleshooting and root cause analysis; reduced infrastructure complexity by consolidating disparate systems; enhanced correlation between metrics, logs, and traces for deeper insights; and cost optimization through efficient resource utilization. The ability to ingest and analyze both structured and semi-structured data is emphasized, catering to the heterogeneous nature of observability data sources.

Furthermore, the post positions GreptimeDB as a cost-effective alternative to existing solutions, offering open-source flexibility and avoiding vendor lock-in. It champions the concept of "metrics-native" logging and tracing, arguing that integrating these data types directly into the metrics database simplifies the overall observability pipeline. The blog post concludes with a call to action, encouraging readers to explore GreptimeDB and contribute to its open-source community, envisioning a future where unified observability empowers organizations to achieve comprehensive system understanding and efficient operations.
Summary of Comments ( 42 )
https://news.ycombinator.com/item?id=43789625

Hacker News users discussed GreptimeDB's potential, questioning its novelty compared to existing time-series databases like ClickHouse and InfluxDB. Some debated its suitability for metrics versus logs and traces, with skepticism around its "one size fits all" approach. Performance claims were met with requests for benchmarks and comparisons. Several commenters expressed interest in the open-source aspect and the potential for SQL-based querying on time-series data, while others pointed out the challenges of schema design and query optimization in such a system. The lack of clarity around the distributed nature of GreptimeDB also prompted inquiries. Overall, the comments reflected a cautious curiosity about the technology, with a desire for more concrete evidence to support its claims.

The Hacker News post "Observability 2.0 and the Database for It" linking to a Greptime blog post has generated a modest discussion with several interesting points raised.

One commenter questions the framing of "Observability 2.0," expressing skepticism about the need for a new definition of observability. They argue that existing tools and practices already adequately address the core principles of observability (metrics, logs, and traces) and suggest that the term "2.0" is primarily a marketing tactic. They also point out the potential for vendor lock-in with specialized databases like GreptimeDB.

Another commenter echoes this sentiment, finding the concept of "Observability 2.0" vague and buzzword-heavy. They express concern that the industry is overcomplicating a relatively straightforward concept and that the focus should remain on effectively utilizing existing tools and methodologies.

A different commenter shifts the focus to the technical aspects, inquiring about the indexing mechanism employed by GreptimeDB and its suitability for handling high-cardinality data. They also raise a practical question regarding the database's ability to ingest data directly from Prometheus, a popular open-source monitoring system.

One commenter, seemingly affiliated with Greptime, responds to this query by clarifying that GreptimeDB utilizes a novel indexing technique designed to efficiently manage high-cardinality data. They confirm that direct ingestion from Prometheus is supported through the PromQL interface and outline the roadmap for future integrations with other data sources. They further elaborate on GreptimeDB's architecture, highlighting its distributed nature and the use of Apache Arrow for columnar storage.

Another commenter expresses interest in the open-source nature of GreptimeDB, appreciating the transparency and community involvement. They inquire about the licensing model and the potential for contributing to the project.

Finally, a commenter raises a broader point about the challenges of managing and analyzing large volumes of observability data. They acknowledge the limitations of traditional databases in this context and express optimism that specialized databases like GreptimeDB might offer a more effective solution. They also highlight the importance of cost-effectiveness in this domain, given the ever-increasing scale of data generated by modern systems.
Why Does My eBPF Program Work on One Kernel but Fail on Another?

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Posted: 2025-04-23 07:17:16

eBPF program portability can be tricky due to differences in kernel versions and configurations. The blog post highlights how seemingly minor variations, such as a missing helper function or a change in struct layout, can cause a program that works perfectly on one kernel to fail on another. It emphasizes the importance of using the bpftool utility for introspection, allowing developers to compare kernel features and identify discrepancies that might be causing compatibility issues. Additionally, building eBPF programs against the oldest supported kernel and strategically employing the LINUX_VERSION_CODE macro can enhance portability and minimize unexpected behavior across different kernel versions.

The blog post "Why Does My eBPF Program Work on One Kernel but Fail on Another?" explores the common frustration of eBPF programs behaving inconsistently across different Linux kernel versions. It delves into the reasons behind this incompatibility, focusing on the volatile nature of the eBPF verifier and its dependencies on kernel internals.

The author begins by acknowledging the seemingly random nature of these failures, where a functioning eBPF program on one kernel version might inexplicably break on another, even with seemingly minor version differences. This fragility stems from the eBPF verifier, a crucial component responsible for ensuring the safety and stability of eBPF programs before they are loaded into the kernel. The verifier analyzes the program's bytecode, meticulously checking for potential issues like infinite loops, out-of-bounds memory accesses, and other unsafe operations that could compromise the kernel's integrity.

A key factor contributing to the verifier's volatility is its reliance on internal kernel data structures and functions. These internals can change between kernel versions, sometimes subtly and without explicit documentation. As a result, a verifier that accepts a program on one kernel might reject it on another due to altered offsets, data structure layouts, or function signatures. Even seemingly minor changes in the kernel's internal workings can have cascading effects on the verifier's logic and lead to program rejection.

The blog post emphasizes that relying on undocumented kernel internals is a primary culprit in these cross-kernel incompatibilities. eBPF programs often interact with kernel functions and data structures that are not part of the official kernel API. While accessing these internals might offer powerful capabilities, it creates a tight coupling between the eBPF program and the specific kernel version it was developed on. Any changes to these undocumented elements in a newer kernel can render the eBPF program unusable.

The author then highlights several specific examples of internal kernel changes impacting eBPF program compatibility, including modifications to context structures and helper functions. These examples illustrate how even seemingly innocuous changes can break existing eBPF programs.

Finally, the post offers strategies for mitigating these compatibility challenges. One approach involves using the bpftool utility to inspect the verifier's log and understand the reasons for program rejection. This can provide valuable insights into the specific kernel changes causing the incompatibility. Another strategy is to avoid relying on undocumented kernel internals whenever possible. Sticking to the stable kernel API can minimize the risk of breakage across kernel versions. The post concludes by encouraging developers to embrace the dynamic nature of the eBPF ecosystem and proactively address potential compatibility issues. Using tools and best practices can help ensure that eBPF programs remain functional and portable across different kernel versions.
- eBPF
- Kernel
- compatibility
- Debugging
- Troubleshooting
- Linux
- BPF
- kernel version
- portability
- system calls
- kernel modules
- performance
- Observability
Summary of Comments ( 12 )
https://news.ycombinator.com/item?id=43769461

The Hacker News comments discuss potential reasons for eBPF program incompatibility across different kernels, focusing primarily on kernel version discrepancies and configuration variations. Some commenters highlight the rapid evolution of the eBPF ecosystem, leading to frequent breaking changes between kernel releases. Others point to the importance of checking for specific kernel features and configurations (like CONFIG_BPF_JIT) that might be enabled on one system but not another, especially when using newer eBPF functionalities. The use of CO-RE (Compile Once – Run Everywhere) and its limitations are also brought up, with users encountering problems despite its intent to improve portability. Finally, some suggest practical debugging strategies, such as using bpftool to inspect program behavior and verify kernel support for required features. A few commenters mention the challenge of staying up-to-date with eBPF's rapid development, emphasizing the need for careful testing across target kernel versions.

The Hacker News post "Why Does My eBPF Program Work on One Kernel but Fail on Another?" with the ID 43769461 has several comments discussing the intricacies and challenges of working with eBPF across different kernel versions.

Several commenters highlight the rapid pace of eBPF development and the resulting instability across kernel versions. One commenter points out that the constant evolution, while beneficial in the long run, makes it difficult for developers to maintain compatibility. They mention the frequent changes in verifier rules and helper functions as primary culprits. Another echoes this sentiment, stating that keeping up with these changes can be a full-time job, particularly when dealing with complex eBPF programs. This rapid evolution necessitates careful attention to kernel version compatibility during development and deployment.

The discussion also delves into the specifics of eBPF program loading and verification. One commenter explains how the behavior of the eBPF verifier can change between kernel versions, leading to programs that work on one kernel but fail on another. They mention that seemingly minor kernel upgrades can sometimes introduce breaking changes in the verifier's logic, causing previously valid programs to be rejected. This emphasizes the need for thorough testing across different target kernels.

Another thread focuses on the challenges of debugging eBPF programs. A user shares their experience of encountering cryptic error messages from the verifier, making it difficult to pinpoint the root cause of the issue. They suggest that improved tooling and more descriptive error messages would significantly ease the debugging process. Another commenter suggests using dynamic tracing tools like bpftrace to gain insights into the program's execution and identify potential problems.

The complexities of eBPF helper functions are also addressed. One commenter points out that the availability and behavior of helper functions can vary across kernels. They recommend consulting the kernel documentation and checking for changes in helper function signatures between kernel versions. Another user advises against relying on undocumented helper functions, as their behavior might change unexpectedly.

Finally, several commenters emphasize the importance of staying updated with the latest eBPF developments. They recommend subscribing to mailing lists, following relevant communities, and keeping track of kernel release notes to anticipate potential compatibility issues. They also advocate for better documentation and tooling to simplify eBPF development and improve cross-kernel compatibility.
Launch HN: Sift Dev (YC W25) – AI-Powered Datadog Alternative

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Posted: 2025-03-11 17:00:46

Sift Dev, a Y Combinator-backed startup, has launched an AI-powered alternative to Datadog for observability. It aims to simplify debugging and troubleshooting by using AI to automatically analyze logs, metrics, and traces, identifying the root cause of issues and surfacing relevant information without manual querying. Sift Dev offers a free tier and integrates with existing tools and platforms. The goal is to reduce the time and complexity involved in resolving incidents and improve developer productivity.

A new company called Sift Dev, a participant in the Winter 2025 batch of Y Combinator, has launched and is presenting itself as an AI-powered alternative to Datadog. Their offering aims to simplify the complex process of debugging and understanding performance issues in software applications. Instead of requiring engineers to manually sift through extensive logs, metrics, and traces, Sift Dev leverages artificial intelligence to automatically identify the root causes of problems. This automated root cause analysis promises to dramatically reduce the time and effort required to diagnose and resolve issues, theoretically leading to faster debugging cycles and increased developer productivity. The announcement on Hacker News links to the Sift Dev website, where interested individuals can sign up for early access to the platform. The post highlights the difficulty and time-consuming nature of traditional debugging methods, positioning Sift Dev's AI-driven approach as a significant improvement over existing tools. While the post doesn't delve into the specifics of the AI technology utilized, it implicitly suggests a more streamlined and intuitive debugging experience compared to established solutions like Datadog. The focus is on empowering developers to quickly pinpoint and address performance bottlenecks, ultimately leading to more stable and performant applications.
- Sift Dev
- YC W25
- Y Combinator
- AI
- artificial intelligence
- Datadog
- Monitoring
- Observability
- DevOps
- SaaS
- startup
- Software
- Alternative
- logs
- Metrics
- Traces
- APM
- application performance monitoring
- Cloud Monitoring
- Infrastructure Monitoring
Summary of Comments ( 31 )
https://news.ycombinator.com/item?id=43334589

The Hacker News comments section for Sift Dev reveals a generally skeptical, yet curious, audience. Several commenters question the value proposition of another observability tool, particularly one focused on AI, expressing concerns about potential noise and the need for explainability. Some see the potential for AI to be useful in filtering and correlating events, but emphasize the importance of not obscuring underlying data. A few users ask for clarification on pricing and how Sift Dev differs from existing solutions. Others are interested in the specific AI techniques used and how they contribute to root cause analysis. Overall, the comments express cautious interest, with a desire for more concrete details about the platform's functionality and benefits over established alternatives.

The Hacker News post for "Launch HN: Sift Dev (YC W25) – AI-Powered Datadog Alternative" has generated several comments discussing various aspects of the product and the market it's entering.

Several commenters express skepticism about the value proposition of using AI in this context. One commenter questions whether AI genuinely adds value for debugging or if it's primarily a marketing buzzword. They argue that traditional methods, like structured logging and effective dashboards, are already sufficient for most debugging scenarios. Another echoes this sentiment, pointing out that experienced engineers often rely on simpler tools and their own intuition. They suggest that AI might only be beneficial in very specific niche cases, not as a general replacement for established monitoring solutions.

Some discussion revolves around the cost and complexity of implementing and maintaining an AI-powered monitoring system. One commenter raises concerns about the potential for increased costs compared to existing solutions, questioning whether the benefits justify the expense. Another user highlights the potential difficulty in understanding and troubleshooting issues arising from the AI's analysis itself, introducing another layer of complexity to the debugging process.

A few commenters express interest in specific features or ask clarifying questions about the product. One asks about the platform's support for various programming languages and frameworks. Another inquires about the pricing model and whether a free tier is available. These comments demonstrate a genuine interest from potential users, seeking practical information about the tool.

Some of the comments offer alternative perspectives on the use of AI in observability. One commenter suggests that AI could be more useful in predicting potential issues rather than just reacting to existing ones. This proactive approach, they argue, could be a significant advantage. Another user proposes that the real value of AI lies in automating tasks like log analysis and anomaly detection, freeing up developers to focus on more complex problems.

Finally, a few comments touch upon the competitive landscape. Some acknowledge the dominance of Datadog in the market and question whether a new entrant, even with AI capabilities, can realistically compete. Others express a desire for more open-source alternatives in the observability space and see potential in Sift Dev if it embraces open-source principles.
Strobelight: A profiling service built on open source technology

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Posted: 2025-03-07 14:43:24

Meta developed Strobelight, an internal performance profiling service built on open-source technologies like eBPF and Spark. It provides continuous, low-overhead profiling of their C++ services, allowing engineers to identify performance bottlenecks and optimize CPU usage without deploying special builds or restarting services. Strobelight leverages randomized sampling and aggregation to minimize performance impact while offering flexible filtering and analysis capabilities. This helps Meta improve resource utilization, reduce costs, and ultimately deliver faster, more efficient services to users.

Facebook engineers have developed and deployed Strobelight, a comprehensive profiling service designed to analyze and optimize the performance of their vast server fleet. This system leverages the power of open-source technologies, including Linux's extended Berkeley Packet Filter (eBPF) and the Parca project, to provide continuous, low-overhead profiling capabilities across diverse workloads and languages. Strobelight's primary goal is to identify performance bottlenecks and inefficiencies, ultimately reducing infrastructure costs and enhancing the user experience across Facebook's platforms.

Strobelight addresses the limitations of traditional profiling methods, which are often intrusive, require recompilation or restarts, and provide only sporadic snapshots of performance. Instead, Strobelight operates continuously in production environments, collecting performance data with minimal impact on the running services. This continuous profiling enables engineers to gain a deeper understanding of long-term performance trends, identify transient issues, and observe the impact of code changes in real-time.

The architecture of Strobelight centers around eBPF, a powerful technology that allows dynamic insertion of code into the Linux kernel. This allows Strobelight to efficiently collect performance data directly from the operating system without requiring modifications to application code. Leveraging eBPF, Strobelight gathers CPU profiling data, including stack traces and timestamps, revealing the precise functions and code paths consuming CPU resources. This information is crucial for pinpointing performance hotspots and identifying areas for optimization.

Collected profiling data is then processed and stored using Parca, an open-source continuous profiling project. Parca provides a robust and scalable platform for storing, querying, and visualizing profiling data. It allows engineers to explore performance data over time, correlate performance with specific events, and conduct comparative analyses to understand the impact of code changes. This rich dataset empowers engineers to make data-driven decisions regarding performance optimization and resource allocation.

Strobelight integrates seamlessly with Facebook's internal infrastructure and tooling, allowing for streamlined access to profiling data and integration with existing monitoring and alerting systems. This integration simplifies the process of identifying and addressing performance issues, facilitating rapid iteration and improvement.

By adopting a continuous profiling approach based on open-source technologies, Facebook has achieved significant gains in performance visibility and optimization capabilities. Strobelight represents a significant advancement in performance engineering, enabling Facebook to proactively address performance bottlenecks, reduce infrastructure costs, and ultimately deliver a smoother and more responsive experience for its billions of users worldwide. This focus on continuous profiling reflects a broader industry trend towards proactive performance management and the adoption of open-source tools for performance analysis.
Summary of Comments ( 7 )
https://news.ycombinator.com/item?id=43290555

Hacker News commenters generally praised Facebook/Meta's release of Strobelight as a positive contribution to the open-source profiling ecosystem. Some expressed excitement about its use of eBPF and its potential for performance analysis. Several users compared it favorably to other profiling tools, noting its ease of use and comprehensive data visualization. A few commenters raised questions about its scalability and overhead, particularly in large-scale production environments. Others discussed its potential applications beyond the initially stated use cases, including debugging and optimization in various programming languages and frameworks. A small number of commenters also touched upon Facebook's history with open source, expressing cautious optimism about the project's long-term support and development.

The Hacker News post discussing Facebook's Strobelight profiling service generated several comments, mostly focusing on comparisons with existing profiling tools and some skepticism about Facebook's open-source contributions.

One commenter highlights the similarities between Strobelight and existing open-source continuous profiling tools like Parca, pyroscope, and conprof, questioning the novelty of Facebook's solution. They suggest that Facebook could have contributed to these projects instead of creating a new one. This sentiment is echoed by another user who mentions contributing to async-profiler, a Java profiler, and expresses disappointment that large companies often reinvent the wheel instead of collaborating with existing open-source efforts.

Another commenter focuses on the perceived "open-washing" aspect, arguing that Facebook's history with open source has been more about taking than giving back. They express doubt that Strobelight will be truly open and actively maintained, suggesting it might be abandoned like other Facebook open-source projects.

A few users discuss the technical details of Strobelight, comparing its eBPF-based approach with other profiling methods and speculating about its performance characteristics. One commenter mentions using a custom-built eBPF profiler similar to Strobelight and shares their experience, providing a practical perspective on the technology.

Some comments also touch upon the challenges of profiling in production environments and the complexities of performance analysis. One user raises the question of whether Strobelight addresses the issue of "noisy neighbors" in shared infrastructure, highlighting a common problem in cloud-native environments.

Overall, the comments express a mix of curiosity about the technical aspects of Strobelight, skepticism about Facebook's open-source commitment, and comparisons with existing profiling solutions. Several users advocate for collaboration with existing open-source projects instead of reinventing the wheel. The conversation provides a glimpse into the perspectives of developers and engineers familiar with profiling tools and the challenges of performance optimization.
AI: Where in the Loop Should Humans Go?

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Posted: 2025-03-04 20:57:36

The Honeycomb blog post explores the optimal role of humans in AI systems, advocating for a shift from "human-in-the-loop" to "human-in-the-design" approach. While acknowledging the current focus on using humans for labeling training data and validating outputs, the post argues that this reactive approach limits AI's potential. Instead, it emphasizes the importance of human expertise in shaping the entire AI lifecycle, from defining the problem and selecting data to evaluating performance and iterating on design. This proactive involvement leverages human understanding to create more robust, reliable, and ethical AI systems that effectively address real-world needs.

The Honeycomb blog post, "AI: Where in the Loop Should Humans Go?" explores the evolving relationship between humans and artificial intelligence, specifically focusing on the concept of "human-in-the-loop" systems. It meticulously dissects the various stages of AI development and deployment where human intervention is not only beneficial but often crucial for ensuring accuracy, reliability, and ethical considerations. The article posits that the optimal placement of human oversight within these systems is dynamic and depends heavily on the specific application and the maturity of the AI model in question.

The piece begins by outlining the spectrum of human involvement, ranging from complete human control, where the AI acts as a supporting tool, to fully autonomous systems where human intervention is minimal or reserved for exceptional circumstances. The authors argue that the initial stages of AI development necessitate a high degree of human oversight. This "human-in-the-loop" approach allows developers to train and refine the model by providing labeled data, correcting errors, and addressing biases. As the AI matures and demonstrates increased proficiency, the level of human involvement can gradually decrease, shifting towards a "human-on-the-loop" model. In this scenario, humans primarily monitor the AI's performance, intervening only when the system encounters unfamiliar situations, produces unexpected outputs, or requires adjustments based on evolving real-world conditions.

The blog post further emphasizes the importance of human judgment in handling edge cases, scenarios that fall outside the typical training data and may represent complex or ambiguous situations. AI models, particularly those trained on large but finite datasets, can struggle with these edge cases, potentially leading to inaccurate or inappropriate responses. Human intervention is essential to ensure that the AI handles these situations appropriately and ethically. Furthermore, the authors highlight the role of humans in defining and refining the objectives and constraints of the AI system. By establishing clear goals and ethical boundaries, humans can steer the AI towards desirable outcomes and prevent unintended consequences.

The article also explores the practical implications of integrating human oversight into AI systems, acknowledging the challenges associated with effectively incorporating human feedback. It underscores the need for user-friendly interfaces and streamlined workflows that enable seamless collaboration between humans and AI. The authors suggest that the design of these interfaces should prioritize clarity, efficiency, and minimize cognitive load on human operators. Ultimately, the blog post advocates for a thoughtful and adaptable approach to human-in-the-loop systems, recognizing that the optimal level of human involvement is a constantly evolving equation that must be continuously reevaluated and adjusted based on the specific needs and characteristics of each AI application. It concludes by emphasizing that the future of AI hinges on a synergistic partnership between humans and machines, leveraging the strengths of both to achieve optimal performance, reliability, and ethical outcomes.
Summary of Comments ( 1 )
https://news.ycombinator.com/item?id=43259742

HN users discuss various aspects of human involvement in AI systems. Some argue for human oversight in critical decisions, particularly in fields like medicine and law, emphasizing the need for accountability and preventing biases. Others suggest humans are best suited for defining goals and evaluating outcomes, leaving the execution to AI. The role of humans in training and refining AI models is also highlighted, with suggestions for incorporating human feedback loops to improve accuracy and address edge cases. Several comments mention the importance of understanding context and nuance, areas where humans currently outperform AI. Finally, the potential for humans to focus on creative and strategic tasks, leveraging AI for automation and efficiency, is explored.

The Hacker News post "AI: Where in the Loop Should Humans Go?" discussing the Honeycomb blog post of the same name generated a moderate amount of discussion with several insightful comments.

A recurring theme is the tension between fully automated AI solutions and human-in-the-loop systems. One commenter highlights the value of human intuition and experience, arguing that while AI excels at identifying patterns, humans are better equipped to understand context and nuance, especially in complex situations. They suggest a collaborative approach where AI serves as a tool to augment human capabilities rather than replace them entirely. This sentiment is echoed by another commenter who stresses the importance of human oversight in ensuring the ethical and responsible use of AI, particularly in sensitive areas like healthcare and law enforcement.

Another commenter points out the economic incentives driving the push for full automation, arguing that businesses are motivated by the potential cost savings of eliminating human labor. They acknowledge the benefits of automation for repetitive tasks but caution against blindly pursuing full automation without considering the potential downsides. This leads to a discussion about the trade-offs between efficiency and reliability, with some arguing that human-in-the-loop systems, while potentially slower, offer greater accuracy and adaptability.

The "human-out-of-the-loop" approach is also discussed, with a commenter questioning the feasibility of truly removing humans from the equation. They argue that even in highly automated systems, humans are still involved in tasks like designing, training, and maintaining the AI, highlighting the ongoing need for human expertise.

Finally, several commenters emphasize the importance of careful consideration of the specific task and context when deciding where humans should fit in the loop. They suggest that different applications require different levels of human involvement, with some tasks being more amenable to full automation than others. The consensus seems to be that a nuanced, context-dependent approach is necessary to effectively leverage the strengths of both AI and human intelligence.
Telescope – an open-source web-based log viewer for logs stored in ClickHouse

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Posted: 2025-02-26 08:28:21
Telescope is an open-source, web-based log viewer designed specifically for ClickHouse. It provides a user-friendly interface for querying, filtering, and visualizing logs stored within ClickHouse databases. Features include full-text search, support for various log formats, customizable dashboards, and real-time log streaming. Telescope aims to simplify the process of exploring and analyzing large volumes of log data, making it easier to identify trends, debug issues, and monitor system performance.
Telescope is an open-source, web-based log viewer specifically designed for efficiently querying and visualizing logs stored within ClickHouse, a high-performance column-oriented database management system. It provides a user-friendly interface for exploring and analyzing vast quantities of log data, enabling users to quickly identify trends, pinpoint errors, and gain deeper insights into their application's behavior.

Key features of Telescope include:
- ClickHouse Integration: Telescope is tightly coupled with ClickHouse, leveraging its powerful analytical capabilities for fast and efficient log processing. This allows for interactive querying and analysis of massive datasets, making it suitable for handling the high volume of logs generated by modern applications.
- Intuitive Web Interface: The project offers a clean and intuitive web UI, enabling users to easily browse, search, and filter logs without needing to write complex ClickHouse queries. The interface simplifies the process of navigating through large log files and facilitates quick identification of relevant information.
- Real-time Log Streaming: Telescope supports real-time log streaming, providing up-to-date visibility into application activity. This allows users to monitor live logs and immediately detect and react to emerging issues.
- Customizable Dashboards and Visualizations: Users can create custom dashboards and visualizations to track key metrics and gain a comprehensive overview of their application's performance. This allows for tailored insights based on specific needs and facilitates the identification of patterns and anomalies.
- Search and Filtering: Telescope offers robust search and filtering capabilities, allowing users to quickly locate specific log entries based on various criteria. This includes filtering by timestamps, log levels, keywords, and other relevant attributes.
- Open Source and Extensible: Being open-source, Telescope allows for community contributions and customization. Users can adapt the tool to their specific requirements and integrate it seamlessly into their existing logging infrastructure.
- Simplified Log Management: By centralizing log viewing and analysis within a single web interface, Telescope simplifies log management and reduces the complexity of working with ClickHouse directly.
In essence, Telescope bridges the gap between the raw power of ClickHouse and the need for an accessible and user-friendly log exploration tool. It empowers users to harness the performance of ClickHouse for log analysis without requiring in-depth knowledge of ClickHouse query language or database administration. This makes Telescope a valuable tool for developers, DevOps engineers, and anyone dealing with large volumes of log data stored in ClickHouse.
Summary of Comments ( 36 )
https://news.ycombinator.com/item?id=43181862

Hacker News users generally praised Telescope's clean interface and the smart choice of using ClickHouse for storage, highlighting its performance capabilities. Some questioned the need for another log viewer, citing existing solutions like Grafana Loki and Kibana, but acknowledged Telescope's potential niche for users already invested in ClickHouse. A few commenters expressed interest in specific features like query language support and the ability to ingest logs directly. Others focused on the practical aspects of deploying and managing Telescope, inquiring about resource consumption and single-sign-on integration. The discussion also touched on alternative approaches to log analysis and visualization, including using command-line tools or more specialized log aggregation systems.

The Hacker News post for Telescope, an open-source web-based log viewer for ClickHouse, has several comments discussing its features, alternatives, and potential use cases.

Several commenters express interest in the project and its capabilities. One user highlights the benefit of having a GUI for ClickHouse, especially for tasks like filtering logs. Another appreciates the straightforward setup process and the user-friendly interface, contrasting it with more complex alternatives. The simplicity of Telescope compared to Grafana is also mentioned favorably.

Some discussion revolves around comparisons to other existing tools. One commenter mentions using Kibana with ClickHouse, while another brings up Grafana and Loki as a comparable setup. The discussion points out that Telescope is specifically designed for ClickHouse, unlike more general-purpose tools. This focus potentially offers a more tailored and efficient experience for users working exclusively with ClickHouse.

The ability to write SQL queries within Telescope is highlighted as a major advantage. This direct access to ClickHouse's power is seen as a significant plus for those familiar with SQL.

A few comments delve into more technical aspects. One user asks about the performance implications of running Telescope, particularly regarding resource usage. Another commenter inquires about the support for distributed ClickHouse setups, showcasing interest in scaling the log viewing capabilities.

The thread also touches upon the potential uses of Telescope. One commenter mentions using a similar setup (ClickHouse and a custom web UI) for security log analysis, while others discuss the general benefits of having a visual tool for navigating large log datasets. This illustrates the broad applicability of Telescope in various domains.

Finally, there's some discussion around the project's open-source nature and future development. Users express interest in contributing and suggest features like adding authentication or improving performance. This active community engagement suggests a healthy and evolving project.
Agent-Less System Monitoring with Elixir Broadway

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

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

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.
Grafana: Why observability needs FinOps, and vice versa

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Posted: 2025-02-06 19:13:34

Observability and FinOps are increasingly intertwined, and integrating them provides significant benefits. This blog post highlights the newly launched Vantage integration with Grafana Cloud, which allows users to combine cost data with observability metrics. By correlating resource usage with cost, teams can identify optimization opportunities, understand the financial impact of performance issues, and make informed decisions about resource allocation. This integration enables better control over cloud spending, faster troubleshooting, and more efficient infrastructure management by providing a single pane of glass for both technical performance and financial analysis. Ultimately, it empowers organizations to achieve a balance between performance and cost.

The Grafana blog post, "Why observability needs FinOps, and vice versa: The Vantage integration with Grafana Cloud," emphasizes the synergistic relationship between observability and FinOps (cloud financial operations), arguing that each discipline significantly enhances the other, leading to more efficient and cost-effective cloud usage. The integration of Vantage, a FinOps platform by Google Cloud, with Grafana Cloud is presented as a practical example of this synergy.

The post begins by highlighting the challenges faced by organizations adopting cloud technologies, particularly the difficulty in understanding and managing cloud costs. It argues that traditional cost management tools are insufficient for the dynamic and complex nature of cloud environments. Observability, with its focus on detailed insights into system performance and behavior, is positioned as a crucial component for gaining a deeper understanding of cost drivers. By correlating cost data with operational metrics, organizations can identify areas of inefficiency, optimize resource allocation, and ultimately reduce cloud spend.

Conversely, the post argues that FinOps practices benefit observability efforts. By understanding the cost implications of different observability strategies, organizations can make informed decisions about data collection, retention, and analysis. This prevents overspending on excessive data ingestion and storage while ensuring that sufficient data is available for effective monitoring and troubleshooting.

The integration of Vantage with Grafana Cloud is presented as a key enabler of this bidirectional benefit. Vantage brings granular cost and usage data into the Grafana ecosystem, allowing users to visualize, analyze, and correlate cost information with other operational metrics within a single platform. This unified view empowers teams to pinpoint cost anomalies, investigate their root causes, and implement corrective actions.

The post provides specific examples of how the integration can be leveraged, such as identifying idle or underutilized resources, tracking the cost of specific applications or services, and analyzing the impact of code changes on cloud spend. It highlights features like cost-optimized alerting, which allows users to set thresholds for cost-related metrics and receive notifications when those thresholds are exceeded. This proactive approach enables teams to address cost issues before they escalate.

Furthermore, the blog post emphasizes the collaborative aspect of FinOps and observability, suggesting that bringing together engineering, finance, and operations teams through a shared platform fosters better communication and alignment around cost optimization goals. This cross-functional collaboration is crucial for implementing effective FinOps strategies and realizing the full potential of cloud cost savings. The post concludes by reiterating the importance of integrating FinOps and observability for achieving sustainable cloud financial management and driving business value.
Summary of Comments ( 5 )
https://news.ycombinator.com/item?id=42965499

HN commenters generally express skepticism about the purported synergy between FinOps and observability. Several suggest that while cost visibility is important, integrating FinOps directly into observability platforms like Grafana might be overkill, creating unnecessary complexity and vendor lock-in. They argue for maintaining separate tools and focusing on clear cost allocation tagging strategies instead. Some also point out potential conflicts of interest, with engineering teams prioritizing performance over cost and finance teams lacking the technical expertise to interpret complex observability data. A few commenters see some value in the integration for specific use cases like anomaly detection and right-sizing resources, but the prevailing sentiment is one of cautious pragmatism.

The Hacker News post "Grafana: Why observability needs FinOps, and vice versa" has generated a few comments, primarily focusing on the increasing costs associated with observability tools and the complexities of managing them effectively.

One commenter highlights the irony of needing cost management tools for the very systems meant to monitor and optimize other systems. They express a sentiment that the ever-expanding tooling ecosystem for cloud infrastructure creates a cycle of needing more tools to manage the previous set of tools. This resonates with the idea that observability, while crucial, can become a significant expense if not carefully managed.

Another commenter points out the inherent conflict between the detailed data collection required for effective observability and the associated costs. They argue that "observability is in direct tension with saving money." This implies that the desire for granular insights often leads to increased storage and processing costs, creating a trade-off between visibility and affordability. They further suggest that cost analysis within observability systems should be a core feature, not an afterthought, to help manage this tension.

A third commenter expresses frustration with the current state of observability and monitoring tools. They claim that such tools often become bloated and difficult to manage. They call for simpler, more focused tools that provide crucial metrics without unnecessary complexity, ultimately aiming for a more manageable and cost-effective solution. This sentiment aligns with the overall discussion around the escalating costs and complexities of maintaining comprehensive observability.

The discussion, while concise, revolves around the practical challenges of implementing observability. The comments emphasize the need for better cost management practices within observability tools themselves, highlighting the growing tension between the benefits of detailed monitoring and the increasing financial burden it can impose.
Show HN: Perforator – cluster-wide profiling tool for large data centers

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Posted: 2025-02-01 08:00:34

Perforator is an open-source, cluster-wide profiling tool developed by Yandex for analyzing performance in large data centers. It uses hardware performance counters to collect low-overhead, detailed performance data across thousands of machines simultaneously, aiming to identify performance bottlenecks and optimize resource utilization. The tool offers a web interface for visualization and analysis, and allows users to drill down into specific nodes and processes for deeper investigation. Perforator supports various profiling modes, including CPU, memory, and I/O, and can be integrated with existing monitoring systems.

Yandex has unveiled Perforator, a novel performance profiling tool designed specifically for the challenges of large-scale data centers. This open-source solution aims to provide comprehensive and granular insights into the performance bottlenecks that can plague complex distributed systems. Unlike traditional profilers that often focus on individual machines, Perforator adopts a cluster-wide approach, enabling administrators and developers to analyze performance across numerous interconnected servers simultaneously. This holistic perspective is crucial for understanding the interplay between different components within a distributed environment and identifying the root causes of performance issues that might be obscured by isolated machine-level analysis.

Perforator utilizes Linux's extended Berkeley Packet Filter (eBPF) technology for efficient data collection. eBPF allows for dynamic tracing and performance monitoring within the kernel with minimal overhead, making it well-suited for the demands of high-traffic, production environments. By leveraging eBPF, Perforator can capture detailed performance metrics without significantly impacting the performance of the systems being monitored.

The tool offers a range of features designed to streamline performance analysis. It provides flame graphs, a powerful visualization technique for understanding the hierarchical relationships between function calls and identifying performance hotspots. Furthermore, Perforator incorporates differential flame graphs, allowing for direct comparisons between different performance profiles, enabling developers to pinpoint the impact of code changes or configuration adjustments on overall system performance. The tool also offers call graphs, which provide a visual representation of the flow of execution within the system, further aiding in understanding complex interactions between different services and components.

Perforator is designed to be easily deployable and integrated within existing infrastructure. It aims to minimize the operational burden associated with performance monitoring and analysis, providing valuable insights without requiring extensive configuration or specialized expertise. By offering a comprehensive and efficient solution for cluster-wide profiling, Perforator empowers engineers to optimize the performance of their large-scale data centers and deliver improved service reliability and efficiency. Its focus on distributed systems and its utilization of cutting-edge technologies like eBPF position Perforator as a valuable tool for anyone working with the complexities of modern data center operations.
Summary of Comments ( 9 )
https://news.ycombinator.com/item?id=42896716

Several commenters on Hacker News expressed interest in Perforator, particularly its ability to profile at scale and its low overhead. Some questioned the choice of Python for the agent, citing potential performance issues, while others appreciated its ease of use and integration with existing Python-based infrastructure. A few commenters compared it favorably to existing tools like BCC and eBPF, highlighting Perforator's distributed nature as a key differentiator. The discussion also touched on the challenges of profiling in production environments, with some sharing their experiences and suggesting potential improvements to Perforator. Overall, the comments indicated a positive reception to the tool, with many eager to try it in their own environments.

The Hacker News post titled "Show HN: Perforator – cluster-wide profiling tool for large data centers" (https://news.ycombinator.com/item?id=42896716) has generated a modest number of comments, primarily focusing on comparisons to existing profiling tools and discussing the practical applications and limitations of Perforator.

Several commenters brought up alternative profiling solutions, highlighting their strengths and weaknesses in comparison to Perforator. One commenter mentioned Coz, emphasizing its user-friendliness and integration with flame graphs. Another suggested the combination of Linux perf and eBPF as a powerful alternative, especially for kernel-level profiling. The discussion around these alternatives touched upon the trade-offs between ease of use, performance overhead, and the level of detail provided.

The practicality of deploying Perforator in large-scale production environments was also a key topic. One commenter questioned the feasibility of using Perforator continuously, citing concerns about performance impact and the potential for data overload. This prompted a discussion about the importance of sampling and filtering in mitigating these issues. The creator of Perforator (a Yandex employee) responded to some of these queries, clarifying the tool's design choices and addressing concerns about its overhead. They explained that Perforator is intended for targeted profiling of specific issues rather than continuous monitoring, and highlighted the tool's ability to filter data based on various criteria. They also explained how the overhead of continuous profiling was minimized.

A few comments focused on specific features of Perforator, such as its support for different profiling methods (perf, eBPF) and its visualization capabilities. One commenter inquired about the integration with other observability tools, while another expressed interest in the underlying data format and the possibility of analyzing it with external tools.

Overall, the comments section provides valuable insights into the potential use cases and limitations of Perforator. The discussion highlights the complexities of performance profiling in large data centers and the need for tools that balance performance overhead, data richness, and ease of use. The comments do not delve deeply into the technical intricacies of Perforator, but rather focus on its practical implications and its position within the existing ecosystem of profiling tools.
Case Study: ByteDance Uses eBPF to Enhance Networking Performance

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Posted: 2025-01-29 15:58:20

ByteDance, facing challenges with high connection counts and complex network topologies across its global services, leveraged eBPF to significantly improve networking performance. They developed several in-house eBPF-based tools, including a high-performance load balancer and a connection management system, to optimize resource utilization and reduce latency. These tools allowed for more efficient traffic distribution, connection concurrency control, and real-time performance monitoring, leading to improved stability and resource efficiency in their data centers. The adoption of eBPF enabled ByteDance to overcome limitations of traditional kernel-based networking solutions and achieve greater scalability and control over their network infrastructure.

This case study details how ByteDance, the parent company of popular social media platforms like TikTok and Douyin, leveraged extended Berkeley Packet Filter (eBPF) technology to significantly improve their network performance and observability. ByteDance operates a massive, globally distributed network infrastructure handling immense traffic volumes, necessitating highly optimized and efficient network operations. Traditional network monitoring and troubleshooting methods proved inadequate for their scale and complexity, often involving complex deployments and limited visibility.

eBPF presented a compelling solution due to its ability to dynamically attach custom programs to various kernel hooks without requiring kernel recompilation or module loading. This flexibility allows for real-time performance analysis and targeted modifications to network behavior. ByteDance utilized eBPF in several key areas:

1. Gateway Load Balancing: By implementing an eBPF-based load balancer at their gateway layer, ByteDance optimized traffic distribution across multiple backend servers. This approach bypassed the limitations of traditional load balancing methods, enabling more granular control and improved resource utilization. The eBPF program dynamically adjusted traffic flow based on real-time network conditions, ensuring optimal performance even under fluctuating loads. This directly addressed issues with connection stickiness experienced with traditional layer-4 load balancing, achieving more effective distribution across backend servers.

2. Network Namespace Isolation: ByteDance employs network namespaces to isolate different services and applications. Managing inter-namespace communication efficiently is crucial. They utilized eBPF to optimize traffic forwarding between namespaces, significantly reducing latency and overhead associated with virtual network interfaces. This facilitated smoother and faster communication between services.

3. Short-lived Connection Optimization: Short-lived connections, common in microservice architectures and high-volume applications, create significant overhead in connection establishment and teardown. ByteDance used eBPF to optimize the handling of these connections, specifically TCP short-lived connections within data centers, by optimizing the TCP stack behavior within the kernel. This optimization reduced the computational burden on servers and improved the efficiency of these transient connections, especially benefiting applications like online gaming and live streaming that rely heavily on quick, short bursts of communication. By offloading connection management to the kernel via eBPF, they bypassed userspace context switching and system calls, resulting in substantial latency reduction.

4. Network Performance Monitoring and Troubleshooting: eBPF provided enhanced visibility into network traffic, allowing ByteDance to identify and diagnose performance bottlenecks quickly. By attaching eBPF programs to specific points in the network stack, they gathered detailed metrics on packet flow, latency, and errors. This real-time data enabled proactive identification and resolution of performance issues, contributing to improved overall system stability and reduced downtime. Specifically, they gained insight into traffic distribution across servers, latency between services, and other critical performance indicators, enabling them to pinpoint and address bottlenecks proactively.

Overall, the adoption of eBPF empowered ByteDance to achieve significant improvements in network performance, scalability, and observability. The dynamic nature and flexibility of eBPF enabled them to tailor network operations precisely to their specific needs, resulting in more efficient resource utilization, reduced latency, and improved user experience. This case study demonstrates the potential of eBPF as a powerful tool for optimizing complex, high-traffic network infrastructures.
Summary of Comments ( 7 )
https://news.ycombinator.com/item?id=42866572

Hacker News users discussed ByteDance's use of eBPF for network performance, focusing on the challenges of deploying such a complex system. Several commenters questioned the actual performance gains, highlighting the lack of quantifiable data in the case study. Some expressed skepticism about the complexity introduced by eBPF, arguing that simpler solutions might be more effective. The discussion also touched on the benefits of XDP for DDoS mitigation and the potential for eBPF to revolutionize networking, while acknowledging the steep learning curve. Several users pointed out the missing details in the case study, such as specific implementations and comparative benchmarks, making it difficult to assess the true impact of ByteDance's approach.

The Hacker News post titled "Case Study: ByteDance Uses eBPF to Enhance Networking Performance" has generated a moderate discussion with several insightful comments. Many commenters focus on the practical implications and broader trends surrounding eBPF adoption.

Several comments highlight the growing significance of eBPF for performance optimization, echoing the case study's findings. One commenter emphasizes how eBPF allows bypassing the kernel's general-purpose networking stack, enabling tailored optimizations for specific applications. This aligns with another comment pointing out the power of shifting complex logic from userspace into the kernel using eBPF, improving efficiency without requiring kernel modifications. The inherent flexibility and safety of eBPF are also lauded, with one user mentioning how these attributes make it a compelling alternative to traditional kernel modules.

The discussion also touches on the expanding use cases of eBPF beyond networking. One commenter notes the growing adoption of eBPF for security and observability, showcasing its versatility. Another comment mentions its use in tracing and profiling, furthering the narrative of eBPF as a powerful tool for diverse performance-related tasks.

A recurring theme is the potential of eBPF to reshape the networking landscape. One commenter speculates on the possibility of eBPF programs becoming the primary way to interact with the network stack in the future, suggesting a shift away from traditional methods. Another comment emphasizes the rising importance of eBPF expertise, predicting a surge in demand for skilled professionals in this area.

Some comments provide context and further information related to the case study. One user mentions Cilium, an eBPF-based networking project, and its relevance to service mesh implementations. Another user notes the increasing popularity of eBPF among large organizations and points to Meta (Facebook) as another prominent adopter.

While expressing enthusiasm for eBPF, some comments also acknowledge its complexities. One user mentions the challenges associated with debugging and managing eBPF programs, hinting at the potential learning curve involved.

Overall, the comments on the Hacker News post paint a picture of eBPF as a rapidly maturing technology with significant potential for performance enhancement across various domains. The discussion reflects the growing excitement surrounding eBPF and its potential to revolutionize networking and other areas of system optimization.
SigNoz (YC W21) Is hiring back end engineers to build open-source Datadog

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Posted: 2025-01-26 07:02:13

SigNoz, a Y Combinator-backed company, is hiring backend engineers to contribute to their open-source application performance monitoring (APM) and observability platform. They aim to build an open-source alternative to Datadog, providing a unified platform for metrics, traces, and logs. The ideal candidate is proficient in Go and possesses experience with distributed systems, databases, and cloud-native technologies like Kubernetes.

SigNoz, a company that participated in Y Combinator's Winter 2021 cohort, is actively seeking back-end engineers to contribute to the development of their open-source application performance monitoring (APM) and observability platform. They aim to build a compelling open-source alternative to Datadog, a popular proprietary observability platform. The company emphasizes the opportunity for engineers to work on a modern technology stack, specifically mentioning ClickHouse, a high-performance column-oriented database management system, and OpenTelemetry, a collection of APIs, SDKs, tooling, and specifications around creating and managing telemetry data (logs, metrics, and traces).

This role presents a chance to delve into the complexities of distributed systems and contribute to a project with a rapidly growing open-source community. The ideal candidate will be passionate about building high-quality, scalable, and performant back-end systems. They will be involved in designing, developing, and maintaining core components of the SigNoz platform, directly impacting the user experience and contributing to the growth and adoption of the open-source project. This position offers the opportunity to work on a project with significant real-world impact, helping developers and organizations gain deeper insights into the performance and behavior of their applications. The advertisement highlights the rewarding nature of contributing to open source and the potential to shape the future of observability tooling. While specific requirements are not explicitly stated in the post, the emphasis on a modern technology stack and the nature of the project suggests a preference for candidates with experience in back-end development, distributed systems, and potentially database technologies.
Summary of Comments ( 0 )
https://news.ycombinator.com/item?id=42828294

HN commenters are largely skeptical of SigNoz's claim to be building an "open-source Datadog." Several point out that open-source observability tools already exist and question the need for another. Some criticize the post's focus on hiring rather than discussing the technical challenges of building such a tool. Others question the viability of the open-source business model, particularly in a crowded market. A few commenters express interest in the project, but the overall sentiment is one of cautious skepticism.

The Hacker News post discussing SigNoz's hiring of backend engineers generated several comments, primarily focusing on the challenges and nuances of building an open-source alternative to Datadog.

One commenter questioned the viability of directly competing with Datadog, highlighting the significant network effects and established user base Datadog enjoys. They argued that focusing on a niche or underserved area within the observability space might be a more successful approach. Another commenter echoed this sentiment, suggesting that specializing in a specific vertical, like Kubernetes monitoring, could differentiate SigNoz from the established players.

Several comments centered on the complexities of open-source monetization. One user pointed out the difficulty of converting open-source users into paying customers, especially in the monitoring and observability market. They suggested focusing on enterprise features and support as a potential revenue stream. Another commenter questioned the specific open-source licensing model SigNoz employs, and how it impacts the ability for other companies to utilize and potentially compete with their product.

Some commenters expressed interest in the technical details of SigNoz, inquiring about the specific technologies used and the architecture of the platform. One comment asked about the choice of ClickHouse as the underlying database and its performance characteristics. Another commenter expressed skepticism about the long-term viability of relying solely on open-source components, suggesting that certain proprietary technologies might be necessary for optimal performance and scalability.

There was also discussion regarding the challenges of scaling an open-source project and building a community around it. One commenter stressed the importance of clear documentation and active community engagement to attract contributors and users. Another commenter suggested that offering paid support and consulting services could be a way to sustain the project and incentivize contributions.

Finally, a few comments touched upon the competitive landscape of the observability market, mentioning other open-source and commercial alternatives to Datadog. One commenter specifically mentioned Prometheus and Grafana as popular open-source options, and questioned how SigNoz differentiates itself from these existing tools. Another user highlighted the growing demand for observability solutions and the opportunity for new players to enter the market.
HyperDX (YC S22) is hiring engineers to build open source observability

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Posted: 2025-01-21 21:01:02

HyperDX, a Y Combinator-backed company, is hiring engineers to build an open-source observability platform. They're looking for individuals passionate about open source, distributed systems, and developer tools to join their team and contribute to projects involving eBPF, Wasm, and cloud-native technologies. The roles offer the opportunity to shape the future of observability and work on a product used by a large community. Experience with Go, Rust, or C++ is desired, but a strong engineering background and a willingness to learn are key.

HyperDX, a company incubated within the prestigious Y Combinator Summer 2022 cohort, is actively seeking talented software engineers to contribute to the development of their open-source observability platform. This platform aims to provide comprehensive insights into the performance and behavior of complex software systems, empowering developers and operators to effectively monitor, troubleshoot, and optimize their applications and infrastructure. HyperDX is particularly interested in candidates with a strong background and demonstrable proficiency in systems programming, distributed systems, and performance analysis. The successful applicants will have the opportunity to contribute to a cutting-edge project within a fast-paced, innovative startup environment, playing a crucial role in shaping the future of open-source observability tools. They will work alongside a team of experienced engineers, tackling challenging technical problems and contributing to a project with the potential to significantly impact the software development landscape. The company's commitment to open source principles signifies that the resulting platform will be freely available to the wider community, fostering collaboration and innovation across the industry. This position represents a unique opportunity to not only build a valuable tool but also contribute to a broader movement towards more transparent and accessible observability solutions. While specific details regarding compensation and benefits are not explicitly outlined, the association with Y Combinator suggests a competitive package and the potential for significant career growth within a rapidly expanding company. HyperDX is looking for individuals who are passionate about open source, driven to solve complex problems, and eager to contribute to a project with significant real-world impact.
- Observability
- Open Source
- Hiring
- software engineering
- Engineers
- Jobs
- YC
- Y Combinator
- startup
- HyperDX
- S22
- developer tools
- Monitoring
- DevOps
Summary of Comments ( 0 )
https://news.ycombinator.com/item?id=42785137

Hacker News users discuss HyperDX's open-source approach, questioning its viability given the competitive landscape. Some express skepticism about building a sustainable business model around open-source observability tools, citing the dominance of established players and the difficulty of monetizing such products. Others are more optimistic, praising the team's experience and the potential for innovation in the space. A few commenters offer practical advice regarding specific technologies and go-to-market strategies. The overall sentiment is cautious interest, with many waiting to see how HyperDX differentiates itself and builds a successful business.

The Hacker News post discussing HyperDX's hiring of engineers for open-source observability has generated a moderate amount of discussion, with several commenters focusing on specific aspects of the job posting and the company's approach.

One commenter highlights the importance of focusing on a specific niche within the observability space, arguing that simply being open-source isn't enough to guarantee success. They suggest HyperDX needs a clear differentiator to stand out in a crowded market. This commenter uses the analogy of databases, pointing out that while many open-source databases exist, successful ones like Postgres carved out a specific area of expertise. They encourage HyperDX to identify a similar niche in observability.

Another commenter questions the practicality of relying solely on an open-source model, wondering how HyperDX plans to generate revenue. They acknowledge the potential for a successful open-source business but emphasize the need for a clear monetization strategy. This comment sparks a small discussion about potential revenue models for open-source companies, including hosted services, enterprise support, and proprietary features.

A separate comment thread discusses the challenges and potential benefits of contributing to open-source projects while working at a startup. Some commenters express concerns about the pressure to prioritize the company's goals over contributing to the wider open-source community. Others, however, see it as a positive opportunity to work on impactful projects and build a strong reputation within the open-source ecosystem.

Finally, a few commenters express interest in the positions themselves, inquiring about specific technologies used and the company culture. These comments are generally brief and focused on gathering more information about the job opportunities.

Overall, the comments section reflects a cautious but generally positive sentiment towards HyperDX. While some commenters express skepticism about the viability of their open-source approach, others see the potential for success if they can differentiate themselves in a competitive market and establish a clear path to monetization. The discussion also touches upon the broader challenges and opportunities associated with building an open-source business, especially within the context of a startup.
Bpftune uses BPF to auto-tune Linux systems

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Posted: 2024-11-17 11:38:35

bpftune is a new open-source tool from Oracle that leverages eBPF (extended Berkeley Packet Filter) to automatically tune Linux system parameters. It dynamically adjusts settings related to networking, memory management, and other kernel subsystems based on real-time workload characteristics and system performance. The goal is to optimize performance and resource utilization without requiring manual intervention or system-specific expertise, making it easier to adapt to changing workloads and achieve optimal system behavior.

The project bpftune, hosted on GitHub by Oracle, introduces a novel approach to automatically tuning Linux systems using Berkeley Packet Filter (BPF) technology. This tool aims to dynamically optimize system parameters in real-time based on observed system behavior, rather than relying on static configurations or manual adjustments.

bpftune leverages the power and flexibility of eBPF to monitor various system metrics and resource utilization. By hooking into critical kernel functions, it gathers data on CPU usage, memory allocation, I/O operations, network traffic, and other relevant performance indicators. This data is then analyzed to identify potential bottlenecks and areas for improvement.

The core functionality of bpftune revolves around its ability to automatically adjust system parameters based on the insights derived from the collected data. This dynamic tuning mechanism allows the system to adapt to changing workloads and optimize its performance accordingly. For instance, if bpftune detects high network latency, it might adjust TCP buffer sizes or other network parameters to mitigate the issue. Similarly, if it observes excessive disk I/O, it could modify scheduler settings or I/O queue depths to improve throughput.

The project emphasizes a safe and controlled approach to system tuning. Changes to system parameters are implemented incrementally and cautiously to avoid unintended consequences or instability. Furthermore, bpftune provides mechanisms for reverting changes and monitoring the impact of adjustments, allowing administrators to maintain control over the tuning process.

bpftune is designed to be extensible and adaptable to various workloads and environments. Users can customize the tool's behavior by configuring the specific metrics to monitor, the tuning algorithms to employ, and the thresholds for triggering adjustments. This flexibility makes it suitable for a wide range of applications, from optimizing server performance in data centers to enhancing the responsiveness of desktop systems. The project aims to simplify the complex task of system tuning, making it more accessible to a broader audience and enabling users to achieve optimal performance without requiring in-depth technical expertise. By using BPF, it aims to offer a low-overhead, high-performance solution for dynamic system optimization.
Summary of Comments ( 73 )
https://news.ycombinator.com/item?id=42163597

Hacker News commenters generally expressed interest in bpftune and its potential. Some questioned the overhead of constantly monitoring and tuning, while others highlighted the benefits for dynamic workloads. A few users pointed out existing tools like tuned-adm, expressing curiosity about bpftune's advantages over them. The project's novelty and use of eBPF were appreciated, with some anticipating its integration into existing performance tuning workflows. A desire for clear documentation and examples of real-world usage was also expressed. Several commenters were specifically intrigued by the network latency use case, hoping for more details and benchmarks.

The Hacker News post titled "Bpftune uses BPF to auto-tune Linux systems" (https://news.ycombinator.com/item?id=42163597) has several comments discussing the project and its implications.

Several commenters express excitement and interest in the project, seeing it as a valuable tool for system administrators and developers seeking performance optimization. The use of BPF is praised for its efficiency and ability to dynamically adjust system parameters. One commenter highlights the potential of bpftune to simplify complex tuning tasks, suggesting it could be particularly helpful for those less experienced in performance optimization.

Some discussion revolves around the specific parameters bpftune adjusts. One commenter asks for clarification on which parameters are targeted, while another expresses concern about the potential for unintended side effects when automatically modifying system settings. This leads to a brief exchange about the importance of understanding the implications of any changes made and the need for careful monitoring.

A few comments delve into the technical aspects of the project. One commenter inquires about the learning algorithms employed by bpftune and how it determines the optimal parameter values. Another discusses the possibility of integrating bpftune with existing monitoring tools and automation frameworks. The maintainability of the BPF programs used by the tool is also raised as a potential concern.

The practical applications of bpftune are also a topic of conversation. Commenters mention potential use cases in various environments, including cloud deployments, high-performance computing, and database systems. The ability to dynamically adapt to changing workloads is seen as a key advantage.

Some skepticism is expressed regarding the project's long-term viability and the potential for over-reliance on automated tuning tools. One commenter cautions against blindly trusting automated solutions and emphasizes the importance of human oversight. The potential for unforeseen interactions with other system components and the need for thorough testing are also highlighted.

Overall, the comments on the Hacker News post reflect a generally positive reception of bpftune while also acknowledging the complexities and potential challenges associated with automated system tuning. The commenters express interest in the project's development and its potential to simplify performance optimization, but also emphasize the need for careful consideration of its implications and the importance of ongoing monitoring and evaluation.

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