Best of Observability — October 2024

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    Article
    Avatar of communityCommunity Picks·1y

    5 Tips for Structured Logging in Spring Boot 3.4

    Spring Boot 3.4 introduces native support for structured logging using formats like Elastic Common Schema (ECS) and Logstash. This reduces dependency management and enhances observability in applications by capturing logs in a structured manner. The post emphasizes the importance of logs in understanding application behavior, particularly in distributed systems, and highlights best practices for logging. It also underscores the role of tools like OpenTelemetry and Digma in achieving comprehensive observability.

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    Article
    Avatar of portkeyportkey·1y

    Elevate Your ToolJet Experience with Portkey AI

    Integrating Portkey with ToolJet enhances app performance, scalability, and reliability through advanced features like observability, caching, API management, and routing. Portkey's tools provide real-time monitoring, seamless interoperability, intelligent caching, smart routing, and built-in security guardrails, ensuring smooth and secure application workflows.

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    Article
    Avatar of grafanaGrafana Labs·2y

    Grafana's Prometheus libraries: How we built libraries to create a truly vendor-neutral data source

    Grafana Labs has decoupled its Prometheus data source from Grafana, enabling the creation of vendor-neutral data sources. This update includes a dedicated Amazon Managed Service for Prometheus plugin while maintaining core functionalities and stability. The change aims to foster better observability and reusability in the open-source community. Key principles like DRY and modularity were emphasized, and significant efforts ensured the code's independence and scalability. The project required extensive internal collaboration, POCs, and CI/CD enhancements to achieve this milestone.

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    Article
    Avatar of ebpfeBPF·2y

    What is eBPF? A Beginner’s Guide to Kernel-Level Observability and Tra

    eBPF allows developers to dynamically load custom code into the Linux kernel, enhancing capabilities for observability, tracing, and security without modifying the kernel itself. By distinguishing between kernel space and user space, the post underscores the advantages of running code with kernel-level privileges. It explores various methods for executing custom logic in the kernel, including Kernel Modules, adding programs via a kernel patch, using Kernel Hooks, employing System Tap and DTrace, and leveraging eBPF. eBPF stands out for its efficiency, security, and support for high-level languages.

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    Article
    Avatar of grafanaGrafana Labs·2y

    Key Prometheus concepts every Grafana user should know

    Prometheus is a robust monitoring framework, far beyond just a time series database, offering features like metric scraping, powerful querying (PromQL), alerting, and service discovery. Key components include client libraries for instrumentation, the node exporter for OS-level metrics, and general exporters for diverse sources. PromQL enables complex queries and supports recording rules for optimizing data retrieval and storage. Prometheus excels in dynamic environments, adapting well with Grafana for enhanced data visualization. Understanding these capabilities, especially through resources like 'Prometheus Up & Running,' can significantly improve your observability practices.

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    Article
    Avatar of ebpfeBPF·2y

    Can eBPF Detect Redis Message Patterns Before They Become Problems?

    Redis, a popular in-memory key-value store, faces performance issues like memory exhaustion and CPU spikes. Active monitoring is essential to address these challenges effectively. eBPF can be used to trace Redis operations at the syscall level, providing granular insights into each Redis command. This reduces latency and CPU spikes by allowing better isolation and debugging of issues. Tests show that eBPF adds minimal overhead to system performance, offering a valuable trade-off for the detailed observability it provides.

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    Article
    Avatar of grafanaGrafana Labs·1y

    How to quickly configure Grafana Cloud Application Observability with Open Telemetry Operator

    Monitoring application health is crucial, similar to monitoring personal health. This guide describes how to use Grafana Cloud Application Observability and OpenTelemetry Operator to monitor key application performance indicators such as CPU usage, memory consumption, and response times. It delves into the benefits of auto-instrumentation, a zero-code approach for app tracing, and provides a step-by-step guide for setting this up in a Kubernetes cluster, focusing on ease of deployment and configuration.

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