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Kubernetes Julia Evans Sentry Clear all

01Sentry · architecture
Sentry JavaScript SDK v11 adds tracing for all JS platforms
Sentry replaced its OpenTelemetry-based JavaScript SDK instrumentation with a source-code transformation approach using diagnostics channels in version 11. This change removes reliance on Node-specific module loader hooks, enabling reliable tracing across non-Node runtimes like Deno, Bun, and Cloudflare Workers, while also supporting bundled applications. The result is a simplified architecture that reduces bundle weight and fixes previous limitations with load order and immutable exports.
DEPTH ■■■■■13 min read2 weeks ago
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02Kubernetes · migration
The Shift to cgroup v2 in Kubernetes: What You Need to Know
Kubernetes has deprecated cgroup v1 in favor of cgroup v2, with complete removal scheduled for future releases. The transition requires migrating Linux nodes, updating container runtimes, and verifying kernel compatibility. Adopting cgroup v2 enables advanced features such as Memory QoS, improved OOM handling, and pressure stall information metrics.
DEPTH ■■■■■12 min read2 weeks ago
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03Sentry · performance
Reducing Android scope-sync overhead in Sentry Flutter
The Sentry Flutter team investigated scope-sync overhead on Android and found redundant data normalization across isolates alongside unnecessary buffer copies during JSON encoding. By removing the duplicate normalization and encoding directly to UTF-8 bytes, the team reduced processing time by 27 to 30 percent across benchmarked payloads.
DEPTH ■■■■■4 min read2 weeks ago
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04Kubernetes · scaling
Scaling Kubernetes Workloads with Node Swap
Kubernetes memory limits often cap node density for bursty agentic AI workloads that sit idle with large memory footprints. To solve this, the engineering team benchmarked Kubernetes v1.34 node swap backed by fast NVMe Local SSDs across CI/CD builds, headless browsers, and Python sandboxes. The approach successfully increased pod density up to three times without significant latency costs.
DEPTH ■■■■■6 min read2 weeks ago
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05Sentry · performance
The Sentry Android SDK is now 20-30% faster!
The Sentry team optimized the Android SDK to reduce initialization time by 20-30% and background CPU usage by up to 10%. Improvements were achieved by profiling with Perfetto, replacing slow runtime manifest parsing and DSN parsing with build-time bytecode writing and lightweight parsers, and reusing thread pools. Upgrading to the latest SDK version allows applications to realize these startup performance gains.
DEPTH ■■■■■4 min read5 days ago
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06Sentry · tooling
Unreal MCP now speaks Sentry
The Sentry team integrated their crash reporting plugin with Unreal Engine's new experimental Model Context Protocol (MCP) server, allowing coding agents to fully automate setup and debugging. By combining editor tools and Sentry MCP, an agent successfully configured the SDK, uploaded debug symbols, packaged a build, induced a crash, and traced the root cause without manual file editing. The integration streamlines crash reporting setup, though it currently suffers from occasional agent tool-discovery issues and server fragility across editor restarts.
DEPTH ■■■■■7 min read2 weeks ago
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07Kubernetes · tooling
Why Is My Kubernetes Job Still Pending? Tracing Kueue Admission with Headlamp
Kubernetes jobs managed by Kueue can remain pending while waiting for admission checks and resource allocation. The team implemented a Kueue plugin for Headlamp to trace pending workloads through local queues, cluster queues, and resource flavors within a unified interface. This approach allows engineers to quickly determine whether a job is blocked by admission policies or standard pod scheduling.
DEPTH ■■■■■9 min read11 days ago
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08Sentry · practice
Should you use a log or a metric?
This post explains the distinction between logs and metrics in application monitoring, noting that logs capture what was true during specific events for debugging while metrics track ongoing numerical trends over time. The author demonstrates how to implement both using Sentry SDKs during a user signup flow and outlines criteria for choosing between them. The resulting takeaway helps engineers decide whether to log an event or track it as a metric based on the questions they need to answer.
DEPTH ■■■■■12 min read3 days ago
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09Sentry · practice
How Etsy gets its mobile apps ready for peak traffic
Etsy prepares its mobile apps for peak holiday traffic through advance planning, operational guardrails, and risk management rather than relying on capacity scaling or the ability to quickly revert binaries. The engineering team utilizes a phased code slush, dual crash reporting, remote feature flags, and custom error tagging to surface and mitigate issues effectively under heavy loads. By connecting crash rates to business metrics and automating manual triage workflows incrementally, the organization maintains high visibility and reliability when updates cannot be instantly rolled back.
DEPTH ■■■■■11 min read3 weeks ago
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10Julia Evans · practice
Replacing the old battery on rechargeable bike lights
An individual attempted to repair old rechargeable bike lights with degraded batteries by cutting open the casing, desoldering the dead cells, and soldering in replacements. Despite lacking electronics experience and losing some screws, the repair was successful and restored the functionality of the lights.
DEPTH ■■■■■5 min read4 weeks ago
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11Sentry · performance
Application Metrics caught my broken size estimator
An engineer integrated application metrics into a client-side video conversion tool to monitor file size estimation accuracy without using invasive telemetry. By tracking key performance indicators and analyzing the metric distributions, they discovered that an AI-generated estimation formula was flawed and producing results off by up to 83 percent. Replacing the broken logic with a correct bitrate-based formula brought the size estimation accuracy to within five percent of real-world results.
DEPTH ■■■■■15 min read7 weeks ago
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12Sentry · tooling
Seer, the Sentry MCP and CLI, or your own coding agent: where each one fits
Engineers often struggle to choose between native debugging agents, Model Context Protocols, and command-line tools when integrating error tracking into their workflows. Sentry demonstrates how its debugging assistant Seer, the Sentry MCP, the Sentry CLI, and various coding agent integrations complement each other rather than compete. The resulting setup allows teams to diagnose complex application errors and automatically generate pull requests through whichever surface they prefer.
DEPTH ■■■■■11 min read3 weeks ago
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13Sentry · tooling
Measuring the back/forward cache with Application Metrics
Engineering teams often lack visibility into back/forward cache performance because successful cache hits leave no telemetry traces and browser heuristics frequently change. To solve this blind spot, the team introduced an opt-in application metrics integration in their JavaScript SDK that models browser bfcache signals into hits, specific miss reasons, and reload durations. As a result, developers can track, query, and fix silent navigation regressions using customizable dashboards.
DEPTH ■■■■■6 min read4 weeks ago
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14Sentry · architecture
Building Sentry's Laravel AI Integration
The Sentry team built a Laravel AI integration to support Agent Tracing during an internal hackweek. Because Laravel AI lacked events for individual LLM invocations, the integration correlates outgoing HTTP requests with active invocations using provider URL prefixes. The resulting zero-config feature is available in sentry-laravel 4.27 and automatically traces agents, LLM requests, and tool calls.
DEPTH ■■■■■4 min read4 weeks ago
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15Sentry · tooling
Debugging our AI search assistant with agent tracing
The Sentry team developed a Search Query Assistant to convert natural language prompts into search syntax, which initially failed on custom numerical attributes due to incorrect field formatting in the system prompt. By utilizing their own AI Conversation view and agent tracing, they identified the root cause in the tool call results and system prompt generation. This observability workflow enabled them to reproduce, fix, and verify the issue both locally and in production.
DEPTH ■■■■■4 min read6 weeks ago
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