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Deep, recent reads ranked by depth and freshness.

Microservicesremove filterRedisremove filterReliabilityremove filterObservabilityremove filterClear all

  1. 01Cloudflare · architecture

    Origin Range Requests: Serving large files without range-request tuning

    Cloudflare replaced a legacy, fixed-size range caching design that suffered from bandwidth waste and memory pressure with a Pingora-based chunk coordinator. The new implementation dynamically combines cached chunks and aligned origin fetches to serve range requests accurately without manual tuning. This approach cut p99 time to first byte in half for R2 storage and reduced tiered cache traffic by six percent.

    DEPTH ■■■■■8 min read2 days ago

    DatabasesPerformanceRedis

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  2. 02Grab · architecture

    App stability at scale with a layered defense

    To maintain app stability amidst rapid weekly releases and AI-generated code, Grab uses a layered defense strategy covering prevention, release gates, and incident response. This pipeline includes static analysis via custom lint rules, multiple tiers of automated and manual testing, a pre-production early access build, staged rollouts, and feature flags with instant kill switches. As a result, critical defects are caught early in the development lifecycle before reaching the wider production user base.

    DEPTH ■■■■■16 min read4 days ago

    MobileReliability

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  3. 03GitHub · architecture

    From 2,500 requests to 24: Rebuilding star history without the stargazers

    GitHub faced abuse and performance issues because tools were scraping user identities to reconstruct repository star history. The engineering team collaborated with maintainers to build a purpose-built aggregate endpoint that returns daily star counts without identities and filters by time before aggregation. This reduced request volume from thousands to a few dozen and eliminated heavy database scans.

    DEPTH ■■■■■8 min read11 days ago

    Microservices

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  4. 04Grab · architecture

    Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs

    Grab addressed runtime data reliability issues in its data mesh by implementing automated Data Production Issues (DPIs). The system uses an incident orchestrator to detect contract breaches, a Data Health API for cross-platform root cause analysis, and Hugo for automated remediation. This approach resolved 86.9 percent of incidents automatically and made mean time to resolve six times faster.

    DEPTH ■■■■■12 min read8 weeks ago

    DatabasesObservability

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  5. 05Stack Overflow · practice

    Taking a look under your agent’s hood

    This post discusses the challenges and methods of applying observability to AI agents, which combine traditional distributed systems telemetry with new requirements for monitoring LLMs and multi-step tool calls. The discussion highlights how non-deterministic agent behavior and increased speed of execution make continuous production feedback loops essential for upstream development. Ultimately, integrating observability data into automated testing and development loops helps teams iterate on agent performance.

    DEPTH ■■■■■25 min read2 weeks ago

    Machine LearningObservability

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