InfoQ Homepage Performance Content on InfoQ
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/articles/adaptive-hedged-requests-p99-latency/en/smallimage/thumbnail-1779785816730.jpg)
Stragglers, Not Failures: How Adaptive Hedged Requests Reduce p99 Latency by 74 Percent
In fan-out microservice architectures, slow-but-completing requests accumulate across services and drive p99 latency far higher than per-service metrics suggest. This article presents an adaptive hedging mechanism that uses DDSketch for real-time quantile estimation, windowed rotation to handle distribution drift, and a token-bucket budget to prevent load amplification.
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/articles/capacity-planning-queue-recovery/en/smallimage/The-Mathematics-of-Backlogs-Capacity-Planning-for-Queue-Recovery-thumb-1778227922596.jpg)
The Mathematics of Backlogs: Capacity Planning for Queue Recovery
Backlogs in distributed systems are arithmetic problems, not mysteries. This article provides practical formulas for calculating backlog drain time, sizing consumer headroom, and setting auto-scaling triggers. It covers key failure modes — retry amplification, metastable states, and cascading pipeline bottlenecks — plus when to shed load instead of draining.
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/articles/ebpf-for-security-observability/en/smallimage/ebpf-for-security-observability-thumbnail-1778674557176.jpg)
Kernel-Level Ground Truth: Why eBPF is Replacing User-Space Agents for Security Observability
eBPF is emerging as a preferred method for security observability over traditional user-space agents. By attaching probes directly to the Linux kernel's syscall interface, it provides consistent visibility even during container-level compromises. eBPF reduces security-related CPU consumption and limits data volume by performing filtering at the kernel level, enhancing operational efficiency.
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/articles/local-first-ai-inference-cloud/en/smallimage/Local-First-AI-Inference-A-Cloud-Architecture-Pattern-for-Cost-Effective-Document-Processing-thumb-image-1778141518292.jpg)
Local-First AI Inference: a Cloud Architecture Pattern for Cost-Effective Document Processing
The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.
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/articles/metrics-driven-approach-ios-performance/en/smallimage/metrics-driven-approach-ios-performance-thumbnail-1777624958302.jpg)
Beyond the Benchmark: a Metrics-Driven Approach to Sustained iOS Performance on Real Devices
iOS performance engineering often defaults to a mental model where performance is a property of a component. Performance is instead an emergent behavior of the interaction between application code, device hardware, OS resource management, network conditions, and user behavior patterns over time. This article gives a direct, first-party path to capturing performance issues using Xcode Instruments.
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/articles/platform-reliability-cycle/en/smallimage/platform-reliability-cycle-thumbnail-1777467114542.jpg)
Three Pillars of Platform Engineering: a Virtuous Cycle
Platform engineering succeeds when reliability and ergonomics reinforce each other rather than compete. This article explores three foundational pillars: automated reliability, developer ergonomics, and operator ergonomics. Together, they establish a virtuous cycle that strengthens system stability, reduces operational burden, and empowers teams to scale infrastructure with confidence.
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/articles/securing-autonomous-ai-agents-kubernetes/en/smallimage/securing-autonomous-ai-agents-kubernetes-thumbnail-1777378848477.jpg)
Securing Autonomous AI Agents on Kubernetes: Trust Boundaries, Secrets, and Observability for a New Category of Cloud Workload
Autonomous AI agents break Kubernetes security assumptions with dynamic dependencies, multi-domain credentials, and unpredictable resource use. This article covers production-tested patterns: Job-based isolation, Vault for scoped short-lived credentials, a four-phase trust model from shadow mode to autonomous operation, and observability for non-deterministic reasoning cycles.
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/articles/mcp-java-architectural-strategy-llm-integrations/en/smallimage/mcp-java-architectural-strategy-llm-integrations-thumbnail-1776772947180.jpg)
MCP in the Java World: Bringing Architectural Strategy to LLM Integrations
Discover how the Model Context Protocol (MCP) Java SDK is establishing a new architectural discipline for enterprise LLM integrations. By defining explicit contracts and leveraging MCP servers as anti-corruption layers, it ensures governance, loose coupling, and security alignment with the JVM ecosystem and existing operational practices, moving integrations beyond fragility to resilience.
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/articles/enterprise-grade-observability-extension-docker/en/smallimage/enterprise-grade-observability-extension-docker-thumbnail-1775560652994.jpg)
Beyond One-Click: Designing an Enterprise-Grade Observability Extension for Docker
Docker Extensions boost developer speed but create a "visibility gap" by isolating telemetry. To meet enterprise needs, extensions must act as bridges to centralized platforms. This article details how to use OpenTelemetry, policy-as-code, and encryption to build secure pipelines. Learn to balance developer productivity with the governance required for scalable, compliant observability.
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/articles/replacing-database-sequences/en/smallimage/replacing-database-sequences-thumbnail-1774603762739.jpg)
Replacing Database Sequences at Scale without Breaking 100+ Services
The article discusses the challenges faced during a migration from a relational database to NoSQL, focusing on the importance of database sequences for unique identifiers. It outlines the development of a new sequence service using DynamoDB and a two-tier caching architecture.
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/articles/change-metrics-system-reliability/en/smallimage/change-metrics-systems-reliability-thumbnail-1772787617464.jpg)
Change as Metrics: Measuring System Reliability through Change Delivery Signals
System changes are the primary driver of production incidents, making change-related metrics essential reliability signals. A minimal metric set of Change Lead Time, Change Success Rate, and Incident Leakage Rate assesses delivery efficiency and reliability, supported by actionable technical metrics and an event-centric data warehouse for unified change observability.
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/articles/read-copy-update/en/smallimage/read-copy-update-thumbnail-1772618412119.jpg)
Read-Copy-Update (RCU): the Secret to Lock-Free Performance
Innovative software engineer with expertise in optimizing concurrency through advanced techniques like Read-Copy-Update (RCU). Proven track record of boosting read performance by over 110% in read-heavy workloads. Skilled in leveraging RCU principles across production systems, enhancing architecture efficiency, and streamlining data handling to maximize scalability and minimize overhead.