Durable Functions Monitor is an open-source UI for monitoring orchestrations. It provides visibility into running, completed, and failed workflows. Deployment Deploy as a sidecar Function App or embed in your existing app: Access the UI at /api/durable-functions-monitor. You can view instance history, rewind failed orchestrations, and purge completed instances.
Read more →AWS Lambda Powertools for .NET: Complete Observability Guide
Observability in serverless is challenging. Lambda functions execute in ephemeral environments, making traditional APM tools ineffective. AWS Lambda Powertools for .NET provides structured logging, distributed tracing, and custom metrics out of the box—implementing AWS Well-Architected best practices without boilerplate. This guide covers installation, configuration, and production patterns for each Powertools utility. The Observability Challenge in […]
Read more →GitHub Copilot GA: Enterprise Adoption Guide
GitHub Copilot is now Generally Available. For enterprises, adoption requires addressing IP concerns, security, and developer training. Key Settings Suggestions Matching Public Code: Disable to avoid copyright concerns. Allow Suggestions from Public Repos: Enable for better accuracy (toggleable). Productivity Impact In our pilot, developers reported 30-40% reduction in boilerplate time. However, code review time increased […]
Read more →Azure Monitor: Application Insights Sampling Strategies
At scale, telemetry volume overwhelms budgets. Sampling reduces data while preserving insights. Adaptive Sampling (Default) Automatically adjusts rate to stay within 5 items/second per server. Good for most cases. Fixed-Rate Sampling Tip: Exclude failures and slow requests from sampling to ensure you never miss issues.
Read more →AWS CloudWatch Logs Insights: Query Patterns
CloudWatch Logs Insights uses a SQL-like query language. Here are my most-used patterns. Find Errors Latency Percentiles
Read more →Azure Cosmos DB: Hierarchical Partition Keys
Cosmos DB now supports multi-level partition keys. This solves the “hot partition” problem for multi-tenant applications. Data is now partitioned by Tenant → Year → Order. This distributes write load across many physical partitions while allowing efficient queries within a tenant.
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