Observed Signal · Jul 19, 2026 · Technical Release · Source: DEV Community · Impact: 4/5 · Sentiment: Positive
EF Core 10: 12 Performance Tips for Faster Queries
A technical guide listing 12 practical performance tips for Entity Framework Core (EF Core) and .NET 10. The author highlights common EF Core inefficiencies and provides code examples and recommendations — including AsNoTracking for read-only queries, ExecuteUpdateAsync for bulk updates, compiled queries, projecting to DTOs, fixing N+1 queries, pagination, AsSplitQuery, and indexes. The article also calls out new EF Core 10 features such as LeftJoin, native JSON columns, and vector search for AI workloads. The author cites benchmark gains of 25–50% simply by upgrading to .NET 10 and provides a quick checklist for production optimizations.
EF Core 10 includes new database/query features (LeftJoin, native JSON, vector search) and upgrading to .NET 10 is claimed to deliver 25–50% baseline query performance gains, which can materially affect backend performance for systems used in advertising and marketing technology.
Track Microsoft Signals & Market Shifts in Real-Time
Polaris7 autonomous intelligence agents track regulatory filings, primary sources, executive changes, and deal flow 24/7. Create your free Explorer workspace to monitor these entities.
Key Takeaways & Evidence Grounding
- Entity Framework Core (EF Core) is described as the most popular ORM in .NET and frequently a performance bottleneck.
- Benchmarks cited show 25–50% faster query execution when upgrading to .NET 10.
- The article lists 12 performance tips, including AsNoTracking, ExecuteUpdateAsync for bulk updates, compiled queries, projection to DTOs, fixing N+1 queries, pagination, and adding indexes.
- EF Core 10 introduces new features: LeftJoin operator, native JSON columns, and vector search support for AI workloads.
Connected Companies & Entities
1 Entity mapped“Benchmarks show 25–50% faster query execution just from upgrading to .NET 10....”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
10 Next.js Performance Mistakes Slowing Production
This technical guide lists ten common performance mistakes that cause Next.js applications to slow in production and provides fixes and best practices. It emphasizes preferring Server Components over indiscriminate "use client" usage, fetching data on the server to avoid client-side waterfalls, using next/image and next/font for optimized image and font delivery, and choosing static generation or ISR over unnecessary per-request server rendering. The article also recommends avoiding default no-store caching, dynamically loading large client libraries, memoizing expensive client-side work where necessary, using Suspense boundaries for slow data, and regularly measuring real production metrics (bundle sizes, Lighthouse, Core Web Vitals, bundle-analyzer). The guidance is framed to reduce hydration cost, network overhead, layout shift, and backend load.
Native AOT in .NET 10 Boosts C# Performance
The article explains Microsoft’s Native AOT (Ahead‑of‑Time compilation) support in .NET 10 and shows how it shifts trade-offs away from JIT compilation toward instant startup, smaller footprints and improved security. It includes a step‑by‑step sample solution (JitApp, AotApp, BenchmarkRunner), csproj settings to enable Native AOT (PublishAot and PublishTrimmed targeting net10.0), build/publish commands, and a BenchmarkRunner that measures total execution time including startup. A published benchmark on GitHub and terminal results in the post show a JIT build at 170 ms versus a Native AOT build at 69 ms (~60% reduction) for the provided workload. The article targets C#/.NET developers evaluating AOT for low‑latency, resource‑constrained and large‑scale container scenarios.
Optimize API Latency in C# .NET Applications
This technical guide explains causes, measurement methods, and optimization techniques for API latency in C# .NET applications. It defines latency types (network, processing, database), shows measurement approaches (Stopwatch, middleware timing, Application Insights), and recommends best practices such as async/await, in-memory and distributed caching (Redis), response compression, efficient serialization (System.Text.Json), and middleware hygiene. Advanced techniques covered include message queues, distributed caching, database sharding, and HTTP/2/3 upgrades. A case study reports reducing an e-commerce checkout latency from ~3s to under 500ms by introducing async calls, adding database indexes, and applying Redis caching. The article also lists monitoring tools (Azure Monitor, Prometheus + Grafana, New Relic) and emphasizes continuous production monitoring and load testing.
Track Real-Time Market Signals & Shifts
Set up custom watchlists to receive automated, evidence-grounded executive digests whenever material signals or shifts occur across your tracked landscape.
