Join the .NET Architects Club: https://www.skool.com/mj-tech-community-5418/about
Get the 2026 .NET Developer roadmap here → https://the-dotnet-weekly.ck.page/2026-roadmap
Want to master Clean Architecture? Go here: https://bit.ly/3PupkOJ
Want to master Modular Monoliths? Go here: https://bit.ly/3SXlzSt

In this video, I test 5 different ways to optimize database updates in .NET.

We usually spend a lot of time thinking about query performance. But update performance matters too, especially when you need to modify hundreds or thousands of rows. In this video, I walk through a progression from a naive approach to much faster options using EF Core, Dapper, raw SQL, and Postgres unnest.

You’ll see:
- Why row-by-row updates are slow
- How EF Core batching helps
- Why loading entities into memory adds overhead
- How a single batched SQL update improves performance
- How Postgres unnest can push things even further

I also compare the performance of each approach with simple benchmarks. For 1,000 records, the unnest approach finished in 12ms. For 10,000 records, it finished in 40ms and came out on top again.

If you work with EF Core, Dapper, Postgres, or high-throughput .NET applications, this video will give you a few practical ideas you can apply right away.

Let me know which approach you’d use in a real project.

Check out my courses:
https://www.milanjovanovic.tech/courses

Read my Blog here:
https://www.milanjovanovic.tech/blog

Join my weekly .NET newsletter:
https://www.milanjovanovic.tech

Chapters

We Are .NET

A step-by-step performance comparison of five approaches to batch-updating database records in .NET using PostgreSQL. Starting from a naive Dapper loop (326ms for 1,000 records), progressing through EF Core with change tracking, a single-statement batched Dapper update using VALUES, the equivalent EF Core raw SQL version, and finally PostgreSQL's unnest() function. Benchmarks show unnest() is the fastest approach—completing 10,000 record updates in ~40ms versus 3 seconds for the naive loop—while EF Core adds overhead compared to raw Dapper for equivalent strategies.

How to Optimize Database Updates in .NET