// infrastructure & data engineering
Hi, I'm Sven.
I build and run data-heavy infrastructure: Kubernetes clusters, ClickHouse at billions of rows, and the pipelines in between. I write about what I measure, from benchmarks to migrations to the occasional dead end, with real numbers attached.
- kubernetes
- clickhouse
- postgresql
- data-engineering
- benchmarks
# writing
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Can LLMs Write ClickHouse SQL? 22 Models, Two Generations, One Benchmark 22 LLMs from two model generations write ClickHouse SQL for the same 50 questions: strict and adjusted accuracy, cost per correct query, and what six months of model progress actually changed. -
ClickHouse Projections vs Materialized Views: A Practical Benchmark Projections and materialized views benchmarked on 200M rows of web analytics data in ClickHouse: query speedup, storage and ingest cost, and the unmerged-parts case where both are slower than the base table. -
ClickHouse Compression Codecs for Time-Series Data: A Benchmark Five ClickHouse codec configurations benchmarked on 100M rows of time-series data: storage, query latency and ingest throughput, with a decision tree for codec selection. -
Kubernetes Trouble-Cheat-Sheet with 8 most important best practices (PDF, direct download) Download the Kubernetes Trouble-Cheat-Sheet PDF with 8 essential best practices to improve your workflow and prevent future issues -
Benchmarking Hetzner's Storage Classes for Database Workloads on Kubernetes Hetzner Storage Classes Comparison on Kubernetes -
2.5x Performance: PostgreSQL to Clickhouse without Kafka with MaterializedPostgreSQL -
Bun+ElysiaJS vs Node+Express.js Using a Single Real-World Request Dive into my benchmark comparison of Bun+ElysiaJS and Node+Express.js, featuring a real-world request scenario. -
Decoding Plot Data with ChatGPT: A Journey into Chart Analysis Explore the fascinating journey of using ChatGPT to interpret data dashboard charts in this insightful article. Discover how a simple observation of a custo