技术博主在同一台Hetzner服务器上对DuckDB和SQLite进行了全面基准测试，对比两个嵌入式数据库处理可观测性数据的能力。[1]测试涵盖了指标、跨度和日志三类数据的读写性能，在相同硬件配置下得出了明显的性能差异。[1]
在读取性能上，DuckDB实现了100倍的性能提升，其峰值吞吐量达到1亿行，而SQLite仅为100万行。[1]写入方面，DuckDB的优势同样突出：指标数据的写入吞吐量达到53,091点/秒、跨度为36,577条/秒、日志为30,442条/秒，相比SQLite提升3至15倍。[1]其中日志处理成为DuckDB最大的优势领域，读取峰值提升100倍、写入速度提升15倍。[1]
在存储效率方面，DuckDB能在单机上存储10亿个指标数据点，占用磁盘空间为10.8GB，相当于压缩后每点约10.8字节。[1]博主建议的最优配置为设置DUCKDB_MEMORY_LIMIT=4GB和DUCKDB_CHECKPOINT_THRESHOLD=256MB。[1]此外，测试过程中发现并修复了摄入路径中的一个崩溃漏洞，该问题与admit gate对并发处理的限制相关。[1]
A comprehensive performance comparison of DuckDB and SQLite on identical hardware has demonstrated substantial advantages for DuckDB in processing observability workloads.[1] The benchmark, conducted on a Hetzner CCX13 server costing $16.49 per month, tested both embedded database engines across metrics, spans, and logs handling.[1]
DuckDB achieved dramatically superior read performance, with peak throughput reaching 100 million rows compared to SQLite's 1 million rows for the same signal processing task—representing a 100-fold improvement.[1] Write throughput gains were also significant, with DuckDB delivering 53,091 metrics points per second, 36,577 spans per second, and 30,442 log entries per second, translating to a 3 to 15 times improvement over SQLite depending on the data type.[1] Log processing emerged as DuckDB's strongest advantage, showing a 15-fold write speed increase and a 100-fold read peak improvement.[1]
The single-machine deployment demonstrated practical scalability, storing one billion metric data points in 10.8 GB of disk space—approximately 10.8 bytes per point after compression.[1] The tester recommended configuring DuckDB with a memory limit of 4 GB and checkpoint threshold of 256 MB for optimal performance.[1] During testing, a crash bug in the custom ingestion pipeline was discovered and fixed, related to admission gate concurrency controls.[1] The benchmark results represent single runs; a median-based approach with triple repetition remains under consideration for future validation.[1]