challenge

Lakebase Postgres employs a disaggregated storage model that enhances data management through efficient caching, utilizing an object store like S3 for backups. The caching operates on two layers: within distributed storage for optimizing write and read performance, and on the Postgres compute side for ultra-fast access to frequently accessed pages. Traditional Postgres caching involves shared buffers and the OS page cache, which leads to double buffering and inefficiencies. Lakebase Postgres addresses these issues by implementing a local file cache (LFC) and larger shared buffers, allowing for more effective memory utilization without the drawbacks of the OS page cache. The shared buffers are set to a maximum of 1 GB, while the LFC can utilize up to 75% of DRAM. The introduction of huge pages reduces memory management overhead and improves performance, resulting in significant throughput increases and reduced latency in production environments. Recent enhancements have shown up to 2× throughput improvements and substantial reductions in CPU usage. The focus is now on extending these benefits to autoscaling Postgres computes, with plans to implement dynamic shared buffers and autoscaling huge pages.