Lightning Talk: Lazy DBMS Storage Design with Computational Storage

The size of datasets in DBMSs are growing super-linearly, increasing the demand for storage devices as data can no longer fit entirely in main memory. Data movement between the CPU and storage devices often limits performance, primarily due to poor data locality, which is difficult to optimize in DBMSs. An emerging solution to this challenge is the use of Computational Storage Devices (CSDs), which offload certain computation tasks to the storage hardware. However, due to the high cost of fully programmable CSDs, current solutions often offer limited computation capabilities, such as the Scaleflux CSD-3000, which features transparent compression. Transparent compression allows for more flexibility in the storage layout of data structures and DBMS storage formats. As a result, traditional techniques for reducing storage overhead in B$^+$-Trees can be simplified or eliminated, leading to higher bandwidth and reduced storage overhead. In this work, we evaluate the CSD-3000 with transparent compression by examining commonly used DBMS data structures and storage layouts. Our experiments cover a variety of DBMSs with different storage formats. The results demonstrate that transparent compression reduces storage overhead while improving bandwidth through better data locality.