DevConf.US 2022

Over the past few years, large-scale real-time data analytics has soared in popularity as the demand for analyzing fresh data is growing. Hence, modern systems must bridge the need for transactional and analytical, often referred as Hybrid Transactional/Analytical Processing (HTAP). Analytical systems typically use a columnar layout to access only the desired fields. On the contrary, storing data row-first works great for accessing, inserting, or updating entire rows. But, transforming rows to columns at runtime is expensive. So, many analytical systems ingest row-major data and eventually load them to a columnar system or in-memory accelerator for future analytical queries. However, these systems generally suffer from high complexity, high materialization cost, and heavy book-keeping overheads.
How will this design change if the optimal layout was always available?
We present a radically new approach, termed Relational Memory (RM), that converts rows into columns at runtime. We rely on a hardware accelerator that sits between the CPU and main memory and transparently converts base data to any group of columns with minimal overhead. To support different layouts over the same base data, we introduce ephemeral variables, a special type of variables that never instantiated in main memory. Instead, upon accessing them, the underlying machinery generates a projection of the requested columns according to the format that maximizes data locality.
We implement and deploy RM in a commercially available platform that includes CPUs and FPGA. We demonstrate that RM provides a significant performance advantage; accessing the desired columns up to 1.63x faster than row-wise counterpart, while matching the performance of columnar access for low projectivity, and outperforming it by up to 1.87x as projectivity increases. Our next steps include supporting selection in hardware to reduce unnecessary data movements and integrating the proposed design within a DDR4 memory controller.