A New Technique to Reduce Latency in Managing Market Data
Summary of the white paper: Pipelining Vector-Based Statistical Functions for In-Memory Analytics, A New Technique to Reduce Latency in Managing Market Data
Market data typically takes the form of a time series, or repeated measurements of some value over time. Database management systems (DBMSs) have evolved specialized techniques to speed up time series processing, including column-based handling of such data. This paper examines the column-based approach as implemented by McObject’s eXtremeDB® for HPC (formerly the Financial Edition,) database system. It looks at how eXtremeDB implements columns, with database designs that support hybrid column- and row-based data handling – and focuses on a key, related eXtremeDB feature for reducing latency: its library of vector-based statistical functions, which are designed to execute over data sequences (columns). The paper shows how columnar storage improves performance by maximizing the proportion of relevant market data that is loaded into CPU cache, and how pipelining
of vector-based statistical functions maximizes performance by eliminating costly transfers between CPU cache and DRAM.
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