Author: Kevin Smyth has worked as a consultant for some of the world’s leading financial institutions. Based in London, Kevin has implemented data capture and high-frequency data analysis projects across a large number of mainstream and alternative asset classes.
Tables in a database define a relationship between different types of data, whether that relationship is static, dynamic (i.e. fluctuating as part of a time series) or a mixture of both. In general, it is regularly the case that database queries will require data from multiple tables for enrichment and aggregation purposes and so a key aspect of database design is developing ways in which data from several tables is mapped together quickly and efficiently. Although kdb+ contains a very rich set of functions for joining tables in real time, if permanent and well-defined relationships between different tables can be established in advance then data retrieval latency and related memory usage may be significantly reduced.
This whitepaper will discuss foreign keys and linked columns in a kdb+ context, two ways whereby table structure and organisation can be optimised to successfully retrieve and store data in large-scale time series databases.
Tests performed using kdb+ 3.0 (2013.04.05).
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