Handling Update Hotspots in Distributed Database Systems Motivation Database systems must deal with the fact that real workloads often exhibit hotspots: Some items at certain times are accessed by concurrent transactions with high probability. This arises in telecoms, sensing, stock trading, shopping, banking, and numerous applications. Some are as simple as counting events, such as user votes or advertisement impressions in Web sites. Some of these applications, such as prepaid telco plans, selling event tickets, or keeping track of remaining inventory, in addition to counting, also need to enforce a bound invariant, that ensures that the quantity being tracked does not […]

There is always one more data bug If we, as data scientists, receive a dataset from a reliable source, we should go ahead with the analysis (classification, clustering, deep learning, etc), right? Well, yes, that’s what most of us (myself included) often do, especially if there is a tight deadline. However, this could be dangerous. Let me describe some rude awakenings I suffered over the past decades, as well as remind you some fast and easy preventive measures.   Examples (a.k.a. horror stories) E1 Geographical data Two decades ago, we got access to a public dataset of cross-roads in California, […]