Quantifying Isolation Anomalies

Choosing a weak isolation level such as Read Committed is understood as a trade-off, where less isolation means that higher performance is gained but there is an increased possibility that data integrity will be lost. Previously, one side of this trade-off has been carefully studied quantitatively – there are well-known metrics for performance such as transactions per minute, standardized benchmarks that measure these in a controlled way, and analytic models that can predict how performance is influenced by system parameters like multiprogramming level. This paper contributes to quantifying the other aspect of the trade-off. We define a novel microbenchmark that measures how rapidly integrity violations are produced at different isolation levels, for a simple set of transactions. We explore how this rate is impacted by configuration factors such as multiprogramming level, or contention frequency. For the isolation levels in multi-version platforms (Snapshot Isolation and the multiversion variant of Read Committed), we offer a simple probabilistic model that predicts the rate of integrity violations in our microbenchmark from configuration parameters. We validate the predictive model against measurements from the microbenchmark. The model identifies a region of the configuration space where a surprising inversion occurs: for these parameter settings, more integrity violations happen with Snapshot Isolation than with multi-version Read Committed, even though the latter is considered a lower isolation level.