USENIX

OSDI '22 - Debugging the OmniTable Way

The talk introduced the OmniTable query model, a novel debugging approach that records a program’s execution as a massive relational table and lets developers interrogate it with SQL. By decoupling observation from runtime, the model promises to replace ad‑hoc instrumentation and print‑statement debugging with declarative queries. Traditional debugging tools force developers to embed costly instrumentation or manage multiple data structures, inflating both code complexity and execution overhead. The speaker demonstrated that Diane’s cache‑leak investigation required dozens of lines of procedural code, whereas an equivalent OmniTable query can be expressed in a handful of SQL statements. Empirical evaluation across five open‑source bugs showed a geometric‑mean reduction of 15× in code size and a 95× speedup in query execution compared with GDB Python scripts. A concrete example involved computing the working‑set size of a cache by sliding a 10,000‑call window over “get_item” invocations and counting distinct keys. The SQL query leveraged derived views such as “functions” and “stores,” producing a time‑series that could be plotted instantly. The presenter highlighted that the prototype, SteamDrill, uses lazy materialization and deterministic record‑and‑replay to avoid the prohibitive storage cost of capturing every instruction. If the approach scales, developers could debug production systems with near‑zero overhead, iterating faster and reducing the risk of introducing new bugs during investigation. The combination of database‑style optimization and replay‑based execution positions OmniTable as a potential new standard for high‑performance, low‑complexity debugging in complex software stacks.