Debugging the OmniTable Way

Debugging is time-consuming, accounting for roughly 50% of a developer's time. To identify the cause of a failure, a developer usually tracks the state of their program as it executes on a failing input. Unfortunately, most debugging tools make it difficult for a developer to specify the program state that they wish to observe and computationally expensive to observe execution state. Moreover, existing work to improve our debugging tools often restrict the state that a developer can track by either exposing incomplete execution state or requiring manual instrumentation.

In this paper, we propose an OmniTable, an abstraction that captures all execution state as a large queryable data table. We build a query model around an OmniTable that supports SQL to simplify debugging without restricting the state that a developer can observe: we find that OmniTable debugging queries are more succinct than equivalent logic specified using existing tools. An OmniTable decouples debugging logic from the original execution, which SteamDrill, our prototype, uses to reduce the performance overhead of debugging. The system employs lazy materialization: it uses deterministic record/replay to store the execution associated with each OmniTable and resolves queries by inspecting replay executions. It employs a novel multi-replay strategy that partitions query resolution across multiple replays and a parallel resolution strategy that simultaneously observes state at multiple points-in-time. We find that SteamDrill queries are an order-of-magnitude faster than existing debugging tools.