Debugging big data analytics often requires a root cause analysis to pinpoint the precise culprit records in an input dataset responsible for incorrect or anomalous output. Existing debugging or data provenance approaches do not track fine-grained control and data flows in user-defined application code; thus, the returned culprit data is often too large for manual inspection and expensive post-mortem analysis is required. We design FlowDebug to identify a highly precise set of input records based on two key insights. First, FlowDebug precisely tracks control and data flow within user-defined functions to propagate taints at a fine-grained level by inserting custom data abstractions through automated source to source transformation. Second, it introduces a novel notion of influence-based provenance for many-to-one dependencies to prioritize which input records are more responsible than others by analyzing the semantics of a user-defined function used for aggregation. By design, our approach does not require any modification to the framework's runtime and can be applied to existing applications easily. FlowDebug significantly improves the precision of debugging results by up to 99.9 percentage points and avoids repetitive re-runs required for post-mortem analysis by a factor of 33 while incurring an instrumentation overhead of 0.4X - 6.1X on vanillamore »
OptDebug: Fault-Inducing Operation Isolation for Dataflow Applications
Fault-isolation is extremely challenging in large scale data processing in cloud environments. Data provenance is a dominant existing approach to isolate data records responsible for a given output. However, data provenance concerns fault isolation only in the data-space, as opposed to fault isolation in the code-space---how can we precisely localize operations or APIs responsible for a given suspicious or incorrect result?
We present OptDebug that identifies fault-inducing operations in a dataflow application using three insights. First, debugging is easier with a small-scale input than a large-scale input. So it uses data provenance to simplify the original input records to a smaller set leading to test failures and test successes. Second, keeping track of operation provenance is crucial for debugging. Thus, it leverages automated taint analysis to propagate the lineage of operations downstream with individual records. Lastly, each operation may contribute to test failures to a different degree. Thus OptDebug ranks each operation's spectra---the relative participation frequency in failing vs. passing tests. In our experiments, OptDebug achieves 100% recall and 86% precision in terms of detecting faulty operations and reduces the debugging time by 17x compared to a naïve approach. Overall, OptDebug shows great promise in improving developer productivity in today's complex more »
- Award ID(s):
- 1764077
- Publication Date:
- NSF-PAR ID:
- 10342446
- Journal Name:
- ACM Symposium on Cloud Computing 2021
- Page Range or eLocation-ID:
- 359 to 372
- Sponsoring Org:
- National Science Foundation
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