Name
Abstract | ||
|---|---|---|
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 vanilla Spark.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3419111.3421292 | SoCC '20: ACM Symposium on Cloud Computing
Virtual Event
USA
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-8137-6 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason Teoh | 1 | 1 | 0.35 |
| Muhammad Ali Gulzar | 2 | 90 | 10.07 |
| Miryung Kim | 3 | 1856 | 82.00 |