Influence-Based Provenance for Dataflow Applications with Taint Propagation (SoCC 2020)
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
This project is developed by Professor Miryung Kim's Software Engineering and Analysis Laboratory at UCLA. If you encounter any problems, please open an issue or feel free to contact us:
Jason Teoh: PhD student, [email protected];
Muhammad Ali Gulzar: Professor at Virginia Tech, [email protected];
Miryung Kim: Professor at UCLA, [email protected];
Please refer to our SoCC 2020 paper, Influence-Based Provenance for Dataflow Applications with Taint Propagation for more details.
@inproceedings{10.1145/3419111.3421292, author = {Teoh, Jason and Gulzar, Muhammad Ali and Kim, Miryung}, title = {Influence-Based Provenance for Dataflow Applications with Taint Propagation}, year = {2020}, isbn = {9781450381376}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3419111.3421292}, doi = {10.1145/3419111.3421292}, 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.}, booktitle = {Proceedings of the 11th ACM Symposium on Cloud Computing}, pages = {372–386}, numpages = {15}, keywords = {data intensive scalable computing, fault localization, big data systems, data provenance, taint analysis}, location = {Virtual Event, USA}, series = {SoCC '20} }
- The current codebase contains code for running another provenance debugging tool, BigSift. If you do not need to run BigSift, you can exclude or remove the corresponding files/packages (src/main/examples/benchmarks/bigsift*).
- The repository depends on spark and spark-sql. You can either provide your own (e.g. create a lib/ folder and place spark jars inside) or modify the build.sbt file to include those dependencies (which are currently commented out). Note that RoaringBitmap is explicitly included in
build.sbt, so be sure to remove that dependency if manually including jars.- If using BigSift, you can add the bigsift spark build here instead.