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Hello,
we(@HongyuHe) at eth-easl found some discrepancy in the AzureFunctionsDataset2019 trace.
Looking at each of the 14-day traces, we have found many duplicate apps and functions, some missing duration or memory stats.
dup : duplicate Hash Owner, Hash App, Hash Function with different invocations, durations, or memory
missing stats : Function or App with Hash is present in one trace file but missing in another trace file
These discrepancies make it hard for us to accurately analyze the trace.
Is it reasonable to treat the duplicates as separate entities, or should we merge them?
Would discarding traces with missing data be the only way to clean up the traces?
We would appreciate it if you could provide a way to clean up these issues. Thanks.
The text was updated successfully, but these errors were encountered:
Sorry for the inconsistencies, we did our best to fetch this data from the existing records, and can't clean this after the fact.
Unfortunately I would suggest you ignore the functions for which there are missing duration and or memory stats, if you need these. For the duplicates, I would suggest you take the latest record, instead of merging.
Hello,
we(@HongyuHe) at eth-easl found some discrepancy in the AzureFunctionsDataset2019 trace.
Looking at each of the 14-day traces, we have found many duplicate apps and functions, some missing duration or memory stats.
- 377 apps missing memory stats
- 422 apps missing memory stats
- 380 apps missing memory stats
- 425 apps missing memory stats
- 386 apps missing memory stats
- 429 apps missing memory stats
- 465 apps missing memory stats
- 397 apps missing memory stats
- 440 apps missing memory stats
- 705 apps missing memory stats
- 750 apps missing memory stats
- 379 apps missing memory stats
- 453 apps missing memory stats
- 398 apps missing memory stats
- 439 apps missing memory stats
- 394 apps missing memory stats
- 444 apps missing memory stats
- 388 apps missing memory stats
- 436 apps missing memory stats
- 440 apps missing memory stats
These discrepancies make it hard for us to accurately analyze the trace.
Is it reasonable to treat the duplicates as separate entities, or should we merge them?
Would discarding traces with missing data be the only way to clean up the traces?
We would appreciate it if you could provide a way to clean up these issues. Thanks.
The text was updated successfully, but these errors were encountered: