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Using GUI-based workflows for data analysis is an iterative process. During each iteration, an analyst makes changes to the workflow to improve it, generating a new version each time. The results produced by executing these versions are materialized to help users refer to them in the future. In many cases, a new version of the workflow, when submitted for execution, produces a result equivalent to that of a previous one. Identifying such equivalence can save computational resources and time by reusing the materialized result. One way to optimize the performance of executing a new version is to compare the current version with a previous one and test if they produce the same results using a workflow version equivalence verifier. As the number of versions grows, this testing can become a computational bottleneck. In this paper, we present Raven, an optimization framework to accelerate the execution of a new version request by detecting and reusing the results of previous equivalent versions with the help of a version equivalence verifier. Raven ranks and prunes the set of prior versions to quickly identify those that may produce an equivalent result to the version execution request. Additionally, when the verifier performs computation to verify the equivalence of a version pair, there may be a significant overlap with previously tested version pairs. Raven identifies and avoids such repeated computations by extending the verifier to reuse previous knowledge of equivalence tests. We evaluated the effectiveness of Raven compared to baselines on real workflows and datasets.
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Drove: Tracking Execution Results of Workflows on Large Datasets
Data analytics using workflows is an iterative process, in which an analyst makes many iterations of changes, such as additions, deletions, and alterations of operators and their links. In many cases, the analyst wants to compare these workflow versions and their execution results to help in deciding the next iterations of changes. Moreover, the analyst needs to know which versions produced undesired results to avoid refining the workflow in those versions. To enable the analyst to get an overview of the workflow versions and their results, we introduce Drove, a framework that manages the end-to-end lifecycle of constructing, refining, and executing workflows on large data sets and provides a dashboard to monitor these execution results. In many cases, the result of an execution is the same as the result of a prior execution. Identifying such equivalence between the execution results of different workflow versions is important for two reasons. First, it can help us reduce the storage cost of the results by storing equivalent results only once. Second, stored results of early executions can be reused for future executions with the same results. Existing tools that track such executions are geared towards small-scale data and lack the means to reuse existing results in future executions. In Drove, we reason the semantic equivalence of the workflow versions to reduce the storage space and reuse the materialized results.
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- Award ID(s):
- 2107150
- PAR ID:
- 10442817
- Date Published:
- Journal Name:
- PhD Workshop at VLDB 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
