Demonstration of interactive runtime debugging of distributed dataflows in Texera

SQL is the most commonly used front-end language for data-intensive scalable computing (DISC) applications due to its broad presence in new and legacy workflows and shallow learning curve. However, DISC-backed SQL introduces several layers of abstraction that significantly reduce the visibility and transparency of workflows, making it challenging for developers to find and fix errors in a query. When a query returns incorrect outputs, it takes a non-trivial effort to comprehend every stage of the query execution and find the root cause among the input data and complex SQL query. We aim to bring the benefits of step-through interactive debugging to DISC-powered SQL with DeSQL. Due to the declarative nature of SQL, there are no ordered atomic statements to place a breakpoint to monitor the flow of data. DeSQL’s automated query decomposition breaks a SQL query into its constituent sub queries, offering natural locations for setting breakpoints and monitoring intermediate data. However, due to advanced query optimization and translation in DISC systems, a user query rarely matches the physical execution, making it challenging to associate subqueries with their intermediate data. DeSQL performs fine-grained taint analysis to dynamically map the subqueries to their intermediate data, while also recognizing subqueries removed by the optimizers. For such subqueries, DeSQL efficiently regenerates the intermediate data from a nearby subquery’s data. On the popular TPC-DC benchmark, DeSQL provides a complete debugging view in 13% less time than the original job time while incurring an average overhead of 10% in addition to retaining Apache Spark’s scalability. In a user study comprising 15 participants engaged in two debugging tasks, we find that participants utilizing DeSQL identify the root cause behind a wrong query output in 74% less time than the de-facto, manual debugging.

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Demonstration of accelerating machine learning inference queries with correlative proxy models

Yang

Huang

Wang

et al. 2022

Proc. VLDB Endow.

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We will demonstrate a prototype query-processing engine, which utilizes correlations among predicates to accelerate machine learning (ML) inference queries on unstructured data. Expensive operators such as feature extractors and classifiers are deployed as user-defined functions (UDFs), which are not penetrable by classic query optimization techniques such as predicate push-down. Recent optimization schemes (e.g., Probabilistic Predicates or PP) build a cheap proxy model for each predicate offline, and inject proxy models in the front of expensive ML UDFs under the independence assumption in queries. Input records that do not satisfy query predicates are filtered early by proxy models to bypass ML UDFs. But enforcing the independence assumption may result in sub-optimal plans. We use correlative proxy models to better exploit predicate correlations and accelerate ML queries. We will demonstrate our query optimizer called CORE, which builds proxy models online, allocates parameters to each model, and reorders them. We will also show end-to-end query processing with or without proxy models.

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Demonstration of interactive runtime debugging of distributed dataflows in Texera

Cited by 4 publications

References 11 publications

Demonstration of Udon: Line-by-line Debugging of User-Defined Functions in Data Workflows

Demonstration of Udon: Line-by-line Debugging of User-Defined Functions in Data Workflows

DeSQL: Interactive Debugging of SQL in Data-Intensive Scalable Computing

Demonstration of accelerating machine learning inference queries with correlative proxy models

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