PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices
Serverless query processing has become increasingly popular due to its advantages, including automated resource management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users to transform their complex and evolving data analytic needs into proper SQL queries and select a serverless query service that delivers satisfactory performance and price for each type of query. This paper presents PixelsDB, an open-source data analytic system that allows users who lack system or SQL expertise to explore data efficiently. It allows users to generate and debug SQL queries using a natural language interface powered by fine-tuned language models. The queries are then executed by a serverless query engine that offers varying prices for different performance service levels (SLAs). The performance SLAs are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We demonstrate that the combination of a serverless paradigm, a natural-language-aided interface, and flexible SLAs and prices will substantially improve the usability of cloud data analytic systems.
Code (1)
Tasks
SchedulingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Serverless GPU Architecture for Enterprise HR Analytics: A Production-Scale BDaaS Implementation
Industrial and government organizations increasingly depend on data-driven analytics for workforce, finance, and regulated decision processes, where timeliness, cost efficiency, and compliance are critical. Distributed f…
Reproducible and Portable Big Data Analytics in the Cloud
Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analy…
Cloud ComputingCPUDescriptiveGPUA Serverless Cloud-Fog Platform for DNN-Based Video Analytics with Incremental Learning
DNN-based video analytics have empowered many new applications (e.g., automated retail). Meanwhile, the proliferation of fog devices provides developers with more design options to improve performance and save cost. To t…
Incremental LearningManagementStratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks
With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development i…
BIG-bench Machine LearningEdge-computingManagementScalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions
As data-intensive applications grow, batch processing in limited-resource environments faces scalability and resource management challenges. Serverless computing offers a flexible alternative, enabling dynamic resource a…
ManagementSentiment Analysis