Progressive Big Data Analytics Engine for Cost-Effective Query Processing
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Solution Overview
Problem
Analytics over large data volumes in cloud clusters becomes expensive due to the pay-as-you-go paradigm, where computation costs increase linearly with query execution time, and conventional systems require queries to complete before issues are diagnosed, often after hours of expensive computation.
Innovation Solution
A distributed progressive analytics engine that uses annotated data items with progress markers for deterministic, massively parallel processing, allowing early results and efficient query processing without requiring system-level sampling strategies or confidence estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems execute queries to completion, then query results are accurate and complete, but computation time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by processing data in progressive batches and generating intermediate results that can be used for early termination. The query processor is configured to stop execution once a predetermined threshold of accuracy is achieved, rather than requiring complete data processing. This allows the system to obtain sufficiently accurate results faster, resolving the contradiction between result accuracy and execution time.
2Adaptability or versatility
If cloud analytics use pay-as-you-go paradigm, then resource utilization is flexible, but computation costs increase linearly with query execution time
Solution Approach 1:
The system implements feedback mechanisms where the query processor monitors progress markers and intermediate result quality in real-time. Based on this feedback, the system can determine when the accuracy threshold is met and terminate computation early. This feedback-driven early termination significantly reduces the total computation time and associated costs while maintaining sufficient result accuracy, directly addressing the linear cost-time relationship in cloud analytics.
3Adaptability or versatility
If exploratory analytics submit many iterative queries, then query refinement is achieved, but computation resources are wasted on off-target queries
Solution Approach 1:
The system performs preliminary validation of query parameters and data schemas before full execution. By checking progress markers and intermediate results early in the processing pipeline, the system can quickly identify off-target queries and terminate them before consuming significant resources. This preliminary action layer enables exploratory analytics to safely iterate through multiple queries without wasting computation on obviously incorrect or redundant queries.
4Measurement precision
If conventional systems wait for query completion before diagnosing issues, then complete results are obtained, but diagnostic time is delayed
Solution Approach 1:
The system implements continuous feedback through progress markers that track processing status in real-time. These markers provide ongoing information about query progress, data quality, and potential issues without requiring completion. Stakeholders can monitor query status and diagnose problems during execution rather than waiting for completion, significantly reducing diagnostic time while maintaining the ability to obtain complete results when necessary.
Data Source
AI summary
A plurality of data items that are annotated with progress markers may be obtained. The progress markers may indicate progress points associated with atemporal processing progress of the respective data items. Deterministic, massively parallel, progressive processing may be initiated on the plurality of data items on a plurality of devices, the progress markers indicating which of the plurality of data items are to be incorporated into results of the progressive processing, the progress markers further indicating an ordering for incorporation of the respective data items into the results.