Query Processing Using Membership Structures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As data processing systems become increasingly complex and data volumes grow, efficiently accessing and processing data updates in a timely manner becomes challenging, particularly when determining whether intermediate data is relevant to a query, leading to prolonged response times.
Innovation Solution
Implementing a processing pipeline with membership structures, such as Bloom filters, to quickly determine if intermediate data is likely to contain responsive data, allowing for probabilistic responses and incremental refinement of query results based on freshness specifications, thereby optimizing query performance and result timeliness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a processing pipeline processes a large amount of data updates, then the system can handle more data volume, but it takes a relatively long period of time before the data updates are available for access by queries
Solution Approach 1:
The patent segments the data processing system into multiple processing stages (ingest stage, intermediate stages, authority stage) that can operate independently and process data in parallel. Each stage handles portions of the data stream, allowing the system to process large volumes of data updates simultaneously without sequentially blocking query access to intermediate results.
Solution Approach 2:
The patent implements preliminary action by allowing queries to access intermediate data from processing stages before the complete data processing pipeline finishes. The system proactively makes intermediate results available for querying while updates continue to be processed, rather than waiting for all processing to complete before allowing any access.
2Measurement precision
If the system examines intermediate data to determine relevance to a query, then query accuracy improves, but the determination process takes a relatively long time when intermediate data volume is large
Solution Approach 1:
The patent introduces membership structures (such as Bloom filters) as intermediary data structures associated with intermediate data from processing stages. These membership structures serve as mediators that enable rapid determination of whether intermediate data contains results responsive to a query, without requiring examination of the actual intermediate data content.
Solution Approach 2:
The patent creates simplified copies of intermediate data in the form of membership structures that capture the essential membership information needed for query relevance determination. These membership structure copies allow the system to check for data relevance without processing or examining the full intermediate data sets, dramatically reducing determination time.
3Reliability
If users require up-to-date results from the processing pipeline, then data freshness is improved, but query response time decreases due to the need to examine intermediate data
Solution Approach 1:
The patent implements dynamic query response strategies where the system can adjust between providing immediate responses from authority data and refining responses by examining intermediate data, based on user-specified freshness requirements. The system dynamically adapts its processing depth and data access patterns to match the user's freshness needs, rather than always performing complete intermediate data examination.
Solution Approach 2:
The patent allows users to specify freshness parameters that control the trade-off between data freshness and query response time. The system changes its operational parameters based on these specifications, adjusting whether to examine intermediate data and to what extent, thereby optimizing query processing efficiency according to the required data freshness level.
Data Source
AI summary
A data processing system includes a plurality of processing stages. In response to a query, a membership structure is accessed to determine whether partially processed data from a particular one of the processing stages.