Probabilistic Database Indexing for Faster Large-Scale Queries
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Solution Overview
Problem
Existing database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, leading to inefficiencies in handling large volumes of data.
Innovation Solution
A database system utilizing probabilistic indexing and parallel processing across multiple computing devices and nodes, enabling efficient storage and retrieval of massive datasets through segmenting, restructuring, and optimizing query plans for concurrent execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database storage and processing methods are used, then hardware constraints and data storage limitations are accepted, but processing speed and efficiency deteriorate when handling large volumes of data
Solution Approach 1:
The patent segments data into fixed-length buckets and organizes them into hierarchical levels (L0, L1, L2, etc.) with different compression and indexing strategies. This segmentation enables parallel processing of multiple buckets simultaneously while maintaining manageable complexity through standardized bucket structures and level-based organization.
Solution Approach 2:
The patent introduces probabilistic indexes as intermediary structures that approximate data locations without requiring exact matches. These probabilistic indexes act as mediators between query requests and actual data buckets, enabling fast approximate queries while avoiding the complexity of maintaining precise indexes for all data.
2Quantity of substance
If data is stored in a manner optimized for storage efficiency, then storage capacity is improved, but retrieval speed deteriorates due to hardware and storage method limitations
Solution Approach 1:
Data is divided into fixed-length buckets of standardized size, allowing efficient storage packing while enabling parallel retrieval operations. Multiple buckets can be accessed simultaneously at different storage levels, improving retrieval speed without sacrificing storage density.
Solution Approach 2:
The patent introduces a hierarchical dimension to storage organization with multiple levels (L0, L1, L2, etc.), where each level provides different trade-offs between storage efficiency and retrieval speed. This multi-dimensional organization allows simultaneous optimization for both capacity and speed by directing queries to appropriate levels.
3Speed
If co-processing options are restricted, then system simplicity is maintained, but execution speed deteriorates for database operations
Solution Approach 1:
Query processing is segmented into independent bucket-level operations that can be executed in parallel across multiple processing units. Each processing unit handles specific buckets independently, enabling co-processing without requiring complex coordination between processors.
Solution Approach 2:
The patent creates simplified copies of data in probabilistic index structures that can be queried without accessing the full data buckets. These copies enable fast approximate queries to be executed in parallel while the actual data remains stored in the original format, reducing co-processing complexity.
4Measurement precision
If exact indexing methods are used, then query accuracy is improved, but processing overhead and execution time worsen for large datasets
Solution Approach 1:
The patent uses inexpensive probabilistic index structures that can be quickly built and discarded for approximate queries. These lightweight index structures enable fast querying without the overhead of maintaining complex exact indexes, accepting approximate results in exchange for dramatically reduced execution time.
Solution Approach 2:
The system performs partial indexing by creating probabilistic indexes for only certain data levels and buckets rather than indexing all data exactly. This partial action provides sufficient accuracy for many queries while avoiding the time cost of complete exact indexing.
Data Source
AI summary
A database system operates by: storing a plurality of variable-length data of a column of a plurality of rows; storing a plurality of fixed-length index values of a probabilistic indexing scheme for the column; and identifying a filtered subset of the plurality of rows having variable-length data of the column equal to a given value based on: identifying a first subset of rows as a proper subset of the plurality of rows based on the plurality of fixed-length index values; and comparing the variable-length data of only rows in the first subset of rows to the given value to identify the filtered subset as a subset of the first subset of rows.