Database Array Field Indexing for Null Value Segmentation
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Solution Overview
Problem
Database systems face inefficiencies in processing queries due to limitations in handling null values and missing data, which affect execution speed and accuracy.
Innovation Solution
The implementation of a probabilistic index-based system that generates secondary indexes for different segments of data, allowing for efficient querying and handling of null values by selecting appropriate indexing schemes based on local data distribution, enabling flexible and efficient data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional indexing methods are used for database fields, then data retrieval can be performed, but query execution speed deteriorates when dealing with null values and missing data
Solution Approach 1:
The patent divides the database table into multiple segments based on null value distribution. Each segment is indexed independently with appropriate indexing strategies (B-tree for non-null, skip list for null-dense), allowing queries to be routed to specific segments rather than scanning the entire table. This segmentation resolves the contradiction by enabling fast query execution on non-null data while reliably handling null values through dedicated segment structures.
Solution Approach 2:
The patent applies different indexing strategies to different segments based on their local characteristics. Non-null dense segments use B-tree indexes for fast equality and range queries, while null-dense segments use skip lists for efficient null value handling. This local quality approach resolves the contradiction by optimizing each segment's query performance according to its specific data distribution, improving overall query speed while maintaining reliable null value handling.
2Productivity
If probabilistic indexing is used to improve query speed, then data retrieval efficiency increases, but accuracy in handling missing data deteriorates
Solution Approach 1:
The patent segments the database based on null value density and applies deterministic indexing (B-tree or skip list) to each segment rather than using probabilistic indexing across the entire table. This segmentation resolves the contradiction by ensuring accurate null value handling through deterministic segment boundaries while maintaining high retrieval efficiency through targeted index selection for each segment type.
Solution Approach 2:
The patent changes the indexing parameter from probabilistic to deterministic based on the segment's null value characteristics. By switching to deterministic indexing methods (B-tree for non-null, skip list for null-dense) tailored to each segment's parameters, the system achieves both high data retrieval efficiency and accurate null value handling, resolving the contradiction between productivity and measurement precision.
3Device complexity
If a single indexing strategy is applied to the entire database, then implementation is simple, but query performance deteriorates for diverse data distributions
Solution Approach 1:
The patent divides the database into segments and applies different indexing strategies to each segment based on null value density. This segmentation resolves the contradiction by allowing optimized query performance for diverse data distributions while keeping implementation complexity manageable through automated segment identification and index selection algorithms that require minimal manual configuration.
Solution Approach 2:
The patent implements a dynamic indexing system that automatically selects the appropriate index type (B-tree or skip list) for each segment based on its data characteristics. This dynamic approach resolves the contradiction by adapting the indexing strategy to each segment's specific needs, achieving high query performance for diverse distributions while maintaining simple implementation through automated decision-making algorithms.
4Productivity
If automated index selection is implemented to optimize query performance, then execution efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements automated index selection at the segment level rather than the table level, which reduces the complexity of the automation system. By dividing the database into segments with relatively homogeneous characteristics, the automated system only needs to make indexing decisions for smaller, more uniform data sets, improving query execution efficiency while keeping the automation complexity manageable.
Solution Approach 2:
The patent uses clear parameter thresholds (null value density percentages) to automate index selection, which simplifies the automation logic. By defining specific parameters and thresholds for determining when to use B-tree versus skip list indexes, the system achieves high query execution efficiency through automated optimization while maintaining low system complexity through straightforward decision rules.
Data Source
AI summary
A database system operates by storing a plurality of array field values for an array field of a plurality of rows and generating index data for the array field based on: indexing non-null element values of the plurality of array fields for the plurality of rows, indexing null-valued ones of the plurality of array fields for the plurality of rows, indexing ones of the plurality of array fields for the plurality of rows having an empty set of elements, and indexing ones of the plurality of fields for the plurality of rows having at least one null element value. Wherein the database system further operates by: determining a query including a query predicate indicating an array operation for the array field; and applying an IO pipeline in conjunction with execution of the query.