Nested Relationship Operators for Efficient Database Query Execution
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Solution Overview
Problem
Conventional database management systems are not computationally efficient in executing queries on table group-structured data, as existing query plan operators examine all rows in a group, leading to inefficient execution plans.
Innovation Solution
The introduction of two new operators, ancestor lookup and branch lookup, which transform a unique key (hkey) to efficiently probe a table group for additional rows, allowing for more efficient execution plans by focusing on specific rows rather than examining all rows in the group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing query plan operators examine all rows in a group, then complete data coverage is achieved, but computational efficiency deteriorates
Solution Approach 1:
The patent extracts and utilizes hierarchical keys (hkeys) that encode the hierarchical structure of table groups. By extracting the essential identifying information from full row examinations, the system can target specific rows using transformed hkeys, thereby achieving complete data coverage only where necessary while improving computational efficiency.
Solution Approach 2:
The patent transforms hkeys through modification operations (shortening, appending table identifiers) to change the search parameters. This transformation allows the system to probe table groups for specific rows rather than examining all rows, resolving the contradiction between complete coverage and computational efficiency.
2Reliability
If operators operate on all rows in a group, then no rows are missed, but processing time increases
Solution Approach 1:
The patent performs preliminary transformation of hkeys before probing the table group. By pre-modifying the hkey to contain the necessary identifying information for specific rows, the system avoids unnecessary examination of unrelated rows, thereby reducing processing time while maintaining accurate row identification.
Solution Approach 2:
The transformed hkey acts as an intermediary that bridges the input row and the target row in the table group. This intermediary contains encoded hierarchical information that guides the probe operation to the correct rows, eliminating the need to examine all rows while ensuring no relevant rows are missed.
3Ease of manufacture
If conventional operators are used on table group data, then implementation is straightforward, but execution plan efficiency is poor
Solution Approach 1:
The patent segments the hkey transformation process into distinct operator types (ancestor lookup, branch lookup, descendant lookup). Each operator handles a specific direction of hierarchical navigation, making implementation systematic and manageable while achieving efficient execution plans through targeted row probing.
Data Source
AI summary
A set of operators on group-structured data enable creation of efficient execution plans. These operators are of two (2) distinct types. They operate similarly. For each row that matches an input row type, an hkey is obtained. The hkey uniquely identifies a table row within a table group. The hkey is transformed into a modified hkey associated with an output row type. Starting with a row of interest associated with the modified hkey, a table group is probed to identify one or more additional rows. As the additional rows are identified, they are written into an output stream.