Queriable Table With Common And Field-Varying Columns
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Solution Overview
Problem
Conventional centralized logging systems using sparse tables become inefficient with numerous event types, requiring complex query operations and union operations across multiple tables, which complicates the process of querying and analyzing events from multiple computing systems.
Innovation Solution
Implementing a queriable table with common and field-varying columns, where common columns store shared fields across event types and field-varying columns store type-dependent fields, allowing for efficient population and querying of events in two stages, simplifying the query process while reducing the number of columns and improving table density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a sparse table is used to store events of multiple types, then the model for issuing queries is simple and user-friendly, but the table becomes sparsely populated with many columns, reducing efficiency when dealing with numerous event types
Solution Approach 1:
The patent segments the table structure into common columns (shared across all event types) and type-specific columns (unique to each event type). This segmentation allows the system to maintain a consolidated table structure while efficiently handling diverse event types, improving both query simplicity and processing efficiency.
Solution Approach 2:
The patent creates a universal table structure that can handle multiple event types through common columns that apply to all events and type-specific columns for event-type-dependent fields. This multi-functional design eliminates the need for separate tables for each event type while maintaining query simplicity and improving processing efficiency.
2Adaptability or versatility
If a sparse table with many columns is used to accommodate various event types, then all event fields can be stored, but the table becomes complex and requires union operations across multiple tables for querying
Solution Approach 1:
The patent divides the table structure into common columns (applicable to all event types) and type-specific columns (unique to each event type). This segmentation reduces table complexity by organizing fields logically while maintaining the ability to store and query all event types efficiently.
Solution Approach 2:
The patent introduces a new dimension to the table structure by adding an event type identifier that links common columns with type-specific columns. This dimensional change allows the system to accommodate diverse event types without increasing overall table complexity, as the structure remains consolidated rather than requiring multiple separate tables.
3Measurement precision
If union operations are used to query events across multiple tables, then detailed querying is possible, but the query process becomes complicated and time-consuming
Solution Approach 1:
The patent merges multiple event type tables into a single consolidated table structure with common columns and type-specific columns. This merging eliminates the need for union operations across multiple tables, reducing query complexity and execution time while maintaining the ability to perform detailed queries on specific event types through the common event type identifier.
Data Source
AI summary
The storage of events of multiple types in a queriable table. The queriable table has at least one common column that corresponds to a field that is common across events regardless of event type. The queriable table also has at least one field-varying column that corresponds to a type-dependent field that depends on event type. The queriable table is populated using multiple events. For instance, the event could be at least some log events that are received from multiple computing systems. The population occurs by assigning each event to a row of the queriable table. The common column is populated with values taken the same common field across event types. On the other hand, the field-varying column is populated with values of different fields from those events depending on the event type.