Workbook Input Tables with Uncommitted Edits for Low-Latency Display
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Solution Overview
Problem
Modern businesses face challenges in efficiently manipulating and composing complex database queries for cloud-based data warehouses, which often exceed the capabilities of most users, leading to high latency in data presentation.
Innovation Solution
An intermediary computing system generates database statements that incorporate uncommitted edits from an edit queue, combining them with data retrieved from a cloud-based data warehouse to provide up-to-date input tables with reduced latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If complex database queries are constructed and issued directly to cloud-based data warehouses, then data retrieval accuracy is maintained, but user-perceived latency increases and ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary computing system that sits between the user interface and the cloud-based data warehouse. This intermediary pre-processes complex database queries, breaking them down into simpler components that can be executed more efficiently. The intermediary system maintains query execution plans and cached results, allowing it to respond to user requests without always needing to execute full complex queries against the remote database, thereby reducing perceived latency while maintaining operational simplicity for users.
Solution Approach 2:
The system performs preliminary actions by pre-compiling and optimizing database queries before they are actually needed. Query execution plans are generated in advance and stored in the intermediary system. When users request data, the system can leverage these pre-prepared execution plans rather than constructing and optimizing queries in real-time, significantly reducing the time users wait for results while keeping the interface simple and easy to use.
2Adaptability or versatility
If complex queries are constructed to manipulate data in remote databases, then data manipulation capability is achieved, but query composition complexity increases beyond most users' abilities
Solution Approach 1:
The intermediary computing system acts as a intelligent mediator that translates simple user requests into complex database operations. Users interact with a simplified interface that doesn't require knowledge of complex SQL queries or database schema. The intermediary system automatically generates the necessary complex queries, joins, filters, and aggregations based on user-friendly input, thereby providing powerful data manipulation capabilities while hiding the underlying complexity from users.
Solution Approach 2:
The system creates and maintains copies of query execution plans and intermediate query results in the intermediary computing system. Instead of requiring users to construct complex queries each time, the system stores optimized query templates and execution plans that can be reused. This copying mechanism allows the system to provide complex data manipulation capabilities through simple interfaces, as the heavy lifting of query composition is done once and reused multiple times.
3Measurement precision
If data is retrieved directly from cloud-based data warehouses, then data accuracy is maintained, but retrieval time increases due to remote access
Solution Approach 1:
The intermediary computing system performs preliminary data retrieval and processing operations. It pre-queries the cloud-based data warehouse for commonly accessed data and caches the results locally. When users request data, the intermediary system serves from the cache when possible, dramatically reducing retrieval time. For data not in cache, the system has already established optimized query paths and execution plans, making subsequent retrievals faster while maintaining data accuracy by pulling from the authoritative cloud-based data warehouse when needed.
Solution Approach 2:
The system implements local quality by maintaining a local cache and intermediary data store between the user and the remote cloud-based data warehouse. This local component stores frequently accessed data and query execution plans, allowing the system to serve data locally when possible (improving speed) while still maintaining the ability to retrieve accurate data from the remote warehouse when the local cache doesn't have the information. The intermediary system ensures data accuracy by validating and syncing with the authoritative remote source.
Data Source
AI summary
Generating edited input tables for low-latency presentation including receiving, by a workbook manager on an intermediary computing system from a first workbook client on a first client computing system, a request for an input table on a cloud-based data warehouse, wherein the intermediary computing system and the cloud-based data warehouse are separate and distinct systems; generating, by the workbook manager, a database statement comprising an uncommitted edit, wherein the database statement configured to retrieve the input table from the cloud-based data warehouse and combine the input table with the uncommitted edit; and providing, by the workbook manager, the input table comprising the uncommitted edit to the first workbook client on the first client computing system.