Multi-dimensional Dataset for Efficient Data Querying
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Solution Overview
Problem
The SALESFORCE platform faces limitations in efficiently manipulating and querying large datasets due to governors restricting loop operations, which are necessary for complex calculations using multiple dimensions, hindering its ability to process insights and generate outputs effectively.
Innovation Solution
An in-memory multi-dimensional dataset is created with a mechanism for efficient interrogation, allowing data items with key-values to be stored and queried, using composite keys and a main data map structure to facilitate rapid data retrieval and reduce the need for excessive looping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If loop operations are used for complex calculations using multiple dimensions in a dataset, then the calculations can be performed, but the governors limit how many times a process can loop through a dataset in any single context
Solution Approach 1:
The patent segments the data structure into a multi-dimensional dataset with separate dimensions for different keys (e.g., time, category, region). This segmentation allows the system to access data along different dimensions without requiring nested loop operations, as each dimension can be queried independently through the interrogation process.
Solution Approach 2:
The patent introduces a multi-dimensional data structure that adds dimensional organization to the data. By organizing data along multiple dimensions (time, category, region, etc.), the system enables efficient querying along any dimension without requiring traditional looping approaches, thus resolving the contradiction between calculation capability and processing efficiency.
2Quantity of substance
If traditional data structures are used, then data can be stored, but manipulation of even moderately large datasets is difficult due to governors limiting loop operations
Solution Approach 1:
The multi-dimensional dataset structure serves multiple functions: it stores large quantities of data, enables efficient querying along any dimension, and provides a unified interface through the interrogation process. This universal structure eliminates the need for different manipulation approaches for different data sizes or types, making operation easier regardless of dataset scale.
Solution Approach 2:
The patent introduces an interrogation process as an intermediary between the user and the multi-dimensional dataset. This intermediary handles the complexity of data manipulation internally, allowing users to query data along different dimensions without directly implementing complex loop operations, thus ease of operation while maintaining large data capacity.
3Reliability
If looping operations are restricted by governors, then system stability is maintained, but the ability to efficiently query datasets to obtain insights is limited
Solution Approach 1:
The patent extracts the looping logic from the user's code and relocates it to the internal implementation of the multi-dimensional dataset and interrogation process. This extraction allows the system to maintain governor constraints on user-facing loop operations while internally performing efficient data retrieval and manipulation without excessive looping, thus maintaining stability while improving productivity.
Solution Approach 2:
The multi-dimensional dataset performs self-service by internally managing the complexity of multi-dimensional queries. The interrogation process automatically handles the coordination of multiple dimensions and data retrieval operations without requiring user-implemented loops, allowing the system to maintain governor constraints while efficiently generating insights from the data.
Data Source
AI summary
The disclosed embodiments include a computer-implemented method that can include generating a sub-map for each of multiple data items, which each includes keys, key values, and a data item value, and where each sub-map maps keys to key values. The method can further include creating a composite key for each sub-map, where a particular composite key is a composite of key values of a particular data item. The method can further include generating, based on the sub-maps, a multi-dimensional data structure including a dimension for each key of the sub-maps where each dimension maps any key values of the dimension's key to any composite key that includes the key value, and where the multi-dimensional data structure is searchable instead of searching the plurality of data items.