Dynamic Schema On-Demand Data Loading
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Solution Overview
Problem
In cloud computing environments, managing and analyzing increasing amounts of data becomes challenging due to the difficulty in efficiently accessing, interacting with, and providing reports for vast data resources, particularly in enterprise and IT systems, where traditional methods require loading all data into memory at startup, leading to inefficiencies and increased costs.
Innovation Solution
The implementation of a dynamic schema system that uses a schema definition language (SDL) to create custom APIs, allowing clients to specify data requirements through query language APIs, enabling on-demand data loading and analysis by parsing schemas and loading only the necessary data, thereby improving data repository systems and reducing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all data is loaded into memory at startup, then data access is fast and immediate, but memory resource consumption increases significantly and system efficiency decreases
Solution Approach 1:
The system performs preliminary actions by pre-defining schemas and data structures before runtime, allowing for efficient on-demand data retrieval without loading all data into memory at startup. The schema is compiled and prepared in advance, enabling fast access patterns when queries are executed.
Solution Approach 2:
The system implements dynamic data loading where data is loaded into memory only when needed based on query requirements. The schema allows for flexible, dynamic construction of data models at runtime, enabling the system to adapt memory usage to actual access patterns rather than pre-loading everything statically.
2Reliability
If traditional data loading methods are used, then all data is available for analysis, but system resource utilization increases and efficiency decreases
Solution Approach 1:
The system extracts only the necessary data from the larger dataset based on schema definitions and query requirements. By using schemas to define data structures, the system can selectively load and process only relevant portions of data, leaving the rest in storage until needed.
Solution Approach 2:
The system applies partial action by loading a subset of data into memory rather than all data. The schema enables the system to load exactly the right amount of data needed for each operation, avoiding the excess resource consumption of loading complete datasets when only partial data is required.
3Stability of the object's composition
If static schemas are used, then data structure is stable and predictable, but system flexibility and adaptability to schema changes decrease
Solution Approach 1:
The system implements dynamic schemas that can be modified and recompiled at runtime. The schema language allows for flexible definition and modification of data structures, enabling the system to adapt to changing requirements while maintaining structural integrity through formal schema definitions.
Solution Approach 2:
The schema system provides self-service capabilities where schema changes can be applied and the system can automatically recompile and adapt to new schema definitions without requiring complete system restarts or manual reconfiguration, enabling continuous operation with evolving data structures.
4Loss of information
If complete data sets are loaded for analysis, then comprehensive data access is enabled, but memory usage and processing overhead increase
Solution Approach 1:
The system extracts and loads only the specific data elements required by queries into memory, using schemas to define and enforce data structures. This selective extraction maintains data completeness for queried operations while avoiding the processing overhead of handling complete datasets in memory.
Solution Approach 2:
The system applies partial action by loading minimal necessary data subsets into memory based on query requirements defined by schemas. This approach prevents excessive data loading and processing overhead while ensuring all necessary data for each operation is available, achieving the right balance between completeness and efficiency.
Data Source
AI summary
A computing system includes a server. The server is communicatively coupled to a data repository and is configured to store a data in the data repository. The server is further configured to receive as input, via a server communicatively coupled to a data repository and configured to store a data in the data repository, a dynamic schema, the dynamic schema comprising a dynamic object, wherein the dynamic object is representative of information stored in at least one server object. The server is additionally configured to provide an application programming interface (API) to interface with the dynamic object based on the dynamic schema. The server is further configured to retrieve a result when the dynamic object is executed via a call to the API, wherein retrieving the result comprises loading the server object or a portion of the server object into a memory of the server only after the call to the API.