JSON File Parsing Engine with Dynamic Fallback
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Solution Overview
Problem
Existing data processing systems, particularly those using Java Virtual Machines (JVMs), are inefficient when parsing JSON files due to their broad feature support and inherent overhead, leading to suboptimal performance in processing certain JSON files.
Innovation Solution
Implementing a processing engine optimized for specific JSON files, which allows for quicker parsing and information ingestion, and incorporating a mechanism to fallback to alternative engines in case of errors, thereby optimizing file processing and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a Java Virtual Machine (JVM) is used to process JSON files, then broad feature support is achieved, but processing speed and efficiency deteriorate due to inherent overhead
Solution Approach 1:
The system segments the monolithic JVM processing approach into multiple specialized processing engines, each optimized for specific JSON file types or processing scenarios. This allows the system to select the most appropriate engine for each file, achieving both specialization (speed) and versatility (feature support) without relying solely on the general-purpose JVM.
Solution Approach 2:
The system changes the operational parameters by introducing multiple processing engines with different optimization characteristics. Instead of using a single JVM with fixed parameters, the system dynamically selects engines based on file characteristics, effectively changing the processing parameters to match the specific requirements of each JSON file type.
2Productivity
If an optimized processing engine is used for specific JSON files, then processing speed improves, but adaptability to different file types deteriorates
Solution Approach 1:
The system implements a multi-functional processing framework where multiple specialized engines work together to provide universal coverage. Each engine is optimized for specific file types, but the collective system handles diverse JSON formats through engine selection and coordination, achieving both specialization benefits and broad adaptability.
Solution Approach 2:
The system introduces an intermediary component that sits between the input JSON files and the processing engines. This intermediary analyzes file characteristics and routes them to the appropriate optimized engine, enabling the system to maintain both speed optimization and adaptability without requiring each engine to handle all file types.
3Device complexity
If a single processing engine is used for all JSON files, then system complexity is reduced, but processing efficiency for certain file types deteriorates
Solution Approach 1:
The system implements a dynamic engine selection mechanism that adapts to different JSON file types at runtime. Instead of a static single-engine architecture, the system dynamically chooses the most appropriate processing engine based on file characteristics, achieving high efficiency without requiring a complex manual configuration of multiple dedicated systems.
Data Source
AI summary
The present application discloses a method, system, and computer system for parsing files. The method includes receiving an indication that a first file is to be processed, determining to begin processing the first file using a first processing engine based at least in part on one or more predefined heuristics, indicating to process the first file using a first processing engine, determining whether a particular error in processing the first file using the first processing engine has been detected, in response to determining that the particular error has been detected, indicate to stop processing the first file using the first processing engine and indicate to continue processing using a second processing engine, and storing in memory information obtained based on processing the first file by one or more of the first processing engine and the second processing engine.