Data Ingestion Error Handling Using Column Error Indicators
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Solution Overview
Problem
Existing data platforms face challenges in efficiently handling errors during the ingestion of data from external files, particularly in parsing and expression evaluation, with inconsistent error handling across different file formats and a reliance on customized SQL function implementations.
Innovation Solution
Implementing a consistent and generic expression error handling mechanism that decouples error handling from scanner components, using error indicators to manage errors within a data ingestion pipeline, allowing for flexible and extendable error handling across various file types and SQL queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customized SQL function implementations are used for error handling in data ingestion, then specific file formats can be handled, but error handling becomes inconsistent across different file formats and increases system complexity
Solution Approach 1:
The patent implements a universal error handling mechanism that works across all file formats and data types. Instead of having separate error handling logic for each file format, a single generic error handling framework is introduced that can handle errors from any source (parsing, expression evaluation, data type mismatches) in a unified manner. This resolves the contradiction by providing adaptability through a single mechanism while reducing complexity by eliminating the need for multiple customized implementations.
2Reliability
If error handling is coupled with scanner components, then parsing errors can be detected, but error handling becomes format-specific and reduces system flexibility
Solution Approach 1:
The patent extracts error handling functionality from scanner components and creates a separate, independent error handling mechanism. The scanner components focus solely on data extraction and parsing, while errors are captured and managed by a dedicated error handling framework. This separation allows the system to maintain high reliability in error detection while achieving versatility, as the error handling mechanism can process errors from any scanner or file format without being tied to specific implementation details.
3Productivity
If multiple customized error handling implementations are maintained for different file formats, then each format can be optimized, but maintenance difficulty and time consumption increase
Solution Approach 1:
The patent merges multiple file-format-specific error handling implementations into a single unified error handling framework. Instead of maintaining separate error handling code for CSV, JSON, XML, and other formats, all error handling logic is consolidated into one generic mechanism that adapts to different formats through standardized interfaces. This resolves the contradiction by maintaining productivity through efficient unified processing while dramatically reducing maintenance time, as updates or bug fixes need to be made in only one location rather than multiple format-specific implementations.
Data Source
AI summary
A data platform including an error handling framework for loading of input data. The data platform generates input data columns based on an input file and generates result data columns based on the input data columns and evaluating expressions. The data platform detects projection errors during the generating of the result data columns and stores result error indicators in error indicator arrays of the result data columns based on the projection errors. The data platform generates filtered result data columns based on the result data columns and the result error indicator arrays of the result data columns and stores the filtered result data columns in a database of the data platform.