Requirements

We're building a CSV parser. This isn't just about splitting lines by commas; we need a robust, flexible solution capable of handling various CSV dialects (different delimiters, quote characters, escape characters), complex data types, and error conditions gracefully. Imagine processing massive financial datasets, user profiles from various systems, or even scientific measurement logs – each with its own quirky CSV format. Our parser needs to be adaptable and reliable. The core challenge lies in designing a system that can easily accommodate new CSV formats without requiring significant code changes. We need to consider different parsing strategies, data validation rules, and error handling mechanisms. Furthermore, the system needs to be performant and thread-safe, especially when dealing with large files. # Requirements ## Functional Requirements 1. **Delimiter Handling:** Support different delimiters (e.g., comma, semicolon, tab, pipe). 2. **Quote Handling:** Support different quote characters (e.g., single quote, double quote) to enclose fields containing delimiters. 3. **Escape Character Handling:** Support escape characters to include quote characters within quoted fields (e.g., `""` escapes `"`). 4. **Line Ending Handling:** Support different line endings (e.g., CRLF, LF). 5. **Header Row Handling:** Ability to detect and skip header rows. 6. **Data Type Conversion:** Ability to convert CSV fields to specific data types (e.g., integer, double, boolean, date). This should be extensible to support custom data types. 7. **Error Handling:** Comprehensive error reporting, including line number and column number of the error. Ability to configure error handling strategies (e.g., skip invalid rows, throw exceptions, log errors). 8. **Null Value Handling:** Support configurable null value representations. 9. **Column Mapping:** Ability to map CSV columns to specific fields in a data object, allowing for flexible data extraction regardless of column order. 10. **Optional Fields**: Handle missing fields in a row and provide a way to specify default values. 11. **Variable Number of Columns**: Handle CSVs with rows containing differing number of columns. Support strategies like truncating longer rows or padding shorter rows with null values. 12. **Comment Handling**: Support ignoring lines that begin with a comment character (e.g., '#'). 13. **File Input**: Read CSV data from a file path. 14. **Stream Input**: Read CSV data from an input stream or String. ## Non-Functional Requirements 1. **Extensibility:** Easy to add support for new CSV dialects, data types, and error handling strategies without modifying core classes. 2. **Modularity:** The code should be organized into well-defined modules with clear responsibilities. 3. **Testability:** The code should be easily testable, with unit tests for each module. 4. **Thread Safety:** The parser should be thread-safe to allow concurrent processing of multiple CSV files. The parsing configuration should be immutable or properly synchronized. 5. **Performance:** The parser should be efficient in terms of memory usage and processing time. Consider buffering strategies for large files. 6. **SOLID Principles:** Adhere to SOLID principles to ensure maintainability and flexibility. 7. **Error Handling:** Provide meaningful error messages and flexible error handling mechanisms. 8. **Configuration**: Provide a flexible configuration mechanism for specifying CSV dialects, data types, error handling, and column mapping. ## Core Entities 1. **CsvParser:** The main class responsible for parsing CSV data. It uses a `CsvConfiguration` and `CsvDialect` and `RowParser` to parse the data. 2. **CsvConfiguration:** Encapsulates the configuration options for the CSV parser, such as delimiter, quote character, escape character, line ending, header row handling, error handling strategy, and column mapping. Should be immutable to ensure thread-safety. 3. **CsvDialect:** Encapsulates the specific "flavor" of CSV being parsed. Should contain the delimiter, quote character, escape character, and line ending characters. Can be used to define pre-configured CSV formats (e.g., RFC4180, TSV). 4. **RowParser**: Abstraction for the process of parsing a single row, extracting values, and converting those values to their respective types. 5. **TypeConverter:** An interface for converting CSV strings to specific data types. Implementations would include `IntegerConverter`, `DoubleConverter`, `BooleanConverter`, `DateConverter`, and a `StringConverter` (the default). The design should easily accommodate custom `TypeConverter` implementations. 6. **ColumnDefinition**: Defines a CSV column's properties like name, data type, target field name in the object, null value representation, and default value. 7. **CsvRow:** Represents a single row of CSV data, typically as a `Map ` where the key is the column name and the value is the parsed data. It may also be a simple `List ` if column names are not used or available. 8. **CsvParsingException:** A custom exception class to represent errors encountered during CSV parsing. 9. **ErrorHandler**: An interface for handling parsing errors. Implementations could include `ThrowExceptionHandler`, `LogExceptionHandler`, and `SkipRowErrorHandler`.

Design CSV parser

Requirements

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Functional Requirements

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Non-Functional Requirements

Core Entity Identification

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