How to parse a large CSV file with NimbleCSV?

Use the parse_stream function with a file stream, like piping File.stream! to MyParser.parse_stream() for lazy processing. This allows efficient handling of big files without loading everything into memory, as shown in the README example.

NimbleCSV vs CSV elixir library – which is better?

NimbleCSV is faster and more customizable, ideal for performance-critical tasks and custom parsers, while CSV.elixir might offer more convenience with built-in features like type casting. Choose based on whether you prioritize speed or out-of-the-box functionality.

How to define a custom CSV parser in NimbleCSV?

Call NimbleCSV.define with your module name and options like separator and escape. This generates a parser module for parsing and dumping, as demonstrated in the README with examples for tab-separated values.

Does NimbleCSV handle CSV headers automatically?

No, NimbleCSV does not manage headers; you need to skip or process the first row manually in your code after parsing. This keeps the library simple but adds overhead for header-rich workflows.

How to dump data back to CSV with NimbleCSV?

Use the dump function on your defined parser module, e.g., MyParser.dump([["name", "age"]]). It outputs a CSV string, allowing bidirectional data handling without additional dependencies.

What's the performance benchmark for NimbleCSV?

NimbleCSV is designed for high performance using binary patterns, making it significantly faster for large files compared to more feature-rich libraries. However, specific benchmarks aren't provided in the README, so testing with your data is recommended.

nimble_csv — Fast CSV Library for Elixir

What is nimble_csv?

NimbleCSV is a CSV parsing and dumping library for Elixir that emphasizes simplicity and high performance. It allows developers to define custom parsers using binary patterns for efficiency and supports both eager and lazy parsing modes. The library solves the need for fast, flexible CSV handling in Elixir applications without imposing data transformation rules.

Target Audience

Elixir developers who need to parse or generate CSV data efficiently, especially those working with large files or requiring custom parsing logic.

Value Proposition

Developers choose NimbleCSV for its speed, simplicity, and composability—it avoids bloat by focusing on parsing raw data and letting users handle transformations, making it ideal for performance-sensitive and customizable CSV processing.

A simple and fast CSV parsing and dumping library for Elixir

Use Cases

Best For

Parsing large CSV files efficiently with streaming
Defining custom CSV parsers with specific separators and escapes
Dumping Elixir data structures to CSV format
Integrating CSV processing into Elixir pipelines and streams
Handling RFC4180-compliant CSV data with a default parser
Building lightweight CSV tools without heavy dependencies

Not Ideal For

Projects requiring automatic data type conversion and header management out of the box
Teams needing comprehensive error handling and validation for malformed CSV files
Applications where CSV parsing must be highly configurable without writing custom code
Environments prioritizing quick, drop-in solutions over performance and customization

Pros & Cons

Pros

Custom Parser Flexibility

Allows defining parsers with specific separators and escapes using NimbleCSV.define, enabling efficient binary pattern matching for tailored CSV formats, as shown in the README example with custom tab-separated parsers.

Efficient Streaming Support

Provides parse_stream for lazy parsing of large files, integrating seamlessly with Elixir's Stream module for memory-efficient processing, demonstrated in the README with file streaming pipelines.

Minimalist Composable Design

Focuses solely on parsing raw data without imposing casting or header management, letting developers build transformations post-parsing, which aligns with Elixir's functional composition style.

Default RFC4180 Parser

Ships with a built-in parser for standard CSV formats using commas and double-quotes, reducing initial setup for common use cases, as highlighted in the README with aliasing examples.

Cons

No Automatic Data Casting

Lacks built-in type inference or conversion; developers must manually handle integer parsing, date conversion, etc., as admitted in the philosophy where it avoids data transformation.

Manual Header Processing

Requires additional code to manage CSV headers, unlike libraries with out-of-the-box header support, forcing extra steps for common tasks like mapping rows to structs.

Limited Error Handling

Focuses on speed and simplicity, providing minimal error messages or recovery for malformed CSV files, which can complicate debugging in production environments with irregular data.

What is nimble_csv?

Target Audience

Elixir developers who need to parse or generate CSV data efficiently, especially those working with large files or requiring custom parsing logic.

Value Proposition

Use Cases

Best For

Parsing large CSV files efficiently with streaming
Defining custom CSV parsers with specific separators and escapes
Dumping Elixir data structures to CSV format
Integrating CSV processing into Elixir pipelines and streams
Handling RFC4180-compliant CSV data with a default parser
Building lightweight CSV tools without heavy dependencies

Not Ideal For

Projects requiring automatic data type conversion and header management out of the box
Teams needing comprehensive error handling and validation for malformed CSV files
Applications where CSV parsing must be highly configurable without writing custom code
Environments prioritizing quick, drop-in solutions over performance and customization

Pros & Cons

Pros

Custom Parser Flexibility

Efficient Streaming Support

Provides parse_stream for lazy parsing of large files, integrating seamlessly with Elixir's Stream module for memory-efficient processing, demonstrated in the README with file streaming pipelines.

Minimalist Composable Design

Focuses solely on parsing raw data without imposing casting or header management, letting developers build transformations post-parsing, which aligns with Elixir's functional composition style.

Default RFC4180 Parser

Ships with a built-in parser for standard CSV formats using commas and double-quotes, reducing initial setup for common use cases, as highlighted in the README with aliasing examples.

Cons

No Automatic Data Casting

Lacks built-in type inference or conversion; developers must manually handle integer parsing, date conversion, etc., as admitted in the philosophy where it avoids data transformation.

Manual Header Processing

Requires additional code to manage CSV headers, unlike libraries with out-of-the-box header support, forcing extra steps for common tasks like mapping rows to structs.

Limited Error Handling

Focuses on speed and simplicity, providing minimal error messages or recovery for malformed CSV files, which can complicate debugging in production environments with irregular data.

nimble_csv

What is nimble_csv?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

nimble_csv

What is nimble_csv?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?