concurrentlineprocessor

Concurrent Line Processor

A high-performance, concurrent line-by-line processor for large files and streams in Go. This package enables efficient processing of large files by splitting input into chunks and processing each line concurrently using multiple goroutines.

Features

• Concurrent Processing: Process lines concurrently using configurable number of worker goroutines
• Memory Efficient: Uses memory pooling and streaming to handle large files without loading everything into memory
• Customizable: Support for custom line processing functions
• Metrics: Built-in performance metrics (bytes read, rows processed, processing time)
• Standard Interface: Implements io.Reader for seamless integration with existing Go I/O patterns
• Flexible Configuration: Configurable chunk size, worker count, and row limits

Installation

go get github.com/anvesh9652/concurrent-line-processor

Quick Start

Basic Usage

package main

import (
    "fmt"
    "io"
    "os"
    
    clp "github.com/anvesh9652/concurrent-line-processor"
)

func main() {
    // Open a large file
    file, err := os.Open("large-file.txt")
    if err != nil {
        panic(err)
    }
    defer file.Close()

    // Create a concurrent line processor
    processor := clp.NewConcurrentLineProcessor(file)

    // Read all processed output
    output, err := io.ReadAll(processor)
    if err != nil {
        panic(err)
    }

    fmt.Println(string(output))
    
    // Print processing metrics
    metrics := processor.Metrics()
    fmt.Printf("Processed %d rows, %d bytes in %s\n", 
        metrics.RowsRead, metrics.BytesRead, metrics.TimeTook)
}

Custom Line Processing

Transform each line using a custom processor function:

package main

import (
    "bytes"
    "encoding/json"
    "io"
    "os"
    "strings"
    
    clp "github.com/anvesh9652/concurrent-line-processor"
)

func main() {
    file, err := os.Open("data.csv")
    if err != nil {
        panic(err)
    }
    defer file.Close()

    // Convert CSV lines to uppercase
    upperCaseProcessor := func(line []byte) ([]byte, error) {
        return bytes.ToUpper(line), nil
    }

    processor := clp.NewConcurrentLineProcessor(file,
        clp.WithCustomLineProcessor(upperCaseProcessor),
        clp.WithWorkers(8),
        clp.WithChunkSize(1024*1024), // 1MB chunks
    )

    output, err := io.ReadAll(processor)
    if err != nil {
        panic(err)
    }

    // Write to output file
    err = os.WriteFile("output.csv", output, 0644)
    if err != nil {
        panic(err)
    }
}

CSV to JSONL Conversion

package main

import (
    "bytes"
    "encoding/csv"
    "encoding/json"
    "io"
    "os"
    
    clp "github.com/anvesh9652/concurrent-line-processor"
)

func convertCSVToJSONL(inputFile, outputFile string, headers []string) error {
    input, err := os.Open(inputFile)
    if err != nil {
        return err
    }
    defer input.Close()

    output, err := os.Create(outputFile)
    if err != nil {
        return err
    }
    defer output.Close()

    // Custom processor to convert CSV row to JSON
    csvToJSONProcessor := func(line []byte) ([]byte, error) {
        reader := csv.NewReader(bytes.NewReader(line))
        row, err := reader.Read()
        if err != nil {
            return nil, err
        }

        // Create map from headers and row values
        record := make(map[string]string)
        for i, header := range headers {
            if i < len(row) {
                record[header] = row[i]
            }
        }

        return json.Marshal(record)
    }

    processor := clp.NewConcurrentLineProcessor(input,
        clp.WithCustomLineProcessor(csvToJSONProcessor),
        clp.WithWorkers(4),
        clp.WithRowsReadLimit(-1), // Process all rows
    )

    _, err = io.Copy(output, processor)
    return err
}

Processing with Row Limit

Process only the first N rows of a large file:

package main

import (
    "io"
    "os"
    
    clp "github.com/anvesh9652/concurrent-line-processor"
)

func processFirstThousandRows(filename string) error {
    file, err := os.Open(filename)
    if err != nil {
        return err
    }
    defer file.Close()

    processor := clp.NewConcurrentLineProcessor(file,
        clp.WithRowsReadLimit(1000), // Only process first 1000 rows
        clp.WithWorkers(2),
    )

    // Process and discard output (useful for analysis)
    _, err = io.Copy(io.Discard, processor)
    if err != nil {
        return err
    }

    metrics := processor.Metrics()
    fmt.Printf("Processed %d rows in %s\n", metrics.RowsRead, metrics.TimeTook)
    
    return nil
}

Configuration Options

The processor can be configured using the following options:

WithWorkers(n int)

Sets the number of worker goroutines for concurrent processing.

• Default: runtime.NumCPU()
• Use 1 for sequential processing
• Higher values may improve performance for CPU-intensive operations

WithChunkSize(size int)

Sets the size of chunks read from the source.

• Default: 30KB (1024 * 30)
• Larger chunks may improve performance but use more memory
• Smaller chunks may reduce memory usage but increase overhead

WithCustomLineProcessor(processor LineProcessor)

Sets a custom function to process each line.

• Function signature: func([]byte) ([]byte, error)
• Must be thread-safe
• Should not modify external state without proper synchronization

WithRowsReadLimit(limit int)

Sets a limit on the number of rows to read.

• Default: -1 (no limit)
• Useful for processing subsets of large files
• Processing stops when the limit is reached

Performance Considerations

Memory Usage

• The processor uses memory pooling to minimize allocations
• Memory usage scales with chunk size and number of workers
• For very large files, consider smaller chunk sizes

Worker Count

• More workers help with CPU-intensive line processing
• For I/O-bound operations, more workers may not help
• Start with runtime.NumCPU() and adjust based on your use case

Chunk Size

• Larger chunks reduce overhead but increase memory usage
• Smaller chunks are more memory-efficient but may have higher overhead
• The default 30KB works well for most use cases

Examples

The examples/ directory contains complete examples demonstrating:

• Basic file processing
• CSV to JSONL conversion
• JSONL to CSV conversion
• Custom line transformations
• Performance profiling

Metrics

The processor provides detailed metrics accessible via the Metrics() method:

type Metrics struct {
    BytesRead        int64  `json:"bytes_read"`        // Total bytes read from source
    TransformedBytes int64  `json:"transformed_bytes"` // Total bytes after processing
    RowsRead         int64  `json:"rows_read"`         // Total rows processed
    TimeTook         string `json:"time_took"`         // Total processing time
}

Thread Safety

• The ConcurrentLineProcessor itself is safe for concurrent use
• Custom line processor functions must be thread-safe
• Metrics can be safely accessed concurrently
• The processor implements io.Reader and can be used safely by one goroutine at a time

Requirements

• Go 1.24.0 or later
• No external dependencies beyond golang.org/x/sync

License

This project is licensed under the MIT License - see the LICENSE file for details.

API Documentation

For complete API documentation, visit pkg.go.dev.