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csv2

Table of Contents

CSV Reader

#include <csv2/reader.hpp>

int main() {
  csv2::Reader<delimiter<','>, 
               quote_character<'"'>, 
               first_row_is_header<true>,
               trim_policy::trim_whitespace> csv;
               
  if (csv.mmap("foo.csv")) {
    const auto header = csv.header();
    for (const auto row: csv) {
      for (const auto cell: row) {
        // Do something with cell value
        // std::string value;
        // cell.read_value(value);
      }
    }
  }
}

Performance Benchmark

This benchmark measures the average execution time (of 5 runs after 3 warmup runs) for csv2 to memory-map the input CSV file and iterate over every cell in the CSV. See benchmark/main.cpp for more details.

cd benchmark
g++ -I../include -O3 -std=c++11 -o main main.cpp
./main <csv_file>

Hardware

MacBook Pro (15-inch, 2019)
Processor: 2.4 GHz 8-Core Intel Core i9
Memory: 32 GB 2400 MHz DDR4
Operating System: macOS Catalina version 10.15.3

Results (as of 23 APR 2020)

Dataset File Size Rows Cols Time
Denver Crime Data 111 MB 479,100 19 0.174s
AirBnb Paris Listings 196 MB 141,730 96 0.289s
2015 Flight Delays and Cancellations 574 MB 5,819,079 31 1.047s
StackLite: Stack Overflow questions 870 MB 17,203,824 7 1.505s
Used Cars Dataset 1.4 GB 539,768 25 1.979s
Title-Based Semantic Subject Indexing 3.7 GB 12,834,026 4 5.929s
Bitcoin tweets - 16M tweets 4 GB 47,478,748 9 7.040s
DDoS Balanced Dataset 6.3 GB 12,794,627 85 12.648s
Seattle Checkouts by Title 7.1 GB 34,892,623 11 12.883s
SHA-1 password hash dump 11 GB 2,62,974,241 2 19.505s
DOHUI NOH scaled_data 16 GB 496,782 3213 32.780s

Reader API

Here is the public API available to you:

template <class delimiter = delimiter<','>, 
          class quote_character = quote_character<'"'>,
          class first_row_is_header = first_row_is_header<true>,
          class trim_policy = trim_policy::trim_whitespace>
class Reader {
public:
  
  // Use this if you'd like to mmap and read from file
  bool mmap(string_type filename);

  // Use this if you have the CSV contents in std::string already
  bool parse(string_type contents);

  // Shape
  size_t rows() const;
  size_t cols() const;
  
  // Row iterator
  // If first_row_is_header, row iteration will start
  // from the second row
  RowIterator begin() const;
  RowIterator end() const;

  // Access the first row of the CSV
  Row header() const;
};

Here's the Row class:

// Row class
class Row {
public:
  // Get raw contents of the row
  void read_raw_value(Container& value) const;
  
  // Cell iterator
  CellIterator begin() const;
  CellIterator end() const;
};

and here's the Cell class:

// Cell class
class Cell {
public:
  // Get raw contents of the cell
  void read_raw_value(Container& value) const;
  
  // Get converted contents of the cell
  // Handles escaped content, e.g., 
  // """foo""" => ""foo""
  void read_value(Container& value) const;
};

CSV Writer

This library also provides a basic csv2::Writer class - one that can be used to write CSV rows to file. Here's a basic usage:

#include <csv2/writer.hpp>
#include <vector>
#include <string>
using namespace csv2;

int main() {
    std::ofstream stream("foo.csv");
    Writer<delimiter<','>> writer(stream);

    std::vector<std::vector<std::string>> rows = 
        {
            {"a", "b", "c"},
            {"1", "2", "3"},
            {"4", "5", "6"}
        };

    writer.write_rows(rows);
    stream.close();
}

Writer API

Here is the public API available to you:

template <class delimiter = delimiter<','>>
class Writer {
public:
  
  // Construct using an std::ofstream
  Writer(output_file_stream stream);

  // Use this to write a single row to file
  void write_row(container_of_strings row);

  // Use this to write a list of rows to file
  void write_rows(container_of_rows rows);

Compiling Tests

mkdir build && cd build
cmake -DCSV2_TEST=ON ..
make
cd test
./csv2_test

Generating Single Header

python3 utils/amalgamate/amalgamate.py -c single_include.json -s .

Contributing

Contributions are welcome, have a look at the CONTRIBUTING.md document for more information.

License

The project is available under the MIT license.


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