.. default-domain:: cpp

.. cpp:namespace:: arrow::csv

Arrow provides a fast CSV reader allowing ingestion of external data to create Arrow Tables or a stream of Arrow RecordBatches.

.. seealso::
   :ref:`CSV reader/writer API reference <cpp-api-csv>`.

Data in a CSV file can either be read in as a single Arrow Table using :class:`~arrow::csv::TableReader` or streamed as RecordBatches using :class:`~arrow::csv::StreamingReader`. See :ref:`Tradeoffs <cpp-csv-tradeoffs>` for a discussion of the tradeoffs between the two methods.

Both these readers require an :class:`arrow::io::InputStream` instance representing the input file. Their behavior can be customized using a combination of :class:`~arrow::csv::ReadOptions`, :class:`~arrow::csv::ParseOptions`, and :class:`~arrow::csv::ConvertOptions`.

#include "arrow/csv/api.h"

{
   // ...
   arrow::io::IOContext io_context = arrow::io::default_io_context();
   std::shared_ptr<arrow::io::InputStream> input = ...;

   auto read_options = arrow::csv::ReadOptions::Defaults();
   auto parse_options = arrow::csv::ParseOptions::Defaults();
   auto convert_options = arrow::csv::ConvertOptions::Defaults();

   // Instantiate TableReader from input stream and options
   auto maybe_reader =
     arrow::csv::TableReader::Make(io_context,
                                   input,
                                   read_options,
                                   parse_options,
                                   convert_options);
   if (!maybe_reader.ok()) {
     // Handle TableReader instantiation error...
   }
   std::shared_ptr<arrow::csv::TableReader> reader = *maybe_reader;

   // Read table from CSV file
   auto maybe_table = reader->Read();
   if (!maybe_table.ok()) {
     // Handle CSV read error
     // (for example a CSV syntax error or failed type conversion)
   }
   std::shared_ptr<arrow::Table> table = *maybe_table;
}

#include "arrow/csv/api.h"

{
   // ...
   arrow::io::IOContext io_context = arrow::io::default_io_context();
   std::shared_ptr<arrow::io::InputStream> input = ...;

   auto read_options = arrow::csv::ReadOptions::Defaults();
   auto parse_options = arrow::csv::ParseOptions::Defaults();
   auto convert_options = arrow::csv::ConvertOptions::Defaults();

   // Instantiate StreamingReader from input stream and options
   auto maybe_reader =
     arrow::csv::StreamingReader::Make(io_context,
                                       input,
                                       read_options,
                                       parse_options,
                                       convert_options);
   if (!maybe_reader.ok()) {
     // Handle StreamingReader instantiation error...
   }
   std::shared_ptr<arrow::csv::StreamingReader> reader = *maybe_reader;

   // Set aside a RecordBatch pointer for re-use while streaming
   std::shared_ptr<RecordBatch> batch;

   while (true) {
       // Attempt to read the first RecordBatch
       arrow::Status status = reader->ReadNext(&batch);

       if (!status.ok()) {
         // Handle read error
       }

       if (batch == NULL) {
         // Handle end of file
         break;
       }

       // Do something with the batch
   }
}

The choice between using :class:`~arrow::csv::TableReader` or :class:`~arrow::csv::StreamingReader` will ultimately depend on the use case but there are a few tradeoffs to be aware of:

1. Memory usage: :class:`~arrow::csv::TableReader` loads all of the data into memory at once and, depending on the amount of data, may require considerably more memory than :class:`~arrow::csv::StreamingReader` which only loads one :class:`~arrow::RecordBatch` at a time. This is likely to be the most significant tradeoff for users.
2. Speed: When reading the entire contents of a CSV, :class:`~arrow::csv::TableReader` will tend to be faster than :class:`~arrow::csv::StreamingReader` because it makes better use of available cores. See :ref:`Performance <cpp-csv-performance>` for more details.
3. Flexibility: :class:`~arrow::csv::StreamingReader` might be considered less flexible than :class:`~arrow::csv::TableReader` because it performs type inference only on the first block that's read in, after which point the types are frozen and any data in subsequent blocks that cannot be converted to those types will cause an error. Note that this can be remedied either by setting :member:`ReadOptions::block_size` to a large enough value or by using :member:`ConvertOptions::column_types` to set the desired data types explicitly.

A CSV file is written to a :class:`~arrow::io::OutputStream`.

#include <arrow/csv/api.h>
{
    // Oneshot write
    // ...
    std::shared_ptr<arrow::io::OutputStream> output = ...;
    auto write_options = arrow::csv::WriteOptions::Defaults();
    if (WriteCSV(table, write_options, output.get()).ok()) {
        // Handle writer error...
    }
}
{
    // Write incrementally
    // ...
    std::shared_ptr<arrow::io::OutputStream> output = ...;
    auto write_options = arrow::csv::WriteOptions::Defaults();
    auto maybe_writer = arrow::csv::MakeCSVWriter(output, schema, write_options);
    if (!maybe_writer.ok()) {
        // Handle writer instantiation error...
    }
    std::shared_ptr<arrow::ipc::RecordBatchWriter> writer = *maybe_writer;

    // Write batches...
    if (!writer->WriteRecordBatch(*batch).ok()) {
        // Handle write error...
    }

    if (!writer->Close().ok()) {
        // Handle close error...
    }
    if (!output->Close().ok()) {
        // Handle file close error...
    }
}

There are three possible ways to infer column names from the CSV file:

• By default, the column names are read from the first row in the CSV file
• If :member:`ReadOptions::column_names` is set, it forces the column names in the table to these values (the first row in the CSV file is read as data)
• If :member:`ReadOptions::autogenerate_column_names` is true, column names will be autogenerated with the pattern "f0", "f1"... (the first row in the CSV file is read as data)

By default, Arrow reads all columns in the CSV file. You can narrow the selection of columns with the :member:`ConvertOptions::include_columns` option. If some columns in :member:`ConvertOptions::include_columns` are missing from the CSV file, an error will be emitted unless :member:`ConvertOptions::include_missing_columns` is true, in which case the missing columns are assumed to contain all-null values.

If both :member:`ReadOptions::column_names` and :member:`ConvertOptions::include_columns` are specified, the :member:`ReadOptions::column_names` are assumed to map to CSV columns, and :member:`ConvertOptions::include_columns` is a subset of those column names that will part of the Arrow Table.

By default, the CSV reader infers the most appropriate data type for each column. Type inference considers the following data types, in order:

• Null
• Int64
• Boolean
• Date32
• Time32 (with seconds unit)
• Timestamp (with seconds unit)
• Timestamp (with nanoseconds unit)
• Float64
• Dictionary<String> (if :member:`ConvertOptions::auto_dict_encode` is true)
• Dictionary<Binary> (if :member:`ConvertOptions::auto_dict_encode` is true)
• String
• Binary

It is possible to override type inference for select columns by setting the :member:`ConvertOptions::column_types` option. Explicit data types can be chosen from the following list:

• Null
• All Integer types
• Float32 and Float64
• Decimal128
• Boolean
• Date32 and Date64
• Time32 and Time64
• Timestamp
• Binary and Large Binary
• String and Large String (with optional UTF8 input validation)
• Fixed-Size Binary
• Dictionary with index type Int32 and value type one of the following: Binary, String, LargeBinary, LargeString, Int32, UInt32, Int64, UInt64, Float32, Float64, Decimal128

Other data types do not support conversion from CSV values and will error out.

If type inference is enabled and :member:`ConvertOptions::auto_dict_encode` is true, the CSV reader first tries to convert string-like columns to a dictionary-encoded string-like array. It switches to a plain string-like array when the threshold in :member:`ConvertOptions::auto_dict_max_cardinality` is reached.

If type inference is enabled, the CSV reader first tries to interpret string-like columns as timestamps. If all rows have some zone offset (e.g. Z or +0100), even if the offsets are inconsistent, then the inferred type will be UTC timestamp. If no rows have a zone offset, then the inferred type will be timestamp without timezone. A mix of rows with/without offsets will result in a string column.

If the type is explicitly specified as a timestamp with/without timezone, then the reader will error on values without/with zone offsets in that column. Note that this means it isn't currently possible to have the reader parse a column of timestamps without zone offsets as local times in a particular timezone; instead, parse the column as timestamp without timezone, then convert the values afterwards using the assume_timezone compute function.

2021-01-01T00:00:00
2021-01-01T00:00:00Z

2021-01-01T00:00:00
2021-01-01T00:00:00Z

2021-01-01T00:00:00
2021-01-01T00:00:00Z

2021-01-01T00:00:00
2021-01-01T00:00:00Z

Null values are recognized from the spellings stored in :member:`ConvertOptions::null_values`. The :func:`ConvertOptions::Defaults` factory method will initialize a number of conventional null spellings such as N/A.

CSV files are expected to be encoded in UTF8. However, non-UTF8 data is accepted for Binary columns.

The format of written CSV files can be customized via :class:`~arrow::csv::WriteOptions`. Currently few options are available; more will be added in future releases.

By default, :class:`~arrow::csv::TableReader` will parallelize reads in order to exploit all CPU cores on your machine. You can change this setting in :member:`ReadOptions::use_threads`. A reasonable expectation is at least 100 MB/s per core on a performant desktop or laptop computer (measured in source CSV bytes, not target Arrow data bytes).