docs/source/cpp/csv.rst
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.. default-domain:: cpp .. highlight:: 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.
.. code-block:: cpp
#include "arrow/csv/api.h"
{ // ... arrow::io::IOContext io_context = arrow::io::default_io_context(); std::shared_ptrarrow::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;
}
.. code-block:: cpp
#include "arrow/csv/api.h"
{ // ... arrow::io::IOContext io_context = arrow::io::default_io_context(); std::shared_ptrarrow::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
}
}
.. _cpp-csv-tradeoffs:
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:
~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.~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.~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.
.. code-block:: cpp
#include <arrow/csv/api.h> { // Oneshot write // ... std::shared_ptrarrow::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_ptrarrow::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_ptrarrow::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...
}
}
.. note:: The writer does not yet support all Arrow types.
There are three possible ways to infer column names from the CSV file:
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)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:
ConvertOptions::auto_dict_encode is true)ConvertOptions::auto_dict_encode is true)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:
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.
+-------------------+------------------------------+-------------------+
| Specified Type | Input CSV | Result Type |
+===================+==============================+===================+
| (inferred) | 2021-01-01T00:00:00 | timestamp[s] |
| +------------------------------+-------------------+
| | 2021-01-01T00:00:00Z | timestamp[s, UTC] |
| +------------------------------+ |
| | 2021-01-01T00:00:00+0100 | |
| +------------------------------+-------------------+
| | :: | string |
| | | |
| | 2021-01-01T00:00:00 | |
| | 2021-01-01T00:00:00Z | |
+-------------------+------------------------------+-------------------+
| timestamp[s] | 2021-01-01T00:00:00 | timestamp[s] |
| +------------------------------+-------------------+
| | 2021-01-01T00:00:00Z | (error) |
| +------------------------------+ |
| | 2021-01-01T00:00:00+0100 | |
| +------------------------------+ |
| | :: | |
| | | |
| | 2021-01-01T00:00:00 | |
| | 2021-01-01T00:00:00Z | |
+-------------------+------------------------------+-------------------+
| timestamp[s, UTC] | 2021-01-01T00:00:00 | (error) |
| +------------------------------+-------------------+
| | 2021-01-01T00:00:00Z | timestamp[s, UTC] |
| +------------------------------+ |
| | 2021-01-01T00:00:00+0100 | |
| +------------------------------+-------------------+
| | :: | (error) |
| | | |
| | 2021-01-01T00:00:00 | |
| | 2021-01-01T00:00:00Z | |
+-------------------+------------------------------+-------------------+
| timestamp[s, | 2021-01-01T00:00:00 | (error) |
| America/New_York] +------------------------------+-------------------+
| | 2021-01-01T00:00:00Z | timestamp[s, |
| +------------------------------+ America/New_York] |
| | 2021-01-01T00:00:00+0100 | |
| +------------------------------+-------------------+
| | :: | (error) |
| | | |
| | 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.
.. _cpp-csv-performance:
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).