.. Licensed to the Apache Software Foundation (ASF) under one .. or more contributor license agreements. See the NOTICE file .. distributed with this work for additional information .. regarding copyright ownership. The ASF licenses this file .. to you under the Apache License, Version 2.0 (the .. "License"); you may not use this file except in compliance .. with the License. You may obtain a copy of the License at .. http://www.apache.org/licenses/LICENSE-2.0 .. Unless required by applicable law or agreed to in writing, .. software distributed under the License is distributed on an .. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY .. KIND, either express or implied. See the License for the .. specific language governing permissions and limitations .. under the License. .. default-domain:: cpp .. highlight:: cpp .. cpp:namespace:: arrow::csv ============================= Reading and Writing CSV files ============================= Arrow provides a fast CSV reader allowing ingestion of external data as Arrow tables. .. seealso:: :ref:`CSV reader/writer API reference `. Basic usage =========== A CSV file is read from a :class:`~arrow::io::InputStream`. .. code-block:: cpp #include "arrow/csv/api.h" { // ... arrow::io::IOContext io_context = arrow::io::default_io_context(); std::shared_ptr 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 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 table = *maybe_table; } A CSV file is written to a :class:`~arrow::io::OutputStream`. .. code-block:: cpp #include { // Oneshot write // ... std::shared_ptr output = ...; auto write_options = arrow::csv::WriteOptions::Defaults(); if (WriteCSV(table, write_options, output.get()).ok()) { // Handle writer error... } } { // Write incrementally // ... std::shared_ptr 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 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. Column names ============ 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) Column selection ================ 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. Interaction with column names ----------------------------- 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. Data types ========== 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 (if :member:`ConvertOptions::auto_dict_encode` is true) * Dictionary (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. Dictionary inference -------------------- 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. Nulls ----- 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``. Character encoding ------------------ CSV files are expected to be encoded in UTF8. However, non-UTF8 data is accepted for Binary columns. Write Options ============= 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. Performance =========== By default, the CSV reader 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).