Reading and writing Parquet files¶

Reading Parquet files¶

The arrow::FileReader class reads data into Arrow Tables and Record Batches.

The StreamReader class allows for data to be read using a C++ input stream approach to read fields column by column and row by row. This approach is offered for ease of use and type-safety. It is of course also useful when data must be streamed as files are read and written incrementally.

Please note that the performance of the StreamReader will not be as good due to the type checking and the fact that column values are processed one at a time.

FileReader¶

To read Parquet data into Arrow structures, use arrow::FileReader. To construct, it requires a ::arrow::io::RandomAccessFile instance representing the input file. To read the whole file at once, use arrow::FileReader::ReadTable():

// #include "arrow/io/api.h"
// #include "arrow/parquet/arrow/reader.h"

arrow::MemoryPool* pool = arrow::default_memory_pool();
std::shared_ptr<arrow::io::RandomAccessFile> input;
ARROW_ASSIGN_OR_RAISE(input, arrow::io::ReadableFile::Open(path_to_file));

// Open Parquet file reader
std::unique_ptr<parquet::arrow::FileReader> arrow_reader;
ARROW_RETURN_NOT_OK(parquet::arrow::OpenFile(input, pool, &arrow_reader));

// Read entire file as a single Arrow table
std::shared_ptr<arrow::Table> table;
ARROW_RETURN_NOT_OK(arrow_reader->ReadTable(&table));

Finer-grained options are available through the arrow::FileReaderBuilder helper class, which accepts the ReaderProperties and ArrowReaderProperties classes.

For reading as a stream of batches, use the arrow::FileReader::GetRecordBatchReader() method to retrieve a arrow::RecordBatchReader. It will use the batch size set in ArrowReaderProperties.

// #include "arrow/io/api.h"
// #include "arrow/parquet/arrow/reader.h"

arrow::MemoryPool* pool = arrow::default_memory_pool();

// Configure general Parquet reader settings
auto reader_properties = parquet::ReaderProperties(pool);
reader_properties.set_buffer_size(4096 * 4);
reader_properties.enable_buffered_stream();

// Configure Arrow-specific Parquet reader settings
auto arrow_reader_props = parquet::ArrowReaderProperties();
arrow_reader_props.set_batch_size(128 * 1024);  // default 64 * 1024

parquet::arrow::FileReaderBuilder reader_builder;
ARROW_RETURN_NOT_OK(
    reader_builder.OpenFile(path_to_file, /*memory_map=*/false, reader_properties));
reader_builder.memory_pool(pool);
reader_builder.properties(arrow_reader_props);

std::unique_ptr<parquet::arrow::FileReader> arrow_reader;
ARROW_ASSIGN_OR_RAISE(arrow_reader, reader_builder.Build());

std::shared_ptr<::arrow::RecordBatchReader> rb_reader;
ARROW_RETURN_NOT_OK(arrow_reader->GetRecordBatchReader(&rb_reader));

for (arrow::Result<std::shared_ptr<arrow::RecordBatch>> maybe_batch : *rb_reader) {
  // Operate on each batch...
}

Performance and Memory Efficiency¶

For remote filesystems, use read coalescing (pre-buffering) to reduce number of API calls:

auto arrow_reader_props = parquet::ArrowReaderProperties();
reader_properties.set_prebuffer(true);

The defaults are generally tuned towards good performance, but parallel column decoding is off by default. Enable it in the constructor of ArrowReaderProperties:

auto arrow_reader_props = parquet::ArrowReaderProperties(/*use_threads=*/true);

If memory efficiency is more important than performance, then:

Do not turn on read coalescing (pre-buffering) in parquet::ArrowReaderProperties.
Read data in batches using arrow::FileReader::GetRecordBatchReader().
Turn on enable_buffered_stream in parquet::ReaderProperties.

In addition, if you know certain columns contain many repeated values, you can read them as dictionary encoded columns. This is enabled with the set_read_dictionary setting on ArrowReaderProperties. If the files were written with Arrow C++ and the store_schema was activated, then the original Arrow schema will be automatically read and will override this setting.

StreamReader¶

The StreamReader allows for Parquet files to be read using standard C++ input operators which ensures type-safety.

Please note that types must match the schema exactly i.e. if the schema field is an unsigned 16-bit integer then you must supply a uint16_t type.

Exceptions are used to signal errors. A ParquetException is thrown in the following circumstances:

Attempt to read field by supplying the incorrect type.
Attempt to read beyond end of row.
Attempt to read beyond end of file.

#include "arrow/io/file.h"
#include "parquet/stream_reader.h"

{
   std::shared_ptr<arrow::io::ReadableFile> infile;

   PARQUET_ASSIGN_OR_THROW(
      infile,
      arrow::io::ReadableFile::Open("test.parquet"));

   parquet::StreamReader stream{parquet::ParquetFileReader::Open(infile)};

   std::string article;
   float price;
   uint32_t quantity;

   while ( !stream.eof() )
   {
      stream >> article >> price >> quantity >> parquet::EndRow;
      // ...
   }
}

Writing Parquet files¶

WriteTable¶

The arrow::WriteTable() function writes an entire ::arrow::Table to an output file.

// #include "parquet/arrow/writer.h"
// #include "arrow/util/type_fwd.h"
using parquet::ArrowWriterProperties;
using parquet::WriterProperties;

ARROW_ASSIGN_OR_RAISE(std::shared_ptr<arrow::Table> table, GetTable());

// Choose compression
std::shared_ptr<WriterProperties> props =
    WriterProperties::Builder().compression(arrow::Compression::SNAPPY)->build();

// Opt to store Arrow schema for easier reads back into Arrow
std::shared_ptr<ArrowWriterProperties> arrow_props =
    ArrowWriterProperties::Builder().store_schema()->build();

std::shared_ptr<arrow::io::FileOutputStream> outfile;
ARROW_ASSIGN_OR_RAISE(outfile, arrow::io::FileOutputStream::Open(path_to_file));

ARROW_RETURN_NOT_OK(parquet::arrow::WriteTable(*table.get(),
                                               arrow::default_memory_pool(), outfile,
                                               /*chunk_size=*/3, props, arrow_props));

Note

Column compression is off by default in C++. See below for how to choose a compression codec in the writer properties.

To write out data batch-by-batch, use arrow::FileWriter.

// #include "parquet/arrow/writer.h"
// #include "arrow/util/type_fwd.h"
using parquet::ArrowWriterProperties;
using parquet::WriterProperties;

// Data is in RBR
std::shared_ptr<arrow::RecordBatchReader> batch_stream;
ARROW_ASSIGN_OR_RAISE(batch_stream, GetRBR());

// Choose compression
std::shared_ptr<WriterProperties> props =
    WriterProperties::Builder().compression(arrow::Compression::SNAPPY)->build();

// Opt to store Arrow schema for easier reads back into Arrow
std::shared_ptr<ArrowWriterProperties> arrow_props =
    ArrowWriterProperties::Builder().store_schema()->build();

// Create a writer
std::shared_ptr<arrow::io::FileOutputStream> outfile;
ARROW_ASSIGN_OR_RAISE(outfile, arrow::io::FileOutputStream::Open(path_to_file));
std::unique_ptr<parquet::arrow::FileWriter> writer;
ARROW_ASSIGN_OR_RAISE(
    writer, parquet::arrow::FileWriter::Open(*batch_stream->schema().get(),
                                             arrow::default_memory_pool(), outfile,
                                             props, arrow_props));

// Write each batch as a row_group
for (arrow::Result<std::shared_ptr<arrow::RecordBatch>> maybe_batch : *batch_stream) {
  ARROW_ASSIGN_OR_RAISE(auto batch, maybe_batch);
  ARROW_ASSIGN_OR_RAISE(auto table,
                        arrow::Table::FromRecordBatches(batch->schema(), {batch}));
  ARROW_RETURN_NOT_OK(writer->WriteTable(*table.get(), batch->num_rows()));
}

// Write file footer and close
ARROW_RETURN_NOT_OK(writer->Close());

StreamWriter¶

The StreamWriter allows for Parquet files to be written using standard C++ output operators, similar to reading with the StreamReader class. This type-safe approach also ensures that rows are written without omitting fields and allows for new row groups to be created automatically (after certain volume of data) or explicitly by using the EndRowGroup stream modifier.

Exceptions are used to signal errors. A ParquetException is thrown in the following circumstances:

Attempt to write a field using an incorrect type.
Attempt to write too many fields in a row.
Attempt to skip a required field.

#include "arrow/io/file.h"
#include "parquet/stream_writer.h"

{
   std::shared_ptr<arrow::io::FileOutputStream> outfile;

   PARQUET_ASSIGN_OR_THROW(
      outfile,
      arrow::io::FileOutputStream::Open("test.parquet"));

   parquet::WriterProperties::Builder builder;
   std::shared_ptr<parquet::schema::GroupNode> schema;

   // Set up builder with required compression type etc.
   // Define schema.
   // ...

   parquet::StreamWriter os{
      parquet::ParquetFileWriter::Open(outfile, schema, builder.build())};

   // Loop over some data structure which provides the required
   // fields to be written and write each row.
   for (const auto& a : getArticles())
   {
      os << a.name() << a.price() << a.quantity() << parquet::EndRow;
   }
}

Writer properties¶

To configure how Parquet files are written, use the WriterProperties::Builder:

#include "parquet/arrow/writer.h"
#include "arrow/util/type_fwd.h"

using parquet::WriterProperties;
using parquet::ParquetVersion;
using parquet::ParquetDataPageVersion;
using arrow::Compression;

std::shared_ptr<WriterProperties> props = WriterProperties::Builder()
   .max_row_group_length(64 * 1024)
   .created_by("My Application")
   .version(ParquetVersion::PARQUET_2_6)
   .data_page_version(ParquetDataPageVersion::V2)
   .compression(Compression::SNAPPY)
   .build();

The max_row_group_length sets an upper bound on the number of rows per row group that takes precedent over the chunk_size passed in the write methods.

You can set the version of Parquet to write with version, which determines which logical types are available. In addition, you can set the data page version with data_page_version. It’s V1 by default; setting to V2 will allow more optimal compression (skipping compressing pages where there isn’t a space benefit), but not all readers support this data page version.

Compression is off by default, but to get the most out of Parquet, you should also choose a compression codec. You can choose one for the whole file or choose one for individual columns. If you choose a mix, the file-level option will apply to columns that don’t have a specific compression codec. See ::arrow::Compression for options.

Column data encodings can likewise be applied at the file-level or at the column level. By default, the writer will attempt to dictionary encode all supported columns, unless the dictionary grows too large. This behavior can be changed at file-level or at the column level with disable_dictionary(). When not using dictionary encoding, it will fallback to the encoding set for the column or the overall file; by default Encoding::PLAIN, but this can be changed with encoding().

#include "parquet/arrow/writer.h"
#include "arrow/util/type_fwd.h"

using parquet::WriterProperties;
using arrow::Compression;
using parquet::Encoding;

std::shared_ptr<WriterProperties> props = WriterProperties::Builder()
  .compression(Compression::SNAPPY)        // Fallback
  ->compression("colA", Compression::ZSTD) // Only applies to column "colA"
  ->encoding(Encoding::BIT_PACKED)         // Fallback
  ->encoding("colB", Encoding::RLE)        // Only applies to column "colB"
  ->disable_dictionary("colB")             // Never dictionary-encode column "colB"
  ->build();

Statistics are enabled by default for all columns. You can disable statistics for all columns or specific columns using disable_statistics on the builder. There is a max_statistics_size which limits the maximum number of bytes that may be used for min and max values, useful for types like strings or binary blobs. If a column has enabled page index using enable_write_page_index, then it does not write statistics to the page header because it is duplicated in the ColumnIndex.

There are also Arrow-specific settings that can be configured with parquet::ArrowWriterProperties:

#include "parquet/arrow/writer.h"

using parquet::ArrowWriterProperties;

std::shared_ptr<ArrowWriterProperties> arrow_props = ArrowWriterProperties::Builder()
   .enable_deprecated_int96_timestamps() // default False
   ->store_schema() // default False
   ->build();

These options mostly dictate how Arrow types are converted to Parquet types. Turning on store_schema will cause the writer to store the serialized Arrow schema within the file metadata. Since there is no bijection between Parquet schemas and Arrow schemas, storing the Arrow schema allows the Arrow reader to more faithfully recreate the original data. This mapping from Parquet types back to original Arrow types includes:

Reading timestamps with original timezone information (Parquet does not support time zones);
Reading Arrow types from their storage types (such as Duration from int64 columns);
Reading string and binary columns back into large variants with 64-bit offsets;
Reading back columns as dictionary encoded (whether an Arrow column and the serialized Parquet version are dictionary encoded are independent).

Supported Parquet features¶

The Parquet format has many features, and Parquet C++ supports a subset of them.

Page types¶

Page type	Notes
DATA_PAGE
DATA_PAGE_V2
DICTIONARY_PAGE

Unsupported page type: INDEX_PAGE. When reading a Parquet file, pages of this type are ignored.

Compression¶

Compression codec	Notes
SNAPPY
GZIP
BROTLI
LZ4	(1)
ZSTD

(1) On the read side, Parquet C++ is able to decompress both the regular LZ4 block format and the ad-hoc Hadoop LZ4 format used by the reference Parquet implementation. On the write side, Parquet C++ always generates the ad-hoc Hadoop LZ4 format.

Unsupported compression codec: LZO.

Encodings¶

Encoding	Reading	Writing	Notes
PLAIN	✓	✓
PLAIN_DICTIONARY	✓	✓
BIT_PACKED	✓	✓	(1)
RLE	✓	✓	(1)
RLE_DICTIONARY	✓	✓	(2)
BYTE_STREAM_SPLIT	✓	✓
DELTA_BINARY_PACKED	✓	✓
DELTA_BYTE_ARRAY	✓
DELTA_LENGTH_BYTE_ARRAY	✓	✓

(1) Only supported for encoding definition and repetition levels, and boolean values.
(2) On the write path, RLE_DICTIONARY is only enabled if Parquet format version 2.4 or greater is selected in WriterProperties::version().

Types¶

Physical types¶

Physical type	Mapped Arrow type	Notes
BOOLEAN	Boolean
INT32	Int32 / other	(1)
INT64	Int64 / other	(1)
INT96	Timestamp (nanoseconds)	(2)
FLOAT	Float32
DOUBLE	Float64
BYTE_ARRAY	Binary / other	(1) (3)
FIXED_LENGTH_BYTE_ARRAY	FixedSizeBinary / other	(1)

(1) Can be mapped to other Arrow types, depending on the logical type (see below).
(2) On the write side, ArrowWriterProperties::support_deprecated_int96_timestamps() must be enabled.
(3) On the write side, an Arrow LargeBinary can also mapped to BYTE_ARRAY.

Logical types¶

Specific logical types can override the default Arrow type mapping for a given physical type.

Logical type	Physical type	Mapped Arrow type	Notes
NULL	Any	Null	(1)
INT	INT32	Int8 / UInt8 / Int16 / UInt16 / Int32 / UInt32
INT	INT64	Int64 / UInt64
DECIMAL	INT32 / INT64 / BYTE_ARRAY / FIXED_LENGTH_BYTE_ARRAY	Decimal128 / Decimal256	(2)
DATE	INT32	Date32	(3)
TIME	INT32	Time32 (milliseconds)
TIME	INT64	Time64 (micro- or nanoseconds)
TIMESTAMP	INT64	Timestamp (milli-, micro- or nanoseconds)
STRING	BYTE_ARRAY	Utf8	(4)
LIST	Any	List	(5)
MAP	Any	Map	(6)

(1) On the write side, the Parquet physical type INT32 is generated.
(2) On the write side, a FIXED_LENGTH_BYTE_ARRAY is always emitted.
(3) On the write side, an Arrow Date64 is also mapped to a Parquet DATE INT32.
(4) On the write side, an Arrow LargeUtf8 is also mapped to a Parquet STRING.
(5) On the write side, an Arrow LargeList or FixedSizedList is also mapped to a Parquet LIST.
(6) On the read side, a key with multiple values does not get deduplicated, in contradiction with the Parquet specification.

Unsupported logical types: JSON, BSON, UUID. If such a type is encountered when reading a Parquet file, the default physical type mapping is used (for example, a Parquet JSON column may be read as Arrow Binary or FixedSizeBinary).

Converted types¶

While converted types are deprecated in the Parquet format (they are superceded by logical types), they are recognized and emitted by the Parquet C++ implementation so as to maximize compatibility with other Parquet implementations.

Special cases¶

An Arrow Extension type is written out as its storage type. It can still be recreated at read time using Parquet metadata (see “Roundtripping Arrow types” below).

An Arrow Dictionary type is written out as its value type. It can still be recreated at read time using Parquet metadata (see “Roundtripping Arrow types” below).

Roundtripping Arrow types¶

While there is no bijection between Arrow types and Parquet types, it is possible to serialize the Arrow schema as part of the Parquet file metadata. This is enabled using ArrowWriterProperties::store_schema().

On the read path, the serialized schema will be automatically recognized and will recreate the original Arrow data, converting the Parquet data as required (for example, a LargeList will be recreated from the Parquet LIST type).

As an example, when serializing an Arrow LargeList to Parquet:

The data is written out as a Parquet LIST
When read back, the Parquet LIST data is decoded as an Arrow LargeList if ArrowWriterProperties::store_schema() was enabled when writing the file; otherwise, it is decoded as an Arrow List.

Serialization details¶

The Arrow schema is serialized as a Arrow IPC schema message, then base64-encoded and stored under the ARROW:schema metadata key in the Parquet file metadata.

Limitations¶

Writing or reading back FixedSizedList data with null entries is not supported.

Encryption¶

Parquet C++ implements all features specified in the encryption specification, except for encryption of column index and bloom filter modules.

More specifically, Parquet C++ supports:

AES_GCM_V1 and AES_GCM_CTR_V1 encryption algorithms.
AAD suffix for Footer, ColumnMetaData, Data Page, Dictionary Page, Data PageHeader, Dictionary PageHeader module types. Other module types (ColumnIndex, OffsetIndex, BloomFilter Header, BloomFilter Bitset) are not supported.
EncryptionWithFooterKey and EncryptionWithColumnKey modes.
Encrypted Footer and Plaintext Footer modes.

Miscellaneous¶

Feature	Reading	Writing	Notes
Column Index	✓	✓	(1)
Offset Index	✓	✓	(1)
Bloom Filter	✓	✓	(2)
CRC checksums	✓	✓

(1) Access to the Column and Offset Index structures is provided, but data read APIs do not currently make any use of them.
(2) APIs are provided for creating, serializing and deserializing Bloom Filters, but they are not integrated into data read APIs.

Reading and Writing ORC files

Reading and Writing CSV files