Reading and writing Parquet files¶

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, not 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.

Reading Parquet files¶

The arrow::FileReader class reads data for an entire file or row group into an ::arrow::Table.

The StreamReader and StreamWriter classes allow for data to be written using a C++ input/output streams approach to read/write 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 and StreamWriter classes will not be as good due to the type checking and the fact that column values are processed one at a time.

FileReader¶

The Parquet arrow::FileReader requires a ::arrow::io::RandomAccessFile instance representing the input file.

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

{
   // ...
   arrow::Status st;
   arrow::MemoryPool* pool = default_memory_pool();
   std::shared_ptr<arrow::io::RandomAccessFile> input = ...;

   // Open Parquet file reader
   std::unique_ptr<parquet::arrow::FileReader> arrow_reader;
   st = parquet::arrow::OpenFile(input, pool, &arrow_reader);
   if (!st.ok()) {
      // Handle error instantiating file reader...
   }

   // Read entire file as a single Arrow table
   std::shared_ptr<arrow::Table> table;
   st = arrow_reader->ReadTable(&table);
   if (!st.ok()) {
      // Handle error reading Parquet data...
   }
}

Finer-grained options are available through the arrow::FileReaderBuilder helper class.

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 os{parquet::ParquetFileReader::Open(infile)};

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

   while ( !os.eof() )
   {
      os >> 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"

{
   std::shared_ptr<arrow::io::FileOutputStream> outfile;
   PARQUET_ASSIGN_OR_THROW(
      outfile,
      arrow::io::FileOutputStream::Open("test.parquet"));

   PARQUET_THROW_NOT_OK(
      parquet::arrow::WriteTable(table, arrow::default_memory_pool(), outfile, 3));
}

StreamWriter¶

The StreamWriter allows for Parquet files to be written using standard C++ output operators. 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;
   }
}

Reading and Writing ORC files

Reading and Writing CSV files