nanoparquet-types: nanoparquet's type maps

In nanoparquet: Read and Write 'Parquet' Files

nanoparquet's type maps

Description

How nanoparquet maps R types to Parquet types.

R's data types

When writing out a data frame, nanoparquet maps R's data types to Parquet logical types. The following table is a summary of the mapping. For the details see below.

R type	Parquet type	Default	Notes
character	STRING (BYTE_ARRAY)	x	I.e. STRSXP. Converted to UTF-8.
"	BYTE_ARRAY
"	FIXED_LEN_BYTE_ARRAY
"	ENUM
"	UUID
Date	DATE	x
difftime	INT64	x	If not hms::hms. Arrow metadata marks it as Duration(NS).
factor	STRING	x	Arrow metadata marks it as a factor.
"	ENUM
hms::hms	TIME(true, MILLIS)	x	Sub-milliseconds precision is lost.
integer	INT(32, true)	x	I.e. INTSXP.
"	INT64
"	INT96
"	DECIMAL (INT32)
"	DECIMAL (INT64)
"	INT(8, *)
"	INT(16, *)
"	INT(32, signed)
list	BYTE_ARRAY		Must be a list of raw vectors. Messing values are NULL.
"	FIXED_LEN_BYTE_ARRAY		Must be a list of raw vectors of the same length. Missing values are NULL.
logical	BOOLEAN	x	I.e. LGLSXP.
numeric	DOUBLE	x	I.e. REALSXP.
"	INT96
"	FLOAT
"	DECIMAL (INT32)
"	DECIMAL (INT64)
"	INT(*, *)
"	FLOAT16
POSIXct	TIMESTAMP(true, MICROS)	x	Sub-microsecond precision is lost.

The non-default mappings can be selected via the schema argument. E.g. to write out a factor column called 'name' as ENUM, use

write_parquet(..., schema = parquet_schema(name = "ENUM"))

The detailed mapping rules are listed below, in order of preference. These rules will likely change until nanoparquet reaches version 1.0.0.

1. Factors (i.e. vectors that inherit the factor class) are converted to character vectors using as.character(), then written as a STRSXP (character vector) type. The fact that a column is a factor is stored in the Arrow metadata (see below), unless the nanoparquet.write_arrow_metadata option is set to FALSE.

2. Dates (i.e. the Date class) is written as DATE logical type, which is an INT32 type internally.

3. hms objects (from the hms package) are written as TIME(true, MILLIS). logical type, which is internally the INT32 Parquet type. Sub-milliseconds precision is lost.

4. POSIXct objects are written as TIMESTAMP(true, MICROS) logical type, which is internally the INT64 Parquet type. Sub-microsecond precision is lost.

5. difftime objects (that are not hms objects, see above), are written as an INT64 Parquet type, and noting in the Arrow metadata (see below) that this column has type Duration with NANOSECONDS unit.

6. Integer vectors (INTSXP) are written as INT(32, true) logical type, which corresponds to the INT32 type.

7. Real vectors (REALSXP) are written as the DOUBLE type.

8. Character vectors (STRSXP) are written as the STRING logical type, which has the BYTE_ARRAY type. They are always converted to UTF-8 before writing.

9. Logical vectors (LGLSXP) are written as the BOOLEAN type.

10. Other vectors error currently.

You can use infer_parquet_schema() on a data frame to map R data types to Parquet data types.

To change the default R to Parquet mapping, use parquet_schema() and the schema argument of write_parquet(). Currently supported non-default mappings are:

• integer to INT64,

• integer to INT96,

• double to INT96,

• double to FLOAT,

• character to BYTE_ARRAY,

• character to FIXED_LEN_BYTE_ARRAY,

• character to ENUM,

• factor to ENUM,

• integer to DECIAML & INT32,

• integer to DECIAML & INT64,

• double to DECIAML & INT32,

• double to DECIAML & INT64,

• integer to ⁠INT(8, *)⁠, ⁠INT(16, *)⁠, INT(32, signed),

• double to ⁠INT(*, *)⁠,

• character to UUID,

• double to FLOAT16,

• list of raw vectors to BYTE_ARRAY,

• list of raw vectors to FIXED_LEN_BYTE_ARRAY.

Parquet's data types

When reading a Parquet file nanoparquet also relies on logical types and the Arrow metadata (if present, see below) in addition to the low level data types. The following table summarizes the mappings. See more details below.

Parquet type	R type	Notes
Logical types
BSON	character
DATE	Date
DECIMAL	numeric	REALSXP, potentially losing precision.
ENUM	character
FLOAT16	numeric	REALSXP
INT(8, *)	integer
INT(16, *)	integer
INT(32, *)	integer	Large unsigned values may overflow!
INT(64, *)	numeric	REALSXP
INTERVAL	list(raw)	Missing values are NULL.
JSON	character
LIST		Not supported.
MAP		Not supported.
STRING	factor	If Arrow metadata says it is a factor. Also UTF8.
"	character	Otherwise. Also UTF8.
TIME	hms::hms	Also TIME_MILLIS and TIME_MICROS.
TIMESTAMP	POSIXct	Also TIMESTAMP_MILLIS and TIMESTAMP_MICROS.
UUID	character	In 00112233-4455-6677-8899-aabbccddeeff form.
UNKNOWN		Not supported.
Primitive types
BOOLEAN	logical
BYTE_ARRAY	factor	If Arrow metadata says it is a factor.
"	list(raw)	Otherwise. Missing values are NULL.
DOUBLE	numeric	REALSXP
FIXED_LEN_BYTE_ARRAY	list(raw)	Missing values are NULL.
FLOAT	numeric	REALSXP
INT32	integer
INT64	numeric	REALSXP
INT96	POSIXct

The exact rules are below. These rules will likely change until nanoparquet reaches version 1.0.0.

1. The BOOLEAN type is read as a logical vector (LGLSXP).

2. The STRING logical type and the UTF8 converted type is read as a character vector with UTF-8 encoding.

3. The DATE logical type and the DATE converted type are read as a Date R object.

4. The TIME logical type and the TIME_MILLIS and TIME_MICROS converted types are read as an hms object, see the hms package.

5. The TIMESTAMP logical type and the TIMESTAMP_MILLIS and TIMESTAMP_MICROS converted types are read as POSIXct objects. If the logical type has the UTC flag set, then the time zone of the POSIXct object is set to UTC.

6. INT32 is read as an integer vector (INTSXP).

7. INT64, DOUBLE and FLOAT are read as real vectors (REALSXP).

8. INT96 is read as a POSIXct read vector with the tzone attribute set to "UTC". It was an old convention to store time stamps as INT96 objects.

9. The DECIMAL converted type (FIXED_LEN_BYTE_ARRAY or BYTE_ARRAY type) is read as a real vector (REALSXP), potentially losing precision.

10. The ENUM logical type is read as a character vector.

11. The UUID logical type is read as a character vector that uses the 00112233-4455-6677-8899-aabbccddeeff form.

12. The FLOAT16 logical type is read as a real vector (REALSXP).

13. BYTE_ARRAY is read as a factor object if the file was written by Arrow and the original data type of the column was a factor. (See 'The Arrow metadata below.)

14. Otherwise BYTE_ARRAY is read a list of raw vectors, with missing values denoted by NULL.

Other logical and converted types are read as their annotated low level types:

1. INT(8, true), INT(16, true) and INT(32, true) are read as integer vectors because they are INT32 internally in Parquet.

2. INT(64, true) is read as a real vector (REALSXP).

3. Unsigned integer types INT(8, false), INT(16, false) and INT(32, false) are read as integer vectors (INTSXP). Large positive values may overflow into negative values, this is a known issue that we will fix.

4. INT(64, false) is read as a real vector (REALSXP). Large positive values may overflow into negative values, this is a known issue that we will fix.

5. INTERVAL is a fixed length byte array, and nanoparquet reads it as a list of raw vectors. Missing values are denoted by NULL.

6. JSON columns are read as character vectors (STRSXP).

7. BSON columns are read as raw vectors (RAWSXP).

These types are not yet supported:

1. Nested types (LIST, MAP) are not supported.

2. The UNKNOWN logical type is not supported.

You can use the read_parquet_schema() function to see how R would read the columns of a Parquet file. Look at the r_type column.

The Arrow metadata

Apache Arrow (i.e. the arrow R package) adds additional metadata to Parquet files when writing them in arrow::write_parquet(). Then, when reading the file in arrow::read_parquet(), it uses this metadata to recreate the same Arrow and R data types as before writing.

nanoparquet::write_parquet() also adds the Arrow metadata to Parquet files, unless the nanoparquet.write_arrow_metadata option is set to FALSE.

Similarly, nanoparquet::read_parquet() uses the Arrow metadata in the Parquet file (if present), unless the nanoparquet.use_arrow_metadata option is set to FALSE.

The Arrow metadata is stored in the file level key-value metadata, with key ARROW:schema.

Currently nanoparquet uses the Arrow metadata for two things:

• It uses it to detect factors. Without the Arrow metadata factors are read as string vectors.

• It uses it to detect difftime objects. Without the arrow metadata these are read as INT64 columns, containing the time difference in nanoseconds.

See Also

nanoparquet-package for options that modify the type mappings.

nanoparquet documentation built on April 3, 2025, 11:26 p.m.