arrow_schema/datatype.rs
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// 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.
use std::fmt;
use std::sync::Arc;
use crate::{Field, FieldRef, Fields, UnionFields};
/// The set of datatypes that are supported by this implementation of Apache Arrow.
///
/// The Arrow specification on data types includes some more types.
/// See also [`Schema.fbs`](https://github.com/apache/arrow/blob/main/format/Schema.fbs)
/// for Arrow's specification.
///
/// The variants of this enum include primitive fixed size types as well as parametric or
/// nested types.
/// Currently the Rust implementation supports the following nested types:
/// - `List<T>`
/// - `LargeList<T>`
/// - `FixedSizeList<T>`
/// - `Struct<T, U, V, ...>`
/// - `Union<T, U, V, ...>`
/// - `Map<K, V>`
///
/// Nested types can themselves be nested within other arrays.
/// For more information on these types please see
/// [the physical memory layout of Apache Arrow](https://arrow.apache.org/docs/format/Columnar.html#physical-memory-layout).
#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub enum DataType {
/// Null type
Null,
/// A boolean datatype representing the values `true` and `false`.
Boolean,
/// A signed 8-bit integer.
Int8,
/// A signed 16-bit integer.
Int16,
/// A signed 32-bit integer.
Int32,
/// A signed 64-bit integer.
Int64,
/// An unsigned 8-bit integer.
UInt8,
/// An unsigned 16-bit integer.
UInt16,
/// An unsigned 32-bit integer.
UInt32,
/// An unsigned 64-bit integer.
UInt64,
/// A 16-bit floating point number.
Float16,
/// A 32-bit floating point number.
Float32,
/// A 64-bit floating point number.
Float64,
/// A timestamp with an optional timezone.
///
/// Time is measured as a Unix epoch, counting the seconds from
/// 00:00:00.000 on 1 January 1970, excluding leap seconds,
/// as a signed 64-bit integer.
///
/// The time zone is a string indicating the name of a time zone, one of:
///
/// * As used in the Olson time zone database (the "tz database" or
/// "tzdata"), such as "America/New_York"
/// * An absolute time zone offset of the form +XX:XX or -XX:XX, such as +07:30
///
/// Timestamps with a non-empty timezone
/// ------------------------------------
///
/// If a Timestamp column has a non-empty timezone value, its epoch is
/// 1970-01-01 00:00:00 (January 1st 1970, midnight) in the *UTC* timezone
/// (the Unix epoch), regardless of the Timestamp's own timezone.
///
/// Therefore, timestamp values with a non-empty timezone correspond to
/// physical points in time together with some additional information about
/// how the data was obtained and/or how to display it (the timezone).
///
/// For example, the timestamp value 0 with the timezone string "Europe/Paris"
/// corresponds to "January 1st 1970, 00h00" in the UTC timezone, but the
/// application may prefer to display it as "January 1st 1970, 01h00" in
/// the Europe/Paris timezone (which is the same physical point in time).
///
/// One consequence is that timestamp values with a non-empty timezone
/// can be compared and ordered directly, since they all share the same
/// well-known point of reference (the Unix epoch).
///
/// Timestamps with an unset / empty timezone
/// -----------------------------------------
///
/// If a Timestamp column has no timezone value, its epoch is
/// 1970-01-01 00:00:00 (January 1st 1970, midnight) in an *unknown* timezone.
///
/// Therefore, timestamp values without a timezone cannot be meaningfully
/// interpreted as physical points in time, but only as calendar / clock
/// indications ("wall clock time") in an unspecified timezone.
///
/// For example, the timestamp value 0 with an empty timezone string
/// corresponds to "January 1st 1970, 00h00" in an unknown timezone: there
/// is not enough information to interpret it as a well-defined physical
/// point in time.
///
/// One consequence is that timestamp values without a timezone cannot
/// be reliably compared or ordered, since they may have different points of
/// reference. In particular, it is *not* possible to interpret an unset
/// or empty timezone as the same as "UTC".
///
/// Conversion between timezones
/// ----------------------------
///
/// If a Timestamp column has a non-empty timezone, changing the timezone
/// to a different non-empty value is a metadata-only operation:
/// the timestamp values need not change as their point of reference remains
/// the same (the Unix epoch).
///
/// However, if a Timestamp column has no timezone value, changing it to a
/// non-empty value requires to think about the desired semantics.
/// One possibility is to assume that the original timestamp values are
/// relative to the epoch of the timezone being set; timestamp values should
/// then adjusted to the Unix epoch (for example, changing the timezone from
/// empty to "Europe/Paris" would require converting the timestamp values
/// from "Europe/Paris" to "UTC", which seems counter-intuitive but is
/// nevertheless correct).
///
/// ```
/// # use arrow_schema::{DataType, TimeUnit};
/// DataType::Timestamp(TimeUnit::Second, None);
/// DataType::Timestamp(TimeUnit::Second, Some("literal".into()));
/// DataType::Timestamp(TimeUnit::Second, Some("string".to_string().into()));
/// ```
Timestamp(TimeUnit, Option<Arc<str>>),
/// A signed 32-bit date representing the elapsed time since UNIX epoch (1970-01-01)
/// in days.
Date32,
/// A signed 64-bit date representing the elapsed time since UNIX epoch (1970-01-01)
/// in milliseconds.
///
/// According to the specification (see [Schema.fbs]), this should be treated as the number of
/// days, in milliseconds, since the UNIX epoch. Therefore, values must be evenly divisible by
/// `86_400_000` (the number of milliseconds in a standard day).
///
/// The reason for this is for compatibility with other language's native libraries,
/// such as Java, which historically lacked a dedicated date type
/// and only supported timestamps.
///
/// Practically, validation that values of this type are evenly divisible by `86_400_000` is not enforced
/// by this library for performance and usability reasons. Date64 values will be treated similarly to the
/// `Timestamp(TimeUnit::Millisecond, None)` type, in that its values will be printed showing the time of
/// day if the value does not represent an exact day, and arithmetic can be done at the millisecond
/// granularity to change the time represented.
///
/// Users should prefer using Date32 to cleanly represent the number of days, or one of the Timestamp
/// variants to include time as part of the representation, depending on their use case.
///
/// For more details, see [#5288](https://github.com/apache/arrow-rs/issues/5288).
///
/// [Schema.fbs]: https://github.com/apache/arrow/blob/main/format/Schema.fbs
Date64,
/// A signed 32-bit time representing the elapsed time since midnight in the unit of `TimeUnit`.
/// Must be either seconds or milliseconds.
Time32(TimeUnit),
/// A signed 64-bit time representing the elapsed time since midnight in the unit of `TimeUnit`.
/// Must be either microseconds or nanoseconds.
Time64(TimeUnit),
/// Measure of elapsed time in either seconds, milliseconds, microseconds or nanoseconds.
Duration(TimeUnit),
/// A "calendar" interval which models types that don't necessarily
/// have a precise duration without the context of a base timestamp (e.g.
/// days can differ in length during day light savings time transitions).
Interval(IntervalUnit),
/// Opaque binary data of variable length.
///
/// A single Binary array can store up to [`i32::MAX`] bytes
/// of binary data in total.
Binary,
/// Opaque binary data of fixed size.
/// Enum parameter specifies the number of bytes per value.
FixedSizeBinary(i32),
/// Opaque binary data of variable length and 64-bit offsets.
///
/// A single LargeBinary array can store up to [`i64::MAX`] bytes
/// of binary data in total.
LargeBinary,
/// (NOT YET FULLY SUPPORTED) Opaque binary data of variable length.
///
/// Note this data type is not yet fully supported. Using it with arrow APIs may result in `panic`s.
///
/// Logically the same as [`Self::Binary`], but the internal representation uses a view
/// struct that contains the string length and either the string's entire data
/// inline (for small strings) or an inlined prefix, an index of another buffer,
/// and an offset pointing to a slice in that buffer (for non-small strings).
BinaryView,
/// A variable-length string in Unicode with UTF-8 encoding.
///
/// A single Utf8 array can store up to [`i32::MAX`] bytes
/// of string data in total.
Utf8,
/// A variable-length string in Unicode with UFT-8 encoding and 64-bit offsets.
///
/// A single LargeUtf8 array can store up to [`i64::MAX`] bytes
/// of string data in total.
LargeUtf8,
/// (NOT YET FULLY SUPPORTED) A variable-length string in Unicode with UTF-8 encoding
///
/// Note this data type is not yet fully supported. Using it with arrow APIs may result in `panic`s.
///
/// Logically the same as [`Self::Utf8`], but the internal representation uses a view
/// struct that contains the string length and either the string's entire data
/// inline (for small strings) or an inlined prefix, an index of another buffer,
/// and an offset pointing to a slice in that buffer (for non-small strings).
Utf8View,
/// A list of some logical data type with variable length.
///
/// A single List array can store up to [`i32::MAX`] elements in total.
List(FieldRef),
/// (NOT YET FULLY SUPPORTED) A list of some logical data type with variable length.
///
/// Note this data type is not yet fully supported. Using it with arrow APIs may result in `panic`s.
///
/// The ListView layout is defined by three buffers:
/// a validity bitmap, an offsets buffer, and an additional sizes buffer.
/// Sizes and offsets are both 32 bits for this type
ListView(FieldRef),
/// A list of some logical data type with fixed length.
FixedSizeList(FieldRef, i32),
/// A list of some logical data type with variable length and 64-bit offsets.
///
/// A single LargeList array can store up to [`i64::MAX`] elements in total.
LargeList(FieldRef),
/// (NOT YET FULLY SUPPORTED) A list of some logical data type with variable length and 64-bit offsets.
///
/// Note this data type is not yet fully supported. Using it with arrow APIs may result in `panic`s.
///
/// The LargeListView layout is defined by three buffers:
/// a validity bitmap, an offsets buffer, and an additional sizes buffer.
/// Sizes and offsets are both 64 bits for this type
LargeListView(FieldRef),
/// A nested datatype that contains a number of sub-fields.
Struct(Fields),
/// A nested datatype that can represent slots of differing types. Components:
///
/// 1. [`UnionFields`]
/// 2. The type of union (Sparse or Dense)
Union(UnionFields, UnionMode),
/// A dictionary encoded array (`key_type`, `value_type`), where
/// each array element is an index of `key_type` into an
/// associated dictionary of `value_type`.
///
/// Dictionary arrays are used to store columns of `value_type`
/// that contain many repeated values using less memory, but with
/// a higher CPU overhead for some operations.
///
/// This type mostly used to represent low cardinality string
/// arrays or a limited set of primitive types as integers.
Dictionary(Box<DataType>, Box<DataType>),
/// Exact 128-bit width decimal value with precision and scale
///
/// * precision is the total number of digits
/// * scale is the number of digits past the decimal
///
/// For example the number 123.45 has precision 5 and scale 2.
///
/// In certain situations, scale could be negative number. For
/// negative scale, it is the number of padding 0 to the right
/// of the digits.
///
/// For example the number 12300 could be treated as a decimal
/// has precision 3 and scale -2.
Decimal128(u8, i8),
/// Exact 256-bit width decimal value with precision and scale
///
/// * precision is the total number of digits
/// * scale is the number of digits past the decimal
///
/// For example the number 123.45 has precision 5 and scale 2.
///
/// In certain situations, scale could be negative number. For
/// negative scale, it is the number of padding 0 to the right
/// of the digits.
///
/// For example the number 12300 could be treated as a decimal
/// has precision 3 and scale -2.
Decimal256(u8, i8),
/// A Map is a logical nested type that is represented as
///
/// `List<entries: Struct<key: K, value: V>>`
///
/// The keys and values are each respectively contiguous.
/// The key and value types are not constrained, but keys should be
/// hashable and unique.
/// Whether the keys are sorted can be set in the `bool` after the `Field`.
///
/// In a field with Map type, the field has a child Struct field, which then
/// has two children: key type and the second the value type. The names of the
/// child fields may be respectively "entries", "key", and "value", but this is
/// not enforced.
Map(FieldRef, bool),
/// A run-end encoding (REE) is a variation of run-length encoding (RLE). These
/// encodings are well-suited for representing data containing sequences of the
/// same value, called runs. Each run is represented as a value and an integer giving
/// the index in the array where the run ends.
///
/// A run-end encoded array has no buffers by itself, but has two child arrays. The
/// first child array, called the run ends array, holds either 16, 32, or 64-bit
/// signed integers. The actual values of each run are held in the second child array.
///
/// These child arrays are prescribed the standard names of "run_ends" and "values"
/// respectively.
RunEndEncoded(FieldRef, FieldRef),
}
/// An absolute length of time in seconds, milliseconds, microseconds or nanoseconds.
#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub enum TimeUnit {
/// Time in seconds.
Second,
/// Time in milliseconds.
Millisecond,
/// Time in microseconds.
Microsecond,
/// Time in nanoseconds.
Nanosecond,
}
/// YEAR_MONTH, DAY_TIME, MONTH_DAY_NANO interval in SQL style.
#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub enum IntervalUnit {
/// Indicates the number of elapsed whole months, stored as 4-byte integers.
YearMonth,
/// Indicates the number of elapsed days and milliseconds,
/// stored as 2 contiguous 32-bit integers (days, milliseconds) (8-bytes in total).
DayTime,
/// A triple of the number of elapsed months, days, and nanoseconds.
/// The values are stored contiguously in 16 byte blocks. Months and
/// days are encoded as 32 bit integers and nanoseconds is encoded as a
/// 64 bit integer. All integers are signed. Each field is independent
/// (e.g. there is no constraint that nanoseconds have the same sign
/// as days or that the quantity of nanoseconds represents less
/// than a day's worth of time).
MonthDayNano,
}
// Sparse or Dense union layouts
#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord, Copy)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub enum UnionMode {
Sparse,
Dense,
}
impl fmt::Display for DataType {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
write!(f, "{self:?}")
}
}
impl DataType {
/// Returns true if the type is primitive: (numeric, temporal).
#[inline]
pub fn is_primitive(&self) -> bool {
self.is_numeric() || self.is_temporal()
}
/// Returns true if this type is numeric: (UInt*, Int*, Float*, Decimal*).
#[inline]
pub fn is_numeric(&self) -> bool {
use DataType::*;
matches!(
self,
UInt8
| UInt16
| UInt32
| UInt64
| Int8
| Int16
| Int32
| Int64
| Float16
| Float32
| Float64
| Decimal128(_, _)
| Decimal256(_, _)
)
}
/// Returns true if this type is temporal: (Date*, Time*, Duration, or Interval).
#[inline]
pub fn is_temporal(&self) -> bool {
use DataType::*;
matches!(
self,
Date32 | Date64 | Timestamp(_, _) | Time32(_) | Time64(_) | Duration(_) | Interval(_)
)
}
/// Returns true if this type is floating: (Float*).
#[inline]
pub fn is_floating(&self) -> bool {
use DataType::*;
matches!(self, Float16 | Float32 | Float64)
}
/// Returns true if this type is integer: (Int*, UInt*).
#[inline]
pub fn is_integer(&self) -> bool {
self.is_signed_integer() || self.is_unsigned_integer()
}
/// Returns true if this type is signed integer: (Int*).
#[inline]
pub fn is_signed_integer(&self) -> bool {
use DataType::*;
matches!(self, Int8 | Int16 | Int32 | Int64)
}
/// Returns true if this type is unsigned integer: (UInt*).
#[inline]
pub fn is_unsigned_integer(&self) -> bool {
use DataType::*;
matches!(self, UInt8 | UInt16 | UInt32 | UInt64)
}
/// Returns true if this type is valid as a dictionary key
#[inline]
pub fn is_dictionary_key_type(&self) -> bool {
self.is_integer()
}
/// Returns true if this type is valid for run-ends array in RunArray
#[inline]
pub fn is_run_ends_type(&self) -> bool {
use DataType::*;
matches!(self, Int16 | Int32 | Int64)
}
/// Returns true if this type is nested (List, FixedSizeList, LargeList, Struct, Union,
/// or Map), or a dictionary of a nested type
#[inline]
pub fn is_nested(&self) -> bool {
use DataType::*;
match self {
Dictionary(_, v) => DataType::is_nested(v.as_ref()),
List(_) | FixedSizeList(_, _) | LargeList(_) | Struct(_) | Union(_, _) | Map(_, _) => {
true
}
_ => false,
}
}
/// Returns true if this type is DataType::Null.
#[inline]
pub fn is_null(&self) -> bool {
use DataType::*;
matches!(self, Null)
}
/// Compares the datatype with another, ignoring nested field names
/// and metadata.
pub fn equals_datatype(&self, other: &DataType) -> bool {
match (&self, other) {
(DataType::List(a), DataType::List(b))
| (DataType::LargeList(a), DataType::LargeList(b)) => {
a.is_nullable() == b.is_nullable() && a.data_type().equals_datatype(b.data_type())
}
(DataType::FixedSizeList(a, a_size), DataType::FixedSizeList(b, b_size)) => {
a_size == b_size
&& a.is_nullable() == b.is_nullable()
&& a.data_type().equals_datatype(b.data_type())
}
(DataType::Struct(a), DataType::Struct(b)) => {
a.len() == b.len()
&& a.iter().zip(b).all(|(a, b)| {
a.is_nullable() == b.is_nullable()
&& a.data_type().equals_datatype(b.data_type())
})
}
(DataType::Map(a_field, a_is_sorted), DataType::Map(b_field, b_is_sorted)) => {
a_field.is_nullable() == b_field.is_nullable()
&& a_field.data_type().equals_datatype(b_field.data_type())
&& a_is_sorted == b_is_sorted
}
(DataType::Dictionary(a_key, a_value), DataType::Dictionary(b_key, b_value)) => {
a_key.equals_datatype(b_key) && a_value.equals_datatype(b_value)
}
(
DataType::RunEndEncoded(a_run_ends, a_values),
DataType::RunEndEncoded(b_run_ends, b_values),
) => {
a_run_ends.is_nullable() == b_run_ends.is_nullable()
&& a_run_ends
.data_type()
.equals_datatype(b_run_ends.data_type())
&& a_values.is_nullable() == b_values.is_nullable()
&& a_values.data_type().equals_datatype(b_values.data_type())
}
(
DataType::Union(a_union_fields, a_union_mode),
DataType::Union(b_union_fields, b_union_mode),
) => {
a_union_mode == b_union_mode
&& a_union_fields.len() == b_union_fields.len()
&& a_union_fields.iter().all(|a| {
b_union_fields.iter().any(|b| {
a.0 == b.0
&& a.1.is_nullable() == b.1.is_nullable()
&& a.1.data_type().equals_datatype(b.1.data_type())
})
})
}
_ => self == other,
}
}
/// Returns the bit width of this type if it is a primitive type
///
/// Returns `None` if not a primitive type
#[inline]
pub fn primitive_width(&self) -> Option<usize> {
match self {
DataType::Null => None,
DataType::Boolean => None,
DataType::Int8 | DataType::UInt8 => Some(1),
DataType::Int16 | DataType::UInt16 | DataType::Float16 => Some(2),
DataType::Int32 | DataType::UInt32 | DataType::Float32 => Some(4),
DataType::Int64 | DataType::UInt64 | DataType::Float64 => Some(8),
DataType::Timestamp(_, _) => Some(8),
DataType::Date32 | DataType::Time32(_) => Some(4),
DataType::Date64 | DataType::Time64(_) => Some(8),
DataType::Duration(_) => Some(8),
DataType::Interval(IntervalUnit::YearMonth) => Some(4),
DataType::Interval(IntervalUnit::DayTime) => Some(8),
DataType::Interval(IntervalUnit::MonthDayNano) => Some(16),
DataType::Decimal128(_, _) => Some(16),
DataType::Decimal256(_, _) => Some(32),
DataType::Utf8 | DataType::LargeUtf8 | DataType::Utf8View => None,
DataType::Binary | DataType::LargeBinary | DataType::BinaryView => None,
DataType::FixedSizeBinary(_) => None,
DataType::List(_)
| DataType::ListView(_)
| DataType::LargeList(_)
| DataType::LargeListView(_)
| DataType::Map(_, _) => None,
DataType::FixedSizeList(_, _) => None,
DataType::Struct(_) => None,
DataType::Union(_, _) => None,
DataType::Dictionary(_, _) => None,
DataType::RunEndEncoded(_, _) => None,
}
}
/// Return size of this instance in bytes.
///
/// Includes the size of `Self`.
pub fn size(&self) -> usize {
std::mem::size_of_val(self)
+ match self {
DataType::Null
| DataType::Boolean
| DataType::Int8
| DataType::Int16
| DataType::Int32
| DataType::Int64
| DataType::UInt8
| DataType::UInt16
| DataType::UInt32
| DataType::UInt64
| DataType::Float16
| DataType::Float32
| DataType::Float64
| DataType::Date32
| DataType::Date64
| DataType::Time32(_)
| DataType::Time64(_)
| DataType::Duration(_)
| DataType::Interval(_)
| DataType::Binary
| DataType::FixedSizeBinary(_)
| DataType::LargeBinary
| DataType::BinaryView
| DataType::Utf8
| DataType::LargeUtf8
| DataType::Utf8View
| DataType::Decimal128(_, _)
| DataType::Decimal256(_, _) => 0,
DataType::Timestamp(_, s) => s.as_ref().map(|s| s.len()).unwrap_or_default(),
DataType::List(field)
| DataType::ListView(field)
| DataType::FixedSizeList(field, _)
| DataType::LargeList(field)
| DataType::LargeListView(field)
| DataType::Map(field, _) => field.size(),
DataType::Struct(fields) => fields.size(),
DataType::Union(fields, _) => fields.size(),
DataType::Dictionary(dt1, dt2) => dt1.size() + dt2.size(),
DataType::RunEndEncoded(run_ends, values) => {
run_ends.size() - std::mem::size_of_val(run_ends) + values.size()
- std::mem::size_of_val(values)
}
}
}
/// Check to see if `self` is a superset of `other`
///
/// If DataType is a nested type, then it will check to see if the nested type is a superset of the other nested type
/// else it will check to see if the DataType is equal to the other DataType
pub fn contains(&self, other: &DataType) -> bool {
match (self, other) {
(DataType::List(f1), DataType::List(f2))
| (DataType::LargeList(f1), DataType::LargeList(f2)) => f1.contains(f2),
(DataType::FixedSizeList(f1, s1), DataType::FixedSizeList(f2, s2)) => {
s1 == s2 && f1.contains(f2)
}
(DataType::Map(f1, s1), DataType::Map(f2, s2)) => s1 == s2 && f1.contains(f2),
(DataType::Struct(f1), DataType::Struct(f2)) => f1.contains(f2),
(DataType::Union(f1, s1), DataType::Union(f2, s2)) => {
s1 == s2
&& f1
.iter()
.all(|f1| f2.iter().any(|f2| f1.0 == f2.0 && f1.1.contains(f2.1)))
}
(DataType::Dictionary(k1, v1), DataType::Dictionary(k2, v2)) => {
k1.contains(k2) && v1.contains(v2)
}
_ => self == other,
}
}
/// Create a [`DataType::List`] with elements of the specified type
/// and nullability, and conventionally named inner [`Field`] (`"item"`).
///
/// To specify field level metadata, construct the inner [`Field`]
/// directly via [`Field::new`] or [`Field::new_list_field`].
pub fn new_list(data_type: DataType, nullable: bool) -> Self {
DataType::List(Arc::new(Field::new_list_field(data_type, nullable)))
}
/// Create a [`DataType::LargeList`] with elements of the specified type
/// and nullability, and conventionally named inner [`Field`] (`"item"`).
///
/// To specify field level metadata, construct the inner [`Field`]
/// directly via [`Field::new`] or [`Field::new_list_field`].
pub fn new_large_list(data_type: DataType, nullable: bool) -> Self {
DataType::LargeList(Arc::new(Field::new_list_field(data_type, nullable)))
}
/// Create a [`DataType::FixedSizeList`] with elements of the specified type, size
/// and nullability, and conventionally named inner [`Field`] (`"item"`).
///
/// To specify field level metadata, construct the inner [`Field`]
/// directly via [`Field::new`] or [`Field::new_list_field`].
pub fn new_fixed_size_list(data_type: DataType, size: i32, nullable: bool) -> Self {
DataType::FixedSizeList(Arc::new(Field::new_list_field(data_type, nullable)), size)
}
}
/// The maximum precision for [DataType::Decimal128] values
pub const DECIMAL128_MAX_PRECISION: u8 = 38;
/// The maximum scale for [DataType::Decimal128] values
pub const DECIMAL128_MAX_SCALE: i8 = 38;
/// The maximum precision for [DataType::Decimal256] values
pub const DECIMAL256_MAX_PRECISION: u8 = 76;
/// The maximum scale for [DataType::Decimal256] values
pub const DECIMAL256_MAX_SCALE: i8 = 76;
/// The default scale for [DataType::Decimal128] and [DataType::Decimal256]
/// values
pub const DECIMAL_DEFAULT_SCALE: i8 = 10;
#[cfg(test)]
mod tests {
use super::*;
#[test]
#[cfg(feature = "serde")]
fn serde_struct_type() {
use std::collections::HashMap;
let kv_array = [("k".to_string(), "v".to_string())];
let field_metadata: HashMap<String, String> = kv_array.iter().cloned().collect();
// Non-empty map: should be converted as JSON obj { ... }
let first_name =
Field::new("first_name", DataType::Utf8, false).with_metadata(field_metadata);
// Empty map: should be omitted.
let last_name =
Field::new("last_name", DataType::Utf8, false).with_metadata(HashMap::default());
let person = DataType::Struct(Fields::from(vec![
first_name,
last_name,
Field::new(
"address",
DataType::Struct(Fields::from(vec![
Field::new("street", DataType::Utf8, false),
Field::new("zip", DataType::UInt16, false),
])),
false,
),
]));
let serialized = serde_json::to_string(&person).unwrap();
// NOTE that this is testing the default (derived) serialization format, not the
// JSON format specified in metadata.md
assert_eq!(
"{\"Struct\":[\
{\"name\":\"first_name\",\"data_type\":\"Utf8\",\"nullable\":false,\"dict_id\":0,\"dict_is_ordered\":false,\"metadata\":{\"k\":\"v\"}},\
{\"name\":\"last_name\",\"data_type\":\"Utf8\",\"nullable\":false,\"dict_id\":0,\"dict_is_ordered\":false,\"metadata\":{}},\
{\"name\":\"address\",\"data_type\":{\"Struct\":\
[{\"name\":\"street\",\"data_type\":\"Utf8\",\"nullable\":false,\"dict_id\":0,\"dict_is_ordered\":false,\"metadata\":{}},\
{\"name\":\"zip\",\"data_type\":\"UInt16\",\"nullable\":false,\"dict_id\":0,\"dict_is_ordered\":false,\"metadata\":{}}\
]},\"nullable\":false,\"dict_id\":0,\"dict_is_ordered\":false,\"metadata\":{}}]}",
serialized
);
let deserialized = serde_json::from_str(&serialized).unwrap();
assert_eq!(person, deserialized);
}
#[test]
fn test_list_datatype_equality() {
// tests that list type equality is checked while ignoring list names
let list_a = DataType::List(Arc::new(Field::new("item", DataType::Int32, true)));
let list_b = DataType::List(Arc::new(Field::new("array", DataType::Int32, true)));
let list_c = DataType::List(Arc::new(Field::new("item", DataType::Int32, false)));
let list_d = DataType::List(Arc::new(Field::new("item", DataType::UInt32, true)));
assert!(list_a.equals_datatype(&list_b));
assert!(!list_a.equals_datatype(&list_c));
assert!(!list_b.equals_datatype(&list_c));
assert!(!list_a.equals_datatype(&list_d));
let list_e =
DataType::FixedSizeList(Arc::new(Field::new("item", list_a.clone(), false)), 3);
let list_f =
DataType::FixedSizeList(Arc::new(Field::new("array", list_b.clone(), false)), 3);
let list_g = DataType::FixedSizeList(
Arc::new(Field::new("item", DataType::FixedSizeBinary(3), true)),
3,
);
assert!(list_e.equals_datatype(&list_f));
assert!(!list_e.equals_datatype(&list_g));
assert!(!list_f.equals_datatype(&list_g));
let list_h = DataType::Struct(Fields::from(vec![Field::new("f1", list_e, true)]));
let list_i = DataType::Struct(Fields::from(vec![Field::new("f1", list_f.clone(), true)]));
let list_j = DataType::Struct(Fields::from(vec![Field::new("f1", list_f.clone(), false)]));
let list_k = DataType::Struct(Fields::from(vec![
Field::new("f1", list_f.clone(), false),
Field::new("f2", list_g.clone(), false),
Field::new("f3", DataType::Utf8, true),
]));
let list_l = DataType::Struct(Fields::from(vec![
Field::new("ff1", list_f.clone(), false),
Field::new("ff2", list_g.clone(), false),
Field::new("ff3", DataType::LargeUtf8, true),
]));
let list_m = DataType::Struct(Fields::from(vec![
Field::new("ff1", list_f, false),
Field::new("ff2", list_g, false),
Field::new("ff3", DataType::Utf8, true),
]));
assert!(list_h.equals_datatype(&list_i));
assert!(!list_h.equals_datatype(&list_j));
assert!(!list_k.equals_datatype(&list_l));
assert!(list_k.equals_datatype(&list_m));
let list_n = DataType::Map(Arc::new(Field::new("f1", list_a.clone(), true)), true);
let list_o = DataType::Map(Arc::new(Field::new("f2", list_b.clone(), true)), true);
let list_p = DataType::Map(Arc::new(Field::new("f2", list_b.clone(), true)), false);
let list_q = DataType::Map(Arc::new(Field::new("f2", list_c.clone(), true)), true);
let list_r = DataType::Map(Arc::new(Field::new("f1", list_a.clone(), false)), true);
assert!(list_n.equals_datatype(&list_o));
assert!(!list_n.equals_datatype(&list_p));
assert!(!list_n.equals_datatype(&list_q));
assert!(!list_n.equals_datatype(&list_r));
let list_s = DataType::Dictionary(Box::new(DataType::UInt8), Box::new(list_a));
let list_t = DataType::Dictionary(Box::new(DataType::UInt8), Box::new(list_b.clone()));
let list_u = DataType::Dictionary(Box::new(DataType::Int8), Box::new(list_b));
let list_v = DataType::Dictionary(Box::new(DataType::UInt8), Box::new(list_c));
assert!(list_s.equals_datatype(&list_t));
assert!(!list_s.equals_datatype(&list_u));
assert!(!list_s.equals_datatype(&list_v));
let union_a = DataType::Union(
UnionFields::new(
vec![1, 2],
vec![
Field::new("f1", DataType::Utf8, false),
Field::new("f2", DataType::UInt8, false),
],
),
UnionMode::Sparse,
);
let union_b = DataType::Union(
UnionFields::new(
vec![1, 2],
vec![
Field::new("ff1", DataType::Utf8, false),
Field::new("ff2", DataType::UInt8, false),
],
),
UnionMode::Sparse,
);
let union_c = DataType::Union(
UnionFields::new(
vec![2, 1],
vec![
Field::new("fff2", DataType::UInt8, false),
Field::new("fff1", DataType::Utf8, false),
],
),
UnionMode::Sparse,
);
let union_d = DataType::Union(
UnionFields::new(
vec![2, 1],
vec![
Field::new("fff1", DataType::Int8, false),
Field::new("fff2", DataType::UInt8, false),
],
),
UnionMode::Sparse,
);
let union_e = DataType::Union(
UnionFields::new(
vec![1, 2],
vec![
Field::new("f1", DataType::Utf8, true),
Field::new("f2", DataType::UInt8, false),
],
),
UnionMode::Sparse,
);
assert!(union_a.equals_datatype(&union_b));
assert!(union_a.equals_datatype(&union_c));
assert!(!union_a.equals_datatype(&union_d));
assert!(!union_a.equals_datatype(&union_e));
let list_w = DataType::RunEndEncoded(
Arc::new(Field::new("f1", DataType::Int64, true)),
Arc::new(Field::new("f2", DataType::Utf8, true)),
);
let list_x = DataType::RunEndEncoded(
Arc::new(Field::new("ff1", DataType::Int64, true)),
Arc::new(Field::new("ff2", DataType::Utf8, true)),
);
let list_y = DataType::RunEndEncoded(
Arc::new(Field::new("ff1", DataType::UInt16, true)),
Arc::new(Field::new("ff2", DataType::Utf8, true)),
);
let list_z = DataType::RunEndEncoded(
Arc::new(Field::new("f1", DataType::Int64, false)),
Arc::new(Field::new("f2", DataType::Utf8, true)),
);
assert!(list_w.equals_datatype(&list_x));
assert!(!list_w.equals_datatype(&list_y));
assert!(!list_w.equals_datatype(&list_z));
}
#[test]
fn create_struct_type() {
let _person = DataType::Struct(Fields::from(vec![
Field::new("first_name", DataType::Utf8, false),
Field::new("last_name", DataType::Utf8, false),
Field::new(
"address",
DataType::Struct(Fields::from(vec![
Field::new("street", DataType::Utf8, false),
Field::new("zip", DataType::UInt16, false),
])),
false,
),
]));
}
#[test]
fn test_nested() {
let list = DataType::List(Arc::new(Field::new("foo", DataType::Utf8, true)));
assert!(!DataType::is_nested(&DataType::Boolean));
assert!(!DataType::is_nested(&DataType::Int32));
assert!(!DataType::is_nested(&DataType::Utf8));
assert!(DataType::is_nested(&list));
assert!(!DataType::is_nested(&DataType::Dictionary(
Box::new(DataType::Int32),
Box::new(DataType::Boolean)
)));
assert!(!DataType::is_nested(&DataType::Dictionary(
Box::new(DataType::Int32),
Box::new(DataType::Int64)
)));
assert!(!DataType::is_nested(&DataType::Dictionary(
Box::new(DataType::Int32),
Box::new(DataType::LargeUtf8)
)));
assert!(DataType::is_nested(&DataType::Dictionary(
Box::new(DataType::Int32),
Box::new(list)
)));
}
#[test]
fn test_integer() {
// is_integer
assert!(DataType::is_integer(&DataType::Int32));
assert!(DataType::is_integer(&DataType::UInt64));
assert!(!DataType::is_integer(&DataType::Float16));
// is_signed_integer
assert!(DataType::is_signed_integer(&DataType::Int32));
assert!(!DataType::is_signed_integer(&DataType::UInt64));
assert!(!DataType::is_signed_integer(&DataType::Float16));
// is_unsigned_integer
assert!(!DataType::is_unsigned_integer(&DataType::Int32));
assert!(DataType::is_unsigned_integer(&DataType::UInt64));
assert!(!DataType::is_unsigned_integer(&DataType::Float16));
// is_dictionary_key_type
assert!(DataType::is_dictionary_key_type(&DataType::Int32));
assert!(DataType::is_dictionary_key_type(&DataType::UInt64));
assert!(!DataType::is_dictionary_key_type(&DataType::Float16));
}
#[test]
fn test_floating() {
assert!(DataType::is_floating(&DataType::Float16));
assert!(!DataType::is_floating(&DataType::Int32));
}
#[test]
fn test_datatype_is_null() {
assert!(DataType::is_null(&DataType::Null));
assert!(!DataType::is_null(&DataType::Int32));
}
#[test]
fn size_should_not_regress() {
assert_eq!(std::mem::size_of::<DataType>(), 24);
}
#[test]
#[should_panic(expected = "duplicate type id: 1")]
fn test_union_with_duplicated_type_id() {
let type_ids = vec![1, 1];
let _union = DataType::Union(
UnionFields::new(
type_ids,
vec![
Field::new("f1", DataType::Int32, false),
Field::new("f2", DataType::Utf8, false),
],
),
UnionMode::Dense,
);
}
}