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feat(logical-types): add NativeType and LogicalType #12853

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Nov 3, 2024
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1 change: 1 addition & 0 deletions datafusion/common/src/lib.rs
Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@ pub mod scalar;
pub mod stats;
pub mod test_util;
pub mod tree_node;
pub mod types;
pub mod utils;

/// Reexport arrow crate
Expand Down
39 changes: 39 additions & 0 deletions datafusion/common/src/types/builtin.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,39 @@
// 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 super::{LogicalType, NativeType};

#[derive(Debug)]
pub struct BuiltinType {
native: NativeType,
}

impl LogicalType for BuiltinType {
fn native(&self) -> &NativeType {
&self.native
}

fn name(&self) -> Option<&str> {
None
}
}

impl From<NativeType> for BuiltinType {
fn from(native: NativeType) -> Self {
Self { native }
}
}
58 changes: 58 additions & 0 deletions datafusion/common/src/types/logical.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,58 @@
// 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 core::fmt;
use std::{cmp::Ordering, hash::Hash, sync::Arc};

use super::NativeType;

/// A reference counted [`LogicalType`]
pub type LogicalTypeRef = Arc<dyn LogicalType>;

pub trait LogicalType: fmt::Debug {
fn native(&self) -> &NativeType;
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I previously propose can_decode_to(DataType) -> bool, so given logical type and DataType, we can know whether they are paired.

How can we do the equivalent check by the current design?

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Given say arrow Int64 data, i want to know whether these is numbers, timestamp, time, date or something else (eg user-defined enum). The fact that any of these hypothetical logical types could be stored as Int64 doesn't help me know. Asking logical type "could you please decode this arrow type?" doesn't help me know.
Thus, going from arrow type to logical type is not an option. We simply need to know what logical type this should be.

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@jayzhan211 jayzhan211 Oct 14, 2024

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I think the idea is that we have LogicalType already. In logical level, they are either LogicalNumber, LogicalTimestamp or LogicalDate, and we can differ them in logical level. They can also decode as i64, i32 in physical level. So asking logical type "could you please decode this arrow type?" is to tell the relationship between logical type and physical type. We don't need to know whether the arrow i64 is number or timestamp, because we already know that.

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I'm not sure I can follow. @jayzhan211 -- can you write a small practical example? I want to make sure I understand the use case. Thanks :)

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impl From<DataType> for NativeType is enough for native type since we can know whether the ArrayRef matches the LogicalType we have. But for LogicalType::UserDefined, I think we need to define what kind of DataType it could be decoded to.

We can figure this out if we meet any practical usage.

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@notfilippo notfilippo Oct 21, 2024

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For any user defined logical type you still know the backing native type (via the native() method), so you should be able to use the same logic to know if your DataType can represent that logical type.

fn name(&self) -> Option<&str>;
}

impl PartialEq for dyn LogicalType {
fn eq(&self, other: &Self) -> bool {
self.native().eq(other.native()) && self.name().eq(&other.name())
}
}

impl Eq for dyn LogicalType {}

impl PartialOrd for dyn LogicalType {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
Some(self.cmp(other))
}
}

impl Ord for dyn LogicalType {
fn cmp(&self, other: &Self) -> Ordering {
self.name()
.cmp(&other.name())
.then(self.native().cmp(other.native()))
}
}

impl Hash for dyn LogicalType {
fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
self.name().hash(state);
self.native().hash(state);
}
}
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Should we implement these traits manually or should we leave to the implementors?

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That's a good question. It all comes form Eq for me.
If we implement Eq by name (or name + native), then we disallow implementations from having any attributes that could affect their semantics (ie an implementation may have a field caching something, but it should effectively be a function of the name). And then we can implement other traits for the type as well.

And in fact this makes sense. We need a way to identify a type. We will store this information in the field metadata, so either type needs to be serializable, or it's name needs to be serializable and resolvable. The latter sounds like the way to go, especially given extension types (#12644), which won't be known at compile time

24 changes: 24 additions & 0 deletions datafusion/common/src/types/mod.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,24 @@
// 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.

mod builtin;
mod logical;
mod native;

pub use builtin::*;
pub use logical::*;
pub use native::*;
254 changes: 254 additions & 0 deletions datafusion/common/src/types/native.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,254 @@
// 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::sync::Arc;

use arrow_schema::{DataType, IntervalUnit, TimeUnit};

#[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)]
pub enum NativeType {
/// 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.
Date,
/// 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.
Binary,
/// Opaque binary data of fixed size.
/// Enum parameter specifies the number of bytes per value.
FixedSizeBinary(i32),
/// A variable-length string in Unicode with UTF-8 encoding.
Utf8,
/// A list of some logical data type with variable length.
List(Box<NativeType>),
/// A list of some logical data type with fixed length.
FixedSizeList(Box<NativeType>, i32),
/// A nested datatype that contains a number of sub-fields.
Struct(Box<[(String, NativeType)]>),
/// A nested datatype that can represent slots of differing types.
Union(Box<[(i8, NativeType)]>),
/// 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(Box<NativeType>),
}

impl From<DataType> for NativeType {
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NativeType name might be confusing or we need more docs? I'm reading the code and first comes to my mind Arrow Types or maybe rust native types? I mean the name should be sufficient to understand what kind of native it is

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Think of it as DataFusion’s type, or its built-in LogicalType. The term NativeType aligns with the concept of native types in Rust, so I believe it’s the preferred name.

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If I read the structure doc

/// Representation of a type that DataFusion can handle natively. It is a subset
 /// of the physical variants in Arrow's native [`DataType`].

its is not a Rust native types its more related to Arrow Types which eventually come to rust types. I feel we need to clarify this a little bit, but I cannot come up with the better phrase right now, lets leave it for follow up PR

fn from(value: DataType) -> Self {
use NativeType::*;
match value {
DataType::Null => Null,
DataType::Boolean => Boolean,
DataType::Int8 => Int8,
DataType::Int16 => Int16,
DataType::Int32 => Int32,
DataType::Int64 => Int64,
DataType::UInt8 => UInt8,
DataType::UInt16 => UInt16,
DataType::UInt32 => UInt32,
DataType::UInt64 => UInt64,
DataType::Float16 => Float16,
DataType::Float32 => Float32,
DataType::Float64 => Float64,
DataType::Timestamp(time_unit, arc) => Timestamp(time_unit, arc),
DataType::Date32 | DataType::Date64 => Date,
DataType::Time32(time_unit) => Time32(time_unit),
DataType::Time64(time_unit) => Time64(time_unit),
DataType::Duration(time_unit) => Duration(time_unit),
DataType::Interval(interval_unit) => Interval(interval_unit),
DataType::Binary | DataType::LargeBinary | DataType::BinaryView => Binary,
DataType::FixedSizeBinary(size) => FixedSizeBinary(size),
DataType::Utf8 | DataType::LargeUtf8 | DataType::Utf8View => Utf8,
DataType::List(field)
| DataType::ListView(field)
| DataType::LargeList(field)
| DataType::LargeListView(field) => {
List(Box::new(field.data_type().clone().into()))
}
DataType::FixedSizeList(field, size) => {
FixedSizeList(Box::new(field.data_type().clone().into()), size)
}
DataType::Struct(fields) => Struct(
fields
.into_iter()
.map(|field| (field.name().clone(), field.data_type().clone().into()))
.collect(),
),
DataType::Union(union_fields, _) => Union(
union_fields
.iter()
.map(|(i, field)| (i, field.data_type().clone().into()))
.collect(),
),
DataType::Dictionary(_, data_type) => data_type.as_ref().clone().into(),
DataType::Decimal128(p, s) => Decimal128(p, s),
DataType::Decimal256(p, s) => Decimal256(p, s),
DataType::Map(field, _) => Map(Box::new(field.data_type().clone().into())),
DataType::RunEndEncoded(_, field) => field.data_type().clone().into(),
}
}
}