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scalar.rs
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scalar.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.
//! This module provides ScalarValue, an enum that can be used for storage of single elements
use crate::error::{DataFusionError, Result};
use arrow::{
array::*,
compute::kernels::cast::{cast, cast_with_options, CastOptions},
datatypes::{
ArrowDictionaryKeyType, ArrowNativeType, DataType, Field, Float32Type,
Float64Type, Int16Type, Int32Type, Int64Type, Int8Type, IntervalUnit, TimeUnit,
TimestampMicrosecondType, TimestampMillisecondType, TimestampNanosecondType,
TimestampSecondType, UInt16Type, UInt32Type, UInt64Type, UInt8Type,
DECIMAL_MAX_PRECISION,
},
util::decimal::{BasicDecimal, Decimal128},
};
use ordered_float::OrderedFloat;
use std::cmp::Ordering;
use std::convert::{Infallible, TryInto};
use std::str::FromStr;
use std::{convert::TryFrom, fmt, iter::repeat, sync::Arc};
/// Represents a dynamically typed, nullable single value.
/// This is the single-valued counter-part of arrow’s `Array`.
/// https://arrow.apache.org/docs/python/api/datatypes.html
/// https://github.com/apache/arrow/blob/master/format/Schema.fbs#L354-L375
#[derive(Clone)]
pub enum ScalarValue {
/// represents `DataType::Null` (castable to/from any other type)
Null,
/// true or false value
Boolean(Option<bool>),
/// 32bit float
Float32(Option<f32>),
/// 64bit float
Float64(Option<f64>),
/// 128bit decimal, using the i128 to represent the decimal
Decimal128(Option<i128>, usize, usize),
/// signed 8bit int
Int8(Option<i8>),
/// signed 16bit int
Int16(Option<i16>),
/// signed 32bit int
Int32(Option<i32>),
/// signed 64bit int
Int64(Option<i64>),
/// unsigned 8bit int
UInt8(Option<u8>),
/// unsigned 16bit int
UInt16(Option<u16>),
/// unsigned 32bit int
UInt32(Option<u32>),
/// unsigned 64bit int
UInt64(Option<u64>),
/// utf-8 encoded string.
Utf8(Option<String>),
/// utf-8 encoded string representing a LargeString's arrow type.
LargeUtf8(Option<String>),
/// binary
Binary(Option<Vec<u8>>),
/// large binary
LargeBinary(Option<Vec<u8>>),
/// list of nested ScalarValue
List(Option<Vec<ScalarValue>>, Box<Field>),
/// Date stored as a signed 32bit int days since UNIX epoch 1970-01-01
Date32(Option<i32>),
/// Date stored as a signed 64bit int milliseconds since UNIX epoch 1970-01-01
Date64(Option<i64>),
/// Timestamp Second
TimestampSecond(Option<i64>, Option<String>),
/// Timestamp Milliseconds
TimestampMillisecond(Option<i64>, Option<String>),
/// Timestamp Microseconds
TimestampMicrosecond(Option<i64>, Option<String>),
/// Timestamp Nanoseconds
TimestampNanosecond(Option<i64>, Option<String>),
/// Number of elapsed whole months
IntervalYearMonth(Option<i32>),
/// Number of elapsed days and milliseconds (no leap seconds)
/// stored as 2 contiguous 32-bit signed integers
IntervalDayTime(Option<i64>),
/// A triple of the number of elapsed months, days, and nanoseconds.
/// Months and days are encoded as 32-bit signed integers.
/// Nanoseconds is encoded as a 64-bit signed integer (no leap seconds).
IntervalMonthDayNano(Option<i128>),
/// struct of nested ScalarValue
Struct(Option<Vec<ScalarValue>>, Box<Vec<Field>>),
/// Dictionary type: index type and value
Dictionary(Box<DataType>, Box<ScalarValue>),
}
// manual implementation of `PartialEq` that uses OrderedFloat to
// get defined behavior for floating point
impl PartialEq for ScalarValue {
fn eq(&self, other: &Self) -> bool {
use ScalarValue::*;
// This purposely doesn't have a catch-all "(_, _)" so that
// any newly added enum variant will require editing this list
// or else face a compile error
match (self, other) {
(Decimal128(v1, p1, s1), Decimal128(v2, p2, s2)) => {
v1.eq(v2) && p1.eq(p2) && s1.eq(s2)
}
(Decimal128(_, _, _), _) => false,
(Boolean(v1), Boolean(v2)) => v1.eq(v2),
(Boolean(_), _) => false,
(Float32(v1), Float32(v2)) => {
let v1 = v1.map(OrderedFloat);
let v2 = v2.map(OrderedFloat);
v1.eq(&v2)
}
(Float32(_), _) => false,
(Float64(v1), Float64(v2)) => {
let v1 = v1.map(OrderedFloat);
let v2 = v2.map(OrderedFloat);
v1.eq(&v2)
}
(Float64(_), _) => false,
(Int8(v1), Int8(v2)) => v1.eq(v2),
(Int8(_), _) => false,
(Int16(v1), Int16(v2)) => v1.eq(v2),
(Int16(_), _) => false,
(Int32(v1), Int32(v2)) => v1.eq(v2),
(Int32(_), _) => false,
(Int64(v1), Int64(v2)) => v1.eq(v2),
(Int64(_), _) => false,
(UInt8(v1), UInt8(v2)) => v1.eq(v2),
(UInt8(_), _) => false,
(UInt16(v1), UInt16(v2)) => v1.eq(v2),
(UInt16(_), _) => false,
(UInt32(v1), UInt32(v2)) => v1.eq(v2),
(UInt32(_), _) => false,
(UInt64(v1), UInt64(v2)) => v1.eq(v2),
(UInt64(_), _) => false,
(Utf8(v1), Utf8(v2)) => v1.eq(v2),
(Utf8(_), _) => false,
(LargeUtf8(v1), LargeUtf8(v2)) => v1.eq(v2),
(LargeUtf8(_), _) => false,
(Binary(v1), Binary(v2)) => v1.eq(v2),
(Binary(_), _) => false,
(LargeBinary(v1), LargeBinary(v2)) => v1.eq(v2),
(LargeBinary(_), _) => false,
(List(v1, t1), List(v2, t2)) => v1.eq(v2) && t1.eq(t2),
(List(_, _), _) => false,
(Date32(v1), Date32(v2)) => v1.eq(v2),
(Date32(_), _) => false,
(Date64(v1), Date64(v2)) => v1.eq(v2),
(Date64(_), _) => false,
(TimestampSecond(v1, _), TimestampSecond(v2, _)) => v1.eq(v2),
(TimestampSecond(_, _), _) => false,
(TimestampMillisecond(v1, _), TimestampMillisecond(v2, _)) => v1.eq(v2),
(TimestampMillisecond(_, _), _) => false,
(TimestampMicrosecond(v1, _), TimestampMicrosecond(v2, _)) => v1.eq(v2),
(TimestampMicrosecond(_, _), _) => false,
(TimestampNanosecond(v1, _), TimestampNanosecond(v2, _)) => v1.eq(v2),
(TimestampNanosecond(_, _), _) => false,
(IntervalYearMonth(v1), IntervalYearMonth(v2)) => v1.eq(v2),
(IntervalYearMonth(_), _) => false,
(IntervalDayTime(v1), IntervalDayTime(v2)) => v1.eq(v2),
(IntervalDayTime(_), _) => false,
(IntervalMonthDayNano(v1), IntervalMonthDayNano(v2)) => v1.eq(v2),
(IntervalMonthDayNano(_), _) => false,
(Struct(v1, t1), Struct(v2, t2)) => v1.eq(v2) && t1.eq(t2),
(Struct(_, _), _) => false,
(Dictionary(k1, v1), Dictionary(k2, v2)) => k1.eq(k2) && v1.eq(v2),
(Dictionary(_, _), _) => false,
(Null, Null) => true,
(Null, _) => false,
}
}
}
// manual implementation of `PartialOrd` that uses OrderedFloat to
// get defined behavior for floating point
impl PartialOrd for ScalarValue {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
use ScalarValue::*;
// This purposely doesn't have a catch-all "(_, _)" so that
// any newly added enum variant will require editing this list
// or else face a compile error
match (self, other) {
(Decimal128(v1, p1, s1), Decimal128(v2, p2, s2)) => {
if p1.eq(p2) && s1.eq(s2) {
v1.partial_cmp(v2)
} else {
// Two decimal values can be compared if they have the same precision and scale.
None
}
}
(Decimal128(_, _, _), _) => None,
(Boolean(v1), Boolean(v2)) => v1.partial_cmp(v2),
(Boolean(_), _) => None,
(Float32(v1), Float32(v2)) => {
let v1 = v1.map(OrderedFloat);
let v2 = v2.map(OrderedFloat);
v1.partial_cmp(&v2)
}
(Float32(_), _) => None,
(Float64(v1), Float64(v2)) => {
let v1 = v1.map(OrderedFloat);
let v2 = v2.map(OrderedFloat);
v1.partial_cmp(&v2)
}
(Float64(_), _) => None,
(Int8(v1), Int8(v2)) => v1.partial_cmp(v2),
(Int8(_), _) => None,
(Int16(v1), Int16(v2)) => v1.partial_cmp(v2),
(Int16(_), _) => None,
(Int32(v1), Int32(v2)) => v1.partial_cmp(v2),
(Int32(_), _) => None,
(Int64(v1), Int64(v2)) => v1.partial_cmp(v2),
(Int64(_), _) => None,
(UInt8(v1), UInt8(v2)) => v1.partial_cmp(v2),
(UInt8(_), _) => None,
(UInt16(v1), UInt16(v2)) => v1.partial_cmp(v2),
(UInt16(_), _) => None,
(UInt32(v1), UInt32(v2)) => v1.partial_cmp(v2),
(UInt32(_), _) => None,
(UInt64(v1), UInt64(v2)) => v1.partial_cmp(v2),
(UInt64(_), _) => None,
(Utf8(v1), Utf8(v2)) => v1.partial_cmp(v2),
(Utf8(_), _) => None,
(LargeUtf8(v1), LargeUtf8(v2)) => v1.partial_cmp(v2),
(LargeUtf8(_), _) => None,
(Binary(v1), Binary(v2)) => v1.partial_cmp(v2),
(Binary(_), _) => None,
(LargeBinary(v1), LargeBinary(v2)) => v1.partial_cmp(v2),
(LargeBinary(_), _) => None,
(List(v1, t1), List(v2, t2)) => {
if t1.eq(t2) {
v1.partial_cmp(v2)
} else {
None
}
}
(List(_, _), _) => None,
(Date32(v1), Date32(v2)) => v1.partial_cmp(v2),
(Date32(_), _) => None,
(Date64(v1), Date64(v2)) => v1.partial_cmp(v2),
(Date64(_), _) => None,
(TimestampSecond(v1, _), TimestampSecond(v2, _)) => v1.partial_cmp(v2),
(TimestampSecond(_, _), _) => None,
(TimestampMillisecond(v1, _), TimestampMillisecond(v2, _)) => {
v1.partial_cmp(v2)
}
(TimestampMillisecond(_, _), _) => None,
(TimestampMicrosecond(v1, _), TimestampMicrosecond(v2, _)) => {
v1.partial_cmp(v2)
}
(TimestampMicrosecond(_, _), _) => None,
(TimestampNanosecond(v1, _), TimestampNanosecond(v2, _)) => {
v1.partial_cmp(v2)
}
(TimestampNanosecond(_, _), _) => None,
(IntervalYearMonth(v1), IntervalYearMonth(v2)) => v1.partial_cmp(v2),
(IntervalYearMonth(_), _) => None,
(IntervalDayTime(v1), IntervalDayTime(v2)) => v1.partial_cmp(v2),
(IntervalDayTime(_), _) => None,
(IntervalMonthDayNano(v1), IntervalMonthDayNano(v2)) => v1.partial_cmp(v2),
(IntervalMonthDayNano(_), _) => None,
(Struct(v1, t1), Struct(v2, t2)) => {
if t1.eq(t2) {
v1.partial_cmp(v2)
} else {
None
}
}
(Struct(_, _), _) => None,
(Dictionary(k1, v1), Dictionary(k2, v2)) => {
// Don't compare if the key types don't match (it is effectively a different datatype)
if k1 == k2 {
v1.partial_cmp(v2)
} else {
None
}
}
(Dictionary(_, _), _) => None,
(Null, Null) => Some(Ordering::Equal),
(Null, _) => None,
}
}
}
impl Eq for ScalarValue {}
// manual implementation of `Hash` that uses OrderedFloat to
// get defined behavior for floating point
impl std::hash::Hash for ScalarValue {
fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
use ScalarValue::*;
match self {
Decimal128(v, p, s) => {
v.hash(state);
p.hash(state);
s.hash(state)
}
Boolean(v) => v.hash(state),
Float32(v) => {
let v = v.map(OrderedFloat);
v.hash(state)
}
Float64(v) => {
let v = v.map(OrderedFloat);
v.hash(state)
}
Int8(v) => v.hash(state),
Int16(v) => v.hash(state),
Int32(v) => v.hash(state),
Int64(v) => v.hash(state),
UInt8(v) => v.hash(state),
UInt16(v) => v.hash(state),
UInt32(v) => v.hash(state),
UInt64(v) => v.hash(state),
Utf8(v) => v.hash(state),
LargeUtf8(v) => v.hash(state),
Binary(v) => v.hash(state),
LargeBinary(v) => v.hash(state),
List(v, t) => {
v.hash(state);
t.hash(state);
}
Date32(v) => v.hash(state),
Date64(v) => v.hash(state),
TimestampSecond(v, _) => v.hash(state),
TimestampMillisecond(v, _) => v.hash(state),
TimestampMicrosecond(v, _) => v.hash(state),
TimestampNanosecond(v, _) => v.hash(state),
IntervalYearMonth(v) => v.hash(state),
IntervalDayTime(v) => v.hash(state),
IntervalMonthDayNano(v) => v.hash(state),
Struct(v, t) => {
v.hash(state);
t.hash(state);
}
Dictionary(k, v) => {
k.hash(state);
v.hash(state);
}
// stable hash for Null value
Null => 1.hash(state),
}
}
}
/// return a reference to the values array and the index into it for a
/// dictionary array
#[inline]
fn get_dict_value<K: ArrowDictionaryKeyType>(
array: &ArrayRef,
index: usize,
) -> (&ArrayRef, Option<usize>) {
let dict_array = as_dictionary_array::<K>(array);
(dict_array.values(), dict_array.key(index))
}
/// Create a dictionary array representing `value` repeated `size`
/// times
fn dict_from_scalar<K: ArrowDictionaryKeyType>(
value: &ScalarValue,
size: usize,
) -> ArrayRef {
// values array is one element long (the value)
let values_array = value.to_array_of_size(1);
// Create a key array with `size` elements, each of 0
let key_array: PrimitiveArray<K> = std::iter::repeat(Some(K::default_value()))
.take(size)
.collect();
// create a new DictionaryArray
//
// Note: this path could be made faster by using the ArrayData
// APIs and skipping validation, if it every comes up in
// performance traces.
Arc::new(
DictionaryArray::<K>::try_new(&key_array, &values_array)
// should always be valid by construction above
.expect("Can not construct dictionary array"),
)
}
/// Create a dictionary array representing all the values in values
fn dict_from_values<K: ArrowDictionaryKeyType>(
values_array: &dyn Array,
) -> Result<ArrayRef> {
// Create a key array with `size` elements of 0..array_len for all
// non-null value elements
let key_array: PrimitiveArray<K> = (0..values_array.len())
.map(|index| {
if values_array.is_valid(index) {
let native_index = K::Native::from_usize(index).ok_or_else(|| {
DataFusionError::Internal(format!(
"Can not create index of type {} from value {}",
K::DATA_TYPE,
index
))
})?;
Ok(Some(native_index))
} else {
Ok(None)
}
})
.collect::<Result<Vec<_>>>()?
.into_iter()
.collect();
// create a new DictionaryArray
//
// Note: this path could be made faster by using the ArrayData
// APIs and skipping validation, if it every comes up in
// performance traces.
let dict_array = DictionaryArray::<K>::try_new(&key_array, values_array)?;
Ok(Arc::new(dict_array))
}
macro_rules! typed_cast_tz {
($array:expr, $index:expr, $ARRAYTYPE:ident, $SCALAR:ident, $TZ:expr) => {{
let array = $array.as_any().downcast_ref::<$ARRAYTYPE>().unwrap();
ScalarValue::$SCALAR(
match array.is_null($index) {
true => None,
false => Some(array.value($index).into()),
},
$TZ.clone(),
)
}};
}
macro_rules! typed_cast {
($array:expr, $index:expr, $ARRAYTYPE:ident, $SCALAR:ident) => {{
let array = $array.as_any().downcast_ref::<$ARRAYTYPE>().unwrap();
ScalarValue::$SCALAR(match array.is_null($index) {
true => None,
false => Some(array.value($index).into()),
})
}};
}
macro_rules! build_list {
($VALUE_BUILDER_TY:ident, $SCALAR_TY:ident, $VALUES:expr, $SIZE:expr) => {{
match $VALUES {
// the return on the macro is necessary, to short-circuit and return ArrayRef
None => {
return new_null_array(
&DataType::List(Box::new(Field::new(
"item",
DataType::$SCALAR_TY,
true,
))),
$SIZE,
)
}
Some(values) => {
build_values_list!($VALUE_BUILDER_TY, $SCALAR_TY, values, $SIZE)
}
}
}};
}
macro_rules! build_timestamp_list {
($TIME_UNIT:expr, $TIME_ZONE:expr, $VALUES:expr, $SIZE:expr) => {{
match $VALUES {
// the return on the macro is necessary, to short-circuit and return ArrayRef
None => {
return new_null_array(
&DataType::List(Box::new(Field::new(
"item",
DataType::Timestamp($TIME_UNIT, $TIME_ZONE),
true,
))),
$SIZE,
)
}
Some(values) => match $TIME_UNIT {
TimeUnit::Second => {
build_values_list_tz!(
TimestampSecondBuilder,
TimestampSecond,
values,
$SIZE
)
}
TimeUnit::Microsecond => build_values_list_tz!(
TimestampMillisecondBuilder,
TimestampMillisecond,
values,
$SIZE
),
TimeUnit::Millisecond => build_values_list_tz!(
TimestampMicrosecondBuilder,
TimestampMicrosecond,
values,
$SIZE
),
TimeUnit::Nanosecond => build_values_list_tz!(
TimestampNanosecondBuilder,
TimestampNanosecond,
values,
$SIZE
),
},
}
}};
}
macro_rules! build_values_list {
($VALUE_BUILDER_TY:ident, $SCALAR_TY:ident, $VALUES:expr, $SIZE:expr) => {{
let mut builder = ListBuilder::new($VALUE_BUILDER_TY::new($VALUES.len()));
for _ in 0..$SIZE {
for scalar_value in $VALUES {
match scalar_value {
ScalarValue::$SCALAR_TY(Some(v)) => {
builder.values().append_value(v.clone());
}
ScalarValue::$SCALAR_TY(None) => {
builder.values().append_null();
}
_ => panic!("Incompatible ScalarValue for list"),
};
}
builder.append(true);
}
builder.finish()
}};
}
macro_rules! build_values_list_tz {
($VALUE_BUILDER_TY:ident, $SCALAR_TY:ident, $VALUES:expr, $SIZE:expr) => {{
let mut builder = ListBuilder::new($VALUE_BUILDER_TY::new($VALUES.len()));
for _ in 0..$SIZE {
for scalar_value in $VALUES {
match scalar_value {
ScalarValue::$SCALAR_TY(Some(v), _) => {
builder.values().append_value(v.clone());
}
ScalarValue::$SCALAR_TY(None, _) => {
builder.values().append_null();
}
_ => panic!("Incompatible ScalarValue for list"),
};
}
builder.append(true);
}
builder.finish()
}};
}
macro_rules! build_array_from_option {
($DATA_TYPE:ident, $ARRAY_TYPE:ident, $EXPR:expr, $SIZE:expr) => {{
match $EXPR {
Some(value) => Arc::new($ARRAY_TYPE::from_value(*value, $SIZE)),
None => new_null_array(&DataType::$DATA_TYPE, $SIZE),
}
}};
($DATA_TYPE:ident, $ENUM:expr, $ARRAY_TYPE:ident, $EXPR:expr, $SIZE:expr) => {{
match $EXPR {
Some(value) => Arc::new($ARRAY_TYPE::from_value(*value, $SIZE)),
None => new_null_array(&DataType::$DATA_TYPE($ENUM), $SIZE),
}
}};
($DATA_TYPE:ident, $ENUM:expr, $ENUM2:expr, $ARRAY_TYPE:ident, $EXPR:expr, $SIZE:expr) => {{
match $EXPR {
Some(value) => {
let array: ArrayRef = Arc::new($ARRAY_TYPE::from_value(*value, $SIZE));
// Need to call cast to cast to final data type with timezone/extra param
cast(&array, &DataType::$DATA_TYPE($ENUM, $ENUM2))
.expect("cannot do temporal cast")
}
None => new_null_array(&DataType::$DATA_TYPE($ENUM, $ENUM2), $SIZE),
}
}};
}
macro_rules! eq_array_primitive {
($array:expr, $index:expr, $ARRAYTYPE:ident, $VALUE:expr) => {{
let array = $array.as_any().downcast_ref::<$ARRAYTYPE>().unwrap();
let is_valid = array.is_valid($index);
match $VALUE {
Some(val) => is_valid && &array.value($index) == val,
None => !is_valid,
}
}};
}
impl ScalarValue {
/// Create a decimal Scalar from value/precision and scale.
pub fn try_new_decimal128(
value: i128,
precision: usize,
scale: usize,
) -> Result<Self> {
// make sure the precision and scale is valid
if precision <= DECIMAL_MAX_PRECISION && scale <= precision {
return Ok(ScalarValue::Decimal128(Some(value), precision, scale));
}
Err(DataFusionError::Internal(format!(
"Can not new a decimal type ScalarValue for precision {} and scale {}",
precision, scale
)))
}
/// Getter for the `DataType` of the value
pub fn get_datatype(&self) -> DataType {
match self {
ScalarValue::Boolean(_) => DataType::Boolean,
ScalarValue::UInt8(_) => DataType::UInt8,
ScalarValue::UInt16(_) => DataType::UInt16,
ScalarValue::UInt32(_) => DataType::UInt32,
ScalarValue::UInt64(_) => DataType::UInt64,
ScalarValue::Int8(_) => DataType::Int8,
ScalarValue::Int16(_) => DataType::Int16,
ScalarValue::Int32(_) => DataType::Int32,
ScalarValue::Int64(_) => DataType::Int64,
ScalarValue::Decimal128(_, precision, scale) => {
DataType::Decimal(*precision, *scale)
}
ScalarValue::TimestampSecond(_, tz_opt) => {
DataType::Timestamp(TimeUnit::Second, tz_opt.clone())
}
ScalarValue::TimestampMillisecond(_, tz_opt) => {
DataType::Timestamp(TimeUnit::Millisecond, tz_opt.clone())
}
ScalarValue::TimestampMicrosecond(_, tz_opt) => {
DataType::Timestamp(TimeUnit::Microsecond, tz_opt.clone())
}
ScalarValue::TimestampNanosecond(_, tz_opt) => {
DataType::Timestamp(TimeUnit::Nanosecond, tz_opt.clone())
}
ScalarValue::Float32(_) => DataType::Float32,
ScalarValue::Float64(_) => DataType::Float64,
ScalarValue::Utf8(_) => DataType::Utf8,
ScalarValue::LargeUtf8(_) => DataType::LargeUtf8,
ScalarValue::Binary(_) => DataType::Binary,
ScalarValue::LargeBinary(_) => DataType::LargeBinary,
ScalarValue::List(_, field) => DataType::List(Box::new(Field::new(
"item",
field.data_type().clone(),
true,
))),
ScalarValue::Date32(_) => DataType::Date32,
ScalarValue::Date64(_) => DataType::Date64,
ScalarValue::IntervalYearMonth(_) => {
DataType::Interval(IntervalUnit::YearMonth)
}
ScalarValue::IntervalDayTime(_) => DataType::Interval(IntervalUnit::DayTime),
ScalarValue::IntervalMonthDayNano(_) => {
DataType::Interval(IntervalUnit::MonthDayNano)
}
ScalarValue::Struct(_, fields) => DataType::Struct(fields.as_ref().clone()),
ScalarValue::Dictionary(k, v) => {
DataType::Dictionary(k.clone(), Box::new(v.get_datatype()))
}
ScalarValue::Null => DataType::Null,
}
}
/// Calculate arithmetic negation for a scalar value
pub fn arithmetic_negate(&self) -> Self {
match self {
ScalarValue::Boolean(None)
| ScalarValue::Int8(None)
| ScalarValue::Int16(None)
| ScalarValue::Int32(None)
| ScalarValue::Int64(None)
| ScalarValue::Float32(None) => self.clone(),
ScalarValue::Float64(Some(v)) => ScalarValue::Float64(Some(-v)),
ScalarValue::Float32(Some(v)) => ScalarValue::Float32(Some(-v)),
ScalarValue::Int8(Some(v)) => ScalarValue::Int8(Some(-v)),
ScalarValue::Int16(Some(v)) => ScalarValue::Int16(Some(-v)),
ScalarValue::Int32(Some(v)) => ScalarValue::Int32(Some(-v)),
ScalarValue::Int64(Some(v)) => ScalarValue::Int64(Some(-v)),
ScalarValue::Decimal128(Some(v), precision, scale) => {
ScalarValue::Decimal128(Some(-v), *precision, *scale)
}
_ => panic!("Cannot run arithmetic negate on scalar value: {:?}", self),
}
}
/// whether this value is null or not.
pub fn is_null(&self) -> bool {
match self {
ScalarValue::Boolean(v) => v.is_none(),
ScalarValue::Null => true,
ScalarValue::Float32(v) => v.is_none(),
ScalarValue::Float64(v) => v.is_none(),
ScalarValue::Decimal128(v, _, _) => v.is_none(),
ScalarValue::Int8(v) => v.is_none(),
ScalarValue::Int16(v) => v.is_none(),
ScalarValue::Int32(v) => v.is_none(),
ScalarValue::Int64(v) => v.is_none(),
ScalarValue::UInt8(v) => v.is_none(),
ScalarValue::UInt16(v) => v.is_none(),
ScalarValue::UInt32(v) => v.is_none(),
ScalarValue::UInt64(v) => v.is_none(),
ScalarValue::Utf8(v) => v.is_none(),
ScalarValue::LargeUtf8(v) => v.is_none(),
ScalarValue::Binary(v) => v.is_none(),
ScalarValue::LargeBinary(v) => v.is_none(),
ScalarValue::List(v, _) => v.is_none(),
ScalarValue::Date32(v) => v.is_none(),
ScalarValue::Date64(v) => v.is_none(),
ScalarValue::TimestampSecond(v, _) => v.is_none(),
ScalarValue::TimestampMillisecond(v, _) => v.is_none(),
ScalarValue::TimestampMicrosecond(v, _) => v.is_none(),
ScalarValue::TimestampNanosecond(v, _) => v.is_none(),
ScalarValue::IntervalYearMonth(v) => v.is_none(),
ScalarValue::IntervalDayTime(v) => v.is_none(),
ScalarValue::IntervalMonthDayNano(v) => v.is_none(),
ScalarValue::Struct(v, _) => v.is_none(),
ScalarValue::Dictionary(_, v) => v.is_null(),
}
}
/// Converts a scalar value into an 1-row array.
pub fn to_array(&self) -> ArrayRef {
self.to_array_of_size(1)
}
/// Converts an iterator of references [`ScalarValue`] into an [`ArrayRef`]
/// corresponding to those values. For example,
///
/// Returns an error if the iterator is empty or if the
/// [`ScalarValue`]s are not all the same type
///
/// Example
/// ```
/// use datafusion_common::ScalarValue;
/// use arrow::array::{ArrayRef, BooleanArray};
///
/// let scalars = vec![
/// ScalarValue::Boolean(Some(true)),
/// ScalarValue::Boolean(None),
/// ScalarValue::Boolean(Some(false)),
/// ];
///
/// // Build an Array from the list of ScalarValues
/// let array = ScalarValue::iter_to_array(scalars.into_iter())
/// .unwrap();
///
/// let expected: ArrayRef = std::sync::Arc::new(
/// BooleanArray::from(vec![
/// Some(true),
/// None,
/// Some(false)
/// ]
/// ));
///
/// assert_eq!(&array, &expected);
/// ```
pub fn iter_to_array(
scalars: impl IntoIterator<Item = ScalarValue>,
) -> Result<ArrayRef> {
let mut scalars = scalars.into_iter().peekable();
// figure out the type based on the first element
let data_type = match scalars.peek() {
None => {
return Err(DataFusionError::Internal(
"Empty iterator passed to ScalarValue::iter_to_array".to_string(),
));
}
Some(sv) => sv.get_datatype(),
};
/// Creates an array of $ARRAY_TY by unpacking values of
/// SCALAR_TY for primitive types
macro_rules! build_array_primitive {
($ARRAY_TY:ident, $SCALAR_TY:ident) => {{
{
let array = scalars.map(|sv| {
if let ScalarValue::$SCALAR_TY(v) = sv {
Ok(v)
} else {
Err(DataFusionError::Internal(format!(
"Inconsistent types in ScalarValue::iter_to_array. \
Expected {:?}, got {:?}",
data_type, sv
)))
}
})
.collect::<Result<$ARRAY_TY>>()?;
Arc::new(array)
}
}};
}
macro_rules! build_array_primitive_tz {
($ARRAY_TY:ident, $SCALAR_TY:ident) => {{
{
let array = scalars.map(|sv| {
if let ScalarValue::$SCALAR_TY(v, _) = sv {
Ok(v)
} else {
Err(DataFusionError::Internal(format!(
"Inconsistent types in ScalarValue::iter_to_array. \
Expected {:?}, got {:?}",
data_type, sv
)))
}
})
.collect::<Result<$ARRAY_TY>>()?;
Arc::new(array)
}
}};
}
/// Creates an array of $ARRAY_TY by unpacking values of
/// SCALAR_TY for "string-like" types.
macro_rules! build_array_string {
($ARRAY_TY:ident, $SCALAR_TY:ident) => {{
{
let array = scalars.map(|sv| {
if let ScalarValue::$SCALAR_TY(v) = sv {
Ok(v)
} else {
Err(DataFusionError::Internal(format!(
"Inconsistent types in ScalarValue::iter_to_array. \
Expected {:?}, got {:?}",
data_type, sv
)))
}
})
.collect::<Result<$ARRAY_TY>>()?;
Arc::new(array)
}
}};
}
macro_rules! build_array_list_primitive {
($ARRAY_TY:ident, $SCALAR_TY:ident, $NATIVE_TYPE:ident) => {{
Arc::new(ListArray::from_iter_primitive::<$ARRAY_TY, _, _>(
scalars.into_iter().map(|x| match x {
ScalarValue::List(xs, _) => xs.map(|x| {
x.iter().map(|x| match x {
ScalarValue::$SCALAR_TY(i) => *i,
sv => panic!(
"Inconsistent types in ScalarValue::iter_to_array. \
Expected {:?}, got {:?}",
data_type, sv
),
})
.collect::<Vec<Option<$NATIVE_TYPE>>>()
}),
sv => panic!(
"Inconsistent types in ScalarValue::iter_to_array. \
Expected {:?}, got {:?}",
data_type, sv
),
}),
))
}};
}
macro_rules! build_array_list_string {
($BUILDER:ident, $SCALAR_TY:ident) => {{
let mut builder = ListBuilder::new($BUILDER::new(0));
for scalar in scalars.into_iter() {
match scalar {
ScalarValue::List(Some(xs), _) => {
for s in xs {
match s {
ScalarValue::$SCALAR_TY(Some(val)) => {
builder.values().append_value(val);
}
ScalarValue::$SCALAR_TY(None) => {
builder.values().append_null();
}
sv => {
return Err(DataFusionError::Internal(format!(
"Inconsistent types in ScalarValue::iter_to_array. \
Expected Utf8, got {:?}",
sv
)))
}
}
}
builder.append(true);
}
ScalarValue::List(None, _) => {
builder.append(false);
}
sv => {
return Err(DataFusionError::Internal(format!(
"Inconsistent types in ScalarValue::iter_to_array. \
Expected List, got {:?}",
sv
)))
}
}
}
Arc::new(builder.finish())
}};
}
let array: ArrayRef = match &data_type {
DataType::Decimal(precision, scale) => {
let decimal_array =
ScalarValue::iter_to_decimal_array(scalars, precision, scale)?;
Arc::new(decimal_array)
}
DataType::Decimal256(_, _) => {
return Err(DataFusionError::Internal(
"Decimal256 is not supported for ScalarValue".to_string(),
))
}
DataType::Null => ScalarValue::iter_to_null_array(scalars),
DataType::Boolean => build_array_primitive!(BooleanArray, Boolean),
DataType::Float32 => build_array_primitive!(Float32Array, Float32),
DataType::Float64 => build_array_primitive!(Float64Array, Float64),
DataType::Int8 => build_array_primitive!(Int8Array, Int8),
DataType::Int16 => build_array_primitive!(Int16Array, Int16),
DataType::Int32 => build_array_primitive!(Int32Array, Int32),
DataType::Int64 => build_array_primitive!(Int64Array, Int64),
DataType::UInt8 => build_array_primitive!(UInt8Array, UInt8),
DataType::UInt16 => build_array_primitive!(UInt16Array, UInt16),
DataType::UInt32 => build_array_primitive!(UInt32Array, UInt32),
DataType::UInt64 => build_array_primitive!(UInt64Array, UInt64),
DataType::Utf8 => build_array_string!(StringArray, Utf8),
DataType::LargeUtf8 => build_array_string!(LargeStringArray, LargeUtf8),
DataType::Binary => build_array_string!(BinaryArray, Binary),
DataType::LargeBinary => build_array_string!(LargeBinaryArray, LargeBinary),
DataType::Date32 => build_array_primitive!(Date32Array, Date32),
DataType::Date64 => build_array_primitive!(Date64Array, Date64),
DataType::Timestamp(TimeUnit::Second, _) => {
build_array_primitive_tz!(TimestampSecondArray, TimestampSecond)
}
DataType::Timestamp(TimeUnit::Millisecond, _) => {
build_array_primitive_tz!(TimestampMillisecondArray, TimestampMillisecond)
}
DataType::Timestamp(TimeUnit::Microsecond, _) => {
build_array_primitive_tz!(TimestampMicrosecondArray, TimestampMicrosecond)
}
DataType::Timestamp(TimeUnit::Nanosecond, _) => {
build_array_primitive_tz!(TimestampNanosecondArray, TimestampNanosecond)
}
DataType::Interval(IntervalUnit::DayTime) => {
build_array_primitive!(IntervalDayTimeArray, IntervalDayTime)
}
DataType::Interval(IntervalUnit::YearMonth) => {
build_array_primitive!(IntervalYearMonthArray, IntervalYearMonth)
}
DataType::List(fields) if fields.data_type() == &DataType::Int8 => {
build_array_list_primitive!(Int8Type, Int8, i8)
}
DataType::List(fields) if fields.data_type() == &DataType::Int16 => {
build_array_list_primitive!(Int16Type, Int16, i16)
}
DataType::List(fields) if fields.data_type() == &DataType::Int32 => {
build_array_list_primitive!(Int32Type, Int32, i32)
}
DataType::List(fields) if fields.data_type() == &DataType::Int64 => {
build_array_list_primitive!(Int64Type, Int64, i64)
}
DataType::List(fields) if fields.data_type() == &DataType::UInt8 => {
build_array_list_primitive!(UInt8Type, UInt8, u8)
}
DataType::List(fields) if fields.data_type() == &DataType::UInt16 => {
build_array_list_primitive!(UInt16Type, UInt16, u16)
}
DataType::List(fields) if fields.data_type() == &DataType::UInt32 => {
build_array_list_primitive!(UInt32Type, UInt32, u32)
}
DataType::List(fields) if fields.data_type() == &DataType::UInt64 => {
build_array_list_primitive!(UInt64Type, UInt64, u64)
}
DataType::List(fields) if fields.data_type() == &DataType::Float32 => {
build_array_list_primitive!(Float32Type, Float32, f32)
}
DataType::List(fields) if fields.data_type() == &DataType::Float64 => {
build_array_list_primitive!(Float64Type, Float64, f64)
}
DataType::List(fields) if fields.data_type() == &DataType::Utf8 => {
build_array_list_string!(StringBuilder, Utf8)
}
DataType::List(fields) if fields.data_type() == &DataType::LargeUtf8 => {
build_array_list_string!(LargeStringBuilder, LargeUtf8)
}
DataType::List(_) => {
// Fallback case handling homogeneous lists with any ScalarValue element type
let list_array = ScalarValue::iter_to_array_list(scalars, &data_type)?;
Arc::new(list_array)