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apache/datafusion-sandbox/refs/heads/bash-push/./datafusion/physical-expr/src/expressions/binary.rs
blob: ce3d4ced4e3a22810dfd447c2613d9b20560b7b5 [file]
// 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 kernels;
use crate::intervals::cp_solver::{propagate_arithmetic, propagate_comparison};
use crate::PhysicalExpr;
use std::hash::Hash;
use std::{any::Any, sync::Arc};
use arrow::array::*;
use arrow::compute::kernels::boolean::{and_kleene, or_kleene};
use arrow::compute::kernels::cmp::*;
use arrow::compute::kernels::concat_elements::concat_elements_utf8;
use arrow::compute::{
cast, filter_record_batch, ilike, like, nilike, nlike, SlicesIterator,
};
use arrow::datatypes::*;
use arrow::error::ArrowError;
use datafusion_common::cast::as_boolean_array;
use datafusion_common::{internal_err, not_impl_err, Result, ScalarValue};
use datafusion_expr::binary::BinaryTypeCoercer;
use datafusion_expr::interval_arithmetic::{apply_operator, Interval};
use datafusion_expr::sort_properties::ExprProperties;
use datafusion_expr::statistics::Distribution::{Bernoulli, Gaussian};
use datafusion_expr::statistics::{
combine_bernoullis, combine_gaussians, create_bernoulli_from_comparison,
new_generic_from_binary_op, Distribution,
};
use datafusion_expr::{ColumnarValue, Operator};
use datafusion_physical_expr_common::datum::{apply, apply_cmp, apply_cmp_for_nested};
use kernels::{
bitwise_and_dyn, bitwise_and_dyn_scalar, bitwise_or_dyn, bitwise_or_dyn_scalar,
bitwise_shift_left_dyn, bitwise_shift_left_dyn_scalar, bitwise_shift_right_dyn,
bitwise_shift_right_dyn_scalar, bitwise_xor_dyn, bitwise_xor_dyn_scalar,
concat_elements_utf8view, regex_match_dyn, regex_match_dyn_scalar,
};
/// Binary expression
#[derive(Debug, Clone, Eq)]
pub struct BinaryExpr {
left: Arc<dyn PhysicalExpr>,
op: Operator,
right: Arc<dyn PhysicalExpr>,
/// Specifies whether an error is returned on overflow or not
fail_on_overflow: bool,
}
// Manually derive PartialEq and Hash to work around https://github.com/rust-lang/rust/issues/78808
impl PartialEq for BinaryExpr {
fn eq(&self, other: &Self) -> bool {
self.left.eq(&other.left)
&& self.op.eq(&other.op)
&& self.right.eq(&other.right)
&& self.fail_on_overflow.eq(&other.fail_on_overflow)
}
}
impl Hash for BinaryExpr {
fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
self.left.hash(state);
self.op.hash(state);
self.right.hash(state);
self.fail_on_overflow.hash(state);
}
}
impl BinaryExpr {
/// Create new binary expression
pub fn new(
left: Arc<dyn PhysicalExpr>,
op: Operator,
right: Arc<dyn PhysicalExpr>,
) -> Self {
Self {
left,
op,
right,
fail_on_overflow: false,
}
}
/// Create new binary expression with explicit fail_on_overflow value
pub fn with_fail_on_overflow(self, fail_on_overflow: bool) -> Self {
Self {
left: self.left,
op: self.op,
right: self.right,
fail_on_overflow,
}
}
/// Get the left side of the binary expression
pub fn left(&self) -> &Arc<dyn PhysicalExpr> {
&self.left
}
/// Get the right side of the binary expression
pub fn right(&self) -> &Arc<dyn PhysicalExpr> {
&self.right
}
/// Get the operator for this binary expression
pub fn op(&self) -> &Operator {
&self.op
}
}
impl std::fmt::Display for BinaryExpr {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
// Put parentheses around child binary expressions so that we can see the difference
// between `(a OR b) AND c` and `a OR (b AND c)`. We only insert parentheses when needed,
// based on operator precedence. For example, `(a AND b) OR c` and `a AND b OR c` are
// equivalent and the parentheses are not necessary.
fn write_child(
f: &mut std::fmt::Formatter,
expr: &dyn PhysicalExpr,
precedence: u8,
) -> std::fmt::Result {
if let Some(child) = expr.as_any().downcast_ref::<BinaryExpr>() {
let p = child.op.precedence();
if p == 0 || p < precedence {
write!(f, "({child})")?;
} else {
write!(f, "{child}")?;
}
} else {
write!(f, "{expr}")?;
}
Ok(())
}
let precedence = self.op.precedence();
write_child(f, self.left.as_ref(), precedence)?;
write!(f, " {} ", self.op)?;
write_child(f, self.right.as_ref(), precedence)
}
}
/// Invoke a boolean kernel on a pair of arrays
#[inline]
fn boolean_op(
left: &dyn Array,
right: &dyn Array,
op: impl FnOnce(&BooleanArray, &BooleanArray) -> Result<BooleanArray, ArrowError>,
) -> Result<Arc<dyn Array + 'static>, ArrowError> {
let ll = as_boolean_array(left).expect("boolean_op failed to downcast left array");
let rr = as_boolean_array(right).expect("boolean_op failed to downcast right array");
op(ll, rr).map(|t| Arc::new(t) as _)
}
impl PhysicalExpr for BinaryExpr {
/// Return a reference to Any that can be used for downcasting
fn as_any(&self) -> &dyn Any {
self
}
fn data_type(&self, input_schema: &Schema) -> Result<DataType> {
BinaryTypeCoercer::new(
&self.left.data_type(input_schema)?,
&self.op,
&self.right.data_type(input_schema)?,
)
.get_result_type()
}
fn nullable(&self, input_schema: &Schema) -> Result<bool> {
Ok(self.left.nullable(input_schema)? || self.right.nullable(input_schema)?)
}
fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
use arrow::compute::kernels::numeric::*;
// Evaluate left-hand side expression.
let lhs = self.left.evaluate(batch)?;
// Check if we can apply short-circuit evaluation.
match check_short_circuit(&lhs, &self.op) {
ShortCircuitStrategy::None => {}
ShortCircuitStrategy::ReturnLeft => return Ok(lhs),
ShortCircuitStrategy::ReturnRight => {
let rhs = self.right.evaluate(batch)?;
return Ok(rhs);
}
ShortCircuitStrategy::PreSelection(selection) => {
// The function `evaluate_selection` was not called for filtering and calculation,
// as it takes into account cases where the selection contains null values.
let batch = filter_record_batch(batch, selection)?;
let right_ret = self.right.evaluate(&batch)?;
match &right_ret {
ColumnarValue::Array(array) => {
// When the array on the right is all true or all false, skip the scatter process
let boolean_array = array.as_boolean();
let true_count = boolean_array.true_count();
let length = boolean_array.len();
if true_count == length {
return Ok(lhs);
} else if true_count == 0 && boolean_array.null_count() == 0 {
// If the right-hand array is returned at this point,the lengths will be inconsistent;
// returning a scalar can avoid this issue
return Ok(ColumnarValue::Scalar(ScalarValue::Boolean(
Some(false),
)));
}
return pre_selection_scatter(selection, Some(boolean_array));
}
ColumnarValue::Scalar(scalar) => {
if let ScalarValue::Boolean(v) = scalar {
// When the scalar is true or false, skip the scatter process
if let Some(v) = v {
if *v {
return Ok(lhs);
} else {
return Ok(right_ret);
}
} else {
return pre_selection_scatter(selection, None);
}
} else {
return internal_err!(
"Expected boolean scalar value, found: {right_ret:?}"
);
}
}
}
}
}
let rhs = self.right.evaluate(batch)?;
let left_data_type = lhs.data_type();
let right_data_type = rhs.data_type();
let schema = batch.schema();
let input_schema = schema.as_ref();
if left_data_type.is_nested() {
if !left_data_type.equals_datatype(&right_data_type) {
return internal_err!("Cannot evaluate binary expression because of type mismatch: left {}, right {} ", left_data_type, right_data_type);
}
return apply_cmp_for_nested(self.op, &lhs, &rhs);
}
match self.op {
Operator::Plus if self.fail_on_overflow => return apply(&lhs, &rhs, add),
Operator::Plus => return apply(&lhs, &rhs, add_wrapping),
Operator::Minus if self.fail_on_overflow => return apply(&lhs, &rhs, sub),
Operator::Minus => return apply(&lhs, &rhs, sub_wrapping),
Operator::Multiply if self.fail_on_overflow => return apply(&lhs, &rhs, mul),
Operator::Multiply => return apply(&lhs, &rhs, mul_wrapping),
Operator::Divide => return apply(&lhs, &rhs, div),
Operator::Modulo => return apply(&lhs, &rhs, rem),
Operator::Eq => return apply_cmp(&lhs, &rhs, eq),
Operator::NotEq => return apply_cmp(&lhs, &rhs, neq),
Operator::Lt => return apply_cmp(&lhs, &rhs, lt),
Operator::Gt => return apply_cmp(&lhs, &rhs, gt),
Operator::LtEq => return apply_cmp(&lhs, &rhs, lt_eq),
Operator::GtEq => return apply_cmp(&lhs, &rhs, gt_eq),
Operator::IsDistinctFrom => return apply_cmp(&lhs, &rhs, distinct),
Operator::IsNotDistinctFrom => return apply_cmp(&lhs, &rhs, not_distinct),
Operator::LikeMatch => return apply_cmp(&lhs, &rhs, like),
Operator::ILikeMatch => return apply_cmp(&lhs, &rhs, ilike),
Operator::NotLikeMatch => return apply_cmp(&lhs, &rhs, nlike),
Operator::NotILikeMatch => return apply_cmp(&lhs, &rhs, nilike),
_ => {}
}
let result_type = self.data_type(input_schema)?;
// If the left-hand side is an array and the right-hand side is a non-null scalar, try the optimized kernel.
if let (ColumnarValue::Array(array), ColumnarValue::Scalar(ref scalar)) =
(&lhs, &rhs)
{
if !scalar.is_null() {
if let Some(result_array) =
self.evaluate_array_scalar(array, scalar.clone())?
{
let final_array = result_array
.and_then(|a| to_result_type_array(&self.op, a, &result_type));
return final_array.map(ColumnarValue::Array);
}
}
}
// if both arrays or both literals - extract arrays and continue execution
let (left, right) = (
lhs.into_array(batch.num_rows())?,
rhs.into_array(batch.num_rows())?,
);
self.evaluate_with_resolved_args(left, &left_data_type, right, &right_data_type)
.map(ColumnarValue::Array)
}
fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> {
vec![&self.left, &self.right]
}
fn with_new_children(
self: Arc<Self>,
children: Vec<Arc<dyn PhysicalExpr>>,
) -> Result<Arc<dyn PhysicalExpr>> {
Ok(Arc::new(
BinaryExpr::new(Arc::clone(&children[0]), self.op, Arc::clone(&children[1]))
.with_fail_on_overflow(self.fail_on_overflow),
))
}
fn evaluate_bounds(&self, children: &[&Interval]) -> Result<Interval> {
// Get children intervals:
let left_interval = children[0];
let right_interval = children[1];
// Calculate current node's interval:
apply_operator(&self.op, left_interval, right_interval)
}
fn propagate_constraints(
&self,
interval: &Interval,
children: &[&Interval],
) -> Result<Option<Vec<Interval>>> {
// Get children intervals.
let left_interval = children[0];
let right_interval = children[1];
if self.op.eq(&Operator::And) {
if interval.eq(&Interval::CERTAINLY_TRUE) {
// A certainly true logical conjunction can only derive from possibly
// true operands. Otherwise, we prove infeasibility.
Ok((!left_interval.eq(&Interval::CERTAINLY_FALSE)
&& !right_interval.eq(&Interval::CERTAINLY_FALSE))
.then(|| vec![Interval::CERTAINLY_TRUE, Interval::CERTAINLY_TRUE]))
} else if interval.eq(&Interval::CERTAINLY_FALSE) {
// If the logical conjunction is certainly false, one of the
// operands must be false. However, it's not always possible to
// determine which operand is false, leading to different scenarios.
// If one operand is certainly true and the other one is uncertain,
// then the latter must be certainly false.
if left_interval.eq(&Interval::CERTAINLY_TRUE)
&& right_interval.eq(&Interval::UNCERTAIN)
{
Ok(Some(vec![
Interval::CERTAINLY_TRUE,
Interval::CERTAINLY_FALSE,
]))
} else if right_interval.eq(&Interval::CERTAINLY_TRUE)
&& left_interval.eq(&Interval::UNCERTAIN)
{
Ok(Some(vec![
Interval::CERTAINLY_FALSE,
Interval::CERTAINLY_TRUE,
]))
}
// If both children are uncertain, or if one is certainly false,
// we cannot conclusively refine their intervals. In this case,
// propagation does not result in any interval changes.
else {
Ok(Some(vec![]))
}
} else {
// An uncertain logical conjunction result can not shrink the
// end-points of its children.
Ok(Some(vec![]))
}
} else if self.op.eq(&Operator::Or) {
if interval.eq(&Interval::CERTAINLY_FALSE) {
// A certainly false logical disjunction can only derive from certainly
// false operands. Otherwise, we prove infeasibility.
Ok((!left_interval.eq(&Interval::CERTAINLY_TRUE)
&& !right_interval.eq(&Interval::CERTAINLY_TRUE))
.then(|| vec![Interval::CERTAINLY_FALSE, Interval::CERTAINLY_FALSE]))
} else if interval.eq(&Interval::CERTAINLY_TRUE) {
// If the logical disjunction is certainly true, one of the
// operands must be true. However, it's not always possible to
// determine which operand is true, leading to different scenarios.
// If one operand is certainly false and the other one is uncertain,
// then the latter must be certainly true.
if left_interval.eq(&Interval::CERTAINLY_FALSE)
&& right_interval.eq(&Interval::UNCERTAIN)
{
Ok(Some(vec![
Interval::CERTAINLY_FALSE,
Interval::CERTAINLY_TRUE,
]))
} else if right_interval.eq(&Interval::CERTAINLY_FALSE)
&& left_interval.eq(&Interval::UNCERTAIN)
{
Ok(Some(vec![
Interval::CERTAINLY_TRUE,
Interval::CERTAINLY_FALSE,
]))
}
// If both children are uncertain, or if one is certainly true,
// we cannot conclusively refine their intervals. In this case,
// propagation does not result in any interval changes.
else {
Ok(Some(vec![]))
}
} else {
// An uncertain logical disjunction result can not shrink the
// end-points of its children.
Ok(Some(vec![]))
}
} else if self.op.supports_propagation() {
Ok(
propagate_comparison(&self.op, interval, left_interval, right_interval)?
.map(|(left, right)| vec![left, right]),
)
} else {
Ok(
propagate_arithmetic(&self.op, interval, left_interval, right_interval)?
.map(|(left, right)| vec![left, right]),
)
}
}
fn evaluate_statistics(&self, children: &[&Distribution]) -> Result<Distribution> {
let (left, right) = (children[0], children[1]);
if self.op.is_numerical_operators() {
// We might be able to construct the output statistics more accurately,
// without falling back to an unknown distribution, if we are dealing
// with Gaussian distributions and numerical operations.
if let (Gaussian(left), Gaussian(right)) = (left, right) {
if let Some(result) = combine_gaussians(&self.op, left, right)? {
return Ok(Gaussian(result));
}
}
} else if self.op.is_logic_operator() {
// If we are dealing with logical operators, we expect (and can only
// operate on) Bernoulli distributions.
return if let (Bernoulli(left), Bernoulli(right)) = (left, right) {
combine_bernoullis(&self.op, left, right).map(Bernoulli)
} else {
internal_err!(
"Logical operators are only compatible with `Bernoulli` distributions"
)
};
} else if self.op.supports_propagation() {
// If we are handling comparison operators, we expect (and can only
// operate on) numeric distributions.
return create_bernoulli_from_comparison(&self.op, left, right);
}
// Fall back to an unknown distribution with only summary statistics:
new_generic_from_binary_op(&self.op, left, right)
}
/// For each operator, [`BinaryExpr`] has distinct rules.
/// TODO: There may be rules specific to some data types and expression ranges.
fn get_properties(&self, children: &[ExprProperties]) -> Result<ExprProperties> {
let (l_order, l_range) = (children[0].sort_properties, &children[0].range);
let (r_order, r_range) = (children[1].sort_properties, &children[1].range);
match self.op() {
Operator::Plus => Ok(ExprProperties {
sort_properties: l_order.add(&r_order),
range: l_range.add(r_range)?,
preserves_lex_ordering: false,
}),
Operator::Minus => Ok(ExprProperties {
sort_properties: l_order.sub(&r_order),
range: l_range.sub(r_range)?,
preserves_lex_ordering: false,
}),
Operator::Gt => Ok(ExprProperties {
sort_properties: l_order.gt_or_gteq(&r_order),
range: l_range.gt(r_range)?,
preserves_lex_ordering: false,
}),
Operator::GtEq => Ok(ExprProperties {
sort_properties: l_order.gt_or_gteq(&r_order),
range: l_range.gt_eq(r_range)?,
preserves_lex_ordering: false,
}),
Operator::Lt => Ok(ExprProperties {
sort_properties: r_order.gt_or_gteq(&l_order),
range: l_range.lt(r_range)?,
preserves_lex_ordering: false,
}),
Operator::LtEq => Ok(ExprProperties {
sort_properties: r_order.gt_or_gteq(&l_order),
range: l_range.lt_eq(r_range)?,
preserves_lex_ordering: false,
}),
Operator::And => Ok(ExprProperties {
sort_properties: r_order.and_or(&l_order),
range: l_range.and(r_range)?,
preserves_lex_ordering: false,
}),
Operator::Or => Ok(ExprProperties {
sort_properties: r_order.and_or(&l_order),
range: l_range.or(r_range)?,
preserves_lex_ordering: false,
}),
_ => Ok(ExprProperties::new_unknown()),
}
}
fn fmt_sql(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
fn write_child(
f: &mut std::fmt::Formatter,
expr: &dyn PhysicalExpr,
precedence: u8,
) -> std::fmt::Result {
if let Some(child) = expr.as_any().downcast_ref::<BinaryExpr>() {
let p = child.op.precedence();
if p == 0 || p < precedence {
write!(f, "(")?;
child.fmt_sql(f)?;
write!(f, ")")
} else {
child.fmt_sql(f)
}
} else {
expr.fmt_sql(f)
}
}
let precedence = self.op.precedence();
write_child(f, self.left.as_ref(), precedence)?;
write!(f, " {} ", self.op)?;
write_child(f, self.right.as_ref(), precedence)
}
}
/// Casts dictionary array to result type for binary numerical operators. Such operators
/// between array and scalar produce a dictionary array other than primitive array of the
/// same operators between array and array. This leads to inconsistent result types causing
/// errors in the following query execution. For such operators between array and scalar,
/// we cast the dictionary array to primitive array.
fn to_result_type_array(
op: &Operator,
array: ArrayRef,
result_type: &DataType,
) -> Result<ArrayRef> {
if array.data_type() == result_type {
Ok(array)
} else if op.is_numerical_operators() {
match array.data_type() {
DataType::Dictionary(_, value_type) => {
if value_type.as_ref() == result_type {
Ok(cast(&array, result_type)?)
} else {
internal_err!(
"Incompatible Dictionary value type {value_type} with result type {result_type} of Binary operator {op:?}"
)
}
}
_ => Ok(array),
}
} else {
Ok(array)
}
}
impl BinaryExpr {
/// Evaluate the expression of the left input is an array and
/// right is literal - use scalar operations
fn evaluate_array_scalar(
&self,
array: &dyn Array,
scalar: ScalarValue,
) -> Result<Option<Result<ArrayRef>>> {
use Operator::*;
let scalar_result = match &self.op {
RegexMatch => regex_match_dyn_scalar(array, scalar, false, false),
RegexIMatch => regex_match_dyn_scalar(array, scalar, false, true),
RegexNotMatch => regex_match_dyn_scalar(array, scalar, true, false),
RegexNotIMatch => regex_match_dyn_scalar(array, scalar, true, true),
BitwiseAnd => bitwise_and_dyn_scalar(array, scalar),
BitwiseOr => bitwise_or_dyn_scalar(array, scalar),
BitwiseXor => bitwise_xor_dyn_scalar(array, scalar),
BitwiseShiftRight => bitwise_shift_right_dyn_scalar(array, scalar),
BitwiseShiftLeft => bitwise_shift_left_dyn_scalar(array, scalar),
// if scalar operation is not supported - fallback to array implementation
_ => None,
};
Ok(scalar_result)
}
fn evaluate_with_resolved_args(
&self,
left: Arc<dyn Array>,
left_data_type: &DataType,
right: Arc<dyn Array>,
right_data_type: &DataType,
) -> Result<ArrayRef> {
use Operator::*;
match &self.op {
IsDistinctFrom | IsNotDistinctFrom | Lt | LtEq | Gt | GtEq | Eq | NotEq
| Plus | Minus | Multiply | Divide | Modulo | LikeMatch | ILikeMatch
| NotLikeMatch | NotILikeMatch => unreachable!(),
And => {
if left_data_type == &DataType::Boolean {
Ok(boolean_op(&left, &right, and_kleene)?)
} else {
internal_err!(
"Cannot evaluate binary expression {:?} with types {:?} and {:?}",
self.op,
left.data_type(),
right.data_type()
)
}
}
Or => {
if left_data_type == &DataType::Boolean {
Ok(boolean_op(&left, &right, or_kleene)?)
} else {
internal_err!(
"Cannot evaluate binary expression {:?} with types {:?} and {:?}",
self.op,
left_data_type,
right_data_type
)
}
}
RegexMatch => regex_match_dyn(left, right, false, false),
RegexIMatch => regex_match_dyn(left, right, false, true),
RegexNotMatch => regex_match_dyn(left, right, true, false),
RegexNotIMatch => regex_match_dyn(left, right, true, true),
BitwiseAnd => bitwise_and_dyn(left, right),
BitwiseOr => bitwise_or_dyn(left, right),
BitwiseXor => bitwise_xor_dyn(left, right),
BitwiseShiftRight => bitwise_shift_right_dyn(left, right),
BitwiseShiftLeft => bitwise_shift_left_dyn(left, right),
StringConcat => concat_elements(left, right),
AtArrow | ArrowAt | Arrow | LongArrow | HashArrow | HashLongArrow | AtAt
| HashMinus | AtQuestion | Question | QuestionAnd | QuestionPipe
| IntegerDivide => {
not_impl_err!(
"Binary operator '{:?}' is not supported in the physical expr",
self.op
)
}
}
}
}
enum ShortCircuitStrategy<'a> {
None,
ReturnLeft,
ReturnRight,
PreSelection(&'a BooleanArray),
}
/// Based on the results calculated from the left side of the short-circuit operation,
/// if the proportion of `true` is less than 0.2 and the current operation is an `and`,
/// the `RecordBatch` will be filtered in advance.
const PRE_SELECTION_THRESHOLD: f32 = 0.2;
/// Checks if a logical operator (`AND`/`OR`) can short-circuit evaluation based on the left-hand side (lhs) result.
///
/// Short-circuiting occurs under these circumstances:
/// - For `AND`:
/// - if LHS is all false => short-circuit → return LHS
/// - if LHS is all true => short-circuit → return RHS
/// - if LHS is mixed and true_count/sum_count <= [`PRE_SELECTION_THRESHOLD`] -> pre-selection
/// - For `OR`:
/// - if LHS is all true => short-circuit → return LHS
/// - if LHS is all false => short-circuit → return RHS
/// # Arguments
/// * `lhs` - The left-hand side (lhs) columnar value (array or scalar)
/// * `lhs` - The left-hand side (lhs) columnar value (array or scalar)
/// * `op` - The logical operator (`AND` or `OR`)
///
/// # Implementation Notes
/// 1. Only works with Boolean-typed arguments (other types automatically return `false`)
/// 2. Handles both scalar values and array values
/// 3. For arrays, uses optimized bit counting techniques for boolean arrays
fn check_short_circuit<'a>(
lhs: &'a ColumnarValue,
op: &Operator,
) -> ShortCircuitStrategy<'a> {
// Quick reject for non-logical operators,and quick judgment when op is and
let is_and = match op {
Operator::And => true,
Operator::Or => false,
_ => return ShortCircuitStrategy::None,
};
// Non-boolean types can't be short-circuited
if lhs.data_type() != DataType::Boolean {
return ShortCircuitStrategy::None;
}
match lhs {
ColumnarValue::Array(array) => {
// Fast path for arrays - try to downcast to boolean array
if let Ok(bool_array) = as_boolean_array(array) {
// Arrays with nulls can't be short-circuited
if bool_array.null_count() > 0 {
return ShortCircuitStrategy::None;
}
let len = bool_array.len();
if len == 0 {
return ShortCircuitStrategy::None;
}
let true_count = bool_array.values().count_set_bits();
if is_and {
// For AND, prioritize checking for all-false (short circuit case)
// Uses optimized false_count() method provided by Arrow
// Short circuit if all values are false
if true_count == 0 {
return ShortCircuitStrategy::ReturnLeft;
}
// If no false values, then all must be true
if true_count == len {
return ShortCircuitStrategy::ReturnRight;
}
// determine if we can pre-selection
if true_count as f32 / len as f32 <= PRE_SELECTION_THRESHOLD {
return ShortCircuitStrategy::PreSelection(bool_array);
}
} else {
// For OR, prioritize checking for all-true (short circuit case)
// Uses optimized true_count() method provided by Arrow
// Short circuit if all values are true
if true_count == len {
return ShortCircuitStrategy::ReturnLeft;
}
// If no true values, then all must be false
if true_count == 0 {
return ShortCircuitStrategy::ReturnRight;
}
}
}
}
ColumnarValue::Scalar(scalar) => {
// Fast path for scalar values
if let ScalarValue::Boolean(Some(is_true)) = scalar {
// Return Left for:
// - AND with false value
// - OR with true value
if (is_and && !is_true) || (!is_and && *is_true) {
return ShortCircuitStrategy::ReturnLeft;
} else {
return ShortCircuitStrategy::ReturnRight;
}
}
}
}
// If we can't short-circuit, indicate that normal evaluation should continue
ShortCircuitStrategy::None
}
/// Creates a new boolean array based on the evaluation of the right expression,
/// but only for positions where the left_result is true.
///
/// This function is used for short-circuit evaluation optimization of logical AND operations:
/// - When left_result has few true values, we only evaluate the right expression for those positions
/// - Values are copied from right_array where left_result is true
/// - All other positions are filled with false values
///
/// # Parameters
/// - `left_result` Boolean array with selection mask (typically from left side of AND)
/// - `right_result` Result of evaluating right side of expression (only for selected positions)
///
/// # Returns
/// A combined ColumnarValue with values from right_result where left_result is true
///
/// # Example
/// Initial Data: { 1, 2, 3, 4, 5 }
/// Left Evaluation
/// (Condition: Equal to 2 or 3)
/// ↓
/// Filtered Data: {2, 3}
/// Left Bitmap: { 0, 1, 1, 0, 0 }
/// ↓
/// Right Evaluation
/// (Condition: Even numbers)
/// ↓
/// Right Data: { 2 }
/// Right Bitmap: { 1, 0 }
/// ↓
/// Combine Results
/// Final Bitmap: { 0, 1, 0, 0, 0 }
///
/// # Note
/// Perhaps it would be better to modify `left_result` directly without creating a copy?
/// In practice, `left_result` should have only one owner, so making changes should be safe.
/// However, this is difficult to achieve under the immutable constraints of [`Arc`] and [`BooleanArray`].
fn pre_selection_scatter(
left_result: &BooleanArray,
right_result: Option<&BooleanArray>,
) -> Result<ColumnarValue> {
let result_len = left_result.len();
let mut result_array_builder = BooleanArray::builder(result_len);
// keep track of current position we have in right boolean array
let mut right_array_pos = 0;
// keep track of how much is filled
let mut last_end = 0;
// reduce if condition in for_each
match right_result {
Some(right_result) => {
SlicesIterator::new(left_result).for_each(|(start, end)| {
// the gap needs to be filled with false
if start > last_end {
result_array_builder.append_n(start - last_end, false);
}
// copy values from right array for this slice
let len = end - start;
right_result
.slice(right_array_pos, len)
.iter()
.for_each(|v| result_array_builder.append_option(v));
right_array_pos += len;
last_end = end;
});
}
None => SlicesIterator::new(left_result).for_each(|(start, end)| {
// the gap needs to be filled with false
if start > last_end {
result_array_builder.append_n(start - last_end, false);
}
// append nulls for this slice derictly
let len = end - start;
result_array_builder.append_nulls(len);
last_end = end;
}),
}
// Fill any remaining positions with false
if last_end < result_len {
result_array_builder.append_n(result_len - last_end, false);
}
let boolean_result = result_array_builder.finish();
Ok(ColumnarValue::Array(Arc::new(boolean_result)))
}
fn concat_elements(left: Arc<dyn Array>, right: Arc<dyn Array>) -> Result<ArrayRef> {
Ok(match left.data_type() {
DataType::Utf8 => Arc::new(concat_elements_utf8(
left.as_string::<i32>(),
right.as_string::<i32>(),
)?),
DataType::LargeUtf8 => Arc::new(concat_elements_utf8(
left.as_string::<i64>(),
right.as_string::<i64>(),
)?),
DataType::Utf8View => Arc::new(concat_elements_utf8view(
left.as_string_view(),
right.as_string_view(),
)?),
other => {
return internal_err!(
"Data type {other:?} not supported for binary operation 'concat_elements' on string arrays"
);
}
})
}
/// Create a binary expression whose arguments are correctly coerced.
/// This function errors if it is not possible to coerce the arguments
/// to computational types supported by the operator.
pub fn binary(
lhs: Arc<dyn PhysicalExpr>,
op: Operator,
rhs: Arc<dyn PhysicalExpr>,
_input_schema: &Schema,
) -> Result<Arc<dyn PhysicalExpr>> {
Ok(Arc::new(BinaryExpr::new(lhs, op, rhs)))
}
/// Create a similar to expression
pub fn similar_to(
negated: bool,
case_insensitive: bool,
expr: Arc<dyn PhysicalExpr>,
pattern: Arc<dyn PhysicalExpr>,
) -> Result<Arc<dyn PhysicalExpr>> {
let binary_op = match (negated, case_insensitive) {
(false, false) => Operator::RegexMatch,
(false, true) => Operator::RegexIMatch,
(true, false) => Operator::RegexNotMatch,
(true, true) => Operator::RegexNotIMatch,
};
Ok(Arc::new(BinaryExpr::new(expr, binary_op, pattern)))
}
#[cfg(test)]
mod tests {
use super::*;
use crate::expressions::{col, lit, try_cast, Column, Literal};
use datafusion_expr::lit as expr_lit;
use datafusion_common::plan_datafusion_err;
use datafusion_physical_expr_common::physical_expr::fmt_sql;
use crate::planner::logical2physical;
use arrow::array::BooleanArray;
use datafusion_expr::col as logical_col;
/// Performs a binary operation, applying any type coercion necessary
fn binary_op(
left: Arc<dyn PhysicalExpr>,
op: Operator,
right: Arc<dyn PhysicalExpr>,
schema: &Schema,
) -> Result<Arc<dyn PhysicalExpr>> {
let left_type = left.data_type(schema)?;
let right_type = right.data_type(schema)?;
let (lhs, rhs) =
BinaryTypeCoercer::new(&left_type, &op, &right_type).get_input_types()?;
let left_expr = try_cast(left, schema, lhs)?;
let right_expr = try_cast(right, schema, rhs)?;
binary(left_expr, op, right_expr, schema)
}
#[test]
fn binary_comparison() -> Result<()> {
let schema = Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let b = Int32Array::from(vec![1, 2, 4, 8, 16]);
// expression: "a < b"
let lt = binary(
col("a", &schema)?,
Operator::Lt,
col("b", &schema)?,
&schema,
)?;
let batch =
RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a), Arc::new(b)])?;
let result = lt
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(result.len(), 5);
let expected = [false, false, true, true, true];
let result =
as_boolean_array(&result).expect("failed to downcast to BooleanArray");
for (i, &expected_item) in expected.iter().enumerate().take(5) {
assert_eq!(result.value(i), expected_item);
}
Ok(())
}
#[test]
fn binary_nested() -> Result<()> {
let schema = Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]);
let a = Int32Array::from(vec![2, 4, 6, 8, 10]);
let b = Int32Array::from(vec![2, 5, 4, 8, 8]);
// expression: "a < b OR a == b"
let expr = binary(
binary(
col("a", &schema)?,
Operator::Lt,
col("b", &schema)?,
&schema,
)?,
Operator::Or,
binary(
col("a", &schema)?,
Operator::Eq,
col("b", &schema)?,
&schema,
)?,
&schema,
)?;
let batch =
RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a), Arc::new(b)])?;
assert_eq!("a@0 < b@1 OR a@0 = b@1", format!("{expr}"));
let result = expr
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(result.len(), 5);
let expected = [true, true, false, true, false];
let result =
as_boolean_array(&result).expect("failed to downcast to BooleanArray");
for (i, &expected_item) in expected.iter().enumerate().take(5) {
assert_eq!(result.value(i), expected_item);
}
Ok(())
}
// runs an end-to-end test of physical type coercion:
// 1. construct a record batch with two columns of type A and B
// (*_ARRAY is the Rust Arrow array type, and *_TYPE is the DataType of the elements)
// 2. construct a physical expression of A OP B
// 3. evaluate the expression
// 4. verify that the resulting expression is of type C
// 5. verify that the results of evaluation are $VEC
macro_rules! test_coercion {
($A_ARRAY:ident, $A_TYPE:expr, $A_VEC:expr, $B_ARRAY:ident, $B_TYPE:expr, $B_VEC:expr, $OP:expr, $C_ARRAY:ident, $C_TYPE:expr, $VEC:expr,) => {{
let schema = Schema::new(vec![
Field::new("a", $A_TYPE, false),
Field::new("b", $B_TYPE, false),
]);
let a = $A_ARRAY::from($A_VEC);
let b = $B_ARRAY::from($B_VEC);
let (lhs, rhs) = BinaryTypeCoercer::new(&$A_TYPE, &$OP, &$B_TYPE).get_input_types()?;
let left = try_cast(col("a", &schema)?, &schema, lhs)?;
let right = try_cast(col("b", &schema)?, &schema, rhs)?;
// verify that we can construct the expression
let expression = binary(left, $OP, right, &schema)?;
let batch = RecordBatch::try_new(
Arc::new(schema.clone()),
vec![Arc::new(a), Arc::new(b)],
)?;
// verify that the expression's type is correct
assert_eq!(expression.data_type(&schema)?, $C_TYPE);
// compute
let result = expression.evaluate(&batch)?.into_array(batch.num_rows()).expect("Failed to convert to array");
// verify that the array's data_type is correct
assert_eq!(*result.data_type(), $C_TYPE);
// verify that the data itself is downcastable
let result = result
.as_any()
.downcast_ref::<$C_ARRAY>()
.expect("failed to downcast");
// verify that the result itself is correct
for (i, x) in $VEC.iter().enumerate() {
let v = result.value(i);
assert_eq!(
v,
*x,
"Unexpected output at position {i}:\n\nActual:\n{v}\n\nExpected:\n{x}"
);
}
}};
}
#[test]
fn test_type_coercion() -> Result<()> {
test_coercion!(
Int32Array,
DataType::Int32,
vec![1i32, 2i32],
UInt32Array,
DataType::UInt32,
vec![1u32, 2u32],
Operator::Plus,
Int64Array,
DataType::Int64,
[2i64, 4i64],
);
test_coercion!(
Int32Array,
DataType::Int32,
vec![1i32],
UInt16Array,
DataType::UInt16,
vec![1u16],
Operator::Plus,
Int32Array,
DataType::Int32,
[2i32],
);
test_coercion!(
Float32Array,
DataType::Float32,
vec![1f32],
UInt16Array,
DataType::UInt16,
vec![1u16],
Operator::Plus,
Float32Array,
DataType::Float32,
[2f32],
);
test_coercion!(
Float32Array,
DataType::Float32,
vec![2f32],
UInt16Array,
DataType::UInt16,
vec![1u16],
Operator::Multiply,
Float32Array,
DataType::Float32,
[2f32],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["1994-12-13", "1995-01-26"],
Date32Array,
DataType::Date32,
vec![9112, 9156],
Operator::Eq,
BooleanArray,
DataType::Boolean,
[true, true],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["1994-12-13", "1995-01-26"],
Date32Array,
DataType::Date32,
vec![9113, 9154],
Operator::Lt,
BooleanArray,
DataType::Boolean,
[true, false],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["1994-12-13T12:34:56", "1995-01-26T01:23:45"],
Date64Array,
DataType::Date64,
vec![787322096000, 791083425000],
Operator::Eq,
BooleanArray,
DataType::Boolean,
[true, true],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["1994-12-13T12:34:56", "1995-01-26T01:23:45"],
Date64Array,
DataType::Date64,
vec![787322096001, 791083424999],
Operator::Lt,
BooleanArray,
DataType::Boolean,
[true, false],
);
test_coercion!(
StringViewArray,
DataType::Utf8View,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexMatch,
BooleanArray,
DataType::Boolean,
[true, false, true, false, false],
);
test_coercion!(
StringViewArray,
DataType::Utf8View,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexIMatch,
BooleanArray,
DataType::Boolean,
[true, true, true, true, false],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringViewArray,
DataType::Utf8View,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexNotMatch,
BooleanArray,
DataType::Boolean,
[false, true, false, true, true],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringViewArray,
DataType::Utf8View,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexNotIMatch,
BooleanArray,
DataType::Boolean,
[false, false, false, false, true],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexMatch,
BooleanArray,
DataType::Boolean,
[true, false, true, false, false],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexIMatch,
BooleanArray,
DataType::Boolean,
[true, true, true, true, false],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexNotMatch,
BooleanArray,
DataType::Boolean,
[false, true, false, true, true],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexNotIMatch,
BooleanArray,
DataType::Boolean,
[false, false, false, false, true],
);
test_coercion!(
LargeStringArray,
DataType::LargeUtf8,
vec!["abc"; 5],
LargeStringArray,
DataType::LargeUtf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexMatch,
BooleanArray,
DataType::Boolean,
[true, false, true, false, false],
);
test_coercion!(
LargeStringArray,
DataType::LargeUtf8,
vec!["abc"; 5],
LargeStringArray,
DataType::LargeUtf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexIMatch,
BooleanArray,
DataType::Boolean,
[true, true, true, true, false],
);
test_coercion!(
LargeStringArray,
DataType::LargeUtf8,
vec!["abc"; 5],
LargeStringArray,
DataType::LargeUtf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexNotMatch,
BooleanArray,
DataType::Boolean,
[false, true, false, true, true],
);
test_coercion!(
LargeStringArray,
DataType::LargeUtf8,
vec!["abc"; 5],
LargeStringArray,
DataType::LargeUtf8,
vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
Operator::RegexNotIMatch,
BooleanArray,
DataType::Boolean,
[false, false, false, false, true],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["a__", "A%BC", "A_BC", "abc", "a%C"],
Operator::LikeMatch,
BooleanArray,
DataType::Boolean,
[true, false, false, true, false],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["a__", "A%BC", "A_BC", "abc", "a%C"],
Operator::ILikeMatch,
BooleanArray,
DataType::Boolean,
[true, true, false, true, true],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["a__", "A%BC", "A_BC", "abc", "a%C"],
Operator::NotLikeMatch,
BooleanArray,
DataType::Boolean,
[false, true, true, false, true],
);
test_coercion!(
StringArray,
DataType::Utf8,
vec!["abc"; 5],
StringArray,
DataType::Utf8,
vec!["a__", "A%BC", "A_BC", "abc", "a%C"],
Operator::NotILikeMatch,
BooleanArray,
DataType::Boolean,
[false, false, true, false, false],
);
test_coercion!(
LargeStringArray,
DataType::LargeUtf8,
vec!["abc"; 5],
LargeStringArray,
DataType::LargeUtf8,
vec!["a__", "A%BC", "A_BC", "abc", "a%C"],
Operator::LikeMatch,
BooleanArray,
DataType::Boolean,
[true, false, false, true, false],
);
test_coercion!(
LargeStringArray,
DataType::LargeUtf8,
vec!["abc"; 5],
LargeStringArray,
DataType::LargeUtf8,
vec!["a__", "A%BC", "A_BC", "abc", "a%C"],
Operator::ILikeMatch,
BooleanArray,
DataType::Boolean,
[true, true, false, true, true],
);
test_coercion!(
LargeStringArray,
DataType::LargeUtf8,
vec!["abc"; 5],
LargeStringArray,
DataType::LargeUtf8,
vec!["a__", "A%BC", "A_BC", "abc", "a%C"],
Operator::NotLikeMatch,
BooleanArray,
DataType::Boolean,
[false, true, true, false, true],
);
test_coercion!(
LargeStringArray,
DataType::LargeUtf8,
vec!["abc"; 5],
LargeStringArray,
DataType::LargeUtf8,
vec!["a__", "A%BC", "A_BC", "abc", "a%C"],
Operator::NotILikeMatch,
BooleanArray,
DataType::Boolean,
[false, false, true, false, false],
);
test_coercion!(
Int16Array,
DataType::Int16,
vec![1i16, 2i16, 3i16],
Int64Array,
DataType::Int64,
vec![10i64, 4i64, 5i64],
Operator::BitwiseAnd,
Int64Array,
DataType::Int64,
[0i64, 0i64, 1i64],
);
test_coercion!(
UInt16Array,
DataType::UInt16,
vec![1u16, 2u16, 3u16],
UInt64Array,
DataType::UInt64,
vec![10u64, 4u64, 5u64],
Operator::BitwiseAnd,
UInt64Array,
DataType::UInt64,
[0u64, 0u64, 1u64],
);
test_coercion!(
Int16Array,
DataType::Int16,
vec![3i16, 2i16, 3i16],
Int64Array,
DataType::Int64,
vec![10i64, 6i64, 5i64],
Operator::BitwiseOr,
Int64Array,
DataType::Int64,
[11i64, 6i64, 7i64],
);
test_coercion!(
UInt16Array,
DataType::UInt16,
vec![1u16, 2u16, 3u16],
UInt64Array,
DataType::UInt64,
vec![10u64, 4u64, 5u64],
Operator::BitwiseOr,
UInt64Array,
DataType::UInt64,
[11u64, 6u64, 7u64],
);
test_coercion!(
Int16Array,
DataType::Int16,
vec![3i16, 2i16, 3i16],
Int64Array,
DataType::Int64,
vec![10i64, 6i64, 5i64],
Operator::BitwiseXor,
Int64Array,
DataType::Int64,
[9i64, 4i64, 6i64],
);
test_coercion!(
UInt16Array,
DataType::UInt16,
vec![3u16, 2u16, 3u16],
UInt64Array,
DataType::UInt64,
vec![10u64, 6u64, 5u64],
Operator::BitwiseXor,
UInt64Array,
DataType::UInt64,
[9u64, 4u64, 6u64],
);
test_coercion!(
Int16Array,
DataType::Int16,
vec![4i16, 27i16, 35i16],
Int64Array,
DataType::Int64,
vec![2i64, 3i64, 4i64],
Operator::BitwiseShiftRight,
Int64Array,
DataType::Int64,
[1i64, 3i64, 2i64],
);
test_coercion!(
UInt16Array,
DataType::UInt16,
vec![4u16, 27u16, 35u16],
UInt64Array,
DataType::UInt64,
vec![2u64, 3u64, 4u64],
Operator::BitwiseShiftRight,
UInt64Array,
DataType::UInt64,
[1u64, 3u64, 2u64],
);
test_coercion!(
Int16Array,
DataType::Int16,
vec![2i16, 3i16, 4i16],
Int64Array,
DataType::Int64,
vec![4i64, 12i64, 7i64],
Operator::BitwiseShiftLeft,
Int64Array,
DataType::Int64,
[32i64, 12288i64, 512i64],
);
test_coercion!(
UInt16Array,
DataType::UInt16,
vec![2u16, 3u16, 4u16],
UInt64Array,
DataType::UInt64,
vec![4u64, 12u64, 7u64],
Operator::BitwiseShiftLeft,
UInt64Array,
DataType::UInt64,
[32u64, 12288u64, 512u64],
);
Ok(())
}
// Note it would be nice to use the same test_coercion macro as
// above, but sadly the type of the values of the dictionary are
// not encoded in the rust type of the DictionaryArray. Thus there
// is no way at the time of this writing to create a dictionary
// array using the `From` trait
#[test]
fn test_dictionary_type_to_array_coercion() -> Result<()> {
// Test string a string dictionary
let dict_type =
DataType::Dictionary(Box::new(DataType::Int32), Box::new(DataType::Utf8));
let string_type = DataType::Utf8;
// build dictionary
let mut dict_builder = StringDictionaryBuilder::<Int32Type>::new();
dict_builder.append("one")?;
dict_builder.append_null();
dict_builder.append("three")?;
dict_builder.append("four")?;
let dict_array = Arc::new(dict_builder.finish()) as ArrayRef;
let str_array = Arc::new(StringArray::from(vec![
Some("not one"),
Some("two"),
None,
Some("four"),
])) as ArrayRef;
let schema = Arc::new(Schema::new(vec![
Field::new("a", dict_type.clone(), true),
Field::new("b", string_type.clone(), true),
]));
// Test 1: a = b
let result = BooleanArray::from(vec![Some(false), None, None, Some(true)]);
apply_logic_op(&schema, &dict_array, &str_array, Operator::Eq, result)?;
// Test 2: now test the other direction
// b = a
let schema = Arc::new(Schema::new(vec![
Field::new("a", string_type, true),
Field::new("b", dict_type, true),
]));
let result = BooleanArray::from(vec![Some(false), None, None, Some(true)]);
apply_logic_op(&schema, &str_array, &dict_array, Operator::Eq, result)?;
Ok(())
}
#[test]
fn plus_op() -> Result<()> {
let schema = Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let b = Int32Array::from(vec![1, 2, 4, 8, 16]);
apply_arithmetic::<Int32Type>(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Plus,
Int32Array::from(vec![2, 4, 7, 12, 21]),
)?;
Ok(())
}
#[test]
fn plus_op_dict() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
Field::new(
"b",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let keys = Int8Array::from(vec![Some(0), None, Some(1), Some(3), None]);
let a = DictionaryArray::try_new(keys, Arc::new(a))?;
let b = Int32Array::from(vec![1, 2, 4, 8, 16]);
let keys = Int8Array::from(vec![0, 1, 1, 2, 1]);
let b = DictionaryArray::try_new(keys, Arc::new(b))?;
apply_arithmetic::<Int32Type>(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Plus,
Int32Array::from(vec![Some(2), None, Some(4), Some(8), None]),
)?;
Ok(())
}
#[test]
fn plus_op_dict_decimal() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
Field::new(
"b",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value),
Some(value + 2),
Some(value - 1),
Some(value + 1),
],
10,
0,
));
let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]);
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value + 1),
Some(value + 3),
Some(value),
Some(value + 2),
],
10,
0,
));
let b = DictionaryArray::try_new(keys, decimal_array)?;
apply_arithmetic(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Plus,
create_decimal_array(&[Some(247), None, None, Some(247), Some(246)], 11, 0),
)?;
Ok(())
}
#[test]
fn plus_op_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Plus,
ScalarValue::Int32(Some(1)),
Arc::new(Int32Array::from(vec![2, 3, 4, 5, 6])),
)?;
Ok(())
}
#[test]
fn plus_op_dict_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
)]);
let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new();
dict_builder.append(1)?;
dict_builder.append_null();
dict_builder.append(2)?;
dict_builder.append(5)?;
let a = dict_builder.finish();
let expected: PrimitiveArray<Int32Type> =
PrimitiveArray::from(vec![Some(2), None, Some(3), Some(6)]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Plus,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Int32(Some(1))),
),
Arc::new(expected),
)?;
Ok(())
}
#[test]
fn plus_op_dict_scalar_decimal() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
)]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[Some(value), None, Some(value - 1), Some(value + 1)],
10,
0,
));
let keys = Int8Array::from(vec![0, 2, 1, 3, 0]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value + 1),
Some(value),
None,
Some(value + 2),
Some(value + 1),
],
11,
0,
));
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Plus,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Decimal128(Some(1), 10, 0)),
),
decimal_array,
)?;
Ok(())
}
#[test]
fn minus_op() -> Result<()> {
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]));
let a = Arc::new(Int32Array::from(vec![1, 2, 4, 8, 16]));
let b = Arc::new(Int32Array::from(vec![1, 2, 3, 4, 5]));
apply_arithmetic::<Int32Type>(
Arc::clone(&schema),
vec![
Arc::clone(&a) as Arc<dyn Array>,
Arc::clone(&b) as Arc<dyn Array>,
],
Operator::Minus,
Int32Array::from(vec![0, 0, 1, 4, 11]),
)?;
// should handle have negative values in result (for signed)
apply_arithmetic::<Int32Type>(
schema,
vec![b, a],
Operator::Minus,
Int32Array::from(vec![0, 0, -1, -4, -11]),
)?;
Ok(())
}
#[test]
fn minus_op_dict() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
Field::new(
"b",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let keys = Int8Array::from(vec![Some(0), None, Some(1), Some(3), None]);
let a = DictionaryArray::try_new(keys, Arc::new(a))?;
let b = Int32Array::from(vec![1, 2, 4, 8, 16]);
let keys = Int8Array::from(vec![0, 1, 1, 2, 1]);
let b = DictionaryArray::try_new(keys, Arc::new(b))?;
apply_arithmetic::<Int32Type>(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Minus,
Int32Array::from(vec![Some(0), None, Some(0), Some(0), None]),
)?;
Ok(())
}
#[test]
fn minus_op_dict_decimal() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
Field::new(
"b",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value),
Some(value + 2),
Some(value - 1),
Some(value + 1),
],
10,
0,
));
let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]);
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value + 1),
Some(value + 3),
Some(value),
Some(value + 2),
],
10,
0,
));
let b = DictionaryArray::try_new(keys, decimal_array)?;
apply_arithmetic(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Minus,
create_decimal_array(&[Some(-1), None, None, Some(1), Some(0)], 11, 0),
)?;
Ok(())
}
#[test]
fn minus_op_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Minus,
ScalarValue::Int32(Some(1)),
Arc::new(Int32Array::from(vec![0, 1, 2, 3, 4])),
)?;
Ok(())
}
#[test]
fn minus_op_dict_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
)]);
let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new();
dict_builder.append(1)?;
dict_builder.append_null();
dict_builder.append(2)?;
dict_builder.append(5)?;
let a = dict_builder.finish();
let expected: PrimitiveArray<Int32Type> =
PrimitiveArray::from(vec![Some(0), None, Some(1), Some(4)]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Minus,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Int32(Some(1))),
),
Arc::new(expected),
)?;
Ok(())
}
#[test]
fn minus_op_dict_scalar_decimal() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
)]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[Some(value), None, Some(value - 1), Some(value + 1)],
10,
0,
));
let keys = Int8Array::from(vec![0, 2, 1, 3, 0]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value - 1),
Some(value - 2),
None,
Some(value),
Some(value - 1),
],
11,
0,
));
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Minus,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Decimal128(Some(1), 10, 0)),
),
decimal_array,
)?;
Ok(())
}
#[test]
fn multiply_op() -> Result<()> {
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]));
let a = Arc::new(Int32Array::from(vec![4, 8, 16, 32, 64]));
let b = Arc::new(Int32Array::from(vec![2, 4, 8, 16, 32]));
apply_arithmetic::<Int32Type>(
schema,
vec![a, b],
Operator::Multiply,
Int32Array::from(vec![8, 32, 128, 512, 2048]),
)?;
Ok(())
}
#[test]
fn multiply_op_dict() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
Field::new(
"b",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let keys = Int8Array::from(vec![Some(0), None, Some(1), Some(3), None]);
let a = DictionaryArray::try_new(keys, Arc::new(a))?;
let b = Int32Array::from(vec![1, 2, 4, 8, 16]);
let keys = Int8Array::from(vec![0, 1, 1, 2, 1]);
let b = DictionaryArray::try_new(keys, Arc::new(b))?;
apply_arithmetic::<Int32Type>(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Multiply,
Int32Array::from(vec![Some(1), None, Some(4), Some(16), None]),
)?;
Ok(())
}
#[test]
fn multiply_op_dict_decimal() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
Field::new(
"b",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value),
Some(value + 2),
Some(value - 1),
Some(value + 1),
],
10,
0,
)) as ArrayRef;
let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]);
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value + 1),
Some(value + 3),
Some(value),
Some(value + 2),
],
10,
0,
));
let b = DictionaryArray::try_new(keys, decimal_array)?;
apply_arithmetic(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Multiply,
create_decimal_array(
&[Some(15252), None, None, Some(15252), Some(15129)],
21,
0,
),
)?;
Ok(())
}
#[test]
fn multiply_op_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Multiply,
ScalarValue::Int32(Some(2)),
Arc::new(Int32Array::from(vec![2, 4, 6, 8, 10])),
)?;
Ok(())
}
#[test]
fn multiply_op_dict_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
)]);
let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new();
dict_builder.append(1)?;
dict_builder.append_null();
dict_builder.append(2)?;
dict_builder.append(5)?;
let a = dict_builder.finish();
let expected: PrimitiveArray<Int32Type> =
PrimitiveArray::from(vec![Some(2), None, Some(4), Some(10)]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Multiply,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Int32(Some(2))),
),
Arc::new(expected),
)?;
Ok(())
}
#[test]
fn multiply_op_dict_scalar_decimal() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
)]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[Some(value), None, Some(value - 1), Some(value + 1)],
10,
0,
));
let keys = Int8Array::from(vec![0, 2, 1, 3, 0]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let decimal_array = Arc::new(create_decimal_array(
&[Some(246), Some(244), None, Some(248), Some(246)],
21,
0,
));
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Multiply,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Decimal128(Some(2), 10, 0)),
),
decimal_array,
)?;
Ok(())
}
#[test]
fn divide_op() -> Result<()> {
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]));
let a = Arc::new(Int32Array::from(vec![8, 32, 128, 512, 2048]));
let b = Arc::new(Int32Array::from(vec![2, 4, 8, 16, 32]));
apply_arithmetic::<Int32Type>(
schema,
vec![a, b],
Operator::Divide,
Int32Array::from(vec![4, 8, 16, 32, 64]),
)?;
Ok(())
}
#[test]
fn divide_op_dict() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
Field::new(
"b",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
]);
let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new();
dict_builder.append(1)?;
dict_builder.append_null();
dict_builder.append(2)?;
dict_builder.append(5)?;
dict_builder.append(0)?;
let a = dict_builder.finish();
let b = Int32Array::from(vec![1, 2, 4, 8, 16]);
let keys = Int8Array::from(vec![0, 1, 1, 2, 1]);
let b = DictionaryArray::try_new(keys, Arc::new(b))?;
apply_arithmetic::<Int32Type>(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Divide,
Int32Array::from(vec![Some(1), None, Some(1), Some(1), Some(0)]),
)?;
Ok(())
}
#[test]
fn divide_op_dict_decimal() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
Field::new(
"b",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value),
Some(value + 2),
Some(value - 1),
Some(value + 1),
],
10,
0,
));
let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]);
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value + 1),
Some(value + 3),
Some(value),
Some(value + 2),
],
10,
0,
));
let b = DictionaryArray::try_new(keys, decimal_array)?;
apply_arithmetic(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Divide,
create_decimal_array(
&[
Some(9919), // 0.9919
None,
None,
Some(10081), // 1.0081
Some(10000), // 1.0
],
14,
4,
),
)?;
Ok(())
}
#[test]
fn divide_op_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Divide,
ScalarValue::Int32(Some(2)),
Arc::new(Int32Array::from(vec![0, 1, 1, 2, 2])),
)?;
Ok(())
}
#[test]
fn divide_op_dict_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
)]);
let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new();
dict_builder.append(1)?;
dict_builder.append_null();
dict_builder.append(2)?;
dict_builder.append(5)?;
let a = dict_builder.finish();
let expected: PrimitiveArray<Int32Type> =
PrimitiveArray::from(vec![Some(0), None, Some(1), Some(2)]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Divide,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Int32(Some(2))),
),
Arc::new(expected),
)?;
Ok(())
}
#[test]
fn divide_op_dict_scalar_decimal() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
)]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[Some(value), None, Some(value - 1), Some(value + 1)],
10,
0,
));
let keys = Int8Array::from(vec![0, 2, 1, 3, 0]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let decimal_array = Arc::new(create_decimal_array(
&[Some(615000), Some(610000), None, Some(620000), Some(615000)],
14,
4,
));
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Divide,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Decimal128(Some(2), 10, 0)),
),
decimal_array,
)?;
Ok(())
}
#[test]
fn modulus_op() -> Result<()> {
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]));
let a = Arc::new(Int32Array::from(vec![8, 32, 128, 512, 2048]));
let b = Arc::new(Int32Array::from(vec![2, 4, 7, 14, 32]));
apply_arithmetic::<Int32Type>(
schema,
vec![a, b],
Operator::Modulo,
Int32Array::from(vec![0, 0, 2, 8, 0]),
)?;
Ok(())
}
#[test]
fn modulus_op_dict() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
Field::new(
"b",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
),
]);
let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new();
dict_builder.append(1)?;
dict_builder.append_null();
dict_builder.append(2)?;
dict_builder.append(5)?;
dict_builder.append(0)?;
let a = dict_builder.finish();
let b = Int32Array::from(vec![1, 2, 4, 8, 16]);
let keys = Int8Array::from(vec![0, 1, 1, 2, 1]);
let b = DictionaryArray::try_new(keys, Arc::new(b))?;
apply_arithmetic::<Int32Type>(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Modulo,
Int32Array::from(vec![Some(0), None, Some(0), Some(1), Some(0)]),
)?;
Ok(())
}
#[test]
fn modulus_op_dict_decimal() -> Result<()> {
let schema = Schema::new(vec![
Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
Field::new(
"b",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
),
]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value),
Some(value + 2),
Some(value - 1),
Some(value + 1),
],
10,
0,
));
let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]);
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value + 1),
Some(value + 3),
Some(value),
Some(value + 2),
],
10,
0,
));
let b = DictionaryArray::try_new(keys, decimal_array)?;
apply_arithmetic(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b)],
Operator::Modulo,
create_decimal_array(&[Some(123), None, None, Some(1), Some(0)], 10, 0),
)?;
Ok(())
}
#[test]
fn modulus_op_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Modulo,
ScalarValue::Int32(Some(2)),
Arc::new(Int32Array::from(vec![1, 0, 1, 0, 1])),
)?;
Ok(())
}
#[test]
fn modules_op_dict_scalar() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)),
true,
)]);
let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new();
dict_builder.append(1)?;
dict_builder.append_null();
dict_builder.append(2)?;
dict_builder.append(5)?;
let a = dict_builder.finish();
let expected: PrimitiveArray<Int32Type> =
PrimitiveArray::from(vec![Some(1), None, Some(0), Some(1)]);
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Modulo,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Int32(Some(2))),
),
Arc::new(expected),
)?;
Ok(())
}
#[test]
fn modulus_op_dict_scalar_decimal() -> Result<()> {
let schema = Schema::new(vec![Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(10, 0)),
),
true,
)]);
let value = 123;
let decimal_array = Arc::new(create_decimal_array(
&[Some(value), None, Some(value - 1), Some(value + 1)],
10,
0,
));
let keys = Int8Array::from(vec![0, 2, 1, 3, 0]);
let a = DictionaryArray::try_new(keys, decimal_array)?;
let decimal_array = Arc::new(create_decimal_array(
&[Some(1), Some(0), None, Some(0), Some(1)],
10,
0,
));
apply_arithmetic_scalar(
Arc::new(schema),
vec![Arc::new(a)],
Operator::Modulo,
ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Decimal128(Some(2), 10, 0)),
),
decimal_array,
)?;
Ok(())
}
fn apply_arithmetic<T: ArrowNumericType>(
schema: SchemaRef,
data: Vec<ArrayRef>,
op: Operator,
expected: PrimitiveArray<T>,
) -> Result<()> {
let arithmetic_op =
binary_op(col("a", &schema)?, op, col("b", &schema)?, &schema)?;
let batch = RecordBatch::try_new(schema, data)?;
let result = arithmetic_op
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(result.as_ref(), &expected);
Ok(())
}
fn apply_arithmetic_scalar(
schema: SchemaRef,
data: Vec<ArrayRef>,
op: Operator,
literal: ScalarValue,
expected: ArrayRef,
) -> Result<()> {
let lit = Arc::new(Literal::new(literal));
let arithmetic_op = binary_op(col("a", &schema)?, op, lit, &schema)?;
let batch = RecordBatch::try_new(schema, data)?;
let result = arithmetic_op
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(&result, &expected);
Ok(())
}
fn apply_logic_op(
schema: &SchemaRef,
left: &ArrayRef,
right: &ArrayRef,
op: Operator,
expected: BooleanArray,
) -> Result<()> {
let op = binary_op(col("a", schema)?, op, col("b", schema)?, schema)?;
let data: Vec<ArrayRef> = vec![Arc::clone(left), Arc::clone(right)];
let batch = RecordBatch::try_new(Arc::clone(schema), data)?;
let result = op
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(result.as_ref(), &expected);
Ok(())
}
// Test `scalar <op> arr` produces expected
fn apply_logic_op_scalar_arr(
schema: &SchemaRef,
scalar: &ScalarValue,
arr: &ArrayRef,
op: Operator,
expected: &BooleanArray,
) -> Result<()> {
let scalar = lit(scalar.clone());
let op = binary_op(scalar, op, col("a", schema)?, schema)?;
let batch = RecordBatch::try_new(Arc::clone(schema), vec![Arc::clone(arr)])?;
let result = op
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(result.as_ref(), expected);
Ok(())
}
// Test `arr <op> scalar` produces expected
fn apply_logic_op_arr_scalar(
schema: &SchemaRef,
arr: &ArrayRef,
scalar: &ScalarValue,
op: Operator,
expected: &BooleanArray,
) -> Result<()> {
let scalar = lit(scalar.clone());
let op = binary_op(col("a", schema)?, op, scalar, schema)?;
let batch = RecordBatch::try_new(Arc::clone(schema), vec![Arc::clone(arr)])?;
let result = op
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(result.as_ref(), expected);
Ok(())
}
#[test]
fn and_with_nulls_op() -> Result<()> {
let schema = Schema::new(vec![
Field::new("a", DataType::Boolean, true),
Field::new("b", DataType::Boolean, true),
]);
let a = Arc::new(BooleanArray::from(vec![
Some(true),
Some(false),
None,
Some(true),
Some(false),
None,
Some(true),
Some(false),
None,
])) as ArrayRef;
let b = Arc::new(BooleanArray::from(vec![
Some(true),
Some(true),
Some(true),
Some(false),
Some(false),
Some(false),
None,
None,
None,
])) as ArrayRef;
let expected = BooleanArray::from(vec![
Some(true),
Some(false),
None,
Some(false),
Some(false),
Some(false),
None,
Some(false),
None,
]);
apply_logic_op(&Arc::new(schema), &a, &b, Operator::And, expected)?;
Ok(())
}
#[test]
fn regex_with_nulls() -> Result<()> {
let schema = Schema::new(vec![
Field::new("a", DataType::Utf8, true),
Field::new("b", DataType::Utf8, true),
]);
let a = Arc::new(StringArray::from(vec![
Some("abc"),
None,
Some("abc"),
None,
Some("abc"),
])) as ArrayRef;
let b = Arc::new(StringArray::from(vec![
Some("^a"),
Some("^A"),
None,
None,
Some("^(b|c)"),
])) as ArrayRef;
let regex_expected =
BooleanArray::from(vec![Some(true), None, None, None, Some(false)]);
let regex_not_expected =
BooleanArray::from(vec![Some(false), None, None, None, Some(true)]);
apply_logic_op(
&Arc::new(schema.clone()),
&a,
&b,
Operator::RegexMatch,
regex_expected.clone(),
)?;
apply_logic_op(
&Arc::new(schema.clone()),
&a,
&b,
Operator::RegexIMatch,
regex_expected.clone(),
)?;
apply_logic_op(
&Arc::new(schema.clone()),
&a,
&b,
Operator::RegexNotMatch,
regex_not_expected.clone(),
)?;
apply_logic_op(
&Arc::new(schema),
&a,
&b,
Operator::RegexNotIMatch,
regex_not_expected.clone(),
)?;
let schema = Schema::new(vec![
Field::new("a", DataType::LargeUtf8, true),
Field::new("b", DataType::LargeUtf8, true),
]);
let a = Arc::new(LargeStringArray::from(vec![
Some("abc"),
None,
Some("abc"),
None,
Some("abc"),
])) as ArrayRef;
let b = Arc::new(LargeStringArray::from(vec![
Some("^a"),
Some("^A"),
None,
None,
Some("^(b|c)"),
])) as ArrayRef;
apply_logic_op(
&Arc::new(schema.clone()),
&a,
&b,
Operator::RegexMatch,
regex_expected.clone(),
)?;
apply_logic_op(
&Arc::new(schema.clone()),
&a,
&b,
Operator::RegexIMatch,
regex_expected,
)?;
apply_logic_op(
&Arc::new(schema.clone()),
&a,
&b,
Operator::RegexNotMatch,
regex_not_expected.clone(),
)?;
apply_logic_op(
&Arc::new(schema),
&a,
&b,
Operator::RegexNotIMatch,
regex_not_expected,
)?;
Ok(())
}
#[test]
fn or_with_nulls_op() -> Result<()> {
let schema = Schema::new(vec![
Field::new("a", DataType::Boolean, true),
Field::new("b", DataType::Boolean, true),
]);
let a = Arc::new(BooleanArray::from(vec![
Some(true),
Some(false),
None,
Some(true),
Some(false),
None,
Some(true),
Some(false),
None,
])) as ArrayRef;
let b = Arc::new(BooleanArray::from(vec![
Some(true),
Some(true),
Some(true),
Some(false),
Some(false),
Some(false),
None,
None,
None,
])) as ArrayRef;
let expected = BooleanArray::from(vec![
Some(true),
Some(true),
Some(true),
Some(true),
Some(false),
None,
Some(true),
None,
None,
]);
apply_logic_op(&Arc::new(schema), &a, &b, Operator::Or, expected)?;
Ok(())
}
/// Returns (schema, a: BooleanArray, b: BooleanArray) with all possible inputs
///
/// a: [true, true, true, NULL, NULL, NULL, false, false, false]
/// b: [true, NULL, false, true, NULL, false, true, NULL, false]
fn bool_test_arrays() -> (SchemaRef, ArrayRef, ArrayRef) {
let schema = Schema::new(vec![
Field::new("a", DataType::Boolean, true),
Field::new("b", DataType::Boolean, true),
]);
let a: BooleanArray = [
Some(true),
Some(true),
Some(true),
None,
None,
None,
Some(false),
Some(false),
Some(false),
]
.iter()
.collect();
let b: BooleanArray = [
Some(true),
None,
Some(false),
Some(true),
None,
Some(false),
Some(true),
None,
Some(false),
]
.iter()
.collect();
(Arc::new(schema), Arc::new(a), Arc::new(b))
}
/// Returns (schema, BooleanArray) with [true, NULL, false]
fn scalar_bool_test_array() -> (SchemaRef, ArrayRef) {
let schema = Schema::new(vec![Field::new("a", DataType::Boolean, true)]);
let a: BooleanArray = [Some(true), None, Some(false)].iter().collect();
(Arc::new(schema), Arc::new(a))
}
#[test]
fn eq_op_bool() {
let (schema, a, b) = bool_test_arrays();
let expected = [
Some(true),
None,
Some(false),
None,
None,
None,
Some(false),
None,
Some(true),
]
.iter()
.collect();
apply_logic_op(&schema, &a, &b, Operator::Eq, expected).unwrap();
}
#[test]
fn eq_op_bool_scalar() {
let (schema, a) = scalar_bool_test_array();
let expected = [Some(true), None, Some(false)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(true),
&a,
Operator::Eq,
&expected,
)
.unwrap();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(true),
Operator::Eq,
&expected,
)
.unwrap();
let expected = [Some(false), None, Some(true)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(false),
&a,
Operator::Eq,
&expected,
)
.unwrap();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(false),
Operator::Eq,
&expected,
)
.unwrap();
}
#[test]
fn neq_op_bool() {
let (schema, a, b) = bool_test_arrays();
let expected = [
Some(false),
None,
Some(true),
None,
None,
None,
Some(true),
None,
Some(false),
]
.iter()
.collect();
apply_logic_op(&schema, &a, &b, Operator::NotEq, expected).unwrap();
}
#[test]
fn neq_op_bool_scalar() {
let (schema, a) = scalar_bool_test_array();
let expected = [Some(false), None, Some(true)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(true),
&a,
Operator::NotEq,
&expected,
)
.unwrap();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(true),
Operator::NotEq,
&expected,
)
.unwrap();
let expected = [Some(true), None, Some(false)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(false),
&a,
Operator::NotEq,
&expected,
)
.unwrap();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(false),
Operator::NotEq,
&expected,
)
.unwrap();
}
#[test]
fn lt_op_bool() {
let (schema, a, b) = bool_test_arrays();
let expected = [
Some(false),
None,
Some(false),
None,
None,
None,
Some(true),
None,
Some(false),
]
.iter()
.collect();
apply_logic_op(&schema, &a, &b, Operator::Lt, expected).unwrap();
}
#[test]
fn lt_op_bool_scalar() {
let (schema, a) = scalar_bool_test_array();
let expected = [Some(false), None, Some(false)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(true),
&a,
Operator::Lt,
&expected,
)
.unwrap();
let expected = [Some(false), None, Some(true)].iter().collect();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(true),
Operator::Lt,
&expected,
)
.unwrap();
let expected = [Some(true), None, Some(false)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(false),
&a,
Operator::Lt,
&expected,
)
.unwrap();
let expected = [Some(false), None, Some(false)].iter().collect();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(false),
Operator::Lt,
&expected,
)
.unwrap();
}
#[test]
fn lt_eq_op_bool() {
let (schema, a, b) = bool_test_arrays();
let expected = [
Some(true),
None,
Some(false),
None,
None,
None,
Some(true),
None,
Some(true),
]
.iter()
.collect();
apply_logic_op(&schema, &a, &b, Operator::LtEq, expected).unwrap();
}
#[test]
fn lt_eq_op_bool_scalar() {
let (schema, a) = scalar_bool_test_array();
let expected = [Some(true), None, Some(false)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(true),
&a,
Operator::LtEq,
&expected,
)
.unwrap();
let expected = [Some(true), None, Some(true)].iter().collect();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(true),
Operator::LtEq,
&expected,
)
.unwrap();
let expected = [Some(true), None, Some(true)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(false),
&a,
Operator::LtEq,
&expected,
)
.unwrap();
let expected = [Some(false), None, Some(true)].iter().collect();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(false),
Operator::LtEq,
&expected,
)
.unwrap();
}
#[test]
fn gt_op_bool() {
let (schema, a, b) = bool_test_arrays();
let expected = [
Some(false),
None,
Some(true),
None,
None,
None,
Some(false),
None,
Some(false),
]
.iter()
.collect();
apply_logic_op(&schema, &a, &b, Operator::Gt, expected).unwrap();
}
#[test]
fn gt_op_bool_scalar() {
let (schema, a) = scalar_bool_test_array();
let expected = [Some(false), None, Some(true)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(true),
&a,
Operator::Gt,
&expected,
)
.unwrap();
let expected = [Some(false), None, Some(false)].iter().collect();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(true),
Operator::Gt,
&expected,
)
.unwrap();
let expected = [Some(false), None, Some(false)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(false),
&a,
Operator::Gt,
&expected,
)
.unwrap();
let expected = [Some(true), None, Some(false)].iter().collect();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(false),
Operator::Gt,
&expected,
)
.unwrap();
}
#[test]
fn gt_eq_op_bool() {
let (schema, a, b) = bool_test_arrays();
let expected = [
Some(true),
None,
Some(true),
None,
None,
None,
Some(false),
None,
Some(true),
]
.iter()
.collect();
apply_logic_op(&schema, &a, &b, Operator::GtEq, expected).unwrap();
}
#[test]
fn gt_eq_op_bool_scalar() {
let (schema, a) = scalar_bool_test_array();
let expected = [Some(true), None, Some(true)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(true),
&a,
Operator::GtEq,
&expected,
)
.unwrap();
let expected = [Some(true), None, Some(false)].iter().collect();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(true),
Operator::GtEq,
&expected,
)
.unwrap();
let expected = [Some(false), None, Some(true)].iter().collect();
apply_logic_op_scalar_arr(
&schema,
&ScalarValue::from(false),
&a,
Operator::GtEq,
&expected,
)
.unwrap();
let expected = [Some(true), None, Some(true)].iter().collect();
apply_logic_op_arr_scalar(
&schema,
&a,
&ScalarValue::from(false),
Operator::GtEq,
&expected,
)
.unwrap();
}
#[test]
fn is_distinct_from_op_bool() {
let (schema, a, b) = bool_test_arrays();
let expected = [
Some(false),
Some(true),
Some(true),
Some(true),
Some(false),
Some(true),
Some(true),
Some(true),
Some(false),
]
.iter()
.collect();
apply_logic_op(&schema, &a, &b, Operator::IsDistinctFrom, expected).unwrap();
}
#[test]
fn is_not_distinct_from_op_bool() {
let (schema, a, b) = bool_test_arrays();
let expected = [
Some(true),
Some(false),
Some(false),
Some(false),
Some(true),
Some(false),
Some(false),
Some(false),
Some(true),
]
.iter()
.collect();
apply_logic_op(&schema, &a, &b, Operator::IsNotDistinctFrom, expected).unwrap();
}
#[test]
fn relatively_deeply_nested() {
// Reproducer for https://github.com/apache/datafusion/issues/419
// where even relatively shallow binary expressions overflowed
// the stack in debug builds
let input: Vec<_> = vec![1, 2, 3, 4, 5].into_iter().map(Some).collect();
let a: Int32Array = input.iter().collect();
let batch = RecordBatch::try_from_iter(vec![("a", Arc::new(a) as _)]).unwrap();
let schema = batch.schema();
// build a left deep tree ((((a + a) + a) + a ....
let tree_depth: i32 = 100;
let expr = (0..tree_depth)
.map(|_| col("a", schema.as_ref()).unwrap())
.reduce(|l, r| binary(l, Operator::Plus, r, &schema).unwrap())
.unwrap();
let result = expr
.evaluate(&batch)
.expect("evaluation")
.into_array(batch.num_rows())
.expect("Failed to convert to array");
let expected: Int32Array = input
.into_iter()
.map(|i| i.map(|i| i * tree_depth))
.collect();
assert_eq!(result.as_ref(), &expected);
}
fn create_decimal_array(
array: &[Option<i128>],
precision: u8,
scale: i8,
) -> Decimal128Array {
let mut decimal_builder = Decimal128Builder::with_capacity(array.len());
for value in array.iter().copied() {
decimal_builder.append_option(value)
}
decimal_builder
.finish()
.with_precision_and_scale(precision, scale)
.unwrap()
}
#[test]
fn comparison_dict_decimal_scalar_expr_test() -> Result<()> {
// scalar of decimal compare with dictionary decimal array
let value_i128 = 123;
let decimal_scalar = ScalarValue::Dictionary(
Box::new(DataType::Int8),
Box::new(ScalarValue::Decimal128(Some(value_i128), 25, 3)),
);
let schema = Arc::new(Schema::new(vec![Field::new(
"a",
DataType::Dictionary(
Box::new(DataType::Int8),
Box::new(DataType::Decimal128(25, 3)),
),
true,
)]));
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value_i128),
None,
Some(value_i128 - 1),
Some(value_i128 + 1),
],
25,
3,
));
let keys = Int8Array::from(vec![Some(0), None, Some(2), Some(3)]);
let dictionary =
Arc::new(DictionaryArray::try_new(keys, decimal_array)?) as ArrayRef;
// array = scalar
apply_logic_op_arr_scalar(
&schema,
&dictionary,
&decimal_scalar,
Operator::Eq,
&BooleanArray::from(vec![Some(true), None, Some(false), Some(false)]),
)
.unwrap();
// array != scalar
apply_logic_op_arr_scalar(
&schema,
&dictionary,
&decimal_scalar,
Operator::NotEq,
&BooleanArray::from(vec![Some(false), None, Some(true), Some(true)]),
)
.unwrap();
// array < scalar
apply_logic_op_arr_scalar(
&schema,
&dictionary,
&decimal_scalar,
Operator::Lt,
&BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
)
.unwrap();
// array <= scalar
apply_logic_op_arr_scalar(
&schema,
&dictionary,
&decimal_scalar,
Operator::LtEq,
&BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]),
)
.unwrap();
// array > scalar
apply_logic_op_arr_scalar(
&schema,
&dictionary,
&decimal_scalar,
Operator::Gt,
&BooleanArray::from(vec![Some(false), None, Some(false), Some(true)]),
)
.unwrap();
// array >= scalar
apply_logic_op_arr_scalar(
&schema,
&dictionary,
&decimal_scalar,
Operator::GtEq,
&BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
)
.unwrap();
Ok(())
}
#[test]
fn comparison_decimal_expr_test() -> Result<()> {
// scalar of decimal compare with decimal array
let value_i128 = 123;
let decimal_scalar = ScalarValue::Decimal128(Some(value_i128), 25, 3);
let schema = Arc::new(Schema::new(vec![Field::new(
"a",
DataType::Decimal128(25, 3),
true,
)]));
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value_i128),
None,
Some(value_i128 - 1),
Some(value_i128 + 1),
],
25,
3,
)) as ArrayRef;
// array = scalar
apply_logic_op_arr_scalar(
&schema,
&decimal_array,
&decimal_scalar,
Operator::Eq,
&BooleanArray::from(vec![Some(true), None, Some(false), Some(false)]),
)
.unwrap();
// array != scalar
apply_logic_op_arr_scalar(
&schema,
&decimal_array,
&decimal_scalar,
Operator::NotEq,
&BooleanArray::from(vec![Some(false), None, Some(true), Some(true)]),
)
.unwrap();
// array < scalar
apply_logic_op_arr_scalar(
&schema,
&decimal_array,
&decimal_scalar,
Operator::Lt,
&BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
)
.unwrap();
// array <= scalar
apply_logic_op_arr_scalar(
&schema,
&decimal_array,
&decimal_scalar,
Operator::LtEq,
&BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]),
)
.unwrap();
// array > scalar
apply_logic_op_arr_scalar(
&schema,
&decimal_array,
&decimal_scalar,
Operator::Gt,
&BooleanArray::from(vec![Some(false), None, Some(false), Some(true)]),
)
.unwrap();
// array >= scalar
apply_logic_op_arr_scalar(
&schema,
&decimal_array,
&decimal_scalar,
Operator::GtEq,
&BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
)
.unwrap();
// scalar of different data type with decimal array
let decimal_scalar = ScalarValue::Decimal128(Some(123_456), 10, 3);
let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int64, true)]));
// scalar == array
apply_logic_op_scalar_arr(
&schema,
&decimal_scalar,
&(Arc::new(Int64Array::from(vec![Some(124), None])) as ArrayRef),
Operator::Eq,
&BooleanArray::from(vec![Some(false), None]),
)
.unwrap();
// array != scalar
apply_logic_op_arr_scalar(
&schema,
&(Arc::new(Int64Array::from(vec![Some(123), None, Some(1)])) as ArrayRef),
&decimal_scalar,
Operator::NotEq,
&BooleanArray::from(vec![Some(true), None, Some(true)]),
)
.unwrap();
// array < scalar
apply_logic_op_arr_scalar(
&schema,
&(Arc::new(Int64Array::from(vec![Some(123), None, Some(124)])) as ArrayRef),
&decimal_scalar,
Operator::Lt,
&BooleanArray::from(vec![Some(true), None, Some(false)]),
)
.unwrap();
// array > scalar
apply_logic_op_arr_scalar(
&schema,
&(Arc::new(Int64Array::from(vec![Some(123), None, Some(124)])) as ArrayRef),
&decimal_scalar,
Operator::Gt,
&BooleanArray::from(vec![Some(false), None, Some(true)]),
)
.unwrap();
let schema =
Arc::new(Schema::new(vec![Field::new("a", DataType::Float64, true)]));
// array == scalar
apply_logic_op_arr_scalar(
&schema,
&(Arc::new(Float64Array::from(vec![Some(123.456), None, Some(123.457)]))
as ArrayRef),
&decimal_scalar,
Operator::Eq,
&BooleanArray::from(vec![Some(true), None, Some(false)]),
)
.unwrap();
// array <= scalar
apply_logic_op_arr_scalar(
&schema,
&(Arc::new(Float64Array::from(vec![
Some(123.456),
None,
Some(123.457),
Some(123.45),
])) as ArrayRef),
&decimal_scalar,
Operator::LtEq,
&BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
)
.unwrap();
// array >= scalar
apply_logic_op_arr_scalar(
&schema,
&(Arc::new(Float64Array::from(vec![
Some(123.456),
None,
Some(123.457),
Some(123.45),
])) as ArrayRef),
&decimal_scalar,
Operator::GtEq,
&BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]),
)
.unwrap();
let value: i128 = 123;
let decimal_array = Arc::new(create_decimal_array(
&[Some(value), None, Some(value - 1), Some(value + 1)],
10,
0,
)) as ArrayRef;
// comparison array op for decimal array
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Decimal128(10, 0), true),
Field::new("b", DataType::Decimal128(10, 0), true),
]));
let right_decimal_array = Arc::new(create_decimal_array(
&[
Some(value - 1),
Some(value),
Some(value + 1),
Some(value + 1),
],
10,
0,
)) as ArrayRef;
apply_logic_op(
&schema,
&decimal_array,
&right_decimal_array,
Operator::Eq,
BooleanArray::from(vec![Some(false), None, Some(false), Some(true)]),
)
.unwrap();
apply_logic_op(
&schema,
&decimal_array,
&right_decimal_array,
Operator::NotEq,
BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]),
)
.unwrap();
apply_logic_op(
&schema,
&decimal_array,
&right_decimal_array,
Operator::Lt,
BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
)
.unwrap();
apply_logic_op(
&schema,
&decimal_array,
&right_decimal_array,
Operator::LtEq,
BooleanArray::from(vec![Some(false), None, Some(true), Some(true)]),
)
.unwrap();
apply_logic_op(
&schema,
&decimal_array,
&right_decimal_array,
Operator::Gt,
BooleanArray::from(vec![Some(true), None, Some(false), Some(false)]),
)
.unwrap();
apply_logic_op(
&schema,
&decimal_array,
&right_decimal_array,
Operator::GtEq,
BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
)
.unwrap();
// compare decimal array with other array type
let value: i64 = 123;
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int64, true),
Field::new("b", DataType::Decimal128(10, 0), true),
]));
let int64_array = Arc::new(Int64Array::from(vec![
Some(value),
Some(value - 1),
Some(value),
Some(value + 1),
])) as ArrayRef;
// eq: int64array == decimal array
apply_logic_op(
&schema,
&int64_array,
&decimal_array,
Operator::Eq,
BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
)
.unwrap();
// neq: int64array != decimal array
apply_logic_op(
&schema,
&int64_array,
&decimal_array,
Operator::NotEq,
BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
)
.unwrap();
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Float64, true),
Field::new("b", DataType::Decimal128(10, 2), true),
]));
let value: i128 = 123;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value), // 1.23
None,
Some(value - 1), // 1.22
Some(value + 1), // 1.24
],
10,
2,
)) as ArrayRef;
let float64_array = Arc::new(Float64Array::from(vec![
Some(1.23),
Some(1.22),
Some(1.23),
Some(1.24),
])) as ArrayRef;
// lt: float64array < decimal array
apply_logic_op(
&schema,
&float64_array,
&decimal_array,
Operator::Lt,
BooleanArray::from(vec![Some(false), None, Some(false), Some(false)]),
)
.unwrap();
// lt_eq: float64array <= decimal array
apply_logic_op(
&schema,
&float64_array,
&decimal_array,
Operator::LtEq,
BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
)
.unwrap();
// gt: float64array > decimal array
apply_logic_op(
&schema,
&float64_array,
&decimal_array,
Operator::Gt,
BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
)
.unwrap();
apply_logic_op(
&schema,
&float64_array,
&decimal_array,
Operator::GtEq,
BooleanArray::from(vec![Some(true), None, Some(true), Some(true)]),
)
.unwrap();
// is distinct: float64array is distinct decimal array
// TODO: now we do not refactor the `is distinct or is not distinct` rule of coercion.
// traced by https://github.com/apache/datafusion/issues/1590
// the decimal array will be casted to float64array
apply_logic_op(
&schema,
&float64_array,
&decimal_array,
Operator::IsDistinctFrom,
BooleanArray::from(vec![Some(false), Some(true), Some(true), Some(false)]),
)
.unwrap();
// is not distinct
apply_logic_op(
&schema,
&float64_array,
&decimal_array,
Operator::IsNotDistinctFrom,
BooleanArray::from(vec![Some(true), Some(false), Some(false), Some(true)]),
)
.unwrap();
Ok(())
}
fn apply_decimal_arithmetic_op(
schema: &SchemaRef,
left: &ArrayRef,
right: &ArrayRef,
op: Operator,
expected: ArrayRef,
) -> Result<()> {
let arithmetic_op = binary_op(col("a", schema)?, op, col("b", schema)?, schema)?;
let data: Vec<ArrayRef> = vec![Arc::clone(left), Arc::clone(right)];
let batch = RecordBatch::try_new(Arc::clone(schema), data)?;
let result = arithmetic_op
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(result.as_ref(), expected.as_ref());
Ok(())
}
#[test]
fn arithmetic_decimal_expr_test() -> Result<()> {
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, true),
Field::new("b", DataType::Decimal128(10, 2), true),
]));
let value: i128 = 123;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value), // 1.23
None,
Some(value - 1), // 1.22
Some(value + 1), // 1.24
],
10,
2,
)) as ArrayRef;
let int32_array = Arc::new(Int32Array::from(vec![
Some(123),
Some(122),
Some(123),
Some(124),
])) as ArrayRef;
// add: Int32array add decimal array
let expect = Arc::new(create_decimal_array(
&[Some(12423), None, Some(12422), Some(12524)],
13,
2,
)) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&int32_array,
&decimal_array,
Operator::Plus,
expect,
)
.unwrap();
// subtract: decimal array subtract int32 array
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Decimal128(10, 2), true),
Field::new("b", DataType::Int32, true),
]));
let expect = Arc::new(create_decimal_array(
&[Some(-12177), None, Some(-12178), Some(-12276)],
13,
2,
)) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&decimal_array,
&int32_array,
Operator::Minus,
expect,
)
.unwrap();
// multiply: decimal array multiply int32 array
let expect = Arc::new(create_decimal_array(
&[Some(15129), None, Some(15006), Some(15376)],
21,
2,
)) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&decimal_array,
&int32_array,
Operator::Multiply,
expect,
)
.unwrap();
// divide: int32 array divide decimal array
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, true),
Field::new("b", DataType::Decimal128(10, 2), true),
]));
let expect = Arc::new(create_decimal_array(
&[Some(1000000), None, Some(1008196), Some(1000000)],
16,
4,
)) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&int32_array,
&decimal_array,
Operator::Divide,
expect,
)
.unwrap();
// modulus: int32 array modulus decimal array
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, true),
Field::new("b", DataType::Decimal128(10, 2), true),
]));
let expect = Arc::new(create_decimal_array(
&[Some(000), None, Some(100), Some(000)],
10,
2,
)) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&int32_array,
&decimal_array,
Operator::Modulo,
expect,
)
.unwrap();
Ok(())
}
#[test]
fn arithmetic_decimal_float_expr_test() -> Result<()> {
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Float64, true),
Field::new("b", DataType::Decimal128(10, 2), true),
]));
let value: i128 = 123;
let decimal_array = Arc::new(create_decimal_array(
&[
Some(value), // 1.23
None,
Some(value - 1), // 1.22
Some(value + 1), // 1.24
],
10,
2,
)) as ArrayRef;
let float64_array = Arc::new(Float64Array::from(vec![
Some(123.0),
Some(122.0),
Some(123.0),
Some(124.0),
])) as ArrayRef;
// add: float64 array add decimal array
let expect = Arc::new(Float64Array::from(vec![
Some(124.23),
None,
Some(124.22),
Some(125.24),
])) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&float64_array,
&decimal_array,
Operator::Plus,
expect,
)
.unwrap();
// subtract: decimal array subtract float64 array
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Float64, true),
Field::new("b", DataType::Decimal128(10, 2), true),
]));
let expect = Arc::new(Float64Array::from(vec![
Some(121.77),
None,
Some(121.78),
Some(122.76),
])) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&float64_array,
&decimal_array,
Operator::Minus,
expect,
)
.unwrap();
// multiply: decimal array multiply float64 array
let expect = Arc::new(Float64Array::from(vec![
Some(151.29),
None,
Some(150.06),
Some(153.76),
])) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&float64_array,
&decimal_array,
Operator::Multiply,
expect,
)
.unwrap();
// divide: float64 array divide decimal array
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Float64, true),
Field::new("b", DataType::Decimal128(10, 2), true),
]));
let expect = Arc::new(Float64Array::from(vec![
Some(100.0),
None,
Some(100.81967213114754),
Some(100.0),
])) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&float64_array,
&decimal_array,
Operator::Divide,
expect,
)
.unwrap();
// modulus: float64 array modulus decimal array
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Float64, true),
Field::new("b", DataType::Decimal128(10, 2), true),
]));
let expect = Arc::new(Float64Array::from(vec![
Some(1.7763568394002505e-15),
None,
Some(1.0000000000000027),
Some(8.881784197001252e-16),
])) as ArrayRef;
apply_decimal_arithmetic_op(
&schema,
&float64_array,
&decimal_array,
Operator::Modulo,
expect,
)
.unwrap();
Ok(())
}
#[test]
fn arithmetic_divide_zero() -> Result<()> {
// other data type
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, true),
Field::new("b", DataType::Int32, true),
]));
let a = Arc::new(Int32Array::from(vec![100]));
let b = Arc::new(Int32Array::from(vec![0]));
let err = apply_arithmetic::<Int32Type>(
schema,
vec![a, b],
Operator::Divide,
Int32Array::from(vec![Some(4), Some(8), Some(16), Some(32), Some(64)]),
)
.unwrap_err();
let _expected = plan_datafusion_err!("Divide by zero");
assert!(matches!(err, ref _expected), "{err}");
// decimal
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Decimal128(25, 3), true),
Field::new("b", DataType::Decimal128(25, 3), true),
]));
let left_decimal_array = Arc::new(create_decimal_array(&[Some(1234567)], 25, 3));
let right_decimal_array = Arc::new(create_decimal_array(&[Some(0)], 25, 3));
let err = apply_arithmetic::<Decimal128Type>(
schema,
vec![left_decimal_array, right_decimal_array],
Operator::Divide,
create_decimal_array(
&[Some(12345670000000000000000000000000000), None],
38,
29,
),
)
.unwrap_err();
assert!(matches!(err, ref _expected), "{err}");
Ok(())
}
#[test]
fn bitwise_array_test() -> Result<()> {
let left = Arc::new(Int32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef;
let right =
Arc::new(Int32Array::from(vec![Some(1), Some(3), Some(7)])) as ArrayRef;
let mut result = bitwise_and_dyn(Arc::clone(&left), Arc::clone(&right))?;
let expected = Int32Array::from(vec![Some(0), None, Some(3)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_or_dyn(Arc::clone(&left), Arc::clone(&right))?;
let expected = Int32Array::from(vec![Some(13), None, Some(15)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_xor_dyn(Arc::clone(&left), Arc::clone(&right))?;
let expected = Int32Array::from(vec![Some(13), None, Some(12)]);
assert_eq!(result.as_ref(), &expected);
let left =
Arc::new(UInt32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef;
let right =
Arc::new(UInt32Array::from(vec![Some(1), Some(3), Some(7)])) as ArrayRef;
let mut result = bitwise_and_dyn(Arc::clone(&left), Arc::clone(&right))?;
let expected = UInt32Array::from(vec![Some(0), None, Some(3)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_or_dyn(Arc::clone(&left), Arc::clone(&right))?;
let expected = UInt32Array::from(vec![Some(13), None, Some(15)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_xor_dyn(Arc::clone(&left), Arc::clone(&right))?;
let expected = UInt32Array::from(vec![Some(13), None, Some(12)]);
assert_eq!(result.as_ref(), &expected);
Ok(())
}
#[test]
fn bitwise_shift_array_test() -> Result<()> {
let input = Arc::new(Int32Array::from(vec![Some(2), None, Some(10)])) as ArrayRef;
let modules =
Arc::new(Int32Array::from(vec![Some(2), Some(4), Some(8)])) as ArrayRef;
let mut result =
bitwise_shift_left_dyn(Arc::clone(&input), Arc::clone(&modules))?;
let expected = Int32Array::from(vec![Some(8), None, Some(2560)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_shift_right_dyn(Arc::clone(&result), Arc::clone(&modules))?;
assert_eq!(result.as_ref(), &input);
let input =
Arc::new(UInt32Array::from(vec![Some(2), None, Some(10)])) as ArrayRef;
let modules =
Arc::new(UInt32Array::from(vec![Some(2), Some(4), Some(8)])) as ArrayRef;
let mut result =
bitwise_shift_left_dyn(Arc::clone(&input), Arc::clone(&modules))?;
let expected = UInt32Array::from(vec![Some(8), None, Some(2560)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_shift_right_dyn(Arc::clone(&result), Arc::clone(&modules))?;
assert_eq!(result.as_ref(), &input);
Ok(())
}
#[test]
fn bitwise_shift_array_overflow_test() -> Result<()> {
let input = Arc::new(Int32Array::from(vec![Some(2)])) as ArrayRef;
let modules = Arc::new(Int32Array::from(vec![Some(100)])) as ArrayRef;
let result = bitwise_shift_left_dyn(Arc::clone(&input), Arc::clone(&modules))?;
let expected = Int32Array::from(vec![Some(32)]);
assert_eq!(result.as_ref(), &expected);
let input = Arc::new(UInt32Array::from(vec![Some(2)])) as ArrayRef;
let modules = Arc::new(UInt32Array::from(vec![Some(100)])) as ArrayRef;
let result = bitwise_shift_left_dyn(Arc::clone(&input), Arc::clone(&modules))?;
let expected = UInt32Array::from(vec![Some(32)]);
assert_eq!(result.as_ref(), &expected);
Ok(())
}
#[test]
fn bitwise_scalar_test() -> Result<()> {
let left = Arc::new(Int32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef;
let right = ScalarValue::from(3i32);
let mut result = bitwise_and_dyn_scalar(&left, right.clone()).unwrap()?;
let expected = Int32Array::from(vec![Some(0), None, Some(3)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_or_dyn_scalar(&left, right.clone()).unwrap()?;
let expected = Int32Array::from(vec![Some(15), None, Some(11)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_xor_dyn_scalar(&left, right).unwrap()?;
let expected = Int32Array::from(vec![Some(15), None, Some(8)]);
assert_eq!(result.as_ref(), &expected);
let left =
Arc::new(UInt32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef;
let right = ScalarValue::from(3u32);
let mut result = bitwise_and_dyn_scalar(&left, right.clone()).unwrap()?;
let expected = UInt32Array::from(vec![Some(0), None, Some(3)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_or_dyn_scalar(&left, right.clone()).unwrap()?;
let expected = UInt32Array::from(vec![Some(15), None, Some(11)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_xor_dyn_scalar(&left, right).unwrap()?;
let expected = UInt32Array::from(vec![Some(15), None, Some(8)]);
assert_eq!(result.as_ref(), &expected);
Ok(())
}
#[test]
fn bitwise_shift_scalar_test() -> Result<()> {
let input = Arc::new(Int32Array::from(vec![Some(2), None, Some(4)])) as ArrayRef;
let module = ScalarValue::from(10i32);
let mut result =
bitwise_shift_left_dyn_scalar(&input, module.clone()).unwrap()?;
let expected = Int32Array::from(vec![Some(2048), None, Some(4096)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_shift_right_dyn_scalar(&result, module).unwrap()?;
assert_eq!(result.as_ref(), &input);
let input = Arc::new(UInt32Array::from(vec![Some(2), None, Some(4)])) as ArrayRef;
let module = ScalarValue::from(10u32);
let mut result =
bitwise_shift_left_dyn_scalar(&input, module.clone()).unwrap()?;
let expected = UInt32Array::from(vec![Some(2048), None, Some(4096)]);
assert_eq!(result.as_ref(), &expected);
result = bitwise_shift_right_dyn_scalar(&result, module).unwrap()?;
assert_eq!(result.as_ref(), &input);
Ok(())
}
#[test]
fn test_display_and_or_combo() {
let expr = BinaryExpr::new(
Arc::new(BinaryExpr::new(
lit(ScalarValue::from(1)),
Operator::And,
lit(ScalarValue::from(2)),
)),
Operator::And,
Arc::new(BinaryExpr::new(
lit(ScalarValue::from(3)),
Operator::And,
lit(ScalarValue::from(4)),
)),
);
assert_eq!(expr.to_string(), "1 AND 2 AND 3 AND 4");
let expr = BinaryExpr::new(
Arc::new(BinaryExpr::new(
lit(ScalarValue::from(1)),
Operator::Or,
lit(ScalarValue::from(2)),
)),
Operator::Or,
Arc::new(BinaryExpr::new(
lit(ScalarValue::from(3)),
Operator::Or,
lit(ScalarValue::from(4)),
)),
);
assert_eq!(expr.to_string(), "1 OR 2 OR 3 OR 4");
let expr = BinaryExpr::new(
Arc::new(BinaryExpr::new(
lit(ScalarValue::from(1)),
Operator::And,
lit(ScalarValue::from(2)),
)),
Operator::Or,
Arc::new(BinaryExpr::new(
lit(ScalarValue::from(3)),
Operator::And,
lit(ScalarValue::from(4)),
)),
);
assert_eq!(expr.to_string(), "1 AND 2 OR 3 AND 4");
let expr = BinaryExpr::new(
Arc::new(BinaryExpr::new(
lit(ScalarValue::from(1)),
Operator::Or,
lit(ScalarValue::from(2)),
)),
Operator::And,
Arc::new(BinaryExpr::new(
lit(ScalarValue::from(3)),
Operator::Or,
lit(ScalarValue::from(4)),
)),
);
assert_eq!(expr.to_string(), "(1 OR 2) AND (3 OR 4)");
}
#[test]
fn test_to_result_type_array() {
let values = Arc::new(Int32Array::from(vec![1, 2, 3, 4]));
let keys = Int8Array::from(vec![Some(0), None, Some(2), Some(3)]);
let dictionary =
Arc::new(DictionaryArray::try_new(keys, values).unwrap()) as ArrayRef;
// Casting Dictionary to Int32
let casted = to_result_type_array(
&Operator::Plus,
Arc::clone(&dictionary),
&DataType::Int32,
)
.unwrap();
assert_eq!(
&casted,
&(Arc::new(Int32Array::from(vec![Some(1), None, Some(3), Some(4)]))
as ArrayRef)
);
// Array has same datatype as result type, no casting
let casted = to_result_type_array(
&Operator::Plus,
Arc::clone(&dictionary),
dictionary.data_type(),
)
.unwrap();
assert_eq!(&casted, &dictionary);
// Not numerical operator, no casting
let casted = to_result_type_array(
&Operator::Eq,
Arc::clone(&dictionary),
&DataType::Int32,
)
.unwrap();
assert_eq!(&casted, &dictionary);
}
#[test]
fn test_add_with_overflow() -> Result<()> {
// create test data
let l = Arc::new(Int32Array::from(vec![1, i32::MAX]));
let r = Arc::new(Int32Array::from(vec![2, 1]));
let schema = Arc::new(Schema::new(vec![
Field::new("l", DataType::Int32, false),
Field::new("r", DataType::Int32, false),
]));
let batch = RecordBatch::try_new(schema, vec![l, r])?;
// create expression
let expr = BinaryExpr::new(
Arc::new(Column::new("l", 0)),
Operator::Plus,
Arc::new(Column::new("r", 1)),
)
.with_fail_on_overflow(true);
// evaluate expression
let result = expr.evaluate(&batch);
assert!(result
.err()
.unwrap()
.to_string()
.contains("Overflow happened on: 2147483647 + 1"));
Ok(())
}
#[test]
fn test_subtract_with_overflow() -> Result<()> {
// create test data
let l = Arc::new(Int32Array::from(vec![1, i32::MIN]));
let r = Arc::new(Int32Array::from(vec![2, 1]));
let schema = Arc::new(Schema::new(vec![
Field::new("l", DataType::Int32, false),
Field::new("r", DataType::Int32, false),
]));
let batch = RecordBatch::try_new(schema, vec![l, r])?;
// create expression
let expr = BinaryExpr::new(
Arc::new(Column::new("l", 0)),
Operator::Minus,
Arc::new(Column::new("r", 1)),
)
.with_fail_on_overflow(true);
// evaluate expression
let result = expr.evaluate(&batch);
assert!(result
.err()
.unwrap()
.to_string()
.contains("Overflow happened on: -2147483648 - 1"));
Ok(())
}
#[test]
fn test_mul_with_overflow() -> Result<()> {
// create test data
let l = Arc::new(Int32Array::from(vec![1, i32::MAX]));
let r = Arc::new(Int32Array::from(vec![2, 2]));
let schema = Arc::new(Schema::new(vec![
Field::new("l", DataType::Int32, false),
Field::new("r", DataType::Int32, false),
]));
let batch = RecordBatch::try_new(schema, vec![l, r])?;
// create expression
let expr = BinaryExpr::new(
Arc::new(Column::new("l", 0)),
Operator::Multiply,
Arc::new(Column::new("r", 1)),
)
.with_fail_on_overflow(true);
// evaluate expression
let result = expr.evaluate(&batch);
assert!(result
.err()
.unwrap()
.to_string()
.contains("Overflow happened on: 2147483647 * 2"));
Ok(())
}
/// Test helper for SIMILAR TO binary operation
fn apply_similar_to(
schema: &SchemaRef,
va: Vec<&str>,
vb: Vec<&str>,
negated: bool,
case_insensitive: bool,
expected: &BooleanArray,
) -> Result<()> {
let a = StringArray::from(va);
let b = StringArray::from(vb);
let op = similar_to(
negated,
case_insensitive,
col("a", schema)?,
col("b", schema)?,
)?;
let batch =
RecordBatch::try_new(Arc::clone(schema), vec![Arc::new(a), Arc::new(b)])?;
let result = op
.evaluate(&batch)?
.into_array(batch.num_rows())
.expect("Failed to convert to array");
assert_eq!(result.as_ref(), expected);
Ok(())
}
#[test]
fn test_similar_to() {
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Utf8, false),
Field::new("b", DataType::Utf8, false),
]));
let expected = [Some(true), Some(false)].iter().collect();
// case-sensitive
apply_similar_to(
&schema,
vec!["hello world", "Hello World"],
vec!["hello.*", "hello.*"],
false,
false,
&expected,
)
.unwrap();
// case-insensitive
apply_similar_to(
&schema,
vec!["hello world", "bye"],
vec!["hello.*", "hello.*"],
false,
true,
&expected,
)
.unwrap();
}
pub fn binary_expr(
left: Arc<dyn PhysicalExpr>,
op: Operator,
right: Arc<dyn PhysicalExpr>,
schema: &Schema,
) -> Result<BinaryExpr> {
Ok(binary_op(left, op, right, schema)?
.as_any()
.downcast_ref::<BinaryExpr>()
.unwrap()
.clone())
}
/// Test for Uniform-Uniform, Unknown-Uniform, Uniform-Unknown and Unknown-Unknown evaluation.
#[test]
fn test_evaluate_statistics_combination_of_range_holders() -> Result<()> {
let schema = &Schema::new(vec![Field::new("a", DataType::Float64, false)]);
let a = Arc::new(Column::new("a", 0)) as _;
let b = lit(ScalarValue::from(12.0));
let left_interval = Interval::make(Some(0.0), Some(12.0))?;
let right_interval = Interval::make(Some(12.0), Some(36.0))?;
let (left_mean, right_mean) = (ScalarValue::from(6.0), ScalarValue::from(24.0));
let (left_med, right_med) = (ScalarValue::from(6.0), ScalarValue::from(24.0));
for children in [
vec![
&Distribution::new_uniform(left_interval.clone())?,
&Distribution::new_uniform(right_interval.clone())?,
],
vec![
&Distribution::new_generic(
left_mean.clone(),
left_med.clone(),
ScalarValue::Float64(None),
left_interval.clone(),
)?,
&Distribution::new_uniform(right_interval.clone())?,
],
vec![
&Distribution::new_uniform(right_interval.clone())?,
&Distribution::new_generic(
right_mean.clone(),
right_med.clone(),
ScalarValue::Float64(None),
right_interval.clone(),
)?,
],
vec![
&Distribution::new_generic(
left_mean.clone(),
left_med.clone(),
ScalarValue::Float64(None),
left_interval.clone(),
)?,
&Distribution::new_generic(
right_mean.clone(),
right_med.clone(),
ScalarValue::Float64(None),
right_interval.clone(),
)?,
],
] {
let ops = vec![
Operator::Plus,
Operator::Minus,
Operator::Multiply,
Operator::Divide,
];
for op in ops {
let expr = binary_expr(Arc::clone(&a), op, Arc::clone(&b), schema)?;
assert_eq!(
expr.evaluate_statistics(&children)?,
new_generic_from_binary_op(&op, children[0], children[1])?
);
}
}
Ok(())
}
#[test]
fn test_evaluate_statistics_bernoulli() -> Result<()> {
let schema = &Schema::new(vec![
Field::new("a", DataType::Int64, false),
Field::new("b", DataType::Int64, false),
]);
let a = Arc::new(Column::new("a", 0)) as _;
let b = Arc::new(Column::new("b", 1)) as _;
let eq = Arc::new(binary_expr(
Arc::clone(&a),
Operator::Eq,
Arc::clone(&b),
schema,
)?);
let neq = Arc::new(binary_expr(a, Operator::NotEq, b, schema)?);
let left_stat = &Distribution::new_uniform(Interval::make(Some(0), Some(7))?)?;
let right_stat = &Distribution::new_uniform(Interval::make(Some(4), Some(11))?)?;
// Intervals: [0, 7], [4, 11].
// The intersection is [4, 7], so the probability of equality is 4 / 64 = 1 / 16.
assert_eq!(
eq.evaluate_statistics(&[left_stat, right_stat])?,
Distribution::new_bernoulli(ScalarValue::from(1.0 / 16.0))?
);
// The probability of being distinct is 1 - 1 / 16 = 15 / 16.
assert_eq!(
neq.evaluate_statistics(&[left_stat, right_stat])?,
Distribution::new_bernoulli(ScalarValue::from(15.0 / 16.0))?
);
Ok(())
}
#[test]
fn test_propagate_statistics_combination_of_range_holders_arithmetic() -> Result<()> {
let schema = &Schema::new(vec![Field::new("a", DataType::Float64, false)]);
let a = Arc::new(Column::new("a", 0)) as _;
let b = lit(ScalarValue::from(12.0));
let left_interval = Interval::make(Some(0.0), Some(12.0))?;
let right_interval = Interval::make(Some(12.0), Some(36.0))?;
let parent = Distribution::new_uniform(Interval::make(Some(-432.), Some(432.))?)?;
let children = vec![
vec![
Distribution::new_uniform(left_interval.clone())?,
Distribution::new_uniform(right_interval.clone())?,
],
vec![
Distribution::new_generic(
ScalarValue::from(6.),
ScalarValue::from(6.),
ScalarValue::Float64(None),
left_interval.clone(),
)?,
Distribution::new_uniform(right_interval.clone())?,
],
vec![
Distribution::new_uniform(left_interval.clone())?,
Distribution::new_generic(
ScalarValue::from(12.),
ScalarValue::from(12.),
ScalarValue::Float64(None),
right_interval.clone(),
)?,
],
vec![
Distribution::new_generic(
ScalarValue::from(6.),
ScalarValue::from(6.),
ScalarValue::Float64(None),
left_interval.clone(),
)?,
Distribution::new_generic(
ScalarValue::from(12.),
ScalarValue::from(12.),
ScalarValue::Float64(None),
right_interval.clone(),
)?,
],
];
let ops = vec![
Operator::Plus,
Operator::Minus,
Operator::Multiply,
Operator::Divide,
];
for child_view in children {
let child_refs = child_view.iter().collect::<Vec<_>>();
for op in &ops {
let expr = binary_expr(Arc::clone(&a), *op, Arc::clone(&b), schema)?;
assert_eq!(
expr.propagate_statistics(&parent, child_refs.as_slice())?,
Some(child_view.clone())
);
}
}
Ok(())
}
#[test]
fn test_propagate_statistics_combination_of_range_holders_comparison() -> Result<()> {
let schema = &Schema::new(vec![Field::new("a", DataType::Float64, false)]);
let a = Arc::new(Column::new("a", 0)) as _;
let b = lit(ScalarValue::from(12.0));
let left_interval = Interval::make(Some(0.0), Some(12.0))?;
let right_interval = Interval::make(Some(6.0), Some(18.0))?;
let one = ScalarValue::from(1.0);
let parent = Distribution::new_bernoulli(one)?;
let children = vec![
vec![
Distribution::new_uniform(left_interval.clone())?,
Distribution::new_uniform(right_interval.clone())?,
],
vec![
Distribution::new_generic(
ScalarValue::from(6.),
ScalarValue::from(6.),
ScalarValue::Float64(None),
left_interval.clone(),
)?,
Distribution::new_uniform(right_interval.clone())?,
],
vec![
Distribution::new_uniform(left_interval.clone())?,
Distribution::new_generic(
ScalarValue::from(12.),
ScalarValue::from(12.),
ScalarValue::Float64(None),
right_interval.clone(),
)?,
],
vec![
Distribution::new_generic(
ScalarValue::from(6.),
ScalarValue::from(6.),
ScalarValue::Float64(None),
left_interval.clone(),
)?,
Distribution::new_generic(
ScalarValue::from(12.),
ScalarValue::from(12.),
ScalarValue::Float64(None),
right_interval.clone(),
)?,
],
];
let ops = vec![
Operator::Eq,
Operator::Gt,
Operator::GtEq,
Operator::Lt,
Operator::LtEq,
];
for child_view in children {
let child_refs = child_view.iter().collect::<Vec<_>>();
for op in &ops {
let expr = binary_expr(Arc::clone(&a), *op, Arc::clone(&b), schema)?;
assert!(expr
.propagate_statistics(&parent, child_refs.as_slice())?
.is_some());
}
}
Ok(())
}
#[test]
fn test_fmt_sql() -> Result<()> {
let schema = Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]);
// Test basic binary expressions
let simple_expr = binary_expr(
col("a", &schema)?,
Operator::Plus,
col("b", &schema)?,
&schema,
)?;
let display_string = simple_expr.to_string();
assert_eq!(display_string, "a@0 + b@1");
let sql_string = fmt_sql(&simple_expr).to_string();
assert_eq!(sql_string, "a + b");
// Test nested expressions with different operator precedence
let nested_expr = binary_expr(
Arc::new(binary_expr(
col("a", &schema)?,
Operator::Plus,
col("b", &schema)?,
&schema,
)?),
Operator::Multiply,
col("b", &schema)?,
&schema,
)?;
let display_string = nested_expr.to_string();
assert_eq!(display_string, "(a@0 + b@1) * b@1");
let sql_string = fmt_sql(&nested_expr).to_string();
assert_eq!(sql_string, "(a + b) * b");
// Test nested expressions with same operator precedence
let nested_same_prec = binary_expr(
Arc::new(binary_expr(
col("a", &schema)?,
Operator::Plus,
col("b", &schema)?,
&schema,
)?),
Operator::Plus,
col("b", &schema)?,
&schema,
)?;
let display_string = nested_same_prec.to_string();
assert_eq!(display_string, "a@0 + b@1 + b@1");
let sql_string = fmt_sql(&nested_same_prec).to_string();
assert_eq!(sql_string, "a + b + b");
// Test with literals
let lit_expr = binary_expr(
col("a", &schema)?,
Operator::Eq,
lit(ScalarValue::Int32(Some(42))),
&schema,
)?;
let display_string = lit_expr.to_string();
assert_eq!(display_string, "a@0 = 42");
let sql_string = fmt_sql(&lit_expr).to_string();
assert_eq!(sql_string, "a = 42");
Ok(())
}
#[test]
fn test_check_short_circuit() {
// Test with non-nullable arrays
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]));
let a_array = Int32Array::from(vec![1, 3, 4, 5, 6]);
let b_array = Int32Array::from(vec![1, 2, 3, 4, 5]);
let batch = RecordBatch::try_new(
Arc::clone(&schema),
vec![Arc::new(a_array), Arc::new(b_array)],
)
.unwrap();
// op: AND left: all false
let left_expr = logical2physical(&logical_col("a").eq(expr_lit(2)), &schema);
let left_value = left_expr.evaluate(&batch).unwrap();
assert!(matches!(
check_short_circuit(&left_value, &Operator::And),
ShortCircuitStrategy::ReturnLeft
));
// op: AND left: not all false
let left_expr = logical2physical(&logical_col("a").eq(expr_lit(3)), &schema);
let left_value = left_expr.evaluate(&batch).unwrap();
let ColumnarValue::Array(array) = &left_value else {
panic!("Expected ColumnarValue::Array");
};
let ShortCircuitStrategy::PreSelection(value) =
check_short_circuit(&left_value, &Operator::And)
else {
panic!("Expected ShortCircuitStrategy::PreSelection");
};
let expected_boolean_arr: Vec<_> =
as_boolean_array(array).unwrap().iter().collect();
let boolean_arr: Vec<_> = value.iter().collect();
assert_eq!(expected_boolean_arr, boolean_arr);
// op: OR left: all true
let left_expr = logical2physical(&logical_col("a").gt(expr_lit(0)), &schema);
let left_value = left_expr.evaluate(&batch).unwrap();
assert!(matches!(
check_short_circuit(&left_value, &Operator::Or),
ShortCircuitStrategy::ReturnLeft
));
// op: OR left: not all true
let left_expr: Arc<dyn PhysicalExpr> =
logical2physical(&logical_col("a").gt(expr_lit(2)), &schema);
let left_value = left_expr.evaluate(&batch).unwrap();
assert!(matches!(
check_short_circuit(&left_value, &Operator::Or),
ShortCircuitStrategy::None
));
// Test with nullable arrays and null values
let schema_nullable = Arc::new(Schema::new(vec![
Field::new("c", DataType::Boolean, true),
Field::new("d", DataType::Boolean, true),
]));
// Create arrays with null values
let c_array = Arc::new(BooleanArray::from(vec![
Some(true),
Some(false),
None,
Some(true),
None,
])) as ArrayRef;
let d_array = Arc::new(BooleanArray::from(vec![
Some(false),
Some(true),
Some(false),
None,
Some(true),
])) as ArrayRef;
let batch_nullable = RecordBatch::try_new(
Arc::clone(&schema_nullable),
vec![Arc::clone(&c_array), Arc::clone(&d_array)],
)
.unwrap();
// Case: Mixed values with nulls - shouldn't short-circuit for AND
let mixed_nulls = logical2physical(&logical_col("c"), &schema_nullable);
let mixed_nulls_value = mixed_nulls.evaluate(&batch_nullable).unwrap();
assert!(matches!(
check_short_circuit(&mixed_nulls_value, &Operator::And),
ShortCircuitStrategy::None
));
// Case: Mixed values with nulls - shouldn't short-circuit for OR
assert!(matches!(
check_short_circuit(&mixed_nulls_value, &Operator::Or),
ShortCircuitStrategy::None
));
// Test with all nulls
let all_nulls = Arc::new(BooleanArray::from(vec![None, None, None])) as ArrayRef;
let null_batch = RecordBatch::try_new(
Arc::new(Schema::new(vec![Field::new("e", DataType::Boolean, true)])),
vec![all_nulls],
)
.unwrap();
let null_expr = logical2physical(&logical_col("e"), &null_batch.schema());
let null_value = null_expr.evaluate(&null_batch).unwrap();
// All nulls shouldn't short-circuit for AND or OR
assert!(matches!(
check_short_circuit(&null_value, &Operator::And),
ShortCircuitStrategy::None
));
assert!(matches!(
check_short_circuit(&null_value, &Operator::Or),
ShortCircuitStrategy::None
));
// Test with scalar values
// Scalar true
let scalar_true = ColumnarValue::Scalar(ScalarValue::Boolean(Some(true)));
assert!(matches!(
check_short_circuit(&scalar_true, &Operator::Or),
ShortCircuitStrategy::ReturnLeft
)); // Should short-circuit OR
assert!(matches!(
check_short_circuit(&scalar_true, &Operator::And),
ShortCircuitStrategy::ReturnRight
)); // Should return the RHS for AND
// Scalar false
let scalar_false = ColumnarValue::Scalar(ScalarValue::Boolean(Some(false)));
assert!(matches!(
check_short_circuit(&scalar_false, &Operator::And),
ShortCircuitStrategy::ReturnLeft
)); // Should short-circuit AND
assert!(matches!(
check_short_circuit(&scalar_false, &Operator::Or),
ShortCircuitStrategy::ReturnRight
)); // Should return the RHS for OR
// Scalar null
let scalar_null = ColumnarValue::Scalar(ScalarValue::Boolean(None));
assert!(matches!(
check_short_circuit(&scalar_null, &Operator::And),
ShortCircuitStrategy::None
));
assert!(matches!(
check_short_circuit(&scalar_null, &Operator::Or),
ShortCircuitStrategy::None
));
}
/// Test for [pre_selection_scatter]
/// Since [check_short_circuit] ensures that the left side does not contain null and is neither all_true nor all_false, as well as not being empty,
/// the following tests have been designed:
/// 1. Test sparse left with interleaved true/false
/// 2. Test multiple consecutive true blocks
/// 3. Test multiple consecutive true blocks
/// 4. Test single true at first position
/// 5. Test single true at last position
/// 6. Test nulls in right array
#[test]
fn test_pre_selection_scatter() {
fn create_bool_array(bools: Vec<bool>) -> BooleanArray {
BooleanArray::from(bools.into_iter().map(Some).collect::<Vec<_>>())
}
// Test sparse left with interleaved true/false
{
// Left: [T, F, T, F, T]
// Right: [F, T, F] (values for 3 true positions)
let left = create_bool_array(vec![true, false, true, false, true]);
let right = create_bool_array(vec![false, true, false]);
let result = pre_selection_scatter(&left, Some(&right)).unwrap();
let result_arr = result.into_array(left.len()).unwrap();
let expected = create_bool_array(vec![false, false, true, false, false]);
assert_eq!(&expected, result_arr.as_boolean());
}
// Test multiple consecutive true blocks
{
// Left: [F, T, T, F, T, T, T]
// Right: [T, F, F, T, F]
let left =
create_bool_array(vec![false, true, true, false, true, true, true]);
let right = create_bool_array(vec![true, false, false, true, false]);
let result = pre_selection_scatter(&left, Some(&right)).unwrap();
let result_arr = result.into_array(left.len()).unwrap();
let expected =
create_bool_array(vec![false, true, false, false, false, true, false]);
assert_eq!(&expected, result_arr.as_boolean());
}
// Test single true at first position
{
// Left: [T, F, F]
// Right: [F]
let left = create_bool_array(vec![true, false, false]);
let right = create_bool_array(vec![false]);
let result = pre_selection_scatter(&left, Some(&right)).unwrap();
let result_arr = result.into_array(left.len()).unwrap();
let expected = create_bool_array(vec![false, false, false]);
assert_eq!(&expected, result_arr.as_boolean());
}
// Test single true at last position
{
// Left: [F, F, T]
// Right: [F]
let left = create_bool_array(vec![false, false, true]);
let right = create_bool_array(vec![false]);
let result = pre_selection_scatter(&left, Some(&right)).unwrap();
let result_arr = result.into_array(left.len()).unwrap();
let expected = create_bool_array(vec![false, false, false]);
assert_eq!(&expected, result_arr.as_boolean());
}
// Test nulls in right array
{
// Left: [F, T, F, T]
// Right: [None, Some(false)] (with null at first position)
let left = create_bool_array(vec![false, true, false, true]);
let right = BooleanArray::from(vec![None, Some(false)]);
let result = pre_selection_scatter(&left, Some(&right)).unwrap();
let result_arr = result.into_array(left.len()).unwrap();
let expected = BooleanArray::from(vec![
Some(false),
None, // null from right
Some(false),
Some(false),
]);
assert_eq!(&expected, result_arr.as_boolean());
}
}
#[test]
fn test_and_true_preselection_returns_lhs() {
let schema =
Arc::new(Schema::new(vec![Field::new("c", DataType::Boolean, false)]));
let c_array = Arc::new(BooleanArray::from(vec![false, true, false, false, false]))
as ArrayRef;
let batch = RecordBatch::try_new(Arc::clone(&schema), vec![Arc::clone(&c_array)])
.unwrap();
let expr = logical2physical(&logical_col("c").and(expr_lit(true)), &schema);
let result = expr.evaluate(&batch).unwrap();
let ColumnarValue::Array(result_arr) = result else {
panic!("Expected ColumnarValue::Array");
};
let expected: Vec<_> = c_array.as_boolean().iter().collect();
let actual: Vec<_> = result_arr.as_boolean().iter().collect();
assert_eq!(
expected, actual,
"AND with TRUE must equal LHS even with PreSelection"
);
}
#[test]
fn test_evaluate_bounds_int32() {
let schema = Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, false),
]);
let a = Arc::new(Column::new("a", 0)) as _;
let b = Arc::new(Column::new("b", 1)) as _;
// Test addition bounds
let add_expr =
binary_expr(Arc::clone(&a), Operator::Plus, Arc::clone(&b), &schema).unwrap();
let add_bounds = add_expr
.evaluate_bounds(&[
&Interval::make(Some(1), Some(10)).unwrap(),
&Interval::make(Some(5), Some(15)).unwrap(),
])
.unwrap();
assert_eq!(add_bounds, Interval::make(Some(6), Some(25)).unwrap());
// Test subtraction bounds
let sub_expr =
binary_expr(Arc::clone(&a), Operator::Minus, Arc::clone(&b), &schema)
.unwrap();
let sub_bounds = sub_expr
.evaluate_bounds(&[
&Interval::make(Some(1), Some(10)).unwrap(),
&Interval::make(Some(5), Some(15)).unwrap(),
])
.unwrap();
assert_eq!(sub_bounds, Interval::make(Some(-14), Some(5)).unwrap());
// Test multiplication bounds
let mul_expr =
binary_expr(Arc::clone(&a), Operator::Multiply, Arc::clone(&b), &schema)
.unwrap();
let mul_bounds = mul_expr
.evaluate_bounds(&[
&Interval::make(Some(1), Some(10)).unwrap(),
&Interval::make(Some(5), Some(15)).unwrap(),
])
.unwrap();
assert_eq!(mul_bounds, Interval::make(Some(5), Some(150)).unwrap());
// Test division bounds
let div_expr =
binary_expr(Arc::clone(&a), Operator::Divide, Arc::clone(&b), &schema)
.unwrap();
let div_bounds = div_expr
.evaluate_bounds(&[
&Interval::make(Some(10), Some(20)).unwrap(),
&Interval::make(Some(2), Some(5)).unwrap(),
])
.unwrap();
assert_eq!(div_bounds, Interval::make(Some(2), Some(10)).unwrap());
}
#[test]
fn test_evaluate_bounds_bool() {
let schema = Schema::new(vec![
Field::new("a", DataType::Boolean, false),
Field::new("b", DataType::Boolean, false),
]);
let a = Arc::new(Column::new("a", 0)) as _;
let b = Arc::new(Column::new("b", 1)) as _;
// Test OR bounds
let or_expr =
binary_expr(Arc::clone(&a), Operator::Or, Arc::clone(&b), &schema).unwrap();
let or_bounds = or_expr
.evaluate_bounds(&[
&Interval::make(Some(true), Some(true)).unwrap(),
&Interval::make(Some(false), Some(false)).unwrap(),
])
.unwrap();
assert_eq!(or_bounds, Interval::make(Some(true), Some(true)).unwrap());
// Test AND bounds
let and_expr =
binary_expr(Arc::clone(&a), Operator::And, Arc::clone(&b), &schema).unwrap();
let and_bounds = and_expr
.evaluate_bounds(&[
&Interval::make(Some(true), Some(true)).unwrap(),
&Interval::make(Some(false), Some(false)).unwrap(),
])
.unwrap();
assert_eq!(
and_bounds,
Interval::make(Some(false), Some(false)).unwrap()
);
}
#[test]
fn test_evaluate_nested_type() {
let batch_schema = Arc::new(Schema::new(vec![
Field::new(
"a",
DataType::List(Arc::new(Field::new_list_field(DataType::Int32, true))),
true,
),
Field::new(
"b",
DataType::List(Arc::new(Field::new_list_field(DataType::Int32, true))),
true,
),
]));
let mut list_builder_a = ListBuilder::new(Int32Builder::new());
list_builder_a.append_value([Some(1)]);
list_builder_a.append_value([Some(2)]);
list_builder_a.append_value([]);
list_builder_a.append_value([None]);
let list_array_a: ArrayRef = Arc::new(list_builder_a.finish());
let mut list_builder_b = ListBuilder::new(Int32Builder::new());
list_builder_b.append_value([Some(1)]);
list_builder_b.append_value([Some(2)]);
list_builder_b.append_value([]);
list_builder_b.append_value([None]);
let list_array_b: ArrayRef = Arc::new(list_builder_b.finish());
let batch =
RecordBatch::try_new(batch_schema, vec![list_array_a, list_array_b]).unwrap();
let schema = Arc::new(Schema::new(vec![
Field::new(
"a",
DataType::List(Arc::new(Field::new("foo", DataType::Int32, true))),
true,
),
Field::new(
"b",
DataType::List(Arc::new(Field::new("bar", DataType::Int32, true))),
true,
),
]));
let a = Arc::new(Column::new("a", 0)) as _;
let b = Arc::new(Column::new("b", 1)) as _;
let eq_expr =
binary_expr(Arc::clone(&a), Operator::Eq, Arc::clone(&b), &schema).unwrap();
let eq_result = eq_expr.evaluate(&batch).unwrap();
let expected =
BooleanArray::from_iter(vec![Some(true), Some(true), Some(true), Some(true)]);
assert_eq!(eq_result.into_array(4).unwrap().as_boolean(), &expected);
}
}