refactor: remove or deprecated existing UDAF implementation (#5637)

* expand macro

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* remove argmin/argmax (wrong impl)

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* remove mean (unnecessary)

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* documentations

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* clean up

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* clean up

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* remove scipy_*, diff and polyval

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* remove unused errors

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* fix clippy again

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

---------

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

src/common/function/src/aggr/hll.rs

@@ -12,6 +12,16 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+//! Two UDAFs are implemented for HyperLogLog:
+//!
+//! - `hll`: Accepts a string column and aggregates the values into a
+//!   HyperLogLog state.
+//! - `hll_merge`: Accepts a binary column of states generated by `hll`
+//!   and merges them into a single state.
+//!
+//! The states can be then used to estimate the cardinality of the
+//! values in the column by `hll_count` UDF.
+
 use std::sync::Arc;
 
 use common_query::prelude::*;

src/common/function/src/aggr/uddsketch_state.rs

@@ -12,6 +12,12 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+//! Implementation of the `uddsketch_state` UDAF that generate the state of
+//! UDDSketch for a given set of values.
+//!
+//! The generated state can be used to compute approximate quantiles using
+//! `uddsketch_calc` UDF.
+
 use std::sync::Arc;
 
 use common_query::prelude::*;

src/common/function/src/scalars/aggregate.rs

@@ -12,24 +12,16 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-mod argmax;
-mod argmin;
-mod diff;
-mod mean;
-mod polyval;
-mod scipy_stats_norm_cdf;
-mod scipy_stats_norm_pdf;
+//! # Deprecate Warning:
+//!
+//! This module is deprecated and will be removed in the future.
+//! All UDAF implementation here are not maintained and should
+//! not be used before they are refactored into the `src/aggr`
+//! version.
 
 use std::sync::Arc;
 
-pub use argmax::ArgmaxAccumulatorCreator;
-pub use argmin::ArgminAccumulatorCreator;
 use common_query::logical_plan::AggregateFunctionCreatorRef;
-pub use diff::DiffAccumulatorCreator;
-pub use mean::MeanAccumulatorCreator;
-pub use polyval::PolyvalAccumulatorCreator;
-pub use scipy_stats_norm_cdf::ScipyStatsNormCdfAccumulatorCreator;
-pub use scipy_stats_norm_pdf::ScipyStatsNormPdfAccumulatorCreator;
 
 use crate::function_registry::FunctionRegistry;
 use crate::scalars::vector::product::VectorProductCreator;
@@ -76,31 +68,22 @@ pub(crate) struct AggregateFunctions;
 
 impl AggregateFunctions {
     pub fn register(registry: &FunctionRegistry) {
-        macro_rules! register_aggr_func {
-            ($name :expr, $arg_count :expr, $creator :ty) => {
-                registry.register_aggregate_function(Arc::new(AggregateFunctionMeta::new(
-                    $name,
-                    $arg_count,
-                    Arc::new(|| Arc::new(<$creator>::default())),
-                )));
-            };
-        }
-
-        register_aggr_func!("diff", 1, DiffAccumulatorCreator);
-        register_aggr_func!("mean", 1, MeanAccumulatorCreator);
-        register_aggr_func!("polyval", 2, PolyvalAccumulatorCreator);
-        register_aggr_func!("argmax", 1, ArgmaxAccumulatorCreator);
-        register_aggr_func!("argmin", 1, ArgminAccumulatorCreator);
-        register_aggr_func!("scipystatsnormcdf", 2, ScipyStatsNormCdfAccumulatorCreator);
-        register_aggr_func!("scipystatsnormpdf", 2, ScipyStatsNormPdfAccumulatorCreator);
-        register_aggr_func!("vec_sum", 1, VectorSumCreator);
-        register_aggr_func!("vec_product", 1, VectorProductCreator);
+        registry.register_aggregate_function(Arc::new(AggregateFunctionMeta::new(
+            "vec_sum",
+            1,
+            Arc::new(|| Arc::new(VectorSumCreator::default())),
+        )));
+        registry.register_aggregate_function(Arc::new(AggregateFunctionMeta::new(
+            "vec_product",
+            1,
+            Arc::new(|| Arc::new(VectorProductCreator::default())),
+        )));
 
         #[cfg(feature = "geo")]
-        register_aggr_func!(
+        registry.register_aggregate_function(Arc::new(AggregateFunctionMeta::new(
             "json_encode_path",
             3,
-            super::geo::encoding::JsonPathEncodeFunctionCreator
-        );
+            Arc::new(|| Arc::new(super::geo::encoding::JsonPathEncodeFunctionCreator::default())),
+        )));
     }
 }

src/common/function/src/scalars/aggregate/argmax.rs

@@ -1,208 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::cmp::Ordering;
-use std::sync::Arc;
-
-use common_macro::{as_aggr_func_creator, AggrFuncTypeStore};
-use common_query::error::{
-    BadAccumulatorImplSnafu, CreateAccumulatorSnafu, InvalidInputStateSnafu, Result,
-};
-use common_query::logical_plan::accumulator::AggrFuncTypeStore;
-use common_query::logical_plan::{Accumulator, AggregateFunctionCreator};
-use common_query::prelude::*;
-use datatypes::prelude::*;
-use datatypes::types::{LogicalPrimitiveType, WrapperType};
-use datatypes::vectors::{ConstantVector, Helper};
-use datatypes::with_match_primitive_type_id;
-use snafu::ensure;
-
-// https://numpy.org/doc/stable/reference/generated/numpy.argmax.html
-// return the index of the max value
-#[derive(Debug, Default)]
-pub struct Argmax<T> {
-    max: Option<T>,
-    n: u64,
-}
-
-impl<T> Argmax<T>
-where
-    T: PartialOrd + Copy,
-{
-    fn update(&mut self, value: T, index: u64) {
-        if let Some(Ordering::Less) = self.max.partial_cmp(&Some(value)) {
-            self.max = Some(value);
-            self.n = index;
-        }
-    }
-}
-
-impl<T> Accumulator for Argmax<T>
-where
-    T: WrapperType + PartialOrd,
-{
-    fn state(&self) -> Result<Vec<Value>> {
-        match self.max {
-            Some(max) => Ok(vec![max.into(), self.n.into()]),
-            _ => Ok(vec![Value::Null, self.n.into()]),
-        }
-    }
-
-    fn update_batch(&mut self, values: &[VectorRef]) -> Result<()> {
-        if values.is_empty() {
-            return Ok(());
-        }
-
-        let column = &values[0];
-        let column: &<T as Scalar>::VectorType = if column.is_const() {
-            let column: &ConstantVector = unsafe { Helper::static_cast(column) };
-            unsafe { Helper::static_cast(column.inner()) }
-        } else {
-            unsafe { Helper::static_cast(column) }
-        };
-        for (i, v) in column.iter_data().enumerate() {
-            if let Some(value) = v {
-                self.update(value, i as u64);
-            }
-        }
-        Ok(())
-    }
-
-    fn merge_batch(&mut self, states: &[VectorRef]) -> Result<()> {
-        if states.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(
-            states.len() == 2,
-            BadAccumulatorImplSnafu {
-                err_msg: "expect 2 states in `merge_batch`",
-            }
-        );
-
-        let max = &states[0];
-        let index = &states[1];
-        let max: &<T as Scalar>::VectorType = unsafe { Helper::static_cast(max) };
-        let index: &<u64 as Scalar>::VectorType = unsafe { Helper::static_cast(index) };
-        index
-            .iter_data()
-            .flatten()
-            .zip(max.iter_data().flatten())
-            .for_each(|(i, max)| self.update(max, i));
-        Ok(())
-    }
-
-    fn evaluate(&self) -> Result<Value> {
-        match self.max {
-            Some(_) => Ok(self.n.into()),
-            _ => Ok(Value::Null),
-        }
-    }
-}
-
-#[as_aggr_func_creator]
-#[derive(Debug, Default, AggrFuncTypeStore)]
-pub struct ArgmaxAccumulatorCreator {}
-
-impl AggregateFunctionCreator for ArgmaxAccumulatorCreator {
-    fn creator(&self) -> AccumulatorCreatorFunction {
-        let creator: AccumulatorCreatorFunction = Arc::new(move |types: &[ConcreteDataType]| {
-            let input_type = &types[0];
-            with_match_primitive_type_id!(
-                input_type.logical_type_id(),
-                |$S| {
-                    Ok(Box::new(Argmax::<<$S as LogicalPrimitiveType>::Wrapper>::default()))
-                },
-                {
-                    let err_msg = format!(
-                        "\"ARGMAX\" aggregate function not support data type {:?}",
-                        input_type.logical_type_id(),
-                    );
-                    CreateAccumulatorSnafu { err_msg }.fail()?
-                }
-            )
-        });
-        creator
-    }
-
-    fn output_type(&self) -> Result<ConcreteDataType> {
-        Ok(ConcreteDataType::uint64_datatype())
-    }
-
-    fn state_types(&self) -> Result<Vec<ConcreteDataType>> {
-        let input_types = self.input_types()?;
-
-        ensure!(input_types.len() == 1, InvalidInputStateSnafu);
-
-        Ok(vec![
-            input_types.into_iter().next().unwrap(),
-            ConcreteDataType::uint64_datatype(),
-        ])
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use datatypes::vectors::Int32Vector;
-
-    use super::*;
-    #[test]
-    fn test_update_batch() {
-        // test update empty batch, expect not updating anything
-        let mut argmax = Argmax::<i32>::default();
-        argmax.update_batch(&[]).unwrap();
-        assert_eq!(Value::Null, argmax.evaluate().unwrap());
-
-        // test update one not-null value
-        let mut argmax = Argmax::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![Some(42)]))];
-        argmax.update_batch(&v).unwrap();
-        assert_eq!(Value::from(0_u64), argmax.evaluate().unwrap());
-
-        // test update one null value
-        let mut argmax = Argmax::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![Option::<i32>::None]))];
-        argmax.update_batch(&v).unwrap();
-        assert_eq!(Value::Null, argmax.evaluate().unwrap());
-
-        // test update no null-value batch
-        let mut argmax = Argmax::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![
-            Some(-1i32),
-            Some(1),
-            Some(3),
-        ]))];
-        argmax.update_batch(&v).unwrap();
-        assert_eq!(Value::from(2_u64), argmax.evaluate().unwrap());
-
-        // test update null-value batch
-        let mut argmax = Argmax::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![
-            Some(-2i32),
-            None,
-            Some(4),
-        ]))];
-        argmax.update_batch(&v).unwrap();
-        assert_eq!(Value::from(2_u64), argmax.evaluate().unwrap());
-
-        // test update with constant vector
-        let mut argmax = Argmax::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(ConstantVector::new(
-            Arc::new(Int32Vector::from_vec(vec![4])),
-            10,
-        ))];
-        argmax.update_batch(&v).unwrap();
-        assert_eq!(Value::from(0_u64), argmax.evaluate().unwrap());
-    }
-}

src/common/function/src/scalars/aggregate/argmin.rs

@@ -1,216 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::cmp::Ordering;
-use std::sync::Arc;
-
-use common_macro::{as_aggr_func_creator, AggrFuncTypeStore};
-use common_query::error::{
-    BadAccumulatorImplSnafu, CreateAccumulatorSnafu, InvalidInputStateSnafu, Result,
-};
-use common_query::logical_plan::accumulator::AggrFuncTypeStore;
-use common_query::logical_plan::{Accumulator, AggregateFunctionCreator};
-use common_query::prelude::*;
-use datatypes::prelude::*;
-use datatypes::vectors::{ConstantVector, Helper};
-use datatypes::with_match_primitive_type_id;
-use snafu::ensure;
-
-// // https://numpy.org/doc/stable/reference/generated/numpy.argmin.html
-#[derive(Debug, Default)]
-pub struct Argmin<T> {
-    min: Option<T>,
-    n: u32,
-}
-
-impl<T> Argmin<T>
-where
-    T: Copy + PartialOrd,
-{
-    fn update(&mut self, value: T, index: u32) {
-        match self.min {
-            Some(min) => {
-                if let Some(Ordering::Greater) = min.partial_cmp(&value) {
-                    self.min = Some(value);
-                    self.n = index;
-                }
-            }
-            None => {
-                self.min = Some(value);
-                self.n = index;
-            }
-        }
-    }
-}
-
-impl<T> Accumulator for Argmin<T>
-where
-    T: WrapperType + PartialOrd,
-{
-    fn state(&self) -> Result<Vec<Value>> {
-        match self.min {
-            Some(min) => Ok(vec![min.into(), self.n.into()]),
-            _ => Ok(vec![Value::Null, self.n.into()]),
-        }
-    }
-
-    fn update_batch(&mut self, values: &[VectorRef]) -> Result<()> {
-        if values.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(values.len() == 1, InvalidInputStateSnafu);
-
-        let column = &values[0];
-        let column: &<T as Scalar>::VectorType = if column.is_const() {
-            let column: &ConstantVector = unsafe { Helper::static_cast(column) };
-            unsafe { Helper::static_cast(column.inner()) }
-        } else {
-            unsafe { Helper::static_cast(column) }
-        };
-        for (i, v) in column.iter_data().enumerate() {
-            if let Some(value) = v {
-                self.update(value, i as u32);
-            }
-        }
-        Ok(())
-    }
-
-    fn merge_batch(&mut self, states: &[VectorRef]) -> Result<()> {
-        if states.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(
-            states.len() == 2,
-            BadAccumulatorImplSnafu {
-                err_msg: "expect 2 states in `merge_batch`",
-            }
-        );
-
-        let min = &states[0];
-        let index = &states[1];
-        let min: &<T as Scalar>::VectorType = unsafe { Helper::static_cast(min) };
-        let index: &<u32 as Scalar>::VectorType = unsafe { Helper::static_cast(index) };
-        index
-            .iter_data()
-            .flatten()
-            .zip(min.iter_data().flatten())
-            .for_each(|(i, min)| self.update(min, i));
-        Ok(())
-    }
-
-    fn evaluate(&self) -> Result<Value> {
-        match self.min {
-            Some(_) => Ok(self.n.into()),
-            _ => Ok(Value::Null),
-        }
-    }
-}
-
-#[as_aggr_func_creator]
-#[derive(Debug, Default, AggrFuncTypeStore)]
-pub struct ArgminAccumulatorCreator {}
-
-impl AggregateFunctionCreator for ArgminAccumulatorCreator {
-    fn creator(&self) -> AccumulatorCreatorFunction {
-        let creator: AccumulatorCreatorFunction = Arc::new(move |types: &[ConcreteDataType]| {
-            let input_type = &types[0];
-            with_match_primitive_type_id!(
-                input_type.logical_type_id(),
-                |$S| {
-                    Ok(Box::new(Argmin::<<$S as LogicalPrimitiveType>::Wrapper>::default()))
-                },
-                {
-                    let err_msg = format!(
-                        "\"ARGMIN\" aggregate function not support data type {:?}",
-                        input_type.logical_type_id(),
-                    );
-                    CreateAccumulatorSnafu { err_msg }.fail()?
-                }
-            )
-        });
-        creator
-    }
-
-    fn output_type(&self) -> Result<ConcreteDataType> {
-        Ok(ConcreteDataType::uint32_datatype())
-    }
-
-    fn state_types(&self) -> Result<Vec<ConcreteDataType>> {
-        let input_types = self.input_types()?;
-
-        ensure!(input_types.len() == 1, InvalidInputStateSnafu);
-
-        Ok(vec![
-            input_types.into_iter().next().unwrap(),
-            ConcreteDataType::uint32_datatype(),
-        ])
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use datatypes::vectors::Int32Vector;
-
-    use super::*;
-    #[test]
-    fn test_update_batch() {
-        // test update empty batch, expect not updating anything
-        let mut argmin = Argmin::<i32>::default();
-        argmin.update_batch(&[]).unwrap();
-        assert_eq!(Value::Null, argmin.evaluate().unwrap());
-
-        // test update one not-null value
-        let mut argmin = Argmin::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![Some(42)]))];
-        argmin.update_batch(&v).unwrap();
-        assert_eq!(Value::from(0_u32), argmin.evaluate().unwrap());
-
-        // test update one null value
-        let mut argmin = Argmin::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![Option::<i32>::None]))];
-        argmin.update_batch(&v).unwrap();
-        assert_eq!(Value::Null, argmin.evaluate().unwrap());
-
-        // test update no null-value batch
-        let mut argmin = Argmin::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![
-            Some(-1i32),
-            Some(1),
-            Some(3),
-        ]))];
-        argmin.update_batch(&v).unwrap();
-        assert_eq!(Value::from(0_u32), argmin.evaluate().unwrap());
-
-        // test update null-value batch
-        let mut argmin = Argmin::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![
-            Some(-2i32),
-            None,
-            Some(4),
-        ]))];
-        argmin.update_batch(&v).unwrap();
-        assert_eq!(Value::from(0_u32), argmin.evaluate().unwrap());
-
-        // test update with constant vector
-        let mut argmin = Argmin::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(ConstantVector::new(
-            Arc::new(Int32Vector::from_vec(vec![4])),
-            10,
-        ))];
-        argmin.update_batch(&v).unwrap();
-        assert_eq!(Value::from(0_u32), argmin.evaluate().unwrap());
-    }
-}

src/common/function/src/scalars/aggregate/diff.rs

@@ -1,252 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::marker::PhantomData;
-use std::sync::Arc;
-
-use common_macro::{as_aggr_func_creator, AggrFuncTypeStore};
-use common_query::error::{
-    CreateAccumulatorSnafu, DowncastVectorSnafu, FromScalarValueSnafu, InvalidInputStateSnafu,
-    Result,
-};
-use common_query::logical_plan::accumulator::AggrFuncTypeStore;
-use common_query::logical_plan::{Accumulator, AggregateFunctionCreator};
-use common_query::prelude::*;
-use datatypes::prelude::*;
-use datatypes::value::ListValue;
-use datatypes::vectors::{ConstantVector, Helper, ListVector};
-use datatypes::with_match_primitive_type_id;
-use num_traits::AsPrimitive;
-use snafu::{ensure, OptionExt, ResultExt};
-
-// https://numpy.org/doc/stable/reference/generated/numpy.diff.html
-// I is the input type, O is the output type.
-#[derive(Debug, Default)]
-pub struct Diff<I, O> {
-    values: Vec<I>,
-    _phantom: PhantomData<O>,
-}
-
-impl<I, O> Diff<I, O> {
-    fn push(&mut self, value: I) {
-        self.values.push(value);
-    }
-}
-
-impl<I, O> Accumulator for Diff<I, O>
-where
-    I: WrapperType,
-    O: WrapperType,
-    I::Native: AsPrimitive<O::Native>,
-    O::Native: std::ops::Sub<Output = O::Native>,
-{
-    fn state(&self) -> Result<Vec<Value>> {
-        let nums = self
-            .values
-            .iter()
-            .map(|&n| n.into())
-            .collect::<Vec<Value>>();
-        Ok(vec![Value::List(ListValue::new(
-            nums,
-            I::LogicalType::build_data_type(),
-        ))])
-    }
-
-    fn update_batch(&mut self, values: &[VectorRef]) -> Result<()> {
-        if values.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(values.len() == 1, InvalidInputStateSnafu);
-
-        let column = &values[0];
-        let mut len = 1;
-        let column: &<I as Scalar>::VectorType = if column.is_const() {
-            len = column.len();
-            let column: &ConstantVector = unsafe { Helper::static_cast(column) };
-            unsafe { Helper::static_cast(column.inner()) }
-        } else {
-            unsafe { Helper::static_cast(column) }
-        };
-        (0..len).for_each(|_| {
-            for v in column.iter_data().flatten() {
-                self.push(v);
-            }
-        });
-        Ok(())
-    }
-
-    fn merge_batch(&mut self, states: &[VectorRef]) -> Result<()> {
-        if states.is_empty() {
-            return Ok(());
-        }
-
-        let states = &states[0];
-        let states = states
-            .as_any()
-            .downcast_ref::<ListVector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect ListVector, got vector type {}",
-                    states.vector_type_name()
-                ),
-            })?;
-        for state in states.values_iter() {
-            if let Some(state) = state.context(FromScalarValueSnafu)? {
-                self.update_batch(&[state])?;
-            }
-        }
-        Ok(())
-    }
-
-    fn evaluate(&self) -> Result<Value> {
-        if self.values.is_empty() || self.values.len() == 1 {
-            return Ok(Value::Null);
-        }
-        let diff = self
-            .values
-            .windows(2)
-            .map(|x| {
-                let native = x[1].into_native().as_() - x[0].into_native().as_();
-                O::from_native(native).into()
-            })
-            .collect::<Vec<Value>>();
-        let diff = Value::List(ListValue::new(diff, O::LogicalType::build_data_type()));
-        Ok(diff)
-    }
-}
-
-#[as_aggr_func_creator]
-#[derive(Debug, Default, AggrFuncTypeStore)]
-pub struct DiffAccumulatorCreator {}
-
-impl AggregateFunctionCreator for DiffAccumulatorCreator {
-    fn creator(&self) -> AccumulatorCreatorFunction {
-        let creator: AccumulatorCreatorFunction = Arc::new(move |types: &[ConcreteDataType]| {
-            let input_type = &types[0];
-            with_match_primitive_type_id!(
-                input_type.logical_type_id(),
-                |$S| {
-                    Ok(Box::new(Diff::<<$S as LogicalPrimitiveType>::Wrapper, <<$S as LogicalPrimitiveType>::LargestType as LogicalPrimitiveType>::Wrapper>::default()))
-                },
-                {
-                    let err_msg = format!(
-                        "\"DIFF\" aggregate function not support data type {:?}",
-                        input_type.logical_type_id(),
-                    );
-                    CreateAccumulatorSnafu { err_msg }.fail()?
-                }
-            )
-        });
-        creator
-    }
-
-    fn output_type(&self) -> Result<ConcreteDataType> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 1, InvalidInputStateSnafu);
-        with_match_primitive_type_id!(
-            input_types[0].logical_type_id(),
-            |$S| {
-                Ok(ConcreteDataType::list_datatype($S::default().into()))
-            },
-            {
-                unreachable!()
-            }
-        )
-    }
-
-    fn state_types(&self) -> Result<Vec<ConcreteDataType>> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 1, InvalidInputStateSnafu);
-        with_match_primitive_type_id!(
-            input_types[0].logical_type_id(),
-            |$S| {
-                Ok(vec![ConcreteDataType::list_datatype($S::default().into())])
-            },
-            {
-                unreachable!()
-            }
-        )
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use datatypes::vectors::Int32Vector;
-
-    use super::*;
-
-    #[test]
-    fn test_update_batch() {
-        // test update empty batch, expect not updating anything
-        let mut diff = Diff::<i32, i64>::default();
-        diff.update_batch(&[]).unwrap();
-        assert!(diff.values.is_empty());
-        assert_eq!(Value::Null, diff.evaluate().unwrap());
-
-        // test update one not-null value
-        let mut diff = Diff::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![Some(42)]))];
-        diff.update_batch(&v).unwrap();
-        assert_eq!(Value::Null, diff.evaluate().unwrap());
-
-        // test update one null value
-        let mut diff = Diff::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![Option::<i32>::None]))];
-        diff.update_batch(&v).unwrap();
-        assert_eq!(Value::Null, diff.evaluate().unwrap());
-
-        // test update no null-value batch
-        let mut diff = Diff::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![
-            Some(-1i32),
-            Some(1),
-            Some(2),
-        ]))];
-        let values = vec![Value::from(2_i64), Value::from(1_i64)];
-        diff.update_batch(&v).unwrap();
-        assert_eq!(
-            Value::List(ListValue::new(values, ConcreteDataType::int64_datatype())),
-            diff.evaluate().unwrap()
-        );
-
-        // test update null-value batch
-        let mut diff = Diff::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![
-            Some(-2i32),
-            None,
-            Some(3),
-            Some(4),
-        ]))];
-        let values = vec![Value::from(5_i64), Value::from(1_i64)];
-        diff.update_batch(&v).unwrap();
-        assert_eq!(
-            Value::List(ListValue::new(values, ConcreteDataType::int64_datatype())),
-            diff.evaluate().unwrap()
-        );
-
-        // test update with constant vector
-        let mut diff = Diff::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(ConstantVector::new(
-            Arc::new(Int32Vector::from_vec(vec![4])),
-            4,
-        ))];
-        let values = vec![Value::from(0_i64), Value::from(0_i64), Value::from(0_i64)];
-        diff.update_batch(&v).unwrap();
-        assert_eq!(
-            Value::List(ListValue::new(values, ConcreteDataType::int64_datatype())),
-            diff.evaluate().unwrap()
-        );
-    }
-}

src/common/function/src/scalars/aggregate/mean.rs

@@ -1,238 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::marker::PhantomData;
-use std::sync::Arc;
-
-use common_macro::{as_aggr_func_creator, AggrFuncTypeStore};
-use common_query::error::{
-    BadAccumulatorImplSnafu, CreateAccumulatorSnafu, DowncastVectorSnafu, InvalidInputStateSnafu,
-    Result,
-};
-use common_query::logical_plan::accumulator::AggrFuncTypeStore;
-use common_query::logical_plan::{Accumulator, AggregateFunctionCreator};
-use common_query::prelude::*;
-use datatypes::prelude::*;
-use datatypes::types::WrapperType;
-use datatypes::vectors::{ConstantVector, Float64Vector, Helper, UInt64Vector};
-use datatypes::with_match_primitive_type_id;
-use num_traits::AsPrimitive;
-use snafu::{ensure, OptionExt};
-
-#[derive(Debug, Default)]
-pub struct Mean<T> {
-    sum: f64,
-    n: u64,
-    _phantom: PhantomData<T>,
-}
-
-impl<T> Mean<T>
-where
-    T: WrapperType,
-    T::Native: AsPrimitive<f64>,
-{
-    #[inline(always)]
-    fn push(&mut self, value: T) {
-        self.sum += value.into_native().as_();
-        self.n += 1;
-    }
-
-    #[inline(always)]
-    fn update(&mut self, sum: f64, n: u64) {
-        self.sum += sum;
-        self.n += n;
-    }
-}
-
-impl<T> Accumulator for Mean<T>
-where
-    T: WrapperType,
-    T::Native: AsPrimitive<f64>,
-{
-    fn state(&self) -> Result<Vec<Value>> {
-        Ok(vec![self.sum.into(), self.n.into()])
-    }
-
-    fn update_batch(&mut self, values: &[VectorRef]) -> Result<()> {
-        if values.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(values.len() == 1, InvalidInputStateSnafu);
-        let column = &values[0];
-        let mut len = 1;
-        let column: &<T as Scalar>::VectorType = if column.is_const() {
-            len = column.len();
-            let column: &ConstantVector = unsafe { Helper::static_cast(column) };
-            unsafe { Helper::static_cast(column.inner()) }
-        } else {
-            unsafe { Helper::static_cast(column) }
-        };
-        (0..len).for_each(|_| {
-            for v in column.iter_data().flatten() {
-                self.push(v);
-            }
-        });
-
-        Ok(())
-    }
-
-    fn merge_batch(&mut self, states: &[VectorRef]) -> Result<()> {
-        if states.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(
-            states.len() == 2,
-            BadAccumulatorImplSnafu {
-                err_msg: "expect 2 states in `merge_batch`",
-            }
-        );
-
-        let sum = &states[0];
-        let n = &states[1];
-
-        let sum = sum
-            .as_any()
-            .downcast_ref::<Float64Vector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect Float64Vector, got vector type {}",
-                    sum.vector_type_name()
-                ),
-            })?;
-
-        let n = n
-            .as_any()
-            .downcast_ref::<UInt64Vector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect UInt64Vector, got vector type {}",
-                    sum.vector_type_name()
-                ),
-            })?;
-
-        sum.iter_data().zip(n.iter_data()).for_each(|(sum, n)| {
-            if let (Some(sum), Some(n)) = (sum, n) {
-                self.update(sum, n);
-            }
-        });
-        Ok(())
-    }
-
-    fn evaluate(&self) -> Result<Value> {
-        if self.n == 0 {
-            return Ok(Value::Null);
-        }
-        let values = self.sum / self.n as f64;
-        Ok(values.into())
-    }
-}
-
-#[as_aggr_func_creator]
-#[derive(Debug, Default, AggrFuncTypeStore)]
-pub struct MeanAccumulatorCreator {}
-
-impl AggregateFunctionCreator for MeanAccumulatorCreator {
-    fn creator(&self) -> AccumulatorCreatorFunction {
-        let creator: AccumulatorCreatorFunction = Arc::new(move |types: &[ConcreteDataType]| {
-            let input_type = &types[0];
-            with_match_primitive_type_id!(
-                input_type.logical_type_id(),
-                |$S| {
-                    Ok(Box::new(Mean::<<$S as LogicalPrimitiveType>::Native>::default()))
-                },
-                {
-                    let err_msg = format!(
-                        "\"MEAN\" aggregate function not support data type {:?}",
-                        input_type.logical_type_id(),
-                    );
-                    CreateAccumulatorSnafu { err_msg }.fail()?
-                }
-            )
-        });
-        creator
-    }
-
-    fn output_type(&self) -> Result<ConcreteDataType> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 1, InvalidInputStateSnafu);
-        Ok(ConcreteDataType::float64_datatype())
-    }
-
-    fn state_types(&self) -> Result<Vec<ConcreteDataType>> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 1, InvalidInputStateSnafu);
-        Ok(vec![
-            ConcreteDataType::float64_datatype(),
-            ConcreteDataType::uint64_datatype(),
-        ])
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use datatypes::vectors::Int32Vector;
-
-    use super::*;
-    #[test]
-    fn test_update_batch() {
-        // test update empty batch, expect not updating anything
-        let mut mean = Mean::<i32>::default();
-        mean.update_batch(&[]).unwrap();
-        assert_eq!(Value::Null, mean.evaluate().unwrap());
-
-        // test update one not-null value
-        let mut mean = Mean::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![Some(42)]))];
-        mean.update_batch(&v).unwrap();
-        assert_eq!(Value::from(42.0_f64), mean.evaluate().unwrap());
-
-        // test update one null value
-        let mut mean = Mean::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![Option::<i32>::None]))];
-        mean.update_batch(&v).unwrap();
-        assert_eq!(Value::Null, mean.evaluate().unwrap());
-
-        // test update no null-value batch
-        let mut mean = Mean::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![
-            Some(-1i32),
-            Some(1),
-            Some(2),
-        ]))];
-        mean.update_batch(&v).unwrap();
-        assert_eq!(Value::from(0.6666666666666666), mean.evaluate().unwrap());
-
-        // test update null-value batch
-        let mut mean = Mean::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(Int32Vector::from(vec![
-            Some(-2i32),
-            None,
-            Some(3),
-            Some(4),
-        ]))];
-        mean.update_batch(&v).unwrap();
-        assert_eq!(Value::from(1.6666666666666667), mean.evaluate().unwrap());
-
-        // test update with constant vector
-        let mut mean = Mean::<i32>::default();
-        let v: Vec<VectorRef> = vec![Arc::new(ConstantVector::new(
-            Arc::new(Int32Vector::from_vec(vec![4])),
-            10,
-        ))];
-        mean.update_batch(&v).unwrap();
-        assert_eq!(Value::from(4.0), mean.evaluate().unwrap());
-    }
-}

src/common/function/src/scalars/aggregate/polyval.rs

@@ -1,329 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::marker::PhantomData;
-use std::sync::Arc;
-
-use common_macro::{as_aggr_func_creator, AggrFuncTypeStore};
-use common_query::error::{
-    self, BadAccumulatorImplSnafu, CreateAccumulatorSnafu, DowncastVectorSnafu,
-    FromScalarValueSnafu, InvalidInputColSnafu, InvalidInputStateSnafu, Result,
-};
-use common_query::logical_plan::accumulator::AggrFuncTypeStore;
-use common_query::logical_plan::{Accumulator, AggregateFunctionCreator};
-use common_query::prelude::*;
-use datatypes::prelude::*;
-use datatypes::types::{LogicalPrimitiveType, WrapperType};
-use datatypes::value::ListValue;
-use datatypes::vectors::{ConstantVector, Helper, Int64Vector, ListVector};
-use datatypes::with_match_primitive_type_id;
-use num_traits::AsPrimitive;
-use snafu::{ensure, OptionExt, ResultExt};
-
-// https://numpy.org/doc/stable/reference/generated/numpy.polyval.html
-#[derive(Debug, Default)]
-pub struct Polyval<T, PolyT>
-where
-    T: WrapperType,
-    T::Native: AsPrimitive<PolyT::Native>,
-    PolyT: WrapperType,
-    PolyT::Native: std::ops::Mul<Output = PolyT::Native>,
-{
-    values: Vec<T>,
-    // DataFusion casts constant in into i64 type.
-    x: Option<i64>,
-    _phantom: PhantomData<PolyT>,
-}
-
-impl<T, PolyT> Polyval<T, PolyT>
-where
-    T: WrapperType,
-    T::Native: AsPrimitive<PolyT::Native>,
-    PolyT: WrapperType,
-    PolyT::Native: std::ops::Mul<Output = PolyT::Native>,
-{
-    fn push(&mut self, value: T) {
-        self.values.push(value);
-    }
-}
-
-impl<T, PolyT> Accumulator for Polyval<T, PolyT>
-where
-    T: WrapperType,
-    T::Native: AsPrimitive<PolyT::Native>,
-    PolyT: WrapperType + std::iter::Sum<<PolyT as WrapperType>::Native>,
-    PolyT::Native: std::ops::Mul<Output = PolyT::Native> + std::iter::Sum<PolyT::Native>,
-    i64: AsPrimitive<<PolyT as WrapperType>::Native>,
-{
-    fn state(&self) -> Result<Vec<Value>> {
-        let nums = self
-            .values
-            .iter()
-            .map(|&n| n.into())
-            .collect::<Vec<Value>>();
-        Ok(vec![
-            Value::List(ListValue::new(nums, T::LogicalType::build_data_type())),
-            self.x.into(),
-        ])
-    }
-
-    fn update_batch(&mut self, values: &[VectorRef]) -> Result<()> {
-        if values.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(values.len() == 2, InvalidInputStateSnafu);
-        ensure!(values[0].len() == values[1].len(), InvalidInputStateSnafu);
-        if values[0].len() == 0 {
-            return Ok(());
-        }
-        // This is a unary accumulator, so only one column is provided.
-        let column = &values[0];
-        let mut len = 1;
-        let column: &<T as Scalar>::VectorType = if column.is_const() {
-            len = column.len();
-            let column: &ConstantVector = unsafe { Helper::static_cast(column) };
-            unsafe { Helper::static_cast(column.inner()) }
-        } else {
-            unsafe { Helper::static_cast(column) }
-        };
-        (0..len).for_each(|_| {
-            for v in column.iter_data().flatten() {
-                self.push(v);
-            }
-        });
-
-        let x = &values[1];
-        let x = Helper::check_get_scalar::<i64>(x).context(error::InvalidInputTypeSnafu {
-            err_msg: "expecting \"POLYVAL\" function's second argument to be a positive integer",
-        })?;
-        // `get(0)` is safe because we have checked `values[1].len() == values[0].len() != 0`
-        let first = x.get(0);
-        ensure!(!first.is_null(), InvalidInputColSnafu);
-
-        for i in 1..x.len() {
-            ensure!(first == x.get(i), InvalidInputColSnafu);
-        }
-
-        let first = match first {
-            Value::Int64(v) => v,
-            // unreachable because we have checked `first` is not null and is i64 above
-            _ => unreachable!(),
-        };
-        if let Some(x) = self.x {
-            ensure!(x == first, InvalidInputColSnafu);
-        } else {
-            self.x = Some(first);
-        };
-        Ok(())
-    }
-
-    // DataFusion executes accumulators in partitions. In some execution stage, DataFusion will
-    // merge states from other accumulators (returned by `state()` method).
-    fn merge_batch(&mut self, states: &[VectorRef]) -> Result<()> {
-        if states.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(
-            states.len() == 2,
-            BadAccumulatorImplSnafu {
-                err_msg: "expect 2 states in `merge_batch`",
-            }
-        );
-
-        let x = &states[1];
-        let x = x
-            .as_any()
-            .downcast_ref::<Int64Vector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect Int64Vector, got vector type {}",
-                    x.vector_type_name()
-                ),
-            })?;
-        let x = x.get(0);
-        if x.is_null() {
-            return Ok(());
-        }
-        let x = match x {
-            Value::Int64(x) => x,
-            _ => unreachable!(),
-        };
-        self.x = Some(x);
-
-        let values = &states[0];
-        let values = values
-            .as_any()
-            .downcast_ref::<ListVector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect ListVector, got vector type {}",
-                    values.vector_type_name()
-                ),
-            })?;
-        for value in values.values_iter() {
-            if let Some(value) = value.context(FromScalarValueSnafu)? {
-                let column: &<T as Scalar>::VectorType = unsafe { Helper::static_cast(&value) };
-                for v in column.iter_data().flatten() {
-                    self.push(v);
-                }
-            }
-        }
-
-        Ok(())
-    }
-
-    // DataFusion expects this function to return the final value of this aggregator.
-    fn evaluate(&self) -> Result<Value> {
-        if self.values.is_empty() {
-            return Ok(Value::Null);
-        }
-        let x = if let Some(x) = self.x {
-            x
-        } else {
-            return Ok(Value::Null);
-        };
-        let len = self.values.len();
-        let polyval: PolyT = self
-            .values
-            .iter()
-            .enumerate()
-            .map(|(i, &value)| value.into_native().as_() * x.pow((len - 1 - i) as u32).as_())
-            .sum();
-        Ok(polyval.into())
-    }
-}
-
-#[as_aggr_func_creator]
-#[derive(Debug, Default, AggrFuncTypeStore)]
-pub struct PolyvalAccumulatorCreator {}
-
-impl AggregateFunctionCreator for PolyvalAccumulatorCreator {
-    fn creator(&self) -> AccumulatorCreatorFunction {
-        let creator: AccumulatorCreatorFunction = Arc::new(move |types: &[ConcreteDataType]| {
-            let input_type = &types[0];
-            with_match_primitive_type_id!(
-                input_type.logical_type_id(),
-                |$S| {
-                    Ok(Box::new(Polyval::<<$S as LogicalPrimitiveType>::Wrapper, <<$S as LogicalPrimitiveType>::LargestType as LogicalPrimitiveType>::Wrapper>::default()))
-                },
-                {
-                    let err_msg = format!(
-                        "\"POLYVAL\" aggregate function not support data type {:?}",
-                        input_type.logical_type_id(),
-                    );
-                    CreateAccumulatorSnafu { err_msg }.fail()?
-                }
-            )
-        });
-        creator
-    }
-
-    fn output_type(&self) -> Result<ConcreteDataType> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 2, InvalidInputStateSnafu);
-        let input_type = self.input_types()?[0].logical_type_id();
-        with_match_primitive_type_id!(
-            input_type,
-            |$S| {
-                Ok(<<$S as LogicalPrimitiveType>::LargestType as LogicalPrimitiveType>::build_data_type())
-            },
-            {
-                unreachable!()
-            }
-        )
-    }
-
-    fn state_types(&self) -> Result<Vec<ConcreteDataType>> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 2, InvalidInputStateSnafu);
-        Ok(vec![
-            ConcreteDataType::list_datatype(input_types.into_iter().next().unwrap()),
-            ConcreteDataType::int64_datatype(),
-        ])
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use datatypes::vectors::Int32Vector;
-
-    use super::*;
-    #[test]
-    fn test_update_batch() {
-        // test update empty batch, expect not updating anything
-        let mut polyval = Polyval::<i32, i64>::default();
-        polyval.update_batch(&[]).unwrap();
-        assert!(polyval.values.is_empty());
-        assert_eq!(Value::Null, polyval.evaluate().unwrap());
-
-        // test update one not-null value
-        let mut polyval = Polyval::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(Int32Vector::from(vec![Some(3)])),
-            Arc::new(Int64Vector::from(vec![Some(2_i64)])),
-        ];
-        polyval.update_batch(&v).unwrap();
-        assert_eq!(Value::Int64(3), polyval.evaluate().unwrap());
-
-        // test update one null value
-        let mut polyval = Polyval::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(Int32Vector::from(vec![Option::<i32>::None])),
-            Arc::new(Int64Vector::from(vec![Some(2_i64)])),
-        ];
-        polyval.update_batch(&v).unwrap();
-        assert_eq!(Value::Null, polyval.evaluate().unwrap());
-
-        // test update no null-value batch
-        let mut polyval = Polyval::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(Int32Vector::from(vec![Some(3), Some(0), Some(1)])),
-            Arc::new(Int64Vector::from(vec![
-                Some(2_i64),
-                Some(2_i64),
-                Some(2_i64),
-            ])),
-        ];
-        polyval.update_batch(&v).unwrap();
-        assert_eq!(Value::Int64(13), polyval.evaluate().unwrap());
-
-        // test update null-value batch
-        let mut polyval = Polyval::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(Int32Vector::from(vec![Some(3), Some(0), None, Some(1)])),
-            Arc::new(Int64Vector::from(vec![
-                Some(2_i64),
-                Some(2_i64),
-                Some(2_i64),
-                Some(2_i64),
-            ])),
-        ];
-        polyval.update_batch(&v).unwrap();
-        assert_eq!(Value::Int64(13), polyval.evaluate().unwrap());
-
-        // test update with constant vector
-        let mut polyval = Polyval::<i32, i64>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(ConstantVector::new(
-                Arc::new(Int32Vector::from_vec(vec![4])),
-                2,
-            )),
-            Arc::new(Int64Vector::from(vec![Some(5_i64), Some(5_i64)])),
-        ];
-        polyval.update_batch(&v).unwrap();
-        assert_eq!(Value::Int64(24), polyval.evaluate().unwrap());
-    }
-}

src/common/function/src/scalars/aggregate/scipy_stats_norm_cdf.rs

@@ -1,270 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use common_macro::{as_aggr_func_creator, AggrFuncTypeStore};
-use common_query::error::{
-    self, BadAccumulatorImplSnafu, CreateAccumulatorSnafu, DowncastVectorSnafu,
-    FromScalarValueSnafu, GenerateFunctionSnafu, InvalidInputColSnafu, InvalidInputStateSnafu,
-    Result,
-};
-use common_query::logical_plan::accumulator::AggrFuncTypeStore;
-use common_query::logical_plan::{Accumulator, AggregateFunctionCreator};
-use common_query::prelude::*;
-use datatypes::prelude::*;
-use datatypes::value::{ListValue, OrderedFloat};
-use datatypes::vectors::{ConstantVector, Float64Vector, Helper, ListVector};
-use datatypes::with_match_primitive_type_id;
-use num_traits::AsPrimitive;
-use snafu::{ensure, OptionExt, ResultExt};
-use statrs::distribution::{ContinuousCDF, Normal};
-use statrs::statistics::Statistics;
-
-// https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html
-
-#[derive(Debug, Default)]
-pub struct ScipyStatsNormCdf<T> {
-    values: Vec<T>,
-    x: Option<f64>,
-}
-
-impl<T> ScipyStatsNormCdf<T> {
-    fn push(&mut self, value: T) {
-        self.values.push(value);
-    }
-}
-
-impl<T> Accumulator for ScipyStatsNormCdf<T>
-where
-    T: WrapperType + std::iter::Sum<T>,
-    T::Native: AsPrimitive<f64>,
-{
-    fn state(&self) -> Result<Vec<Value>> {
-        let nums = self
-            .values
-            .iter()
-            .map(|&x| x.into())
-            .collect::<Vec<Value>>();
-        Ok(vec![
-            Value::List(ListValue::new(nums, T::LogicalType::build_data_type())),
-            self.x.into(),
-        ])
-    }
-
-    fn update_batch(&mut self, values: &[VectorRef]) -> Result<()> {
-        if values.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(values.len() == 2, InvalidInputStateSnafu);
-        ensure!(values[1].len() == values[0].len(), InvalidInputStateSnafu);
-
-        if values[0].len() == 0 {
-            return Ok(());
-        }
-
-        let column = &values[0];
-        let mut len = 1;
-        let column: &<T as Scalar>::VectorType = if column.is_const() {
-            len = column.len();
-            let column: &ConstantVector = unsafe { Helper::static_cast(column) };
-            unsafe { Helper::static_cast(column.inner()) }
-        } else {
-            unsafe { Helper::static_cast(column) }
-        };
-
-        let x = &values[1];
-        let x = Helper::check_get_scalar::<f64>(x).context(error::InvalidInputTypeSnafu {
-            err_msg: "expecting \"SCIPYSTATSNORMCDF\" function's second argument to be a positive integer",
-        })?;
-        let first = x.get(0);
-        ensure!(!first.is_null(), InvalidInputColSnafu);
-        let first = match first {
-            Value::Float64(OrderedFloat(v)) => v,
-            // unreachable because we have checked `first` is not null and is i64 above
-            _ => unreachable!(),
-        };
-        if let Some(x) = self.x {
-            ensure!(x == first, InvalidInputColSnafu);
-        } else {
-            self.x = Some(first);
-        };
-
-        (0..len).for_each(|_| {
-            for v in column.iter_data().flatten() {
-                self.push(v);
-            }
-        });
-        Ok(())
-    }
-
-    fn merge_batch(&mut self, states: &[VectorRef]) -> Result<()> {
-        if states.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(
-            states.len() == 2,
-            BadAccumulatorImplSnafu {
-                err_msg: "expect 2 states in `merge_batch`",
-            }
-        );
-
-        let x = &states[1];
-        let x = x
-            .as_any()
-            .downcast_ref::<Float64Vector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect Float64Vector, got vector type {}",
-                    x.vector_type_name()
-                ),
-            })?;
-        let x = x.get(0);
-        if x.is_null() {
-            return Ok(());
-        }
-        let x = match x {
-            Value::Float64(OrderedFloat(x)) => x,
-            _ => unreachable!(),
-        };
-        self.x = Some(x);
-
-        let values = &states[0];
-        let values = values
-            .as_any()
-            .downcast_ref::<ListVector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect ListVector, got vector type {}",
-                    values.vector_type_name()
-                ),
-            })?;
-        for value in values.values_iter() {
-            if let Some(value) = value.context(FromScalarValueSnafu)? {
-                let column: &<T as Scalar>::VectorType = unsafe { Helper::static_cast(&value) };
-                for v in column.iter_data().flatten() {
-                    self.push(v);
-                }
-            }
-        }
-        Ok(())
-    }
-
-    fn evaluate(&self) -> Result<Value> {
-        let mean = self.values.iter().map(|v| v.into_native().as_()).mean();
-        let std_dev = self.values.iter().map(|v| v.into_native().as_()).std_dev();
-        if mean.is_nan() || std_dev.is_nan() {
-            Ok(Value::Null)
-        } else {
-            let x = if let Some(x) = self.x {
-                x
-            } else {
-                return Ok(Value::Null);
-            };
-            let n = Normal::new(mean, std_dev).context(GenerateFunctionSnafu)?;
-            Ok(n.cdf(x).into())
-        }
-    }
-}
-
-#[as_aggr_func_creator]
-#[derive(Debug, Default, AggrFuncTypeStore)]
-pub struct ScipyStatsNormCdfAccumulatorCreator {}
-
-impl AggregateFunctionCreator for ScipyStatsNormCdfAccumulatorCreator {
-    fn creator(&self) -> AccumulatorCreatorFunction {
-        let creator: AccumulatorCreatorFunction = Arc::new(move |types: &[ConcreteDataType]| {
-            let input_type = &types[0];
-            with_match_primitive_type_id!(
-                input_type.logical_type_id(),
-                |$S| {
-                    Ok(Box::new(ScipyStatsNormCdf::<<$S as LogicalPrimitiveType>::Wrapper>::default()))
-                },
-                {
-                    let err_msg = format!(
-                        "\"SCIPYSTATSNORMCDF\" aggregate function not support data type {:?}",
-                        input_type.logical_type_id(),
-                    );
-                    CreateAccumulatorSnafu { err_msg }.fail()?
-                }
-            )
-        });
-        creator
-    }
-
-    fn output_type(&self) -> Result<ConcreteDataType> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 2, InvalidInputStateSnafu);
-        Ok(ConcreteDataType::float64_datatype())
-    }
-
-    fn state_types(&self) -> Result<Vec<ConcreteDataType>> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 2, InvalidInputStateSnafu);
-        Ok(vec![
-            ConcreteDataType::list_datatype(input_types[0].clone()),
-            ConcreteDataType::float64_datatype(),
-        ])
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use datatypes::vectors::{Float64Vector, Int32Vector};
-
-    use super::*;
-    #[test]
-    fn test_update_batch() {
-        // test update empty batch, expect not updating anything
-        let mut scipy_stats_norm_cdf = ScipyStatsNormCdf::<i32>::default();
-        scipy_stats_norm_cdf.update_batch(&[]).unwrap();
-        assert!(scipy_stats_norm_cdf.values.is_empty());
-        assert_eq!(Value::Null, scipy_stats_norm_cdf.evaluate().unwrap());
-
-        // test update no null-value batch
-        let mut scipy_stats_norm_cdf = ScipyStatsNormCdf::<i32>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(Int32Vector::from(vec![Some(-1i32), Some(1), Some(2)])),
-            Arc::new(Float64Vector::from(vec![
-                Some(2.0_f64),
-                Some(2.0_f64),
-                Some(2.0_f64),
-            ])),
-        ];
-        scipy_stats_norm_cdf.update_batch(&v).unwrap();
-        assert_eq!(
-            Value::from(0.8086334555398362),
-            scipy_stats_norm_cdf.evaluate().unwrap()
-        );
-
-        // test update null-value batch
-        let mut scipy_stats_norm_cdf = ScipyStatsNormCdf::<i32>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(Int32Vector::from(vec![Some(-2i32), None, Some(3), Some(4)])),
-            Arc::new(Float64Vector::from(vec![
-                Some(2.0_f64),
-                None,
-                Some(2.0_f64),
-                Some(2.0_f64),
-            ])),
-        ];
-        scipy_stats_norm_cdf.update_batch(&v).unwrap();
-        assert_eq!(
-            Value::from(0.5412943699039795),
-            scipy_stats_norm_cdf.evaluate().unwrap()
-        );
-    }
-}

src/common/function/src/scalars/aggregate/scipy_stats_norm_pdf.rs

@@ -1,271 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use common_macro::{as_aggr_func_creator, AggrFuncTypeStore};
-use common_query::error::{
-    self, BadAccumulatorImplSnafu, CreateAccumulatorSnafu, DowncastVectorSnafu,
-    FromScalarValueSnafu, GenerateFunctionSnafu, InvalidInputColSnafu, InvalidInputStateSnafu,
-    Result,
-};
-use common_query::logical_plan::accumulator::AggrFuncTypeStore;
-use common_query::logical_plan::{Accumulator, AggregateFunctionCreator};
-use common_query::prelude::*;
-use datatypes::prelude::*;
-use datatypes::value::{ListValue, OrderedFloat};
-use datatypes::vectors::{ConstantVector, Float64Vector, Helper, ListVector};
-use datatypes::with_match_primitive_type_id;
-use num_traits::AsPrimitive;
-use snafu::{ensure, OptionExt, ResultExt};
-use statrs::distribution::{Continuous, Normal};
-use statrs::statistics::Statistics;
-
-// https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html
-
-#[derive(Debug, Default)]
-pub struct ScipyStatsNormPdf<T> {
-    values: Vec<T>,
-    x: Option<f64>,
-}
-
-impl<T> ScipyStatsNormPdf<T> {
-    fn push(&mut self, value: T) {
-        self.values.push(value);
-    }
-}
-
-impl<T> Accumulator for ScipyStatsNormPdf<T>
-where
-    T: WrapperType,
-    T::Native: AsPrimitive<f64> + std::iter::Sum<T>,
-{
-    fn state(&self) -> Result<Vec<Value>> {
-        let nums = self
-            .values
-            .iter()
-            .map(|&x| x.into())
-            .collect::<Vec<Value>>();
-        Ok(vec![
-            Value::List(ListValue::new(nums, T::LogicalType::build_data_type())),
-            self.x.into(),
-        ])
-    }
-
-    fn update_batch(&mut self, values: &[VectorRef]) -> Result<()> {
-        if values.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(values.len() == 2, InvalidInputStateSnafu);
-        ensure!(values[1].len() == values[0].len(), InvalidInputStateSnafu);
-
-        if values[0].len() == 0 {
-            return Ok(());
-        }
-
-        let column = &values[0];
-        let mut len = 1;
-        let column: &<T as Scalar>::VectorType = if column.is_const() {
-            len = column.len();
-            let column: &ConstantVector = unsafe { Helper::static_cast(column) };
-            unsafe { Helper::static_cast(column.inner()) }
-        } else {
-            unsafe { Helper::static_cast(column) }
-        };
-
-        let x = &values[1];
-        let x = Helper::check_get_scalar::<f64>(x).context(error::InvalidInputTypeSnafu {
-            err_msg: "expecting \"SCIPYSTATSNORMPDF\" function's second argument to be a positive integer",
-        })?;
-        let first = x.get(0);
-        ensure!(!first.is_null(), InvalidInputColSnafu);
-        let first = match first {
-            Value::Float64(OrderedFloat(v)) => v,
-            // unreachable because we have checked `first` is not null and is i64 above
-            _ => unreachable!(),
-        };
-        if let Some(x) = self.x {
-            ensure!(x == first, InvalidInputColSnafu);
-        } else {
-            self.x = Some(first);
-        };
-
-        (0..len).for_each(|_| {
-            for v in column.iter_data().flatten() {
-                self.push(v);
-            }
-        });
-        Ok(())
-    }
-
-    fn merge_batch(&mut self, states: &[VectorRef]) -> Result<()> {
-        if states.is_empty() {
-            return Ok(());
-        }
-
-        ensure!(
-            states.len() == 2,
-            BadAccumulatorImplSnafu {
-                err_msg: "expect 2 states in `merge_batch`",
-            }
-        );
-
-        let x = &states[1];
-        let x = x
-            .as_any()
-            .downcast_ref::<Float64Vector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect Float64Vector, got vector type {}",
-                    x.vector_type_name()
-                ),
-            })?;
-        let x = x.get(0);
-        if x.is_null() {
-            return Ok(());
-        }
-        let x = match x {
-            Value::Float64(OrderedFloat(x)) => x,
-            _ => unreachable!(),
-        };
-        self.x = Some(x);
-
-        let values = &states[0];
-        let values = values
-            .as_any()
-            .downcast_ref::<ListVector>()
-            .with_context(|| DowncastVectorSnafu {
-                err_msg: format!(
-                    "expect ListVector, got vector type {}",
-                    values.vector_type_name()
-                ),
-            })?;
-        for value in values.values_iter() {
-            if let Some(value) = value.context(FromScalarValueSnafu)? {
-                let column: &<T as Scalar>::VectorType = unsafe { Helper::static_cast(&value) };
-                for v in column.iter_data().flatten() {
-                    self.push(v);
-                }
-            }
-        }
-        Ok(())
-    }
-
-    fn evaluate(&self) -> Result<Value> {
-        let mean = self.values.iter().map(|v| v.into_native().as_()).mean();
-        let std_dev = self.values.iter().map(|v| v.into_native().as_()).std_dev();
-
-        if mean.is_nan() || std_dev.is_nan() {
-            Ok(Value::Null)
-        } else {
-            let x = if let Some(x) = self.x {
-                x
-            } else {
-                return Ok(Value::Null);
-            };
-            let n = Normal::new(mean, std_dev).context(GenerateFunctionSnafu)?;
-            Ok(n.pdf(x).into())
-        }
-    }
-}
-
-#[as_aggr_func_creator]
-#[derive(Debug, Default, AggrFuncTypeStore)]
-pub struct ScipyStatsNormPdfAccumulatorCreator {}
-
-impl AggregateFunctionCreator for ScipyStatsNormPdfAccumulatorCreator {
-    fn creator(&self) -> AccumulatorCreatorFunction {
-        let creator: AccumulatorCreatorFunction = Arc::new(move |types: &[ConcreteDataType]| {
-            let input_type = &types[0];
-            with_match_primitive_type_id!(
-                input_type.logical_type_id(),
-                |$S| {
-                    Ok(Box::new(ScipyStatsNormPdf::<<$S as LogicalPrimitiveType>::Wrapper>::default()))
-                },
-                {
-                    let err_msg = format!(
-                        "\"SCIPYSTATSNORMpdf\" aggregate function not support data type {:?}",
-                        input_type.logical_type_id(),
-                    );
-                    CreateAccumulatorSnafu { err_msg }.fail()?
-                }
-            )
-        });
-        creator
-    }
-
-    fn output_type(&self) -> Result<ConcreteDataType> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 2, InvalidInputStateSnafu);
-        Ok(ConcreteDataType::float64_datatype())
-    }
-
-    fn state_types(&self) -> Result<Vec<ConcreteDataType>> {
-        let input_types = self.input_types()?;
-        ensure!(input_types.len() == 2, InvalidInputStateSnafu);
-        Ok(vec![
-            ConcreteDataType::list_datatype(input_types[0].clone()),
-            ConcreteDataType::float64_datatype(),
-        ])
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use datatypes::vectors::{Float64Vector, Int32Vector};
-
-    use super::*;
-    #[test]
-    fn test_update_batch() {
-        // test update empty batch, expect not updating anything
-        let mut scipy_stats_norm_pdf = ScipyStatsNormPdf::<i32>::default();
-        scipy_stats_norm_pdf.update_batch(&[]).unwrap();
-        assert!(scipy_stats_norm_pdf.values.is_empty());
-        assert_eq!(Value::Null, scipy_stats_norm_pdf.evaluate().unwrap());
-
-        // test update no null-value batch
-        let mut scipy_stats_norm_pdf = ScipyStatsNormPdf::<i32>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(Int32Vector::from(vec![Some(-1i32), Some(1), Some(2)])),
-            Arc::new(Float64Vector::from(vec![
-                Some(2.0_f64),
-                Some(2.0_f64),
-                Some(2.0_f64),
-            ])),
-        ];
-        scipy_stats_norm_pdf.update_batch(&v).unwrap();
-        assert_eq!(
-            Value::from(0.17843340219081558),
-            scipy_stats_norm_pdf.evaluate().unwrap()
-        );
-
-        // test update null-value batch
-        let mut scipy_stats_norm_pdf = ScipyStatsNormPdf::<i32>::default();
-        let v: Vec<VectorRef> = vec![
-            Arc::new(Int32Vector::from(vec![Some(-2i32), None, Some(3), Some(4)])),
-            Arc::new(Float64Vector::from(vec![
-                Some(2.0_f64),
-                None,
-                Some(2.0_f64),
-                Some(2.0_f64),
-            ])),
-        ];
-        scipy_stats_norm_pdf.update_batch(&v).unwrap();
-        assert_eq!(
-            Value::from(0.12343972049858312),
-            scipy_stats_norm_pdf.evaluate().unwrap()
-        );
-    }
-}

src/common/query/src/error.rs

@@ -24,7 +24,6 @@ use datatypes::arrow::datatypes::DataType as ArrowDatatype;
 use datatypes::error::Error as DataTypeError;
 use datatypes::prelude::ConcreteDataType;
 use snafu::{Location, Snafu};
-use statrs::StatsError;
 
 #[derive(Snafu)]
 #[snafu(visibility(pub))]
@@ -38,14 +37,6 @@ pub enum Error {
         location: Location,
     },
 
-    #[snafu(display("Failed to generate function"))]
-    GenerateFunction {
-        #[snafu(source)]
-        error: StatsError,
-        #[snafu(implicit)]
-        location: Location,
-    },
-
     #[snafu(display("Failed to cast scalar value into vector"))]
     FromScalarValue {
         #[snafu(implicit)]
@@ -97,12 +88,6 @@ pub enum Error {
         location: Location,
     },
 
-    #[snafu(display("unexpected: not constant column"))]
-    InvalidInputCol {
-        #[snafu(implicit)]
-        location: Location,
-    },
-
     #[snafu(display("General DataFusion error"))]
     GeneralDataFusion {
         #[snafu(source)]
@@ -248,8 +233,6 @@ impl ErrorExt for Error {
             Error::CreateAccumulator { .. }
             | Error::DowncastVector { .. }
             | Error::InvalidInputState { .. }
-            | Error::InvalidInputCol { .. }
-            | Error::GenerateFunction { .. }
             | Error::BadAccumulatorImpl { .. }
             | Error::ToScalarValue { .. }
             | Error::GetScalarVector { .. }

src/query/src/tests.rs

@@ -21,14 +21,8 @@ use table::TableRef;
 use crate::parser::QueryLanguageParser;
 use crate::{QueryEngineFactory, QueryEngineRef};
 
-mod argmax_test;
-mod argmin_test;
-mod mean_test;
 mod my_sum_udaf_example;
-mod polyval_test;
 mod query_engine_test;
-mod scipy_stats_norm_cdf_test;
-mod scipy_stats_norm_pdf;
 mod time_range_filter_test;
 
 mod function;

src/query/src/tests/argmax_test.rs

@@ -1,73 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use datatypes::for_all_primitive_types;
-use datatypes::prelude::*;
-use datatypes::types::WrapperType;
-
-use crate::error::Result;
-use crate::tests::{exec_selection, function};
-use crate::QueryEngine;
-
-#[tokio::test]
-async fn test_argmax_aggregator() -> Result<()> {
-    common_telemetry::init_default_ut_logging();
-    let engine = function::create_query_engine();
-
-    macro_rules! test_argmax {
-        ([], $( { $T:ty } ),*) => {
-            $(
-                let column_name = format!("{}_number", std::any::type_name::<$T>());
-                test_argmax_success::<$T>(&column_name, "numbers", engine.clone()).await?;
-            )*
-        }
-    }
-    for_all_primitive_types! { test_argmax }
-    Ok(())
-}
-
-async fn test_argmax_success<T>(
-    column_name: &str,
-    table_name: &str,
-    engine: Arc<dyn QueryEngine>,
-) -> Result<()>
-where
-    T: WrapperType + PartialOrd,
-{
-    let sql = format!("select ARGMAX({column_name}) as argmax from {table_name}");
-    let result = exec_selection(engine.clone(), &sql).await;
-    let value = function::get_value_from_batches("argmax", result);
-
-    let numbers =
-        function::get_numbers_from_table::<T>(column_name, table_name, engine.clone()).await;
-    let expected_value = match numbers.len() {
-        0 => 0_u64,
-        _ => {
-            let mut index = 0;
-            let mut max = numbers[0];
-            for (i, &number) in numbers.iter().enumerate() {
-                if max < number {
-                    max = number;
-                    index = i;
-                }
-            }
-            index as u64
-        }
-    };
-    let expected_value = Value::from(expected_value);
-    assert_eq!(value, expected_value);
-    Ok(())
-}

src/query/src/tests/argmin_test.rs

@@ -1,73 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use datatypes::for_all_primitive_types;
-use datatypes::prelude::*;
-use datatypes::types::WrapperType;
-
-use crate::error::Result;
-use crate::tests::{exec_selection, function};
-use crate::QueryEngine;
-
-#[tokio::test]
-async fn test_argmin_aggregator() -> Result<()> {
-    common_telemetry::init_default_ut_logging();
-    let engine = function::create_query_engine();
-
-    macro_rules! test_argmin {
-        ([], $( { $T:ty } ),*) => {
-            $(
-                let column_name = format!("{}_number", std::any::type_name::<$T>());
-                test_argmin_success::<$T>(&column_name, "numbers", engine.clone()).await?;
-            )*
-        }
-    }
-    for_all_primitive_types! { test_argmin }
-    Ok(())
-}
-
-async fn test_argmin_success<T>(
-    column_name: &str,
-    table_name: &str,
-    engine: Arc<dyn QueryEngine>,
-) -> Result<()>
-where
-    T: WrapperType + PartialOrd,
-{
-    let sql = format!("select argmin({column_name}) as argmin from {table_name}");
-    let result = exec_selection(engine.clone(), &sql).await;
-    let value = function::get_value_from_batches("argmin", result);
-
-    let numbers =
-        function::get_numbers_from_table::<T>(column_name, table_name, engine.clone()).await;
-    let expected_value = match numbers.len() {
-        0 => 0_u32,
-        _ => {
-            let mut index = 0;
-            let mut min = numbers[0];
-            for (i, &number) in numbers.iter().enumerate() {
-                if min > number {
-                    min = number;
-                    index = i;
-                }
-            }
-            index as u32
-        }
-    };
-    let expected_value = Value::from(expected_value);
-    assert_eq!(value, expected_value);
-    Ok(())
-}

src/query/src/tests/function.rs

@@ -16,42 +16,14 @@ use std::sync::Arc;
 
 use common_function::scalars::vector::impl_conv::veclit_to_binlit;
 use common_recordbatch::RecordBatch;
-use datatypes::for_all_primitive_types;
 use datatypes::prelude::*;
 use datatypes::schema::{ColumnSchema, Schema};
-use datatypes::types::WrapperType;
-use datatypes::vectors::{BinaryVector, Helper};
+use datatypes::vectors::BinaryVector;
 use rand::Rng;
 use table::test_util::MemTable;
 
-use crate::tests::{exec_selection, new_query_engine_with_table};
-use crate::{QueryEngine, QueryEngineRef};
-
-pub fn create_query_engine() -> QueryEngineRef {
-    let mut column_schemas = vec![];
-    let mut columns = vec![];
-    macro_rules! create_number_table {
-        ([], $( { $T:ty } ),*) => {
-            $(
-                let mut rng = rand::thread_rng();
-
-                let column_name = format!("{}_number", std::any::type_name::<$T>());
-                let column_schema = ColumnSchema::new(column_name, Value::from(<$T>::default()).data_type(), true);
-                column_schemas.push(column_schema);
-
-                let numbers = (1..=10).map(|_| rng.gen::<$T>()).collect::<Vec<$T>>();
-                let column: VectorRef = Arc::new(<$T as Scalar>::VectorType::from_vec(numbers.to_vec()));
-                columns.push(column);
-            )*
-        }
-    }
-    for_all_primitive_types! { create_number_table }
-
-    let schema = Arc::new(Schema::new(column_schemas.clone()));
-    let recordbatch = RecordBatch::new(schema, columns).unwrap();
-    let number_table = MemTable::table("numbers", recordbatch);
-    new_query_engine_with_table(number_table)
-}
+use crate::tests::new_query_engine_with_table;
+use crate::QueryEngineRef;
 
 pub fn create_query_engine_for_vector10x3() -> QueryEngineRef {
     let mut column_schemas = vec![];
@@ -81,22 +53,6 @@ pub fn create_query_engine_for_vector10x3() -> QueryEngineRef {
     new_query_engine_with_table(vector_table)
 }
 
-pub async fn get_numbers_from_table<'s, T>(
-    column_name: &'s str,
-    table_name: &'s str,
-    engine: Arc<dyn QueryEngine>,
-) -> Vec<T>
-where
-    T: WrapperType,
-{
-    let sql = format!("SELECT {column_name} FROM {table_name}");
-    let numbers = exec_selection(engine, &sql).await;
-
-    let column = numbers[0].column(0);
-    let column: &<T as Scalar>::VectorType = unsafe { Helper::static_cast(column) };
-    column.iter_data().flatten().collect::<Vec<T>>()
-}
-
 pub fn get_value_from_batches(column_name: &str, batches: Vec<RecordBatch>) -> Value {
     assert_eq!(1, batches.len());
     assert_eq!(batches[0].num_columns(), 1);

src/query/src/tests/mean_test.rs

@@ -1,68 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use datatypes::for_all_primitive_types;
-use datatypes::prelude::*;
-use datatypes::types::WrapperType;
-use datatypes::value::OrderedFloat;
-use num_traits::AsPrimitive;
-
-use crate::error::Result;
-use crate::tests::{exec_selection, function};
-use crate::QueryEngine;
-
-#[tokio::test]
-async fn test_mean_aggregator() -> Result<()> {
-    common_telemetry::init_default_ut_logging();
-    let engine = function::create_query_engine();
-
-    macro_rules! test_mean {
-        ([], $( { $T:ty } ),*) => {
-            $(
-                let column_name = format!("{}_number", std::any::type_name::<$T>());
-                test_mean_success::<$T>(&column_name, "numbers", engine.clone()).await?;
-            )*
-        }
-    }
-    for_all_primitive_types! { test_mean }
-    Ok(())
-}
-
-async fn test_mean_success<T>(
-    column_name: &str,
-    table_name: &str,
-    engine: Arc<dyn QueryEngine>,
-) -> Result<()>
-where
-    T: WrapperType + AsPrimitive<f64>,
-{
-    let sql = format!("select MEAN({column_name}) as mean from {table_name}");
-    let result = exec_selection(engine.clone(), &sql).await;
-    let value = function::get_value_from_batches("mean", result);
-
-    let numbers =
-        function::get_numbers_from_table::<T>(column_name, table_name, engine.clone()).await;
-    let numbers = numbers.iter().map(|&n| n.as_()).collect::<Vec<f64>>();
-    let expected = numbers.iter().sum::<f64>() / (numbers.len() as f64);
-    let Value::Float64(OrderedFloat(value)) = value else {
-        unreachable!()
-    };
-    assert!(
-        (value - expected).abs() < 1e-3,
-        "expected {expected}, actual {value}"
-    );
-    Ok(())
-}

src/query/src/tests/polyval_test.rs

@@ -1,70 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use datatypes::for_all_primitive_types;
-use datatypes::prelude::*;
-use datatypes::types::WrapperType;
-use num_traits::AsPrimitive;
-
-use crate::error::Result;
-use crate::tests::{exec_selection, function};
-use crate::QueryEngine;
-
-#[tokio::test]
-async fn test_polyval_aggregator() -> Result<()> {
-    common_telemetry::init_default_ut_logging();
-    let engine = function::create_query_engine();
-
-    macro_rules! test_polyval {
-        ([], $( { $T:ty } ),*) => {
-            $(
-                let column_name = format!("{}_number", std::any::type_name::<$T>());
-                test_polyval_success::<$T, <<<$T as WrapperType>::LogicalType as LogicalPrimitiveType>::LargestType as LogicalPrimitiveType>::Wrapper>(&column_name, "numbers", engine.clone()).await?;
-            )*
-        }
-    }
-    for_all_primitive_types! { test_polyval }
-    Ok(())
-}
-
-async fn test_polyval_success<T, PolyT>(
-    column_name: &str,
-    table_name: &str,
-    engine: Arc<dyn QueryEngine>,
-) -> Result<()>
-where
-    T: WrapperType,
-    PolyT: WrapperType,
-    T::Native: AsPrimitive<PolyT::Native>,
-    PolyT::Native: std::ops::Mul<Output = PolyT::Native> + std::iter::Sum,
-    i64: AsPrimitive<PolyT::Native>,
-{
-    let sql = format!("select POLYVAL({column_name}, 0) as polyval from {table_name}");
-    let result = exec_selection(engine.clone(), &sql).await;
-    let value = function::get_value_from_batches("polyval", result);
-
-    let numbers =
-        function::get_numbers_from_table::<T>(column_name, table_name, engine.clone()).await;
-    let expected_value = numbers.iter().copied();
-    let x = 0i64;
-    let len = expected_value.len();
-    let expected_native: PolyT::Native = expected_value
-        .enumerate()
-        .map(|(i, v)| v.into_native().as_() * (x.pow((len - 1 - i) as u32)).as_())
-        .sum();
-    assert_eq!(value, PolyT::from_native(expected_native).into());
-    Ok(())
-}

src/query/src/tests/scipy_stats_norm_cdf_test.rs

@@ -1,69 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use datatypes::for_all_primitive_types;
-use datatypes::types::WrapperType;
-use num_traits::AsPrimitive;
-use statrs::distribution::{ContinuousCDF, Normal};
-use statrs::statistics::Statistics;
-
-use crate::error::Result;
-use crate::tests::{exec_selection, function};
-use crate::QueryEngine;
-
-#[tokio::test]
-async fn test_scipy_stats_norm_cdf_aggregator() -> Result<()> {
-    common_telemetry::init_default_ut_logging();
-    let engine = function::create_query_engine();
-
-    macro_rules! test_scipy_stats_norm_cdf {
-        ([], $( { $T:ty } ),*) => {
-            $(
-                let column_name = format!("{}_number", std::any::type_name::<$T>());
-                test_scipy_stats_norm_cdf_success::<$T>(&column_name, "numbers", engine.clone()).await?;
-            )*
-        }
-    }
-    for_all_primitive_types! { test_scipy_stats_norm_cdf }
-    Ok(())
-}
-
-async fn test_scipy_stats_norm_cdf_success<T>(
-    column_name: &str,
-    table_name: &str,
-    engine: Arc<dyn QueryEngine>,
-) -> Result<()>
-where
-    T: WrapperType + AsPrimitive<f64>,
-{
-    let sql = format!(
-        "select SCIPYSTATSNORMCDF({column_name},2.0) as scipy_stats_norm_cdf from {table_name}",
-    );
-    let result = exec_selection(engine.clone(), &sql).await;
-    let value = function::get_value_from_batches("scipy_stats_norm_cdf", result);
-
-    let numbers =
-        function::get_numbers_from_table::<T>(column_name, table_name, engine.clone()).await;
-    let expected_value = numbers.iter().map(|&n| n.as_()).collect::<Vec<f64>>();
-    let mean = expected_value.clone().mean();
-    let stddev = expected_value.std_dev();
-
-    let n = Normal::new(mean, stddev).unwrap();
-    let expected_value = n.cdf(2.0);
-
-    assert_eq!(value, expected_value.into());
-    Ok(())
-}

src/query/src/tests/scipy_stats_norm_pdf.rs

@@ -1,69 +0,0 @@
-// Copyright 2023 Greptime Team
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-use std::sync::Arc;
-
-use datatypes::for_all_primitive_types;
-use datatypes::types::WrapperType;
-use num_traits::AsPrimitive;
-use statrs::distribution::{Continuous, Normal};
-use statrs::statistics::Statistics;
-
-use crate::error::Result;
-use crate::tests::{exec_selection, function};
-use crate::QueryEngine;
-
-#[tokio::test]
-async fn test_scipy_stats_norm_pdf_aggregator() -> Result<()> {
-    common_telemetry::init_default_ut_logging();
-    let engine = function::create_query_engine();
-
-    macro_rules! test_scipy_stats_norm_pdf {
-        ([], $( { $T:ty } ),*) => {
-            $(
-                let column_name = format!("{}_number", std::any::type_name::<$T>());
-                test_scipy_stats_norm_pdf_success::<$T>(&column_name, "numbers", engine.clone()).await?;
-            )*
-        }
-    }
-    for_all_primitive_types! { test_scipy_stats_norm_pdf }
-    Ok(())
-}
-
-async fn test_scipy_stats_norm_pdf_success<T>(
-    column_name: &str,
-    table_name: &str,
-    engine: Arc<dyn QueryEngine>,
-) -> Result<()>
-where
-    T: WrapperType + AsPrimitive<f64>,
-{
-    let sql = format!(
-        "select SCIPYSTATSNORMPDF({column_name},2.0) as scipy_stats_norm_pdf from {table_name}"
-    );
-    let result = exec_selection(engine.clone(), &sql).await;
-    let value = function::get_value_from_batches("scipy_stats_norm_pdf", result);
-
-    let numbers =
-        function::get_numbers_from_table::<T>(column_name, table_name, engine.clone()).await;
-    let expected_value = numbers.iter().map(|&n| n.as_()).collect::<Vec<f64>>();
-    let mean = expected_value.clone().mean();
-    let stddev = expected_value.std_dev();
-
-    let n = Normal::new(mean, stddev).unwrap();
-    let expected_value = n.pdf(2.0);
-
-    assert_eq!(value, expected_value.into());
-    Ok(())
-}