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feat: impl quantile_over_time function (GreptimeTeam#1287)
* fix qualifier alias Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix in another way Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * impl quantile_over_time Signed-off-by: Ruihang Xia <waynestxia@gmail.com> * fix clippy Signed-off-by: Ruihang Xia <waynestxia@gmail.com> --------- Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
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| Original file line number | Diff line number | Diff line change |
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| // 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 datafusion::arrow::array::Float64Array; | ||
| use datafusion::arrow::datatypes::TimeUnit; | ||
| use datafusion::common::DataFusionError; | ||
| use datafusion::logical_expr::{ScalarUDF, Signature, TypeSignature, Volatility}; | ||
| use datafusion::physical_plan::ColumnarValue; | ||
| use datatypes::arrow::array::Array; | ||
| use datatypes::arrow::datatypes::DataType; | ||
|
|
||
| use crate::error; | ||
| use crate::functions::extract_array; | ||
| use crate::range_array::RangeArray; | ||
|
|
||
| pub struct QuantileOverTime { | ||
| quantile: f64, | ||
| } | ||
|
|
||
| impl QuantileOverTime { | ||
| fn new(quantile: f64) -> Self { | ||
| Self { quantile } | ||
| } | ||
|
|
||
| pub const fn name() -> &'static str { | ||
| "prom_quantile_over_time" | ||
| } | ||
|
|
||
| pub fn scalar_udf(quantile: f64) -> ScalarUDF { | ||
| ScalarUDF { | ||
| name: Self::name().to_string(), | ||
| signature: Signature::new( | ||
| TypeSignature::Exact(Self::input_type()), | ||
| Volatility::Immutable, | ||
| ), | ||
| return_type: Arc::new(|_| Ok(Arc::new(Self::return_type()))), | ||
| fun: Arc::new(move |input| Self::new(quantile).calc(input)), | ||
| } | ||
| } | ||
|
|
||
| // time index column and value column | ||
| fn input_type() -> Vec<DataType> { | ||
| vec![ | ||
| RangeArray::convert_data_type(DataType::Timestamp(TimeUnit::Millisecond, None)), | ||
| RangeArray::convert_data_type(DataType::Float64), | ||
| ] | ||
| } | ||
|
|
||
| fn return_type() -> DataType { | ||
| DataType::Float64 | ||
| } | ||
|
|
||
| fn calc(&self, input: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { | ||
| // construct matrix from input. | ||
| // The third one is quantile param, which is included in fields. | ||
| assert_eq!(input.len(), 3); | ||
| let ts_array = extract_array(&input[0])?; | ||
| let value_array = extract_array(&input[1])?; | ||
|
|
||
| let ts_range: RangeArray = RangeArray::try_new(ts_array.data().clone().into())?; | ||
| let value_range: RangeArray = RangeArray::try_new(value_array.data().clone().into())?; | ||
| error::ensure( | ||
| ts_range.len() == value_range.len(), | ||
| DataFusionError::Execution(format!( | ||
| "{}: input arrays should have the same length, found {} and {}", | ||
| Self::name(), | ||
| ts_range.len(), | ||
| value_range.len() | ||
| )), | ||
| )?; | ||
| error::ensure( | ||
| ts_range.value_type() == DataType::Timestamp(TimeUnit::Millisecond, None), | ||
| DataFusionError::Execution(format!( | ||
| "{}: expect TimestampMillisecond as time index array's type, found {}", | ||
| Self::name(), | ||
| ts_range.value_type() | ||
| )), | ||
| )?; | ||
| error::ensure( | ||
| value_range.value_type() == DataType::Float64, | ||
| DataFusionError::Execution(format!( | ||
| "{}: expect Float64 as value array's type, found {}", | ||
| Self::name(), | ||
| value_range.value_type() | ||
| )), | ||
| )?; | ||
|
|
||
| // calculation | ||
| let mut result_array = Vec::with_capacity(ts_range.len()); | ||
|
|
||
| for index in 0..ts_range.len() { | ||
| let timestamps = ts_range.get(index).unwrap(); | ||
| let values = value_range.get(index).unwrap(); | ||
| let values = values | ||
| .as_any() | ||
| .downcast_ref::<Float64Array>() | ||
| .unwrap() | ||
| .values(); | ||
| error::ensure( | ||
| timestamps.len() == values.len(), | ||
| DataFusionError::Execution(format!( | ||
| "{}: input arrays should have the same length, found {} and {}", | ||
| Self::name(), | ||
| timestamps.len(), | ||
| values.len() | ||
| )), | ||
| )?; | ||
|
|
||
| let retule = quantile_impl(values, self.quantile); | ||
|
|
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| result_array.push(retule); | ||
| } | ||
|
|
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| let result = ColumnarValue::Array(Arc::new(Float64Array::from_iter(result_array))); | ||
| Ok(result) | ||
| } | ||
| } | ||
|
|
||
| /// Refer to https://github.com/prometheus/prometheus/blob/6e2905a4d4ff9b47b1f6d201333f5bd53633f921/promql/quantile.go#L357-L386 | ||
| fn quantile_impl(values: &[f64], quantile: f64) -> Option<f64> { | ||
| if quantile.is_nan() || values.is_empty() { | ||
| return Some(f64::NAN); | ||
| } | ||
| if quantile < 0.0 { | ||
| return Some(f64::NEG_INFINITY); | ||
| } | ||
| if quantile > 1.0 { | ||
| return Some(f64::INFINITY); | ||
| } | ||
|
|
||
| let mut values = values.to_vec(); | ||
| values.sort_unstable_by(f64::total_cmp); | ||
|
|
||
| let length = values.len(); | ||
| let rank = quantile * (length - 1) as f64; | ||
|
|
||
| let lower_index = 0.max(rank.floor() as usize); | ||
| let upper_index = (length - 1).min(lower_index + 1); | ||
| let weight = rank - rank.floor(); | ||
|
|
||
| let result = values[lower_index] * (1.0 - weight) + values[upper_index] * weight; | ||
| Some(result) | ||
| } | ||
|
|
||
| #[cfg(test)] | ||
| mod tests { | ||
| use super::*; | ||
|
|
||
| #[test] | ||
| fn test_quantile_impl_empty() { | ||
| let values = &[]; | ||
| let q = 0.5; | ||
| assert!(quantile_impl(values, q).unwrap().is_nan()); | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_quantile_impl_nan() { | ||
| let values = &[1.0, 2.0, 3.0]; | ||
| let q = f64::NAN; | ||
| assert!(quantile_impl(values, q).unwrap().is_nan()); | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_quantile_impl_negative_quantile() { | ||
| let values = &[1.0, 2.0, 3.0]; | ||
| let q = -0.5; | ||
| assert_eq!(quantile_impl(values, q).unwrap(), f64::NEG_INFINITY); | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_quantile_impl_greater_than_one_quantile() { | ||
| let values = &[1.0, 2.0, 3.0]; | ||
| let q = 1.5; | ||
| assert_eq!(quantile_impl(values, q).unwrap(), f64::INFINITY); | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_quantile_impl_single_element() { | ||
| let values = &[1.0]; | ||
| let q = 0.8; | ||
| assert_eq!(quantile_impl(values, q).unwrap(), 1.0); | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_quantile_impl_even_length() { | ||
| let values = &[3.0, 1.0, 5.0, 2.0]; | ||
| let q = 0.5; | ||
| assert_eq!(quantile_impl(values, q).unwrap(), 2.5); | ||
| } | ||
|
|
||
| #[test] | ||
| fn test_quantile_impl_odd_length() { | ||
| let values = &[4.0, 1.0, 3.0, 2.0, 5.0]; | ||
| let q = 0.25; | ||
| assert_eq!(quantile_impl(values, q).unwrap(), 2.0); | ||
| } | ||
| } |
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