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| // Licensed to the Apache Software Foundation (ASF) under one | ||
| // or more contributor license agreements. See the NOTICE file | ||
| // distributed with this work for additional information | ||
| // regarding copyright ownership. The ASF licenses this file | ||
| // to you under the Apache License, Version 2.0 (the | ||
| // "License"); you may not use this file except in compliance | ||
| // with the License. You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, | ||
| // software distributed under the License is distributed on an | ||
| // "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| // KIND, either express or implied. See the License for the | ||
| // specific language governing permissions and limitations | ||
| // under the License. | ||
|
|
||
| use datafusion::{arrow::datatypes::DataType, logical_expr::Volatility}; | ||
| use std::any::Any; | ||
|
|
||
| use arrow::{ | ||
| array::{ArrayRef, AsArray, Float64Array}, | ||
| datatypes::Float64Type, | ||
| }; | ||
| use datafusion::error::Result; | ||
| use datafusion::prelude::*; | ||
| use datafusion_common::ScalarValue; | ||
| use datafusion_expr::{ | ||
| PartitionEvaluator, Signature, WindowFrame, WindowUDF, WindowUDFImpl, | ||
| }; | ||
|
|
||
| /// This example shows how to use the full WindowUDFImpl API to implement a user | ||
| /// defined window function. As in the `simple_udwf.rs` example, this struct implements | ||
| /// a function `partition_evaluator` that returns the `MyPartitionEvaluator` instance. | ||
| /// | ||
| /// To do so, we must implement the `WindowUDFImpl` trait. | ||
| struct SmoothItUdf { | ||
| signature: Signature, | ||
| } | ||
|
|
||
| impl SmoothItUdf { | ||
| /// Create a new instance of the SmoothItUdf struct | ||
| fn new() -> Self { | ||
| Self { | ||
| signature: Signature::exact( | ||
| // this function will always take one arguments of type f64 | ||
| vec![DataType::Float64], | ||
| // this function is deterministic and will always return the same | ||
| // result for the same input | ||
| Volatility::Immutable, | ||
| ), | ||
| } | ||
| } | ||
| } | ||
|
|
||
| impl WindowUDFImpl for SmoothItUdf { | ||
| /// We implement as_any so that we can downcast the WindowUDFImpl trait object | ||
| fn as_any(&self) -> &dyn Any { | ||
| self | ||
| } | ||
|
|
||
| /// Return the name of this function | ||
| fn name(&self) -> &str { | ||
| "smooth_it" | ||
| } | ||
|
|
||
| /// Return the "signature" of this function -- namely that types of arguments it will take | ||
| fn signature(&self) -> &Signature { | ||
| &self.signature | ||
| } | ||
|
|
||
| /// What is the type of value that will be returned by this function. | ||
| fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> { | ||
| Ok(DataType::Float64) | ||
| } | ||
|
|
||
| /// Create a `PartitionEvalutor` to evaluate this function on a new | ||
| /// partition. | ||
| fn partition_evaluator(&self) -> Result<Box<dyn PartitionEvaluator>> { | ||
| Ok(Box::new(MyPartitionEvaluator::new())) | ||
| } | ||
| } | ||
|
|
||
| /// This implements the lowest level evaluation for a window function | ||
| /// | ||
| /// It handles calculating the value of the window function for each | ||
| /// distinct values of `PARTITION BY` (each car type in our example) | ||
| #[derive(Clone, Debug)] | ||
| struct MyPartitionEvaluator {} | ||
|
|
||
| impl MyPartitionEvaluator { | ||
| fn new() -> Self { | ||
| Self {} | ||
| } | ||
| } | ||
|
|
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| /// Different evaluation methods are called depending on the various | ||
| /// settings of WindowUDF. This example uses the simplest and most | ||
| /// general, `evaluate`. See `PartitionEvaluator` for the other more | ||
| /// advanced uses. | ||
| impl PartitionEvaluator for MyPartitionEvaluator { | ||
| /// Tell DataFusion the window function varies based on the value | ||
| /// of the window frame. | ||
| fn uses_window_frame(&self) -> bool { | ||
| true | ||
| } | ||
|
|
||
| /// This function is called once per input row. | ||
| /// | ||
| /// `range`specifies which indexes of `values` should be | ||
| /// considered for the calculation. | ||
| /// | ||
| /// Note this is the SLOWEST, but simplest, way to evaluate a | ||
| /// window function. It is much faster to implement | ||
| /// evaluate_all or evaluate_all_with_rank, if possible | ||
| fn evaluate( | ||
| &mut self, | ||
| values: &[ArrayRef], | ||
| range: &std::ops::Range<usize>, | ||
| ) -> Result<ScalarValue> { | ||
| // Again, the input argument is an array of floating | ||
| // point numbers to calculate a moving average | ||
| let arr: &Float64Array = values[0].as_ref().as_primitive::<Float64Type>(); | ||
|
|
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| let range_len = range.end - range.start; | ||
|
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| // our smoothing function will average all the values in the | ||
| let output = if range_len > 0 { | ||
| let sum: f64 = arr.values().iter().skip(range.start).take(range_len).sum(); | ||
| Some(sum / range_len as f64) | ||
| } else { | ||
| None | ||
| }; | ||
|
|
||
| Ok(ScalarValue::Float64(output)) | ||
| } | ||
| } | ||
|
|
||
| // create local execution context with `cars.csv` registered as a table named `cars` | ||
| async fn create_context() -> Result<SessionContext> { | ||
| // declare a new context. In spark API, this corresponds to a new spark SQL session | ||
| let ctx = SessionContext::new(); | ||
|
|
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| // declare a table in memory. In spark API, this corresponds to createDataFrame(...). | ||
| println!("pwd: {}", std::env::current_dir().unwrap().display()); | ||
| let csv_path = "../../datafusion/core/tests/data/cars.csv".to_string(); | ||
| let read_options = CsvReadOptions::default().has_header(true); | ||
|
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| ctx.register_csv("cars", &csv_path, read_options).await?; | ||
| Ok(ctx) | ||
| } | ||
|
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||
| #[tokio::main] | ||
| async fn main() -> Result<()> { | ||
| let ctx = create_context().await?; | ||
| let smooth_it = WindowUDF::from(SmoothItUdf::new()); | ||
| ctx.register_udwf(smooth_it.clone()); | ||
|
|
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| // Use SQL to run the new window function | ||
| let df = ctx.sql("SELECT * from cars").await?; | ||
| // print the results | ||
| df.show().await?; | ||
|
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| // Use SQL to run the new window function: | ||
| // | ||
| // `PARTITION BY car`:each distinct value of car (red, and green) | ||
| // should be treated as a separate partition (and will result in | ||
| // creating a new `PartitionEvaluator`) | ||
| // | ||
| // `ORDER BY time`: within each partition ('green' or 'red') the | ||
| // rows will be be ordered by the value in the `time` column | ||
| // | ||
| // `evaluate_inside_range` is invoked with a window defined by the | ||
| // SQL. In this case: | ||
| // | ||
| // The first invocation will be passed row 0, the first row in the | ||
| // partition. | ||
| // | ||
| // The second invocation will be passed rows 0 and 1, the first | ||
| // two rows in the partition. | ||
| // | ||
| // etc. | ||
| let df = ctx | ||
| .sql( | ||
| "SELECT \ | ||
| car, \ | ||
| speed, \ | ||
| smooth_it(speed) OVER (PARTITION BY car ORDER BY time) AS smooth_speed,\ | ||
| time \ | ||
| from cars \ | ||
| ORDER BY \ | ||
| car", | ||
| ) | ||
| .await?; | ||
| // print the results | ||
| df.show().await?; | ||
|
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||
| // this time, call the new widow function with an explicit | ||
| // window so evaluate will be invoked with each window. | ||
| // | ||
| // `ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING`: each invocation | ||
| // sees at most 3 rows: the row before, the current row, and the 1 | ||
| // row afterward. | ||
| let df = ctx.sql( | ||
| "SELECT \ | ||
| car, \ | ||
| speed, \ | ||
| smooth_it(speed) OVER (PARTITION BY car ORDER BY time ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS smooth_speed,\ | ||
| time \ | ||
| from cars \ | ||
| ORDER BY \ | ||
| car", | ||
| ).await?; | ||
| // print the results | ||
| df.show().await?; | ||
|
|
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| // Now, run the function using the DataFrame API: | ||
| let window_expr = smooth_it.call( | ||
| vec![col("speed")], // smooth_it(speed) | ||
| vec![col("car")], // PARTITION BY car | ||
| vec![col("time").sort(true, true)], // ORDER BY time ASC | ||
| WindowFrame::new(false), | ||
| ); | ||
| let df = ctx.table("cars").await?.window(vec![window_expr])?; | ||
|
|
||
| // print the results | ||
| df.show().await?; | ||
|
|
||
| Ok(()) | ||
| } |
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