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Support User Defined Window Functions (#6703)
* Support User Defined Window Functions * Apply suggestions from code review Co-authored-by: Mustafa Akur <[email protected]> * Remove left over println * Apply suggestions from code review Co-authored-by: Mustafa Akur <[email protected]> * Apply suggestions from code review Co-authored-by: Mustafa Akur <[email protected]> * fix docs --------- Co-authored-by: Mustafa Akur <[email protected]>
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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. | ||
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| use std::sync::Arc; | ||
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| use arrow::{ | ||
| array::{ArrayRef, AsArray, Float64Array}, | ||
| datatypes::Float64Type, | ||
| }; | ||
| use arrow_schema::DataType; | ||
| use datafusion::datasource::file_format::options::CsvReadOptions; | ||
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| use datafusion::error::Result; | ||
| use datafusion::prelude::*; | ||
| use datafusion_common::{DataFusionError, ScalarValue}; | ||
| use datafusion_expr::{ | ||
| PartitionEvaluator, Signature, Volatility, WindowFrame, WindowUDF, | ||
| }; | ||
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| // 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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| /// In this example we will declare a user defined window function that computes a moving average and then run it using SQL | ||
| #[tokio::main] | ||
| async fn main() -> Result<()> { | ||
| let ctx = create_context().await?; | ||
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| // register the window function with DataFusion so we can call it | ||
| ctx.register_udwf(smooth_it()); | ||
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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),\ | ||
| 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),\ | ||
| 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])?; | ||
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| // print the results | ||
| df.show().await?; | ||
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| Ok(()) | ||
| } | ||
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| fn smooth_it() -> WindowUDF { | ||
| WindowUDF { | ||
| name: String::from("smooth_it"), | ||
| // it will take 1 arguments -- the column to smooth | ||
| signature: Signature::exact(vec![DataType::Float64], Volatility::Immutable), | ||
| return_type: Arc::new(return_type), | ||
| partition_evaluator_factory: Arc::new(make_partition_evaluator), | ||
| } | ||
| } | ||
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| /// Compute the return type of the smooth_it window function given | ||
| /// arguments of `arg_types`. | ||
| fn return_type(arg_types: &[DataType]) -> Result<Arc<DataType>> { | ||
| if arg_types.len() != 1 { | ||
| return Err(DataFusionError::Plan(format!( | ||
| "my_udwf expects 1 argument, got {}: {:?}", | ||
| arg_types.len(), | ||
| arg_types | ||
| ))); | ||
| } | ||
| Ok(Arc::new(arg_types[0].clone())) | ||
| } | ||
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| /// Create a `PartitionEvalutor` to evaluate this function on a new | ||
| /// partition. | ||
| fn make_partition_evaluator() -> Result<Box<dyn PartitionEvaluator>> { | ||
| Ok(Box::new(MyPartitionEvaluator::new())) | ||
| } | ||
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| /// 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 {} | ||
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| 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 | ||
| } | ||
|
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| /// 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 | ||
| }; | ||
|
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| Ok(ScalarValue::Float64(output)) | ||
| } | ||
| } |
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