alamb commented on code in PR #6617: URL: https://github.com/apache/arrow-datafusion/pull/6617#discussion_r1228612672
########## datafusion-examples/examples/simple_udwf.rs: ########## @@ -0,0 +1,210 @@ +// 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 std::sync::Arc; + +use arrow::{ + array::{AsArray, Float64Array}, + datatypes::Float64Type, +}; +use arrow_schema::DataType; +use datafusion::datasource::file_format::options::CsvReadOptions; + +use datafusion::error::Result; +use datafusion::prelude::*; +use datafusion_common::DataFusionError; +use datafusion_expr::{ + partition_evaluator::PartitionEvaluator, Signature, Volatility, WindowUDF, +}; + +// 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 SQLsession + let ctx = SessionContext::new(); + + // declare a table in memory. In spark API, this corresponds to createDataFrame(...). + println!("pwd: {}", std::env::current_dir().unwrap().display()); + let csv_path = format!("datafusion/core/tests/data/cars.csv"); + let read_options = CsvReadOptions::default().has_header(true); + + ctx.register_csv("cars", &csv_path, read_options).await?; + Ok(ctx) +} + +/// 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?; + + // register the window function with DataFusion so wecan call it + ctx.register_udwf(my_average()); + + // Use SQL to run the new window function + let df = ctx.sql("SELECT * from cars").await?; + // print the results + df.show().await?; + + // Use SQL to run the new window function + // `PARTITION BY car`:each distinct value of car (red, and green) should be treated separately + // `ORDER BY time`: within each group (greed or green) the values will be orderd by time + let df = ctx + .sql( + "SELECT car, \ + speed, \ + lag(speed, 1) OVER (PARTITION BY car ORDER BY time),\ + my_average(speed) OVER (PARTITION BY car ORDER BY time),\ + time \ + from cars", + ) + .await?; + // print the results + df.show().await?; + + // // ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING: Run the window functon so that each invocation only sees 5 rows: the 2 before and 2 after) using + // let df = ctx.sql("SELECT car, \ + // speed, \ + // lag(speed, 1) OVER (PARTITION BY car ORDER BY time ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING),\ + // time \ + // from cars").await?; + // // print the results + // df.show().await?; + + // todo show how to run dataframe API as well + + Ok(()) +} + +// TODO make a helper funciton like `crate_udf` that helps to make these signatures + +fn my_average() -> WindowUDF { + WindowUDF { + name: String::from("my_average"), + // it will take 2 arguments -- the column and the window size + signature: Signature::exact(vec![DataType::Int32], Volatility::Immutable), + return_type: Arc::new(return_type), + partition_evaluator: Arc::new(make_partition_evaluator), + } +} + +/// Compute the return type of the function given the argument 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())) +} + +/// Create a partition evaluator for this argument +fn make_partition_evaluator() -> Result<Box<dyn PartitionEvaluator>> { + Ok(Box::new(MyPartitionEvaluator::new())) +} Review Comment: @stuartcarnie I looked into adding the arguments. The primary issue I encountered is that a `WindowUDF` is specified in terms of structures in `datafusion-expr` ( aka it doesn't have access to `PhysicalExpr`s as those are defined in a different crate. Here are some possible signatures we could provide. Do you have any feedback on these possibilities? # Pass in the `Expr`s from the logical plan This is non ideal in my mind as the PartitionEvaluator is created during execution (where the `Expr`s are normally not around anymore) ```rust /// Factory that creates a PartitionEvaluator for the given window function. /// /// This function is passed its input arguments so that cases such as /// constants can be correctly handled. pub type PartitionEvaluatorFunctionFactory = Arc<dyn Fn(&[Expr]) -> Result<Box<dyn PartitionEvaluator>> + Send + Sync>; ``` # Pass in a `ArgType` enum This is also non ideal in my mind as it seemingly artificially limits what the user defined window function can special case (why not Column's for example??) ```rust enum ArgType { /// The argument was a single value Scalar(ScalarValue), /// the argument is something other than a single value Array } /// Factory that creates a PartitionEvaluator for the given window function. /// /// This function is passed its input arguments so that cases such as /// constants can be specially handled if desired. pub type PartitionEvaluatorFunctionFactory = Arc<dyn Fn(args: Vec<ArgType>) -> Result<Box<dyn PartitionEvaluator>> + Send + Sync>; ``` -- This is an automated message from the Apache Git Service. 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