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Implement trait based API for define AggregateUDF #8733
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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 datafusion::{arrow::datatypes::DataType, logical_expr::Volatility}; | ||
| use std::{any::Any, sync::Arc}; | ||
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| use arrow::{ | ||
| array::{ArrayRef, Float32Array}, | ||
| record_batch::RecordBatch, | ||
| }; | ||
| use datafusion::error::Result; | ||
| use datafusion::prelude::*; | ||
| use datafusion_common::{cast::as_float64_array, ScalarValue}; | ||
| use datafusion_expr::{Accumulator, AggregateUDF, AggregateUDFImpl, Signature}; | ||
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| /// This example shows how to use the full AggregateUDFImpl API to implement a user | ||
| /// defined aggregate function. As in the `simple_udaf.rs` example, this struct implements | ||
| /// a function `accumulator` that returns the `Accumulator` instance. | ||
| /// | ||
| /// To do so, we must implement the `AggregateUDFImpl` trait. | ||
| #[derive(Debug, Clone)] | ||
| struct GeoMeanUdf { | ||
| signature: Signature, | ||
| } | ||
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| impl GeoMeanUdf { | ||
| /// Create a new instance of the GeoMeanUdf 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, | ||
| ), | ||
| } | ||
| } | ||
| } | ||
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| impl AggregateUDFImpl for GeoMeanUdf { | ||
| /// We implement as_any so that we can downcast the AggregateUDFImpl trait object | ||
| fn as_any(&self) -> &dyn Any { | ||
| self | ||
| } | ||
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| /// Return the name of this function | ||
| fn name(&self) -> &str { | ||
| "geo_mean" | ||
| } | ||
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| /// Return the "signature" of this function -- namely that types of arguments it will take | ||
| fn signature(&self) -> &Signature { | ||
| &self.signature | ||
| } | ||
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| /// 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) | ||
| } | ||
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| /// This is the accumulator factory; DataFusion uses it to create new accumulators. | ||
| fn accumulator(&self, _arg: &DataType) -> Result<Box<dyn Accumulator>> { | ||
| Ok(Box::new(GeometricMean::new())) | ||
| } | ||
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| /// This is the description of the state. accumulator's state() must match the types here. | ||
| fn state_type(&self, _return_type: &DataType) -> Result<Vec<DataType>> { | ||
| Ok(vec![DataType::Float64, DataType::UInt32]) | ||
| } | ||
| } | ||
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| /// A UDAF has state across multiple rows, and thus we require a `struct` with that state. | ||
| #[derive(Debug)] | ||
| struct GeometricMean { | ||
| n: u32, | ||
| prod: f64, | ||
| } | ||
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| impl GeometricMean { | ||
| // how the struct is initialized | ||
| pub fn new() -> Self { | ||
| GeometricMean { n: 0, prod: 1.0 } | ||
| } | ||
| } | ||
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| // UDAFs are built using the trait `Accumulator`, that offers DataFusion the necessary functions | ||
| // to use them. | ||
| impl Accumulator for GeometricMean { | ||
| // This function serializes our state to `ScalarValue`, which DataFusion uses | ||
| // to pass this state between execution stages. | ||
| // Note that this can be arbitrary data. | ||
| fn state(&self) -> Result<Vec<ScalarValue>> { | ||
| Ok(vec![ | ||
| ScalarValue::from(self.prod), | ||
| ScalarValue::from(self.n), | ||
| ]) | ||
| } | ||
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| // DataFusion expects this function to return the final value of this aggregator. | ||
| // in this case, this is the formula of the geometric mean | ||
| fn evaluate(&self) -> Result<ScalarValue> { | ||
| let value = self.prod.powf(1.0 / self.n as f64); | ||
| Ok(ScalarValue::from(value)) | ||
| } | ||
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| // DataFusion calls this function to update the accumulator's state for a batch | ||
| // of inputs rows. In this case the product is updated with values from the first column | ||
| // and the count is updated based on the row count | ||
| fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> { | ||
| if values.is_empty() { | ||
| return Ok(()); | ||
| } | ||
| let arr = &values[0]; | ||
| (0..arr.len()).try_for_each(|index| { | ||
| let v = ScalarValue::try_from_array(arr, index)?; | ||
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| if let ScalarValue::Float64(Some(value)) = v { | ||
| self.prod *= value; | ||
| self.n += 1; | ||
| } else { | ||
| unreachable!("") | ||
| } | ||
| Ok(()) | ||
| }) | ||
| } | ||
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| // Merge the output of `Self::state()` from other instances of this accumulator | ||
| // into this accumulator's state | ||
| fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> { | ||
| if states.is_empty() { | ||
| return Ok(()); | ||
| } | ||
| let arr = &states[0]; | ||
| (0..arr.len()).try_for_each(|index| { | ||
| let v = states | ||
| .iter() | ||
| .map(|array| ScalarValue::try_from_array(array, index)) | ||
| .collect::<Result<Vec<_>>>()?; | ||
| if let (ScalarValue::Float64(Some(prod)), ScalarValue::UInt32(Some(n))) = | ||
| (&v[0], &v[1]) | ||
| { | ||
| self.prod *= prod; | ||
| self.n += n; | ||
| } else { | ||
| unreachable!("") | ||
| } | ||
| Ok(()) | ||
| }) | ||
| } | ||
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| fn size(&self) -> usize { | ||
| std::mem::size_of_val(self) | ||
| } | ||
| } | ||
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| // create local session context with an in-memory table | ||
| fn create_context() -> Result<SessionContext> { | ||
| use datafusion::arrow::datatypes::{Field, Schema}; | ||
| use datafusion::datasource::MemTable; | ||
| // define a schema. | ||
| let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Float32, false)])); | ||
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| // define data in two partitions | ||
| let batch1 = RecordBatch::try_new( | ||
| schema.clone(), | ||
| vec![Arc::new(Float32Array::from(vec![2.0, 4.0, 8.0]))], | ||
| )?; | ||
| let batch2 = RecordBatch::try_new( | ||
| schema.clone(), | ||
| vec![Arc::new(Float32Array::from(vec![64.0]))], | ||
| )?; | ||
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| // declare a new context. In spark API, this corresponds to a new spark SQLsession | ||
| let ctx = SessionContext::new(); | ||
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| // declare a table in memory. In spark API, this corresponds to createDataFrame(...). | ||
| let provider = MemTable::try_new(schema, vec![vec![batch1], vec![batch2]])?; | ||
| ctx.register_table("t", Arc::new(provider))?; | ||
| Ok(ctx) | ||
| } | ||
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| #[tokio::main] | ||
| async fn main() -> Result<()> { | ||
| let ctx = create_context()?; | ||
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| // create the AggregateUDF | ||
| let geometric_mean = AggregateUDF::from(GeoMeanUdf::new()); | ||
| ctx.register_udaf(geometric_mean.clone()); | ||
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| let sql_df = ctx.sql("SELECT geo_mean(a) FROM t").await?; | ||
| sql_df.show().await?; | ||
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| // get a DataFrame from the context | ||
| // this table has 1 column `a` f32 with values {2,4,8,64}, whose geometric mean is 8.0. | ||
| let df = ctx.table("t").await?; | ||
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| // perform the aggregation | ||
| let df = df.aggregate(vec![], vec![geometric_mean.call(vec![col("a")])])?; | ||
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| // note that "a" is f32, not f64. DataFusion coerces it to match the UDAF's signature. | ||
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| // execute the query | ||
| let results = df.collect().await?; | ||
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| // downcast the array to the expected type | ||
| let result = as_float64_array(results[0].column(0))?; | ||
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| // verify that the calculation is correct | ||
| assert!((result.value(0) - 8.0).abs() < f64::EPSILON); | ||
| println!("The geometric mean of [2,4,8,64] is {}", result.value(0)); | ||
|
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| Ok(()) | ||
| } | ||
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👍
While this "advanced" usage isn't much more advanced than the current "simple" UDAF I think this PR now provides a home / plausible way to implement the full
GroupsAccumulatorAPI for UDAFs (which is the powerful, very performant API used by built in aggregate functions in DataFusion)There was a problem hiding this comment.
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Filed #8793 to track