forked from apache/datafusion
-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'apache_main' into feature/optimize-projections
- Loading branch information
Showing
50 changed files
with
1,110 additions
and
351 deletions.
There are no files selected for viewing
180 changes: 180 additions & 0 deletions
180
datafusion-examples/examples/simplify_udaf_expression.rs
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,180 @@ | ||
| // 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 arrow_schema::{Field, Schema}; | ||
| use datafusion::{arrow::datatypes::DataType, logical_expr::Volatility}; | ||
| use datafusion_expr::function::AggregateFunctionSimplification; | ||
| use datafusion_expr::simplify::SimplifyInfo; | ||
|
|
||
| use std::{any::Any, sync::Arc}; | ||
|
|
||
| use datafusion::arrow::{array::Float32Array, record_batch::RecordBatch}; | ||
| use datafusion::error::Result; | ||
| use datafusion::{assert_batches_eq, prelude::*}; | ||
| use datafusion_common::cast::as_float64_array; | ||
| use datafusion_expr::{ | ||
| expr::{AggregateFunction, AggregateFunctionDefinition}, | ||
| function::AccumulatorArgs, | ||
| Accumulator, AggregateUDF, AggregateUDFImpl, GroupsAccumulator, Signature, | ||
| }; | ||
|
|
||
| /// This example shows how to use the AggregateUDFImpl::simplify API to simplify/replace user | ||
| /// defined aggregate function with a different expression which is defined in the `simplify` method. | ||
|
|
||
| #[derive(Debug, Clone)] | ||
| struct BetterAvgUdaf { | ||
| signature: Signature, | ||
| } | ||
|
|
||
| impl BetterAvgUdaf { | ||
| /// Create a new instance of the GeoMeanUdaf struct | ||
| fn new() -> Self { | ||
| Self { | ||
| signature: Signature::exact(vec![DataType::Float64], Volatility::Immutable), | ||
| } | ||
| } | ||
| } | ||
|
|
||
| impl AggregateUDFImpl for BetterAvgUdaf { | ||
| fn as_any(&self) -> &dyn Any { | ||
| self | ||
| } | ||
|
|
||
| fn name(&self) -> &str { | ||
| "better_avg" | ||
| } | ||
|
|
||
| fn signature(&self) -> &Signature { | ||
| &self.signature | ||
| } | ||
|
|
||
| fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> { | ||
| Ok(DataType::Float64) | ||
| } | ||
|
|
||
| fn accumulator(&self, _acc_args: AccumulatorArgs) -> Result<Box<dyn Accumulator>> { | ||
| unimplemented!("should not be invoked") | ||
| } | ||
|
|
||
| fn state_fields( | ||
| &self, | ||
| _name: &str, | ||
| _value_type: DataType, | ||
| _ordering_fields: Vec<arrow_schema::Field>, | ||
| ) -> Result<Vec<arrow_schema::Field>> { | ||
| unimplemented!("should not be invoked") | ||
| } | ||
|
|
||
| fn groups_accumulator_supported(&self) -> bool { | ||
| true | ||
| } | ||
|
|
||
| fn create_groups_accumulator(&self) -> Result<Box<dyn GroupsAccumulator>> { | ||
| unimplemented!("should not get here"); | ||
| } | ||
| // we override method, to return new expression which would substitute | ||
| // user defined function call | ||
| fn simplify(&self) -> Option<AggregateFunctionSimplification> { | ||
| // as an example for this functionality we replace UDF function | ||
| // with build-in aggregate function to illustrate the use | ||
| let simplify = |aggregate_function: datafusion_expr::expr::AggregateFunction, | ||
| _: &dyn SimplifyInfo| { | ||
| Ok(Expr::AggregateFunction(AggregateFunction { | ||
| func_def: AggregateFunctionDefinition::BuiltIn( | ||
| // yes it is the same Avg, `BetterAvgUdaf` was just a | ||
| // marketing pitch :) | ||
| datafusion_expr::aggregate_function::AggregateFunction::Avg, | ||
| ), | ||
| args: aggregate_function.args, | ||
| distinct: aggregate_function.distinct, | ||
| filter: aggregate_function.filter, | ||
| order_by: aggregate_function.order_by, | ||
| null_treatment: aggregate_function.null_treatment, | ||
| })) | ||
| }; | ||
|
|
||
| Some(Box::new(simplify)) | ||
| } | ||
| } | ||
|
|
||
| // create local session context with an in-memory table | ||
| fn create_context() -> Result<SessionContext> { | ||
| use datafusion::datasource::MemTable; | ||
| // define a schema. | ||
| let schema = Arc::new(Schema::new(vec![ | ||
| Field::new("a", DataType::Float32, false), | ||
| Field::new("b", DataType::Float32, false), | ||
| ])); | ||
|
|
||
| // define data in two partitions | ||
| let batch1 = RecordBatch::try_new( | ||
| schema.clone(), | ||
| vec![ | ||
| Arc::new(Float32Array::from(vec![2.0, 4.0, 8.0])), | ||
| Arc::new(Float32Array::from(vec![2.0, 2.0, 2.0])), | ||
| ], | ||
| )?; | ||
| let batch2 = RecordBatch::try_new( | ||
| schema.clone(), | ||
| vec![ | ||
| Arc::new(Float32Array::from(vec![16.0])), | ||
| Arc::new(Float32Array::from(vec![2.0])), | ||
| ], | ||
| )?; | ||
|
|
||
| let ctx = SessionContext::new(); | ||
|
|
||
| // 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) | ||
| } | ||
|
|
||
| #[tokio::main] | ||
| async fn main() -> Result<()> { | ||
| let ctx = create_context()?; | ||
|
|
||
| let better_avg = AggregateUDF::from(BetterAvgUdaf::new()); | ||
| ctx.register_udaf(better_avg.clone()); | ||
|
|
||
| let result = ctx | ||
| .sql("SELECT better_avg(a) FROM t group by b") | ||
| .await? | ||
| .collect() | ||
| .await?; | ||
|
|
||
| let expected = [ | ||
| "+-----------------+", | ||
| "| better_avg(t.a) |", | ||
| "+-----------------+", | ||
| "| 7.5 |", | ||
| "+-----------------+", | ||
| ]; | ||
|
|
||
| assert_batches_eq!(expected, &result); | ||
|
|
||
| let df = ctx.table("t").await?; | ||
| let df = df.aggregate(vec![], vec![better_avg.call(vec![col("a")])])?; | ||
|
|
||
| let results = df.collect().await?; | ||
| let result = as_float64_array(results[0].column(0))?; | ||
|
|
||
| assert!((result.value(0) - 7.5).abs() < f64::EPSILON); | ||
| println!("The average of [2,4,8,16] is {}", result.value(0)); | ||
|
|
||
| Ok(()) | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.