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Introduce new trait based ScalarUDF API
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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::{ | ||
array::{ArrayRef, Float32Array, Float64Array}, | ||
datatypes::DataType, | ||
record_batch::RecordBatch, | ||
}, | ||
logical_expr::Volatility, | ||
}; | ||
use std::any::Any; | ||
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use arrow::array::{new_null_array, Array, AsArray}; | ||
use arrow::compute; | ||
use arrow::datatypes::Float64Type; | ||
use datafusion::error::Result; | ||
use datafusion::prelude::*; | ||
use datafusion_common::{internal_err, ScalarValue}; | ||
use datafusion_expr::{ColumnarValue, ScalarUDF, ScalarUDFImpl, Signature}; | ||
use std::sync::Arc; | ||
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/// This example shows how to use the full ScalarUDFImpl API to implement a user | ||
/// defined function. As in the `simple_udf.rs` example, this struct implements | ||
/// a function that takes two arguments and returns the first argument raised to | ||
/// the power of the second argument `a^b`. | ||
/// | ||
/// To do so, we must implement the `ScalarUDFImpl` trait. | ||
struct PowUdf { | ||
signature: Signature, | ||
aliases: Vec<String>, | ||
} | ||
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impl PowUdf { | ||
/// Create a new instance of the `PowUdf` struct | ||
fn new() -> Self { | ||
Self { | ||
signature: Signature::exact( | ||
// this function will always take two arguments of type f64 | ||
vec![DataType::Float64, DataType::Float64], | ||
// this function is deterministic and will always return the same | ||
// result for the same input | ||
Volatility::Immutable, | ||
), | ||
// we will also add an alias of "my_pow" | ||
aliases: vec!["my_pow".to_string()], | ||
} | ||
} | ||
} | ||
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impl ScalarUDFImpl for PowUdf { | ||
/// We implement as_any so that we can downcast the ScalarUDFImpl trait object | ||
fn as_any(&self) -> &dyn Any { | ||
self | ||
} | ||
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/// Return the name of this function | ||
fn name(&self) -> &str { | ||
"pow" | ||
} | ||
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/// Return the "signature" of this function -- namely what 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? In | ||
/// this case it will always be a constant value, but it could also be a | ||
/// function of the input types. | ||
fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> { | ||
Ok(DataType::Float64) | ||
} | ||
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/// This is the function that actually calculates the results. | ||
/// | ||
/// This is the same way that functions built into DataFusion are invoked, | ||
/// which permits important special cases when one or both of the arguments | ||
/// are single values (constants). For example `pow(a, 2)` | ||
/// | ||
/// However, it also means the implementation is more complex than when | ||
/// using `create_udf`. | ||
fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> { | ||
// DataFusion has arranged for the correct inputs to be passed to this | ||
// function, but we check again to make sure | ||
assert_eq!(args.len(), 2); | ||
let (base, exp) = (&args[0], &args[1]); | ||
assert_eq!(base.data_type(), DataType::Float64); | ||
assert_eq!(exp.data_type(), DataType::Float64); | ||
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match (base, exp) { | ||
// For demonstration purposes we also implement the scalar / scalar | ||
// case here, but it is not typically required for high performance. | ||
// | ||
// For performance it is most important to optimize cases where at | ||
// least one argument is an array. If all arguments are constants, | ||
// the DataFusion expression simplification logic will often invoke | ||
// this path once during planning, and simply use the result during | ||
// execution. | ||
( | ||
ColumnarValue::Scalar(ScalarValue::Float64(base)), | ||
ColumnarValue::Scalar(ScalarValue::Float64(exp)), | ||
) => { | ||
// compute the output. Note DataFusion treats `None` as NULL. | ||
let res = match (base, exp) { | ||
(Some(base), Some(exp)) => Some(base.powf(*exp)), | ||
// one or both arguments were NULL | ||
_ => None, | ||
}; | ||
Ok(ColumnarValue::Scalar(ScalarValue::from(res))) | ||
} | ||
// special case if the exponent is a constant | ||
( | ||
ColumnarValue::Array(base_array), | ||
ColumnarValue::Scalar(ScalarValue::Float64(exp)), | ||
) => { | ||
let result_array = match exp { | ||
// a ^ null = null | ||
None => new_null_array(base_array.data_type(), base_array.len()), | ||
// a ^ exp | ||
Some(exp) => { | ||
// DataFusion has ensured both arguments are Float64: | ||
let base_array = base_array.as_primitive::<Float64Type>(); | ||
// calculate the result for every row. The `unary` very | ||
// fast, "vectorized" code and handles things like null | ||
// values for us. | ||
let res: Float64Array = | ||
compute::unary(base_array, |base| base.powf(*exp)); | ||
Arc::new(res) | ||
} | ||
}; | ||
Ok(ColumnarValue::Array(result_array)) | ||
} | ||
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// special case if the base is a constant (note this code is quite | ||
// similar to the previous case, so we omit comments) | ||
( | ||
ColumnarValue::Scalar(ScalarValue::Float64(base)), | ||
ColumnarValue::Array(exp_array), | ||
) => { | ||
let res = match base { | ||
None => new_null_array(exp_array.data_type(), exp_array.len()), | ||
Some(base) => { | ||
let exp_array = exp_array.as_primitive::<Float64Type>(); | ||
let res: Float64Array = | ||
compute::unary(exp_array, |exp| base.powf(exp)); | ||
Arc::new(res) | ||
} | ||
}; | ||
Ok(ColumnarValue::Array(res)) | ||
} | ||
// Both arguments are arrays s we have to perform the calculation for every row | ||
(ColumnarValue::Array(base_array), ColumnarValue::Array(exp_array)) => { | ||
let res: Float64Array = compute::binary( | ||
base_array.as_primitive::<Float64Type>(), | ||
exp_array.as_primitive::<Float64Type>(), | ||
|base, exp| base.powf(exp), | ||
)?; | ||
Ok(ColumnarValue::Array(Arc::new(res))) | ||
} | ||
// if the types were not float, it is a bug in DataFusion | ||
_ => { | ||
use datafusion_common::DataFusionError; | ||
internal_err!("Invalid argument types to pow function") | ||
} | ||
} | ||
} | ||
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/// We will also add an alias of "my_pow" | ||
fn aliases(&self) -> &[String] { | ||
&self.aliases | ||
} | ||
} | ||
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/// In this example we register `PowUdf` as a user defined function | ||
/// and invoke it via the DataFrame API and SQL | ||
#[tokio::main] | ||
async fn main() -> Result<()> { | ||
let ctx = create_context()?; | ||
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// create the UDF | ||
let pow = ScalarUDF::from(PowUdf::new()); | ||
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// register the UDF with the context so it can be invoked by name and from SQL | ||
ctx.register_udf(pow.clone()); | ||
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// get a DataFrame from the context for scanning the "t" table | ||
let df = ctx.table("t").await?; | ||
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// Call pow(a, 10) using the DataFrame API | ||
let df = df.select(vec![pow.call(vec![col("a"), lit(10i32)])])?; | ||
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// note that the second argument is passed as an i32, not f64. DataFusion | ||
// automatically coerces the types to match the UDF's defined signature. | ||
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// print the results | ||
df.show().await?; | ||
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// You can also invoke both pow(2, 10) and its alias my_pow(a, b) using SQL | ||
let sql_df = ctx.sql("SELECT pow(2, 10), my_pow(a, b) FROM t").await?; | ||
sql_df.show().await?; | ||
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Ok(()) | ||
} | ||
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/// create local execution context with an in-memory table: | ||
/// | ||
/// ```text | ||
/// +-----+-----+ | ||
/// | a | b | | ||
/// +-----+-----+ | ||
/// | 2.1 | 1.0 | | ||
/// | 3.1 | 2.0 | | ||
/// | 4.1 | 3.0 | | ||
/// | 5.1 | 4.0 | | ||
/// +-----+-----+ | ||
/// ``` | ||
fn create_context() -> Result<SessionContext> { | ||
// define data. | ||
let a: ArrayRef = Arc::new(Float32Array::from(vec![2.1, 3.1, 4.1, 5.1])); | ||
let b: ArrayRef = Arc::new(Float64Array::from(vec![1.0, 2.0, 3.0, 4.0])); | ||
let batch = RecordBatch::try_from_iter(vec![("a", a), ("b", b)])?; | ||
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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(...). | ||
ctx.register_batch("t", batch)?; | ||
Ok(ctx) | ||
} |
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