datafusion_physical_expr/
async_scalar_function.rs

1// Licensed to the Apache Software Foundation (ASF) under one
2// or more contributor license agreements.  See the NOTICE file
3// distributed with this work for additional information
4// regarding copyright ownership.  The ASF licenses this file
5// to you under the Apache License, Version 2.0 (the
6// "License"); you may not use this file except in compliance
7// with the License.  You may obtain a copy of the License at
8//
9//   http://www.apache.org/licenses/LICENSE-2.0
10//
11// Unless required by applicable law or agreed to in writing,
12// software distributed under the License is distributed on an
13// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
14// KIND, either express or implied.  See the License for the
15// specific language governing permissions and limitations
16// under the License.
17
18use crate::ScalarFunctionExpr;
19use arrow::array::RecordBatch;
20use arrow::compute::concat;
21use arrow::datatypes::{DataType, Field, FieldRef, Schema};
22use datafusion_common::Result;
23use datafusion_common::config::ConfigOptions;
24use datafusion_common::{internal_err, not_impl_err};
25use datafusion_expr::ScalarFunctionArgs;
26use datafusion_expr::async_udf::AsyncScalarUDF;
27use datafusion_expr_common::columnar_value::ColumnarValue;
28use datafusion_physical_expr_common::physical_expr::PhysicalExpr;
29use std::fmt::Display;
30use std::hash::{Hash, Hasher};
31use std::sync::Arc;
32
33/// Wrapper around a scalar function that can be evaluated asynchronously
34#[derive(Debug, Clone, Eq)]
35pub struct AsyncFuncExpr {
36    /// The name of the output column this function will generate
37    pub name: String,
38    /// The actual function (always `ScalarFunctionExpr`)
39    pub func: Arc<dyn PhysicalExpr>,
40    /// The field that this function will return
41    return_field: FieldRef,
42}
43
44impl Display for AsyncFuncExpr {
45    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
46        write!(f, "async_expr(name={}, expr={})", self.name, self.func)
47    }
48}
49
50impl PartialEq for AsyncFuncExpr {
51    fn eq(&self, other: &Self) -> bool {
52        self.name == other.name && self.func == Arc::clone(&other.func)
53    }
54}
55
56impl Hash for AsyncFuncExpr {
57    fn hash<H: Hasher>(&self, state: &mut H) {
58        self.name.hash(state);
59        self.func.as_ref().hash(state);
60    }
61}
62
63impl AsyncFuncExpr {
64    /// create a new AsyncFuncExpr
65    pub fn try_new(
66        name: impl Into<String>,
67        func: Arc<dyn PhysicalExpr>,
68        schema: &Schema,
69    ) -> Result<Self> {
70        let Some(_) = func.downcast_ref::<ScalarFunctionExpr>() else {
71            return internal_err!(
72                "unexpected function type, expected ScalarFunctionExpr, got: {:?}",
73                func
74            );
75        };
76
77        let return_field = func.return_field(schema)?;
78        Ok(Self {
79            name: name.into(),
80            func,
81            return_field,
82        })
83    }
84
85    /// return the name of the output column
86    pub fn name(&self) -> &str {
87        &self.name
88    }
89
90    /// Return the output field generated by evaluating this function
91    pub fn field(&self, input_schema: &Schema) -> Result<Field> {
92        Ok(Field::new(
93            &self.name,
94            self.func.data_type(input_schema)?,
95            self.func.nullable(input_schema)?,
96        ))
97    }
98
99    /// Return the ideal batch size for this function
100    pub fn ideal_batch_size(&self) -> Result<Option<usize>> {
101        if let Some(expr) = self.func.downcast_ref::<ScalarFunctionExpr>()
102            && let Some(udf) = expr.fun().inner().downcast_ref::<AsyncScalarUDF>()
103        {
104            return Ok(udf.ideal_batch_size());
105        }
106        not_impl_err!("Can't get ideal_batch_size from {:?}", self.func)
107    }
108
109    /// This (async) function is called for each record batch to evaluate the LLM expressions
110    ///
111    /// The output is the output of evaluating the async expression and the input record batch
112    pub async fn invoke_with_args(
113        &self,
114        batch: &RecordBatch,
115        config_options: Arc<ConfigOptions>,
116    ) -> Result<ColumnarValue> {
117        let Some(scalar_function_expr) = self.func.downcast_ref::<ScalarFunctionExpr>()
118        else {
119            return internal_err!(
120                "unexpected function type, expected ScalarFunctionExpr, got: {:?}",
121                self.func
122            );
123        };
124
125        let Some(async_udf) = scalar_function_expr
126            .fun()
127            .inner()
128            .downcast_ref::<AsyncScalarUDF>()
129        else {
130            return not_impl_err!(
131                "Don't know how to evaluate async function: {:?}",
132                scalar_function_expr
133            );
134        };
135
136        let arg_fields = scalar_function_expr
137            .args()
138            .iter()
139            .map(|e| e.return_field(batch.schema_ref()))
140            .collect::<Result<Vec<_>>>()?;
141
142        let mut result_batches = vec![];
143        if let Some(ideal_batch_size) = self.ideal_batch_size()? {
144            let mut remainder = batch.clone();
145            while remainder.num_rows() > 0 {
146                let size = if ideal_batch_size > remainder.num_rows() {
147                    remainder.num_rows()
148                } else {
149                    ideal_batch_size
150                };
151
152                let current_batch = remainder.slice(0, size); // get next 10 rows
153                remainder = remainder.slice(size, remainder.num_rows() - size);
154                let args = scalar_function_expr
155                    .args()
156                    .iter()
157                    .map(|e| e.evaluate(&current_batch))
158                    .collect::<Result<Vec<_>>>()?;
159                result_batches.push(
160                    async_udf
161                        .invoke_async_with_args(ScalarFunctionArgs {
162                            args,
163                            arg_fields: arg_fields.clone(),
164                            number_rows: current_batch.num_rows(),
165                            return_field: Arc::clone(&self.return_field),
166                            config_options: Arc::clone(&config_options),
167                        })
168                        .await?,
169                );
170            }
171        } else {
172            let args = scalar_function_expr
173                .args()
174                .iter()
175                .map(|e| e.evaluate(batch))
176                .collect::<Result<Vec<_>>>()?;
177
178            result_batches.push(
179                async_udf
180                    .invoke_async_with_args(ScalarFunctionArgs {
181                        args: args.to_vec(),
182                        arg_fields,
183                        number_rows: batch.num_rows(),
184                        return_field: Arc::clone(&self.return_field),
185                        config_options: Arc::clone(&config_options),
186                    })
187                    .await?,
188            );
189        }
190
191        let datas = result_batches
192            .into_iter()
193            .map(|cv| match cv {
194                ColumnarValue::Array(arr) => Ok(arr),
195                ColumnarValue::Scalar(scalar) => Ok(scalar.to_array_of_size(1)?),
196            })
197            .collect::<Result<Vec<_>>>()?;
198
199        // Get references to the arrays as dyn Array to call concat
200        let dyn_arrays = datas
201            .iter()
202            .map(|arr| arr as &dyn arrow::array::Array)
203            .collect::<Vec<_>>();
204        let result_array = concat(&dyn_arrays)?;
205        Ok(ColumnarValue::Array(result_array))
206    }
207}
208
209impl PhysicalExpr for AsyncFuncExpr {
210    fn data_type(&self, input_schema: &Schema) -> Result<DataType> {
211        self.func.data_type(input_schema)
212    }
213
214    fn nullable(&self, input_schema: &Schema) -> Result<bool> {
215        self.func.nullable(input_schema)
216    }
217
218    fn evaluate(&self, _batch: &RecordBatch) -> Result<ColumnarValue> {
219        // TODO: implement this for scalar value input
220        not_impl_err!("AsyncFuncExpr.evaluate")
221    }
222
223    fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> {
224        self.func.children()
225    }
226
227    fn with_new_children(
228        self: Arc<Self>,
229        children: Vec<Arc<dyn PhysicalExpr>>,
230    ) -> Result<Arc<dyn PhysicalExpr>> {
231        let new_func = Arc::clone(&self.func).with_new_children(children)?;
232        Ok(Arc::new(AsyncFuncExpr {
233            name: self.name.clone(),
234            func: new_func,
235            return_field: Arc::clone(&self.return_field),
236        }))
237    }
238
239    fn fmt_sql(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
240        write!(f, "{}", self.func)
241    }
242}
datafusion_physical_expr/async_scalar_function.rs

datafusion_physical_expr/
async_scalar_function.rs
