Add example for PartitionedFile schema #22809
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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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| //! See `main.rs` for how to run it. | ||
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| use arrow::array::{Int32Array, RecordBatch}; | ||
| use arrow_schema::{DataType, Field, Schema, SchemaRef}; | ||
| use datafusion::common::Result; | ||
| use datafusion::datasource::listing::PartitionedFile; | ||
| use datafusion::datasource::object_store::ObjectStoreUrl; | ||
| use datafusion::datasource::physical_plan::{FileScanConfigBuilder, ParquetSource}; | ||
| use datafusion::datasource::source::DataSourceExec; | ||
| use datafusion::execution::TaskContext; | ||
| use datafusion::parquet::arrow::ArrowWriter; | ||
| use datafusion::parquet::file::reader::Length; | ||
| use datafusion::physical_plan::ExecutionPlan; | ||
| use futures::StreamExt; | ||
| use std::fs::File; | ||
| use std::path::Path; | ||
| use std::sync::Arc; | ||
| use tempfile::TempDir; | ||
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| /// Demonstrates how to attach a per-file Arrow schema to a [`PartitionedFile`] | ||
| /// via [`PartitionedFile::with_arrow_schema`]. | ||
| /// | ||
| /// By default DataFusion infers a file's physical schema by reading its | ||
| /// metadata (e.g. the Parquet footer) when the scan begins. When the schema is | ||
| /// already known, it can be supplied up front so this inference step is | ||
| /// skipped, saving an I/O round trip and metadata parse per file. | ||
| /// | ||
| /// The example writes a small Parquet file with a single `Int32` column `a` and | ||
| /// reads it back three ways: | ||
| /// - without a schema, letting DataFusion infer it at query time; | ||
| /// - with the correct schema, skipping inference; | ||
| /// - with a deliberately mismatched schema (`a` typed as `Int64`), which | ||
| /// surfaces as an error since the provided schema does not match the data | ||
| /// actually stored in the file. | ||
| /// | ||
| /// Note that the schema passed to [`PartitionedFile::with_arrow_schema`] must | ||
| /// describe only the columns physically stored in the file and must not include | ||
| /// partition columns. | ||
| pub async fn read_partitioned_file() -> Result<()> { | ||
| let tmpdir = TempDir::new()?; | ||
| let file_path = tmpdir.path().join("partitioned-file"); | ||
| let file = File::create(file_path.as_path())?; | ||
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| let file_schema = | ||
| Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)])); | ||
| let batch = RecordBatch::try_new( | ||
| file_schema.clone(), | ||
| vec![Arc::new(Int32Array::from(vec![1, 2, 3, 4, 5]))], | ||
| )?; | ||
| let mut writer = ArrowWriter::try_new(&file, file_schema.clone(), None)?; | ||
| writer.write(&batch)?; | ||
| writer.finish()?; | ||
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| let table_schema = Arc::new(Schema::new(vec![ | ||
| Field::new("a", DataType::Int32, true), | ||
| // Specify another field in the table which is missing from the file schema. | ||
| // Illustrates that the table schema does not need to match the PartitionedFile schema | ||
| // for a scan to succeed. | ||
| Field::new("b", DataType::Float64, true), | ||
|
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| ])); | ||
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| // Infer file schema at query time. | ||
| { | ||
| let batch = | ||
| read_file(file_path.as_path(), file.len(), table_schema.clone(), None) | ||
| .await?; | ||
| println!("{batch:?}"); | ||
| } | ||
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| // Provide the correct file schema to skip inferring at query time. | ||
| { | ||
| let batch = read_file( | ||
| file_path.as_path(), | ||
| file.len(), | ||
| table_schema.clone(), | ||
| Some(file_schema.clone()), | ||
| ) | ||
| .await?; | ||
| println!("{batch:?}"); | ||
| } | ||
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| // A mismatching file schema returns an error. | ||
| { | ||
| let mismatching_schema = | ||
| Arc::new(Schema::new(vec![Field::new("a", DataType::Int64, false)])); | ||
| let error = read_file( | ||
| file_path.as_path(), | ||
| file.len(), | ||
| table_schema.clone(), | ||
| Some(mismatching_schema), | ||
| ) | ||
| .await | ||
| .unwrap_err(); | ||
| println!("Got schema error: {error:?}"); | ||
| } | ||
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| Ok(()) | ||
| } | ||
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| /// Scans a single Parquet file with the given `source_schema`, optionally | ||
| /// supplying the file's Arrow schema to skip schema inference. A `None` | ||
| /// `file_schema` lets DataFusion infer the schema from the file metadata at | ||
| /// query time. | ||
| async fn read_file( | ||
| file_path: &Path, | ||
| file_len: u64, | ||
| source_schema: SchemaRef, | ||
| file_schema: Option<SchemaRef>, | ||
| ) -> Result<RecordBatch> { | ||
| let mut partitioned_file = | ||
| PartitionedFile::new(file_path.to_string_lossy(), file_len); | ||
| if let Some(schema) = file_schema { | ||
| partitioned_file = partitioned_file.with_arrow_schema(schema); | ||
| } | ||
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| let config = FileScanConfigBuilder::new( | ||
| ObjectStoreUrl::local_filesystem(), | ||
| Arc::new(ParquetSource::new(source_schema)), | ||
| ) | ||
| .with_file(partitioned_file) | ||
| .build(); | ||
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| let exec = DataSourceExec::from_data_source(config); | ||
| let mut result = exec.execute(0, Arc::new(TaskContext::default()))?; | ||
| result.next().await.ok_or_else(|| { | ||
| datafusion::error::DataFusionError::Internal( | ||
| "execution produced no batches".into(), | ||
| ) | ||
| })? | ||
| } | ||
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Thank you -- this is a nice description of what is going on