Igosuki commented on a change in pull request #910: URL: https://github.com/apache/arrow-datafusion/pull/910#discussion_r696541461
########## File path: datafusion/src/avro_to_arrow/reader.rs ########## @@ -0,0 +1,289 @@ +// 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 crate::arrow::datatypes::{Schema, SchemaRef}; +use crate::arrow::record_batch::RecordBatch; +use crate::avro_to_arrow::arrow_array_reader::AvroArrowArrayReader; +use crate::error::Result; +use arrow::error::Result as ArrowResult; +use avro_rs::Reader as AvroReader; +use std::io::{Read, Seek, SeekFrom}; +use std::sync::Arc; + +/// Avro file reader builder +#[derive(Debug)] +pub struct ReaderBuilder { + /// Optional schema for the Avro file + /// + /// If the schema is not supplied, the reader will try to infer the schema + /// based on the Avro structure. + schema: Option<SchemaRef>, + /// Batch size (number of records to load each time) + /// + /// The default batch size when using the `ReaderBuilder` is 1024 records + batch_size: usize, + /// Optional projection for which columns to load (zero-based column indices) + projection: Option<Vec<String>>, +} + +impl Default for ReaderBuilder { + fn default() -> Self { + Self { + schema: None, + batch_size: 1024, + projection: None, + } + } +} + +impl ReaderBuilder { + /// Create a new builder for configuring Avro parsing options. + /// + /// To convert a builder into a reader, call `Reader::from_builder` + /// + /// # Example + /// + /// ``` + /// extern crate avro_rs; + /// + /// use std::fs::File; + /// + /// fn example() -> crate::datafusion::avro_to_arrow::Reader<'static, File> { + /// let file = File::open("test/data/basic.avro").unwrap(); + /// + /// // create a builder, inferring the schema with the first 100 records + /// let builder = crate::datafusion::avro_to_arrow::ReaderBuilder::new().infer_schema().with_batch_size(100); + /// + /// let reader = builder.build::<File>(file).unwrap(); + /// + /// reader + /// } + /// ``` + pub fn new() -> Self { + Self::default() + } + + /// Set the Avro file's schema + pub fn with_schema(mut self, schema: SchemaRef) -> Self { + self.schema = Some(schema); + self + } + + /// Set the Avro reader to infer the schema of the file + pub fn infer_schema(mut self) -> Self { + // remove any schema that is set + self.schema = None; + self + } + + /// Set the batch size (number of records to load at one time) + pub fn with_batch_size(mut self, batch_size: usize) -> Self { + self.batch_size = batch_size; + self + } + + /// Set the reader's column projection + pub fn with_projection(mut self, projection: Vec<String>) -> Self { + self.projection = Some(projection); + self + } + + /// Create a new `Reader` from the `ReaderBuilder` + pub fn build<'a, R>(self, source: R) -> Result<Reader<'a, R>> + where + R: Read + Seek, + { + let mut source = source; + + // check if schema should be inferred + let schema = match self.schema { + Some(schema) => schema, + None => Arc::new(infer_avro_schema_from_reader(&mut source)?), + }; + source.seek(SeekFrom::Start(0))?; + Reader::try_new(source, schema, self.batch_size, self.projection) + } +} + +/// Avro file record reader +pub struct Reader<'a, R: Read> { + array_reader: AvroArrowArrayReader<'a, R>, + schema: SchemaRef, + batch_size: usize, +} + +impl<'a, R: Read> Reader<'a, R> { + /// Create a new Avro Reader from any value that implements the `Read` trait. + /// + /// If reading a `File`, you can customise the Reader, such as to enable schema + /// inference, use `ReaderBuilder`. + pub fn try_new( + reader: R, + schema: SchemaRef, + batch_size: usize, + projection: Option<Vec<String>>, + ) -> Result<Self> { + Ok(Self { + array_reader: AvroArrowArrayReader::try_new( + AvroReader::new(reader)?, + schema.clone(), + projection, + )?, + schema, + batch_size, + }) + } + + /// Returns the schema of the reader, useful for getting the schema without reading + /// record batches + pub fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + /// Read the next batch of records + #[allow(clippy::should_implement_trait)] + pub fn next(&mut self) -> ArrowResult<Option<RecordBatch>> { + self.array_reader.next_batch(self.batch_size) + } +} + +impl<'a, R: Read> Iterator for Reader<'a, R> { + type Item = ArrowResult<RecordBatch>; + + fn next(&mut self) -> Option<Self::Item> { + self.next().transpose() + } +} + +/// Infer Avro schema given a reader +pub fn infer_avro_schema_from_reader<R: Read + Seek>(reader: &mut R) -> Result<Schema> { + let avro_reader = avro_rs::Reader::new(reader)?; + let schema = avro_reader.writer_schema(); + super::to_arrow_schema(schema) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::arrow::array::*; + use crate::arrow::datatypes::{DataType, Field}; + use arrow::datatypes::TimeUnit; + use std::fs::File; + + fn build_reader(name: &str) -> Reader<File> { + let testdata = crate::test_util::arrow_test_data(); + let filename = format!("{}/avro/{}", testdata, name); + let builder = ReaderBuilder::new().infer_schema().with_batch_size(64); + builder.build(File::open(filename).unwrap()).unwrap() + } + + fn get_col<'a, T: 'static>( + batch: &'a RecordBatch, + col: (usize, &Field), + ) -> Option<&'a T> { + batch.column(col.0).as_any().downcast_ref::<T>() + } + + #[test] + fn test_avro_basic() { + let mut reader = build_reader("alltypes_dictionary.avro"); + let batch = reader.next().unwrap().unwrap(); + + assert_eq!(11, batch.num_columns()); + assert_eq!(2, batch.num_rows()); + + let schema = reader.schema(); + let batch_schema = batch.schema(); + assert_eq!(schema, batch_schema); + + let id = schema.column_with_name("id").unwrap(); + assert_eq!(0, id.0); + assert_eq!(&DataType::Int32, id.1.data_type()); + let col = get_col::<Int32Array>(&batch, id).unwrap(); Review comment: No but you're right, with the current recordbatch API being what it is checking the output is likely better. -- This is an automated message from the Apache Git Service. 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