datafusion 7.0.0-alpha

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// to you under the Apache License, Version 2.0 (the
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//   http://www.apache.org/licenses/LICENSE-2.0
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//! Module containing helper methods for the various file formats

pub mod avro;
pub mod csv;
pub mod json;
pub mod parquet;

use std::any::Any;
use std::fmt;
use std::sync::Arc;

use crate::arrow::datatypes::SchemaRef;
use crate::error::Result;
use crate::logical_plan::Expr;
use crate::physical_plan::file_format::FileScanConfig;
use crate::physical_plan::{ExecutionPlan, Statistics};

use async_trait::async_trait;

use super::object_store::{ObjectReader, ObjectReaderStream};

/// This trait abstracts all the file format specific implementations
/// from the `TableProvider`. This helps code re-utilization accross
/// providers that support the the same file formats.
#[async_trait]
pub trait FileFormat: Send + Sync + fmt::Debug {
    /// Returns the table provider as [`Any`](std::any::Any) so that it can be
    /// downcast to a specific implementation.
    fn as_any(&self) -> &dyn Any;

    /// Infer the common schema of the provided objects. The objects will usually
    /// be analysed up to a given number of records or files (as specified in the
    /// format config) then give the estimated common schema. This might fail if
    /// the files have schemas that cannot be merged.
    async fn infer_schema(&self, readers: ObjectReaderStream) -> Result<SchemaRef>;

    /// Infer the statistics for the provided object. The cost and accuracy of the
    /// estimated statistics might vary greatly between file formats.
    async fn infer_stats(&self, reader: Arc<dyn ObjectReader>) -> Result<Statistics>;

    /// Take a list of files and convert it to the appropriate executor
    /// according to this file format.
    async fn create_physical_plan(
        &self,
        conf: FileScanConfig,
        filters: &[Expr],
    ) -> Result<Arc<dyn ExecutionPlan>>;
}