// 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.

//! A complete, safe, native Rust implementation of [Apache Arrow](https://arrow.apache.org), a cross-language
//! development platform for in-memory data.
//!
//! Please see the [arrow crates.io](https://crates.io/crates/arrow)
//! page for feature flags and tips to improve performance.
//!
//! # Columnar Format
//!
//! The [`array`] module provides statically typed implementations of all the array types as defined
//! by the [Arrow Columnar Format](https://arrow.apache.org/docs/format/Columnar.html)
//!
//! For example, an [`Int32Array`](array::Int32Array) represents a nullable array of `i32`
//!
//! ```rust
//! # use arrow::array::{Array, Int32Array};
//! let array = Int32Array::from(vec![Some(1), None, Some(3)]);
//! assert_eq!(array.len(), 3);
//! assert_eq!(array.value(0), 1);
//! assert_eq!(array.is_null(1), true);
//!
//! let collected: Vec<_> = array.iter().collect();
//! assert_eq!(collected, vec![Some(1), None, Some(3)]);
//! assert_eq!(array.values(), &[1, 0, 3])
//! ```
//!
//! It is also possible to write generic code. For example, the following is generic over
//! all primitively typed arrays
//!
//! ```rust
//! # use std::iter::Sum;
//! # use arrow::array::{Float32Array, PrimitiveArray, TimestampNanosecondArray};
//! # use arrow::datatypes::ArrowPrimitiveType;
//! #
//! fn sum<T: ArrowPrimitiveType>(array: &PrimitiveArray<T>) -> T::Native
//! where
//!     T: ArrowPrimitiveType,
//!     T::Native: Sum
//! {
//!     array.iter().map(|v| v.unwrap_or_default()).sum()
//! }
//!
//! assert_eq!(sum(&Float32Array::from(vec![1.1, 2.9, 3.])), 7.);
//! assert_eq!(sum(&TimestampNanosecondArray::from(vec![1, 2, 3])), 6);
//! ```
//!
//! And the following is generic over all arrays with comparable values
//!
//! ```rust
//! # use arrow::array::{ArrayAccessor, ArrayIter, Int32Array, StringArray};
//! # use arrow::datatypes::ArrowPrimitiveType;
//! #
//! fn min<T: ArrayAccessor>(array: T) -> Option<T::Item>
//! where
//!     T::Item: Ord
//! {
//!     ArrayIter::new(array).filter_map(|v| v).min()
//! }
//!
//! assert_eq!(min(&Int32Array::from(vec![4, 2, 1, 6])), Some(1));
//! assert_eq!(min(&StringArray::from(vec!["b", "a", "c"])), Some("a"));
//! ```
//!
//! **For more examples, and details consult the [arrow_array] docs.**
//!
//! # Type Erasure / Trait Objects
//!
//! It is often the case that code wishes to handle any type of array, without necessarily knowing
//! its concrete type. This use-case is catered for by a combination of [`Array`]
//! and [`DataType`](datatypes::DataType), with the former providing a type-erased container for
//! the array, and the latter identifying the concrete type of array.
//!
//! ```rust
//! # use arrow::array::{Array, Float32Array};
//! # use arrow::array::StringArray;
//! # use arrow::datatypes::DataType;
//! #
//! fn impl_string(array: &StringArray) {}
//! fn impl_f32(array: &Float32Array) {}
//!
//! fn impl_dyn(array: &dyn Array) {
//!     match array.data_type() {
//!         DataType::Utf8 => impl_string(array.as_any().downcast_ref().unwrap()),
//!         DataType::Float32 => impl_f32(array.as_any().downcast_ref().unwrap()),
//!         _ => unimplemented!()
//!     }
//! }
//! ```
//!
//! To facilitate downcasting, the [`AsArray`](crate::array::AsArray) extension trait can be used
//!
//! ```rust
//! # use arrow::array::{Array, Float32Array, AsArray};
//! # use arrow::array::StringArray;
//! # use arrow::datatypes::DataType;
//! #
//! fn impl_string(array: &StringArray) {}
//! fn impl_f32(array: &Float32Array) {}
//!
//! fn impl_dyn(array: &dyn Array) {
//!     match array.data_type() {
//!         DataType::Utf8 => impl_string(array.as_string()),
//!         DataType::Float32 => impl_f32(array.as_primitive()),
//!         _ => unimplemented!()
//!     }
//! }
//! ```
//!
//! It is also common to want to write a function that returns one of a number of possible
//! array implementations. [`ArrayRef`] is a type-alias for [`Arc<dyn Array>`](array::Array)
//! which is frequently used for this purpose
//!
//! ```rust
//! # use std::str::FromStr;
//! # use std::sync::Arc;
//! # use arrow::array::{ArrayRef, Int32Array, PrimitiveArray};
//! # use arrow::datatypes::{ArrowPrimitiveType, DataType, Int32Type, UInt32Type};
//! # use arrow::compute::cast;
//! #
//! fn parse_to_primitive<'a, T, I>(iter: I) -> PrimitiveArray<T>
//! where
//!     T: ArrowPrimitiveType,
//!     T::Native: FromStr,
//!     I: IntoIterator<Item=&'a str>,
//! {
//!     PrimitiveArray::from_iter(iter.into_iter().map(|val| T::Native::from_str(val).ok()))
//! }
//!
//! fn parse_strings<'a, I>(iter: I, to_data_type: DataType) -> ArrayRef
//! where
//!     I: IntoIterator<Item=&'a str>,
//! {
//!    match to_data_type {
//!        DataType::Int32 => Arc::new(parse_to_primitive::<Int32Type, _>(iter)) as _,
//!        DataType::UInt32 => Arc::new(parse_to_primitive::<UInt32Type, _>(iter)) as _,
//!        _ => unimplemented!()
//!    }
//! }
//!
//! let array = parse_strings(["1", "2", "3"], DataType::Int32);
//! let integers = array.as_any().downcast_ref::<Int32Array>().unwrap();
//! assert_eq!(integers.values(), &[1, 2, 3])
//! ```
//!
//! # Compute Kernels
//!
//! The [`compute`] module provides optimised implementations of many common operations,
//! for example the `parse_strings` operation above could also be implemented as follows:
//!
//! ```
//! # use std::sync::Arc;
//! # use arrow::error::Result;
//! # use arrow::array::{ArrayRef, StringArray, UInt32Array};
//! # use arrow::datatypes::DataType;
//! #
//! fn parse_strings<'a, I>(iter: I, to_data_type: &DataType) -> Result<ArrayRef>
//! where
//!     I: IntoIterator<Item=&'a str>,
//! {
//!     let array = StringArray::from_iter(iter.into_iter().map(Some));
//!     arrow::compute::cast(&array, to_data_type)
//! }
//!
//! let array = parse_strings(["1", "2", "3"], &DataType::UInt32).unwrap();
//! let integers = array.as_any().downcast_ref::<UInt32Array>().unwrap();
//! assert_eq!(integers.values(), &[1, 2, 3])
//! ```
//!
//! This module also implements many common vertical operations:
//!
//! * All mathematical binary operators, such as [`sub`](compute::kernels::numeric::sub)
//! * All boolean binary operators such as [`equality`](compute::kernels::comparison::eq)
//! * [`cast`](compute::kernels::cast::cast)
//! * [`filter`](compute::kernels::filter::filter)
//! * [`take`](compute::kernels::take::take)
//! * [`sort`](compute::kernels::sort::sort)
//! * some string operators such as [`substring`](compute::kernels::substring::substring) and [`length`](compute::kernels::length::length)
//!
//! ```
//! # use arrow::compute::gt_scalar;
//! # use arrow_array::cast::AsArray;
//! # use arrow_array::Int32Array;
//! # use arrow_array::types::Int32Type;
//! # use arrow_select::filter::filter;
//! let array = Int32Array::from_iter(0..100);
//! let predicate = gt_scalar(&array, 60).unwrap();
//! let filtered = filter(&array, &predicate).unwrap();
//!
//! let expected = Int32Array::from_iter(61..100);
//! assert_eq!(&expected, filtered.as_primitive::<Int32Type>());
//! ```
//!
//! As well as some horizontal operations, such as:
//!
//! * [`min`](compute::kernels::aggregate::min) and [`max`](compute::kernels::aggregate::max)
//! * [`sum`](compute::kernels::aggregate::sum)
//!
//! # Tabular Representation
//!
//! It is common to want to group one or more columns together into a tabular representation. This
//! is provided by [`RecordBatch`] which combines a [`Schema`](datatypes::Schema)
//! and a corresponding list of [`ArrayRef`].
//!
//!
//! ```
//! # use std::sync::Arc;
//! # use arrow::array::{Float32Array, Int32Array};
//! # use arrow::record_batch::RecordBatch;
//! #
//! let col_1 = Arc::new(Int32Array::from_iter([1, 2, 3])) as _;
//! let col_2 = Arc::new(Float32Array::from_iter([1., 6.3, 4.])) as _;
//!
//! let batch = RecordBatch::try_from_iter([("col1", col_1), ("col_2", col_2)]).unwrap();
//! ```
//!
//! # IO
//!
//! This crate provides readers and writers for various formats to/from [`RecordBatch`]
//!
//! * JSON: [`Reader`](json::reader::Reader) and [`Writer`](json::writer::Writer)
//! * CSV: [`Reader`](csv::reader::Reader) and [`Writer`](csv::writer::Writer)
//! * IPC: [`Reader`](ipc::reader::StreamReader) and [`Writer`](ipc::writer::FileWriter)
//!
//! Parquet is published as a [separate crate](https://crates.io/crates/parquet)
//!
//! # Serde Compatibility
//!
//! [`arrow_json::reader::Decoder`] provides a mechanism to convert arbitrary, serde-compatible
//! structures into [`RecordBatch`].
//!
//! Whilst likely less performant than implementing a custom builder, as described in
//! [arrow_array::builder], this provides a simple mechanism to get up and running quickly
//!
//! ```
//! # use std::sync::Arc;
//! # use arrow_json::ReaderBuilder;
//! # use arrow_schema::{DataType, Field, Schema};
//! # use serde::Serialize;
//! # use arrow_array::cast::AsArray;
//! # use arrow_array::types::{Float32Type, Int32Type};
//! #
//! #[derive(Serialize)]
//! struct MyStruct {
//!     int32: i32,
//!     string: String,
//! }
//!
//! let schema = Schema::new(vec![
//!     Field::new("int32", DataType::Int32, false),
//!     Field::new("string", DataType::Utf8, false),
//! ]);
//!
//! let rows = vec![
//!     MyStruct{ int32: 5, string: "bar".to_string() },
//!     MyStruct{ int32: 8, string: "foo".to_string() },
//! ];
//!
//! let mut decoder = ReaderBuilder::new(Arc::new(schema)).build_decoder().unwrap();
//! decoder.serialize(&rows).unwrap();
//!
//! let batch = decoder.flush().unwrap().unwrap();
//!
//! // Expect batch containing two columns
//! let int32 = batch.column(0).as_primitive::<Int32Type>();
//! assert_eq!(int32.values(), &[5, 8]);
//!
//! let string = batch.column(1).as_string::<i32>();
//! assert_eq!(string.value(0), "bar");
//! assert_eq!(string.value(1), "foo");
//! ```
//!
//! # Crate Topology
//!
//! The [`arrow`] project is implemented as multiple sub-crates, which are then re-exported by
//! this top-level crate.
//!
//! Crate authors can choose to depend on this top-level crate, or just
//! the sub-crates they need.
//!
//! The current list of sub-crates is:
//!
//! * [`arrow-arith`][arrow_arith] - arithmetic kernels
//! * [`arrow-array`][arrow_array] - type-safe arrow array abstractions
//! * [`arrow-buffer`][arrow_buffer] - buffer abstractions for arrow arrays
//! * [`arrow-cast`][arrow_cast] - cast kernels for arrow arrays
//! * [`arrow-csv`][arrow_csv] - read/write CSV to arrow format
//! * [`arrow-data`][arrow_data] - the underlying data of arrow arrays
//! * [`arrow-ipc`][arrow_ipc] - read/write IPC to arrow format
//! * [`arrow-json`][arrow_json] - read/write JSON to arrow format
//! * [`arrow-ord`][arrow_ord] - ordering kernels for arrow arrays
//! * [`arrow-row`][arrow_row] - comparable row format
//! * [`arrow-schema`][arrow_schema] - the logical types for arrow arrays
//! * [`arrow-select`][arrow_select] - selection kernels for arrow arrays
//! * [`arrow-string`][arrow_string] - string kernels for arrow arrays
//!
//! Some functionality is also distributed independently of this crate:
//!
//! * [`arrow-flight`] - support for [Arrow Flight RPC]
//! * [`arrow-integration-test`] - support for [Arrow JSON Test Format]
//! * [`parquet`](https://docs.rs/parquet/latest/parquet/) - support for [Apache Parquet]
//!
//! # Safety and Security
//!
//! Like many crates, this crate makes use of unsafe where prudent. However, it endeavours to be
//! sound. Specifically, **it should not be possible to trigger undefined behaviour using safe APIs.**
//!
//! If you think you have found an instance where this is possible, please file
//! a ticket in our [issue tracker] and it will be triaged and fixed. For more information on
//! arrow's use of unsafe, see [here](https://github.com/apache/arrow-rs/tree/master/arrow#safety).
//!
//! # Higher-level Processing
//!
//! This crate aims to provide reusable, low-level primitives for operating on columnar data. For
//! more sophisticated query processing workloads, consider checking out [DataFusion]. This
//! orchestrates the primitives exported by this crate into an embeddable query engine, with
//! SQL and DataFrame frontends, and heavily influences this crate's roadmap.
//!
//! [`arrow`]: https://github.com/apache/arrow-rs
//! [`array`]: mod@array
//! [`Array`]: array::Array
//! [`ArrayRef`]: array::ArrayRef
//! [`ArrayData`]: array::ArrayData
//! [`make_array`]: array::make_array
//! [`Buffer`]: buffer::Buffer
//! [`RecordBatch`]: record_batch::RecordBatch
//! [`arrow-flight`]: https://docs.rs/arrow-flight/latest/arrow_flight/
//! [`arrow-integration-test`]: https://docs.rs/arrow-integration-test/latest/arrow_integration_test/
//! [`parquet`]: https://docs.rs/parquet/latest/parquet/
//! [Arrow Flight RPC]: https://arrow.apache.org/docs/format/Flight.html
//! [Arrow JSON Test Format]: https://github.com/apache/arrow/blob/master/docs/source/format/Integration.rst#json-test-data-format
//! [Apache Parquet]: https://parquet.apache.org/
//! [DataFusion]: https://github.com/apache/arrow-datafusion
//! [issue tracker]: https://github.com/apache/arrow-rs/issues
//!

#![deny(clippy::redundant_clone)]
#![warn(missing_debug_implementations)]
#![allow(rustdoc::invalid_html_tags)]
pub use arrow_array::{downcast_dictionary_array, downcast_primitive_array};

pub use arrow_buffer::{alloc, buffer};

pub mod array;
pub mod compute;
#[cfg(feature = "csv")]
pub use arrow_csv as csv;
pub mod datatypes;
pub mod error;
#[cfg(feature = "ffi")]
pub mod ffi;
#[cfg(feature = "ffi")]
pub mod ffi_stream;
#[cfg(feature = "ipc")]
pub use arrow_ipc as ipc;
#[cfg(feature = "json")]
pub use arrow_json as json;
#[cfg(feature = "pyarrow")]
pub mod pyarrow;

pub mod record_batch {
    pub use arrow_array::{
        RecordBatch, RecordBatchIterator, RecordBatchOptions, RecordBatchReader, RecordBatchWriter,
    };
}
pub use arrow_array::temporal_conversions;
pub use arrow_row as row;
pub mod tensor;
pub mod util;