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| parquet | Support for Parquet columnar file format |[docs.rs](https://docs.rs/parquet/latest)|[(README)][parquet-readme]|
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| arrow-flight | Support for Arrow-Flight IPC protocol |[docs.rs](https://docs.rs/arrow-flight/latest)|[(README)][flight-readme]|
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| object-store | Support for object store interactions (aws, azure, gcp, local, in-memory) |[docs.rs](https://docs.rs/object_store/latest)|[(README)][objectstore-readme]|
|[`arrow-flight`]| Support for Arrow-Flight IPC protocol |[docs.rs](https://docs.rs/arrow-flight/latest)|[(README)][flight-readme]|
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|[`object-store`]| Support for object store interactions (aws, azure, gcp, local, in-memory) |[docs.rs](https://docs.rs/object_store/latest)|[(README)][objectstore-readme]|
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|[`parquet`]| Support for Parquet columnar file format |[docs.rs](https://docs.rs/parquet/latest)|[(README)][parquet-readme]|
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|[`parquet_derive`]| A crate for deriving RecordWriter/RecordReader for arbitrary, simple structs |[docs.rs](https://docs.rs/parquet-derive/latest)|[(README)][parquet-derive-readme]|
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The current development version the API documentation in this repo can be found [here](https://arrow.apache.org/rust).
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There are two related crates in a different repository
Collectively, these crates support a vast array of functionality for analytic computations in Rust.
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### `arrow` and `parquet` crates
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For example, you can write an SQL query or a `DataFrame` (using the `datafusion` crate), run it against a parquet file (using the `parquet` crate), evaluate it in-memory using Arrow's columnar format (using the `arrow` crate), and send to another process (using the `arrow-flight` crate).
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The Arrow Rust project releases approximately monthly and follows [Semantic
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Versioning].
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Generally speaking, the `arrow` crate offers functionality for using Arrow arrays, and `datafusion` offers most operations typically found in SQL, including `join`s and window functions.
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Due to available maintainer and testing bandwidth, [`arrow`] crates ([`arrow`],
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[`arrow-flight`], etc.) are released on the same schedule with the same versions
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as the [`parquet`] and [`parquet-derive`] crates.
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Starting June 2024, we plan to release new major versions with potentially
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breaking API changes at most once a quarter, and release incremental minor versions in
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the intervening months. See [this ticket] for more details.
Collectively, these crates support a wider array of functionality for analytic computations in Rust.
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For example, you can write SQL queries or a `DataFrame` (using the
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[`datafusion`] crate) to read a parquet file (using the [`parquet`] crate),
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evaluate it in-memory using Arrow's columnar format (using the [`arrow`] crate),
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and send to another process (using the [`arrow-flight`] crate).
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Generally speaking, the [`arrow`] crate offers functionality for using Arrow
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arrays, and [`datafusion`] offers most operations typically found in SQL,
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including `join`s and window functions.
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You can find more details about each crate in their respective READMEs.
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## Arrow Rust Community
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The `[email protected]` mailing list serves as the core communication channel for the Arrow community. Instructions for signing up and links to the archives can be found at the [Arrow Community](https://arrow.apache.org/community/) page. All major announcements and communications happen there.
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The `[email protected]` mailing list serves as the core communication channel for the Arrow community. Instructions for signing up and links to the archives can be found on the [Arrow Community](https://arrow.apache.org/community/) page. All major announcements and communications happen there.
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The Rust Arrow community also uses the official [ASF Slack](https://s.apache.org/slack-invite) for informal discussions and coordination. This is
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a great place to meet other contributors and get guidance on where to contribute. Join us in the `#arrow-rust` channel and feel free to ask for an invite via:
@@ -72,8 +125,9 @@ There is more information in the [contributing] guide.
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