alamb commented on a change in pull request #8313: URL: https://github.com/apache/arrow/pull/8313#discussion_r498342317
########## File path: rust/README.md ########## @@ -21,17 +21,38 @@ [](https://codecov.io/gh/apache/arrow?branch=master) -## The Rust implementation of Arrow consists of the following crates +Welcome to the implementation of Arrow, the popular in-memory columnar format, in Rust. + +This part of the Arrow project is divided in 4 main components: | Crate | Description | Documentation | |-----------|-------------|---------------| -|Arrow | Core functionality (memory layout, array builders, low level computations) | [(README)](arrow/README.md) | -|Parquet | Parquet support | [(README)](parquet/README.md) | -|DataFusion | In-memory query engine with SQL support | [(README)](datafusion/README.md) | +|Arrow | Core functionality (memory layout, arrays, low level computations) | [(README)](arrow/README.md) | +|Parquet | Parquet support | [(README)](parquet/README.md) | +|Arrow-flight | Arrow data between processes | [(README)](arrow-flight/README.md) | +|DataFusion | In-memory query engine with SQL support | [(README)](datafusion/README.md) | + +Independently, they support a vast array of functionality for in-memory computations. + +Together, they allow users to write an SQL query or a `DataFrame` (using `DataFusion` crate), run it against a parquet file (using `Parquet` crate) and have it evaluated in-memory using Arrow's columnar format (using the `Arrow` crate), and sent it over to another process (using `Arrow-flight` crate). + +Generally speaking, `Arrow`'s has most functionality to develop under the Arrow format, and `DataFusion` offers most operations typically found in SQL, with the notable execeptions of: Review comment: ```suggestion Generally speaking, the `arrow` crate offers the functionality to develop code that uses Arrow arrays, and `datafusion` offers most operations typically found in SQL, with the notable exceptions of: ``` ########## File path: rust/README.md ########## @@ -21,17 +21,38 @@ [](https://codecov.io/gh/apache/arrow?branch=master) -## The Rust implementation of Arrow consists of the following crates +Welcome to the implementation of Arrow, the popular in-memory columnar format, in Rust. + +This part of the Arrow project is divided in 4 main components: | Crate | Description | Documentation | |-----------|-------------|---------------| -|Arrow | Core functionality (memory layout, array builders, low level computations) | [(README)](arrow/README.md) | -|Parquet | Parquet support | [(README)](parquet/README.md) | -|DataFusion | In-memory query engine with SQL support | [(README)](datafusion/README.md) | +|Arrow | Core functionality (memory layout, arrays, low level computations) | [(README)](arrow/README.md) | +|Parquet | Parquet support | [(README)](parquet/README.md) | +|Arrow-flight | Arrow data between processes | [(README)](arrow-flight/README.md) | +|DataFusion | In-memory query engine with SQL support | [(README)](datafusion/README.md) | + +Independently, they support a vast array of functionality for in-memory computations. + +Together, they allow users to write an SQL query or a `DataFrame` (using `DataFusion` crate), run it against a parquet file (using `Parquet` crate) and have it evaluated in-memory using Arrow's columnar format (using the `Arrow` crate), and sent it over to another process (using `Arrow-flight` crate). Review comment: ```suggestion Together, they allow users to write an SQL query or a `DataFrame` (using `datafusion` crate), run it against a parquet file (using `parquet` crate), evaluate it in-memory using Arrow's columnar format (using the `arrow` crate), and send to another process (using `arrow-flight` crate). ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
