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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2020/10/01 15:41:22 UTC
[GitHub] [arrow] alamb commented on a change in pull request #8313: ARROW-4927: [Rust] Update top level README to describe current functionality
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).
```
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