From c03a6ccd0f211f546f9de5c755d09977d61b8194 Mon Sep 17 00:00:00 2001 From: Jiayu Liu Date: Wed, 23 Jun 2021 18:53:51 +0800 Subject: [PATCH] update docs to reflect recent changes (#489) --- README.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/README.md b/README.md index eafc5c294d74..f947964f11b9 100644 --- a/README.md +++ b/README.md @@ -37,10 +37,7 @@ Independently, they support a vast array of functionality for in-memory computat Together, they allow users to 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). -Generally speaking, the `arrow` crate offers functionality to develop code that uses Arrow arrays, and `datafusion` offers most operations typically found in SQL, with the notable exceptions of: - -- `join` -- `window` functions +Generally speaking, the `arrow` crate offers functionality to develop code that uses Arrow arrays, and `datafusion` offers most operations typically found in SQL, including `join`s and window functions. There are too many features to enumerate here, but some notable mentions: