From d476d7d77b2fd092bbeca026607b3a6e36e0c796 Mon Sep 17 00:00:00 2001
From: rjzamora <rzamora217@gmail.com>
Date: Mon, 16 Sep 2024 10:05:09 -0700
Subject: [PATCH] fix typo again

---
 docs/source/spilling.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/spilling.rst b/docs/source/spilling.rst
index 037b193b..c86b5ce4 100644
--- a/docs/source/spilling.rst
+++ b/docs/source/spilling.rst
@@ -114,7 +114,7 @@ cuDF Spilling
 
 When executing an ETL workflow with `Dask cuDF <https://docs.rapids.ai/api/dask-cudf/stable/>`_
 (i.e. Dask DataFrame), it is usually best to leverage `native spilling support in cuDF
-<https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory>_`.
+<https://docs.rapids.ai/api/cudf/stable/developer_guide/library_design/#spilling-to-host-memory>`_.
 
 Native cuDF spilling has an important advantage over the other methodologies mentioned
 above. When JIT-unspill or default spilling are used, the worker is only able to spill