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Posted to commits@datafusion.apache.org by gi...@apache.org on 2026/08/08 04:08:07 UTC
(datafusion) 01/01: Fix duplicated words in documentation (#24176)
This is an automated email from the ASF dual-hosted git repository.
github-merge-queue[bot] pushed a commit to branch gh-readonly-queue/main/pr-24176-1cb89d2b8134ed76a8324bbc14f75239bb020b7c
in repository https://gitbox.apache.org/repos/asf/datafusion.git
commit e0a05a693da6c56f97f001e0d5b3df1329b11ac2
Author: Recoordinate <la...@gmail.com>
AuthorDate: Sat Aug 8 11:07:46 2026 +0700
Fix duplicated words in documentation (#24176)
A few duplicated words in the docs:
- `docs/source/library-user-guide/building-logical-plans.md`: "functions
that can can be used" -> "that can be used".
- `docs/source/user-guide/explain-usage.md`: "the same query query as"
-> "the same query as"; "Divides the input into into 10" -> "into 10".
- `benchmarks/README.md`: "sort merge joins joins" -> "sort merge
joins".
Documentation only.
Signed-off-by: latent-9 <29...@users.noreply.github.com>
---
benchmarks/README.md | 2 +-
docs/source/library-user-guide/building-logical-plans.md | 2 +-
docs/source/user-guide/explain-usage.md | 4 ++--
3 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/benchmarks/README.md b/benchmarks/README.md
index b6a7705cf9..f357ff4da5 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -953,7 +953,7 @@ Several queries are included to test hash joins under various workloads.
## Sort Merge Join
-This benchmark focuses on the performance of queries with sort merge joins joins, minimizing other overheads such as scanning data sources or evaluating predicates.
+This benchmark focuses on the performance of queries with sort merge joins, minimizing other overheads such as scanning data sources or evaluating predicates.
Several queries are included to test sort merge joins under various workloads.
diff --git a/docs/source/library-user-guide/building-logical-plans.md b/docs/source/library-user-guide/building-logical-plans.md
index 9dc0fcbf31..6efd97879a 100644
--- a/docs/source/library-user-guide/building-logical-plans.md
+++ b/docs/source/library-user-guide/building-logical-plans.md
@@ -86,7 +86,7 @@ Filter: person.id > Int32(500) [id:Int32;N, name:Utf8;N]
DataFusion logical plans can be created using the [LogicalPlanBuilder] struct. There is also a [DataFrame] API which is
a higher-level API that delegates to [LogicalPlanBuilder].
-There are several functions that can can be used to create a new builder, such as
+There are several functions that can be used to create a new builder, such as
- `empty` - create an empty plan with no fields
- `values` - create a plan from a set of literal values
diff --git a/docs/source/user-guide/explain-usage.md b/docs/source/user-guide/explain-usage.md
index 40ff369b58..bc9dace297 100644
--- a/docs/source/user-guide/explain-usage.md
+++ b/docs/source/user-guide/explain-usage.md
@@ -169,7 +169,7 @@ debugging to see why and when DataFusion added and removed operators from a plan
During execution, DataFusion operators collect detailed metrics. You can access
them programmatically via [`ExecutionPlan::metrics`] as well as with the
-`EXPLAIN ANALYZE` command. For example here is the same query query as
+`EXPLAIN ANALYZE` command. For example here is the same query as
above but with `EXPLAIN ANALYZE` (note the output is edited for clarity)
[`executionplan::metrics`]: https://docs.rs/datafusion/latest/datafusion/physical_plan/trait.ExecutionPlan.html#method.metrics
@@ -365,7 +365,7 @@ For this query, let's again read the plan from the bottom to the top:
- `gby=[UserID@0 as UserID]`: Represents `GROUP BY` in the [physical plan] and groups together the same values of `UserID`.
- `aggr=[count(*)]`: Applies the `COUNT` aggregate on all rows for each group.
- `RepartitionExec`
- - `partitioning=Hash([UserID@0], 10)`: Divides the input into into 10 (new) output partitions based on the value of `hash(UserID)`. You can read more about this in the [partitioning] documentation.
+ - `partitioning=Hash([UserID@0], 10)`: Divides the input into 10 (new) output partitions based on the value of `hash(UserID)`. You can read more about this in the [partitioning] documentation.
- `input_partitions=10`: Number of input partitions.
- `CoalesceBatchesExec`
- `target_batch_size=8192`: Combines smaller batches in to larger batches. In this case approximately 8192 rows in each batch.
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