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new f2cb5714cb50 [SPARK-58843][DOC] Fix grammar errors in the SQL tuning,
GraphX and MLlib clustering guides
f2cb5714cb50 is described below
commit f2cb5714cb50d49add1ce3c4c5a2fcb526af5eeb
Author: Uros <[email protected]>
AuthorDate: Tue Aug 18 14:33:15 2026 +0000
[SPARK-58843][DOC] Fix grammar errors in the SQL tuning, GraphX and MLlib
clustering guides
### What changes were proposed in this pull request?
Fixes eight grammatical errors across three documentation guides:
- `sql-performance-tuning.md`: `partitions is close` to `partitions close`;
`only respect` to `only respects`; `is small than this factor multiply` to `is
smaller than this factor multiplying`; `makes use the` to `makes use of the`
- `graphx-programming-guide.md`: `there are relationship with missing user`
to `there is a relationship with a missing user` (twice); `in number of
situations` to `in a number of situations`
- `mllib-clustering.md`: `most commonly used method` to `most commonly
used methods`
### Why are the changes needed?
Each is a plain grammatical error. Several are proven by the same file
writing the phrase correctly elsewhere: `sql-performance-tuning.md` already has
`if its size is larger than this factor multiplying` and `makes use of the
runtime statistics`.
### Does this PR introduce _any_ user-facing change?
No, other than the corrected documentation.
### How was this patch tested?
Documentation only. No code sample, config name, or expected output is
touched.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Code (Opus 4.8)
Closes #58076 from uros-b/typo-docs-guides-grammar.
Authored-by: Uros <[email protected]>
Signed-off-by: Uros Bojanic <[email protected]>
---
docs/graphx-programming-guide.md | 6 +++---
docs/mllib-clustering.md | 2 +-
docs/sql-performance-tuning.md | 8 ++++----
3 files changed, 8 insertions(+), 8 deletions(-)
diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 4791e215f458..5d599bf39da0 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -189,7 +189,7 @@ val users: RDD[(VertexId, (String, String))] =
val relationships: RDD[Edge[String]] =
sc.parallelize(Seq(Edge(3L, 7L, "collab"), Edge(5L, 3L, "advisor"),
Edge(2L, 5L, "colleague"), Edge(5L, 7L, "pi")))
-// Define a default user in case there are relationship with missing user
+// Define a default user in case there is a relationship with a missing user
val defaultUser = ("John Doe", "Missing")
// Build the initial Graph
val graph = Graph(users, relationships, defaultUser)
@@ -413,7 +413,7 @@ implemented efficiently without data movement or
duplication.
The [`subgraph`][Graph.subgraph] operator takes vertex and edge predicates and
returns the graph
containing only the vertices that satisfy the vertex predicate (evaluate to
true) and edges that
satisfy the edge predicate *and connect vertices that satisfy the vertex
predicate*. The `subgraph`
-operator can be used in number of situations to restrict the graph to the
vertices and edges of
+operator can be used in a number of situations to restrict the graph to the
vertices and edges of
interest or eliminate broken links. For example in the following code we
remove broken links:
@@ -428,7 +428,7 @@ val relationships: RDD[Edge[String]] =
sc.parallelize(Seq(Edge(3L, 7L, "collab"), Edge(5L, 3L, "advisor"),
Edge(2L, 5L, "colleague"), Edge(5L, 7L, "pi"),
Edge(4L, 0L, "student"), Edge(5L, 0L, "colleague")))
-// Define a default user in case there are relationship with missing user
+// Define a default user in case there is a relationship with a missing user
val defaultUser = ("John Doe", "Missing")
// Build the initial Graph
val graph = Graph(users, relationships, defaultUser)
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index fde354457f23..da7839c7b6d5 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -384,7 +384,7 @@ Refer to the [`LDA` Java
docs](api/java/org/apache/spark/mllib/clustering/LDA.ht
Bisecting K-means can often be much faster than regular K-means, but it will
generally produce a different clustering.
Bisecting k-means is a kind of [hierarchical
clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering).
-Hierarchical clustering is one of the most commonly used method of cluster
analysis which seeks to build a hierarchy of clusters.
+Hierarchical clustering is one of the most commonly used methods of cluster
analysis which seeks to build a hierarchy of clusters.
Strategies for hierarchical clustering generally fall into two types:
- Agglomerative: This is a "bottom up" approach: each observation starts in
its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
diff --git a/docs/sql-performance-tuning.md b/docs/sql-performance-tuning.md
index 468d482b16ab..fee8801086c4 100644
--- a/docs/sql-performance-tuning.md
+++ b/docs/sql-performance-tuning.md
@@ -101,7 +101,7 @@ Configuration of in-memory caching can be done via
`spark.conf.set` or by runnin
<td>None</td>
<td>
The suggested (not guaranteed) maximum number of split file partitions.
If it is set,
- Spark will rescale each partition to make the number of partitions is
close to this
+ Spark will rescale each partition to make the number of partitions close
to this
value if the initial number of partitions exceeds this value. This
configuration is
effective only when using file-based sources such as Parquet, JSON and
ORC.
</td>
@@ -429,7 +429,7 @@ This feature coalesces the post shuffle partitions based on
the map output stati
<td><code>spark.sql.adaptive.coalescePartitions.parallelismFirst</code></td>
<td>true</td>
<td>
- When true, Spark ignores the target size specified by
<code>spark.sql.adaptive.advisoryPartitionSizeInBytes</code> (default 64MB)
when coalescing contiguous shuffle partitions, and only respect the minimum
partition size specified by
<code>spark.sql.adaptive.coalescePartitions.minPartitionSize</code> (default
1MB), to maximize the parallelism. This is to avoid performance regressions
when enabling adaptive query execution. It's recommended to set this config to
false on a busy clu [...]
+ When true, Spark ignores the target size specified by
<code>spark.sql.adaptive.advisoryPartitionSizeInBytes</code> (default 64MB)
when coalescing contiguous shuffle partitions, and only respects the minimum
partition size specified by
<code>spark.sql.adaptive.coalescePartitions.minPartitionSize</code> (default
1MB), to maximize the parallelism. This is to avoid performance regressions
when enabling adaptive query execution. It's recommended to set this config to
false on a busy cl [...]
</td>
<td>3.2.0</td>
</tr>
@@ -482,7 +482,7 @@ This feature coalesces the post shuffle partitions based on
the map output stati
<td><code>spark.sql.adaptive.rebalancePartitionsSmallPartitionFactor</code></td>
<td>0.2</td>
<td>
- A partition will be merged during splitting if its size is small than
this factor multiply
<code>spark.sql.adaptive.advisoryPartitionSizeInBytes</code>.
+ A partition will be merged during splitting if its size is smaller than
this factor multiplying
<code>spark.sql.adaptive.advisoryPartitionSizeInBytes</code>.
</td>
<td>3.3.0</td>
</tr>
@@ -622,7 +622,7 @@ You can control the details of how AQE works by providing
your own cost evaluato
## Storage Partition Join
-Storage Partition Join (SPJ) is an optimization technique in Spark SQL that
makes use the existing storage layout to avoid the shuffle phase.
+Storage Partition Join (SPJ) is an optimization technique in Spark SQL that
makes use of the existing storage layout to avoid the shuffle phase.
This is a generalization of the concept of Bucket Joins, which is only
applicable for
[bucketed](sql-data-sources-load-save-functions.html#bucketing-sorting-and-partitioning)
tables, to tables partitioned by functions registered in FunctionCatalog.
Storage Partition Joins are currently supported for compatible V2 DataSources.
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