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new a7369b3 [SPARK-35909][DOCS] Fix broken Python Links in
docs/sql-getting-started.md
a7369b3 is described below
commit a7369b3080ec3d76957df63cf905a68e41197ba3
Author: Dhruvil Dave <dhruvil.d...@outlook.com>
AuthorDate: Sun Jun 27 11:34:28 2021 -0700
[SPARK-35909][DOCS] Fix broken Python Links in docs/sql-getting-started.md
### What changes were proposed in this pull request?
The hyperlinks in Python code blocks in [Spark SQL Guide - Getting
Started](https://spark.apache.org/docs/latest/sql-getting-started.html)
currently point to invalid addresses and return 404. This pull request fixes
that issue by pointing them to correct links in Python API docs.
### Why are the changes needed?
Error in documentation classifies as a bug and hence needs to be fixed.
### Does this PR introduce _any_ user-facing change?
Yes. This PR fixes documentation error in
https://spark.apache.org/docs/latest/sql-getting-started.html
### How was this patch tested?
This patch was locally built after cloning the repo from scratch and then
doing a clean build after fixing the required problems.
Closes #33107 from dhruvildave/sql-doc.
Authored-by: Dhruvil Dave <dhruvil.d...@outlook.com>
Signed-off-by: Dongjoon Hyun <dongj...@apache.org>
---
docs/sql-getting-started.md | 14 ++++++--------
1 file changed, 6 insertions(+), 8 deletions(-)
diff --git a/docs/sql-getting-started.md b/docs/sql-getting-started.md
index 5486e73..2403d7b 100644
--- a/docs/sql-getting-started.md
+++ b/docs/sql-getting-started.md
@@ -9,9 +9,9 @@ license: |
The ASF licenses this file to You under the Apache License, Version 2.0
(the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
-
+
http://www.apache.org/licenses/LICENSE-2.0
-
+
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -41,7 +41,7 @@ The entry point into all functionality in Spark is the
[`SparkSession`](api/java
<div data-lang="python" markdown="1">
-The entry point into all functionality in Spark is the
[`SparkSession`](api/python/pyspark.sql.html#pyspark.sql.SparkSession) class.
To create a basic `SparkSession`, just use `SparkSession.builder`:
+The entry point into all functionality in Spark is the
[`SparkSession`](api/python/reference/api/pyspark.sql.SparkSession.html) class.
To create a basic `SparkSession`, just use `SparkSession.builder`:
{% include_example init_session python/sql/basic.py %}
</div>
@@ -104,7 +104,7 @@ As an example, the following creates a DataFrame based on
the content of a JSON
## Untyped Dataset Operations (aka DataFrame Operations)
-DataFrames provide a domain-specific language for structured data manipulation
in [Scala](api/scala/org/apache/spark/sql/Dataset.html),
[Java](api/java/index.html?org/apache/spark/sql/Dataset.html),
[Python](api/python/pyspark.sql.html#pyspark.sql.DataFrame) and
[R](api/R/SparkDataFrame.html).
+DataFrames provide a domain-specific language for structured data manipulation
in [Scala](api/scala/org/apache/spark/sql/Dataset.html),
[Java](api/java/index.html?org/apache/spark/sql/Dataset.html),
[Python](api/python/reference/api/pyspark.sql.DataFrame.html) and
[R](api/R/SparkDataFrame.html).
As mentioned above, in Spark 2.0, DataFrames are just Dataset of `Row`s in
Scala and Java API. These operations are also referred as "untyped
transformations" in contrast to "typed transformations" come with strongly
typed Scala/Java Datasets.
@@ -136,9 +136,9 @@ latter form, which is future proof and won't break with
column names that
are also attributes on the DataFrame class.
{% include_example untyped_ops python/sql/basic.py %}
-For a complete list of the types of operations that can be performed on a
DataFrame refer to the [API
Documentation](api/python/pyspark.sql.html#pyspark.sql.DataFrame).
+For a complete list of the types of operations that can be performed on a
DataFrame refer to the [API
Documentation](api/python/reference/pyspark.sql.html#dataframe-apis).
-In addition to simple column references and expressions, DataFrames also have
a rich library of functions including string manipulation, date arithmetic,
common math operations and more. The complete list is available in the
[DataFrame Function
Reference](api/python/pyspark.sql.html#module-pyspark.sql.functions).
+In addition to simple column references and expressions, DataFrames also have
a rich library of functions including string manipulation, date arithmetic,
common math operations and more. The complete list is available in the
[DataFrame Function Reference](api/python/reference/pyspark.sql.html#functions).
</div>
@@ -356,5 +356,3 @@ Aggregate functions are functions that return a single
value on a group of rows.
Users are not limited to the predefined aggregate functions and can create
their own. For more details
about user defined aggregate functions, please refer to the documentation of
[User Defined Aggregate Functions](sql-ref-functions-udf-aggregate.html).
-
-
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