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new 04b3699 [MINOR][DOCS] Fix typo in documents
04b3699 is described below
commit 04b369942a0fb850a4df8dd0d9370114177d386a
Author: Kazuaki Ishizaki <[email protected]>
AuthorDate: Mon May 4 16:53:50 2020 +0900
[MINOR][DOCS] Fix typo in documents
Fixed typo in `docs` directory and in `project/MimaExcludes.scala`
Better readability of documents
No
No test needed
Closes #28447 from kiszk/typo_20200504.
Authored-by: Kazuaki Ishizaki <[email protected]>
Signed-off-by: HyukjinKwon <[email protected]>
(cherry picked from commit 35fcc8d5c58626836cd4d99a472e7350ea3acd0d)
Signed-off-by: HyukjinKwon <[email protected]>
---
docs/spark-standalone.md | 2 +-
docs/sql-migration-guide.md | 6 +++---
docs/sql-ref-functions-udf-hive.md | 4 ++--
docs/sql-ref-functions.md | 2 +-
docs/sql-ref-syntax-aux-describe-query.md | 2 +-
docs/web-ui.md | 4 ++--
6 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 2c2ed53..1e6f8c5 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -335,7 +335,7 @@ SPARK_WORKER_OPTS supports the following system properties:
overlap with `spark.worker.cleanup.enabled`, as this enables cleanup of
non-shuffle files in
local directories of a dead executor, while `spark.worker.cleanup.enabled`
enables cleanup of
all files/subdirectories of a stopped and timeout application.
- This only affects Standalone mode, support of other cluster manangers can
be added in the future.
+ This only affects Standalone mode, support of other cluster managers can
be added in the future.
</td>
<td>2.4.0</td>
</tr>
diff --git a/docs/sql-migration-guide.md b/docs/sql-migration-guide.md
index 774fb2c..d515b57 100644
--- a/docs/sql-migration-guide.md
+++ b/docs/sql-migration-guide.md
@@ -36,7 +36,7 @@ license: |
- In Spark 3.0, `CREATE TABLE` without a specific provider uses the value of
`spark.sql.sources.default` as its provider. In Spark version 2.4 and below, it
was Hive. To restore the behavior before Spark 3.0, you can set
`spark.sql.legacy.createHiveTableByDefault.enabled` to `true`.
- - In Spark 3.0, when inserting a value into a table column with a different
data type, the type coercion is performed as per ANSI SQL standard. Certain
unreasonable type conversions such as converting `string` to `int` and `double`
to `boolean` are disallowed. A runtime exception is thrown if the value is
out-of-range for the data type of the column. In Spark version 2.4 and below,
type conversions during table insertion are allowed as long as they are valid
`Cast`. When inserting an o [...]
+ - In Spark 3.0, when inserting a value into a table column with a different
data type, the type coercion is performed as per ANSI SQL standard. Certain
unreasonable type conversions such as converting `string` to `int` and `double`
to `boolean` are disallowed. A runtime exception is thrown if the value is
out-of-range for the data type of the column. In Spark version 2.4 and below,
type conversions during table insertion are allowed as long as they are valid
`Cast`. When inserting an o [...]
- The `ADD JAR` command previously returned a result set with the single
value 0. It now returns an empty result set.
@@ -44,7 +44,7 @@ license: |
- Refreshing a cached table would trigger a table uncache operation and then
a table cache (lazily) operation. In Spark version 2.4 and below, the cache
name and storage level are not preserved before the uncache operation.
Therefore, the cache name and storage level could be changed unexpectedly. In
Spark 3.0, cache name and storage level are first preserved for cache
recreation. It helps to maintain a consistent cache behavior upon table
refreshing.
- - In Spark 3.0, the properties listing below become reserved; commands fail
if you specify reserved properties in places like `CREATE DATABASE ... WITH
DBPROPERTIES` and `ALTER TABLE ... SET TBLPROPERTIES`. You need their specific
clauses to specify them, for example, `CREATE DATABASE test COMMENT 'any
comment' LOCATION 'some path'`. You can set
`spark.sql.legacy.notReserveProperties` to `true` to ignore the
`ParseException`, in this case, these properties will be silently removed, for
[...]
+ - In Spark 3.0, the properties listing below become reserved; commands fail
if you specify reserved properties in places like `CREATE DATABASE ... WITH
DBPROPERTIES` and `ALTER TABLE ... SET TBLPROPERTIES`. You need their specific
clauses to specify them, for example, `CREATE DATABASE test COMMENT 'any
comment' LOCATION 'some path'`. You can set
`spark.sql.legacy.notReserveProperties` to `true` to ignore the
`ParseException`, in this case, these properties will be silently removed, for
[...]
| Property (case sensitive) | Database Reserved | Table Reserved | Remarks
|
| ------------------------- | ----------------- | -------------- | -------
|
@@ -124,7 +124,7 @@ license: |
- In Spark 3.0, negative scale of decimal is not allowed by default, for
example, data type of literal like `1E10BD` is `DecimalType(11, 0)`. In Spark
version 2.4 and below, it was `DecimalType(2, -9)`. To restore the behavior
before Spark 3.0, you can set `spark.sql.legacy.allowNegativeScaleOfDecimal` to
`true`.
- - In Spark 3.0, the unary arithmetic operator plus(`+`) only accepts string,
numeric and interval type values as inputs. Besides, `+` with a integral string
representation is coerced to a double value, for example, `+'1'` returns `1.0`.
In Spark version 2.4 and below, this operator is ignored. There is no type
checking for it, thus, all type values with a `+` prefix are valid, for
example, `+ array(1, 2)` is valid and results `[1, 2]`. Besides, there is no
type coercion for it at all, [...]
+ - In Spark 3.0, the unary arithmetic operator plus(`+`) only accepts string,
numeric and interval type values as inputs. Besides, `+` with an integral
string representation is coerced to a double value, for example, `+'1'` returns
`1.0`. In Spark version 2.4 and below, this operator is ignored. There is no
type checking for it, thus, all type values with a `+` prefix are valid, for
example, `+ array(1, 2)` is valid and results `[1, 2]`. Besides, there is no
type coercion for it at all, [...]
- In Spark 3.0, Dataset query fails if it contains ambiguous column
reference that is caused by self join. A typical example: `val df1 = ...; val
df2 = df1.filter(...);`, then `df1.join(df2, df1("a") > df2("a"))` returns an
empty result which is quite confusing. This is because Spark cannot resolve
Dataset column references that point to tables being self joined, and
`df1("a")` is exactly the same as `df2("a")` in Spark. To restore the behavior
before Spark 3.0, you can set `spark.sql. [...]
diff --git a/docs/sql-ref-functions-udf-hive.md
b/docs/sql-ref-functions-udf-hive.md
index 97d72c4..7a7129d 100644
--- a/docs/sql-ref-functions-udf-hive.md
+++ b/docs/sql-ref-functions-udf-hive.md
@@ -30,7 +30,7 @@ An example below uses
[GenericUDFAbs](https://github.com/apache/hive/blob/master
{% highlight sql %}
-- Register `GenericUDFAbs` and use it in Spark SQL.
--- Note that, if you use your own programmed one, you need to add a JAR
containig it
+-- Note that, if you use your own programmed one, you need to add a JAR
containing it
-- into a classpath,
-- e.g., ADD JAR yourHiveUDF.jar;
CREATE TEMPORARY FUNCTION testUDF AS
'org.apache.hadoop.hive.ql.udf.generic.GenericUDFAbs';
@@ -105,4 +105,4 @@ SELECT key, hiveUDAF(value) FROM t GROUP BY key;
| b| 3|
| a| 3|
+---+---------------+
-{% endhighlight %}
\ No newline at end of file
+{% endhighlight %}
diff --git a/docs/sql-ref-functions.md b/docs/sql-ref-functions.md
index 7493b8b..67951a9 100644
--- a/docs/sql-ref-functions.md
+++ b/docs/sql-ref-functions.md
@@ -24,7 +24,7 @@ Built-in functions are commonly used routines that Spark SQL
predefines and a co
### Built-in Functions
-Spark SQL has some categories of frequently-used built-in functions for
aggregtion, arrays/maps, date/timestamp, and JSON data.
+Spark SQL has some categories of frequently-used built-in functions for
aggregation, arrays/maps, date/timestamp, and JSON data.
This subsection presents the usages and descriptions of these functions.
#### Scalar Functions
diff --git a/docs/sql-ref-syntax-aux-describe-query.md
b/docs/sql-ref-syntax-aux-describe-query.md
index 07ac39b..41e66dc 100644
--- a/docs/sql-ref-syntax-aux-describe-query.md
+++ b/docs/sql-ref-syntax-aux-describe-query.md
@@ -75,7 +75,7 @@ DESCRIBE QUERY WITH all_names_cte
| name| string| null|
+--------+---------+-------+
--- Returns column metadata information for a inline table.
+-- Returns column metadata information for an inline table.
DESC QUERY VALUES(100, 'John', 10000.20D) AS employee(id, name, salary);
+--------+---------+-------+
|col_name|data_type|comment|
diff --git a/docs/web-ui.md b/docs/web-ui.md
index e28a689..251e0a0 100644
--- a/docs/web-ui.md
+++ b/docs/web-ui.md
@@ -99,7 +99,7 @@ This page displays the details of a specific job identified
by its job ID.
The Stages tab displays a summary page that shows the current state of all
stages of all jobs in
the Spark application.
-At the beginning of the page is the summary with the count of all stages by
status (active, pending, completed, sikipped, and failed)
+At the beginning of the page is the summary with the count of all stages by
status (active, pending, completed, skipped, and failed)
<p style="text-align: center;">
<img src="img/AllStagesPageDetail1.png" title="Stages header" alt="Stages
header" width="30%">
@@ -136,7 +136,7 @@ Summary metrics for all task are represented in a table and
in a timeline.
* **[Tasks deserialization
time](configuration.html#compression-and-serialization)**
* **Duration of tasks**.
* **GC time** is the total JVM garbage collection time.
-* **Result serialization time** is the time spent serializing the task result
on a executor before sending it back to the driver.
+* **Result serialization time** is the time spent serializing the task result
on an executor before sending it back to the driver.
* **Getting result time** is the time that the driver spends fetching task
results from workers.
* **Scheduler delay** is the time the task waits to be scheduled for execution.
* **Peak execution memory** is the maximum memory used by the internal data
structures created during shuffles, aggregations and joins.
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