GitHub user HyukjinKwon opened a pull request:
https://github.com/apache/spark/pull/15513
[WIP][SPARK-17963][SQL][Documentation] Add examples (extend) in each
function and improve documentation with arguments
## What changes were proposed in this pull request?
This PR proposes to change the documentation for functions.
The changes include
- Re-indent the documentation
- Add arguments
- Add examples in `extended` where the arguments are multiple or specific
format (e.g. xml/ json).
For examples, the documentation was updated as below:
**Before**
- `approx_count_distinct`
```sql
Usage: approx_count_distinct(expr) - Returns the estimated cardinality by
HyperLogLog++.
approx_count_distinct(expr, relativeSD=0.05) - Returns the estimated
cardinality by HyperLogLog++
with relativeSD, the maximum estimation error allowed.
Extended Usage:
No example for approx_count_distinct.
```
- `percentile_approx`
```sql
Usage:
percentile_approx(col, percentage [, accuracy]) - Returns the
approximate percentile value of numeric
column `col` at the given percentage. The value of percentage must be
between 0.0
and 1.0. The `accuracy` parameter (default: 10000) is a positive
integer literal which
controls approximation accuracy at the cost of memory. Higher value
of `accuracy` yields
better accuracy, `1.0/accuracy` is the relative error of the
approximation.
percentile_approx(col, array(percentage1 [, percentage2]...) [,
accuracy]) - Returns the approximate
percentile array of column `col` at the given percentage array. Each
value of the
percentage array must be between 0.0 and 1.0. The `accuracy`
parameter (default: 10000) is
a positive integer literal which controls approximation accuracy at
the cost of memory.
Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is
the relative error of
the approximation.
Extended Usage:
No example for percentile_approx.
```
**After**
- `approx_count_distinct`
```sql
Usage:
approx_count_distinct(expr) - Returns the estimated cardinality by
HyperLogLog++.
Arguments:
expr - any type expression that represents data to collect the
first.
approx_count_distinct(expr, relativeSD) - Returns the estimated
cardinality by HyperLogLog++
with relativeSD, the maximum estimation error allowed.
Arguments:
expr - any type expression that represents data to collect the
first.
relativeSD - any numeric type or any nonnumeric type literal that
can be implicitly
converted to double type, that represents maximum estimation
error allowed
(default = 0.05).
Extended Usage: No example for approx_count_distinct.
```
- `percentile_approx`
```sql
Usage:
percentile_approx(col, percentage [, accuracy]) - Returns the
approximate percentile value of numeric
column `col` at the given percentage. The value of percentage must
be between 0.0
and 1.0. The `accuracy` parameter (default: 10000) is a positive
integer literal which
controls approximation accuracy at the cost of memory. Higher value
of `accuracy` yields
better accuracy, `1.0/accuracy` is the relative error of the
approximation.
Arguments:
col - any numeric type or any nonnumeric type expression that can
be implicitly
converted to double type.
percentage - any numeric type or any nonnumeric type literal that
can be
implicitly converted to double type.
accuracy - any numeric type or any nonnumeric type literal that
can be implicitly
converted to int type.
percentile_approx(col, array(percentage1 [, percentage2]...) [,
accuracy]) - Returns the approximate
percentile array of column `col` at the given percentage array.
Each value of the
percentage array must be between 0.0 and 1.0. The `accuracy`
parameter (default: 10000) is
a positive integer literal which controls approximation accuracy at
the cost of memory.
Higher value of `accuracy` yields better accuracy, `1.0/accuracy`
is the relative error of
the approximation.
Arguments:
col - any numeric type or any nonnumeric type expression that can
be implicitly
converted to double type.
array(...) - an array that contains any numeric type literal that
can be implicitly
converted to double type.
accuracy - any numeric type or any nonnumeric type literal that
can be implicitly
converted to int type.
Extended Usage:
> SELECT percentile_approx(10.0, 0.5, 100);
10.0
> SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100);
[10.0,10.0,10.0]
```
## How was this patch tested?
N/A
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/HyukjinKwon/spark SPARK-17963
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/15513.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #15513
----
commit 2059374537496c9f81512b643e3ec084e43e2594
Author: hyukjinkwon <[email protected]>
Date: 2016-10-17T12:07:52Z
Add examples (extend) in each function and improve documentation with
arguments
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