{"action":"created","comment":{"url":"https://api.github.com/repos/apache/spark/pulls/comments/85635703","pull_request_review_id":6343496,"id":85635703,"diff_hunk":"@@ -82,7 +90,16 @@ case class CreateArray(children: Seq[Expression]) extends Expression {\n  * The children are a flatted sequence of kv pairs, e.g. (key1, value1, key2, value2, ...)\n  */\n @ExpressionDescription(\n-  usage = \"_FUNC_(key0, value0, key1, value1...) - Creates a map with the given key/value pairs.\")\n+  usage = \"_FUNC_(key0, value0, key1, value1...) - Creates a map with the given key/value pairs.\",","path":"sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala","position":22,"original_position":22,"commit_id":"2b437fe169080b53215f280c8987ff1d8e779df8","original_commit_id":"2b437fe169080b53215f280c8987ff1d8e779df8","user":{"login":"gatorsmile","id":11567269,"avatar_url":"https://avatars.githubusercontent.com/u/11567269?v=3","gravatar_id":"","url":"https://api.github.com/users/gatorsmile","html_url":"https://github.com/gatorsmile","followers_url":"https://api.github.com/users/gatorsmile/followers","following_url":"https://api.github.com/users/gatorsmile/following{/other_user}","gists_url":"https://api.github.com/users/gatorsmile/gists{/gist_id}","starred_url":"https://api.github.com/users/gatorsmile/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gatorsmile/subscriptions","organizations_url":"https://api.github.com/users/gatorsmile/orgs","repos_url":"https://api.github.com/users/gatorsmile/repos","events_url":"https://api.github.com/users/gatorsmile/events{/privacy}","received_events_url":"https://api.github.com/users/gatorsmile/received_events","type":"User","site_admin":false},"body":"Nit: please correct all of them to make the usage of `...` consistent. For example, `x, y, z, ...`","created_at":"2016-10-29T07:16:28Z","updated_at":"2016-10-29T07:16:28Z","html_url":"https://github.com/apache/spark/pull/15677#discussion_r85635703","pull_request_url":"https://api.github.com/repos/apache/spark/pulls/15677","_links":{"self":{"href":"https://api.github.com/repos/apache/spark/pulls/comments/85635703"},"html":{"href":"https://github.com/apache/spark/pull/15677#discussion_r85635703"},"pull_request":{"href":"https://api.github.com/repos/apache/spark/pulls/15677"}}},"pull_request":{"url":"https://api.github.com/repos/apache/spark/pulls/15677","id":91472134,"html_url":"https://github.com/apache/spark/pull/15677","diff_url":"https://github.com/apache/spark/pull/15677.diff","patch_url":"https://github.com/apache/spark/pull/15677.patch","issue_url":"https://api.github.com/repos/apache/spark/issues/15677","number":15677,"state":"open","locked":false,"title":"[SPARK-17963][SQL][Documentation] Add examples (extend) in each expression and improve documentation with arguments","user":{"login":"HyukjinKwon","id":6477701,"avatar_url":"https://avatars.githubusercontent.com/u/6477701?v=3","gravatar_id":"","url":"https://api.github.com/users/HyukjinKwon","html_url":"https://github.com/HyukjinKwon","followers_url":"https://api.github.com/users/HyukjinKwon/followers","following_url":"https://api.github.com/users/HyukjinKwon/following{/other_user}","gists_url":"https://api.github.com/users/HyukjinKwon/gists{/gist_id}","starred_url":"https://api.github.com/users/HyukjinKwon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/HyukjinKwon/subscriptions","organizations_url":"https://api.github.com/users/HyukjinKwon/orgs","repos_url":"https://api.github.com/users/HyukjinKwon/repos","events_url":"https://api.github.com/users/HyukjinKwon/events{/privacy}","received_events_url":"https://api.github.com/users/HyukjinKwon/received_events","type":"User","site_admin":false},"body":"## What changes were proposed in this pull request?\r\n\r\nThis PR proposes to change the documentation for functions. Please refer the discussion from https://github.com/apache/spark/pull/15513\r\n\r\nThe changes include\r\n\r\n - Re-indent the documentation\r\n - Add examples/arguments in `extended` where the arguments are multiple or specific format (e.g. xml/ json).\r\n\r\nFor examples, the documentation was updated as below:\r\n\r\n### Functions with single line usage\r\n\r\n**Before**\r\n  - `pow`\r\n\r\n    ```sql\r\n    Usage: pow(x1, x2) - Raise x1 to the power of x2.\r\n    Extended Usage:\r\n    > SELECT pow(2, 3);\r\n     8.0\r\n    ```\r\n\r\n  - `current_timestamp`\r\n\r\n    ```sql\r\n    Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation.\r\n    Extended Usage:\r\n    No example for current_timestamp.\r\n    ```\r\n\r\n**After**\r\n\r\n  - `pow`\r\n\r\n    ```sql\r\n    Usage: pow(expr1, expr2) - Raise expr1 to the power of expr2.\r\n    Extended Usage:\r\n        Arguments:\r\n          expr1 - a numeric expression.\r\n          expr2 - a numeric expression.\r\n\r\n        Examples:\r\n          > SELECT pow(2, 3);\r\n           8.0\r\n    ```\r\n\r\n  - `current_timestamp`\r\n\r\n    ```sql\r\n    Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation.\r\n    Extended Usage:\r\n        No example/arguemnt for current_timestamp.\r\n    ```\r\n\r\n\r\n### Functions with (already) multiple line usage\r\n\r\n**Before**\r\n\r\n  - `approx_count_distinct`\r\n\r\n    ```sql\r\n    Usage: approx_count_distinct(expr) - Returns the estimated cardinality by HyperLogLog++.\r\n        approx_count_distinct(expr, relativeSD=0.05) - Returns the estimated cardinality by HyperLogLog++\r\n          with relativeSD, the maximum estimation error allowed.\r\n\r\n    Extended Usage:\r\n    No example for approx_count_distinct.\r\n    ```\r\n\r\n  - `percentile_approx`\r\n\r\n    ```sql\r\n    Usage:\r\n          percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric\r\n          column `col` at the given percentage. The value of percentage must be between 0.0\r\n          and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which\r\n          controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields\r\n          better accuracy, `1.0/accuracy` is the relative error of the approximation.\r\n\r\n          percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy]) - Returns the approximate\r\n          percentile array of column `col` at the given percentage array. Each value of the\r\n          percentage array must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is\r\n          a positive integer literal which controls approximation accuracy at the cost of memory.\r\n          Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of\r\n          the approximation.\r\n\r\nExtended Usage:\r\nNo example for percentile_approx.\r\n```\r\n\r\n**After**\r\n\r\n  - `approx_count_distinct`\r\n\r\n    ```sql\r\n    Usage:\r\n        approx_count_distinct(expr[, relativeSD]) - Returns the estimated cardinality by HyperLogLog++.\r\n          relativeSD defines the maximum estimation error allowed.\r\n\r\n    Extended Usage:\r\n        Arguments:\r\n          expr - an expression of any type that represents data to count.\r\n          relativeSD - a numeric literal that defines the maximum estimation error allowed.\r\n    ```\r\n\r\n  - `percentile_approx`\r\n\r\n    ```sql\r\n    Usage:\r\n        percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric\r\n          column `col` at the given percentage. The value of `percentage` must be between 0.0\r\n          and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which\r\n          controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields\r\n          better accuracy, `1.0/accuracy` is the relative error of the approximation.\r\n          When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0.\r\n\r\n    Extended Usage:\r\n        Arguments:\r\n          col - a numeric expression.\r\n          percentage - a numeric literal or an array literal of numeric type that defines the\r\n            percentile. For example, 0.5 means 50-percentile.\r\n          accuracy - a numeric literal.\r\n\r\n        Examples:\r\n          > SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100);\r\n           [10.0,10.0,10.0]\r\n          > SELECT percentile_approx(10.0, 0.5, 100);\r\n           10.0\r\n    ```\r\n\r\n## How was this patch tested?\r\n\r\nManually tested\r\n\r\n**When examples are multiple**\r\n\r\n```sql\r\nspark-sql> describe function extended reflect;\r\nFunction: reflect\r\nClass: org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection\r\nUsage: reflect(class, method[, arg1[, arg2 ..]]) - Calls a method with reflection.\r\nExtended Usage:\r\n    Arguments:\r\n      class - a string literal that represents a fully-qualified class name.\r\n      method - a string literal that represents a method name.\r\n      arg - a boolean, string or numeric expression except decimal that represents an argument for\r\n        the method.\r\n\r\n    Examples:\r\n      > SELECT reflect('java.util.UUID', 'randomUUID');\r\n       c33fb387-8500-4bfa-81d2-6e0e3e930df2\r\n      > SELECT reflect('java.util.UUID', 'fromString', 'a5cf6c42-0c85-418f-af6c-3e4e5b1328f2');\r\n       a5cf6c42-0c85-418f-af6c-3e4e5b1328f2\r\n```\r\n\r\n**When `Usage` is in single line**\r\n\r\n```sql\r\nspark-sql> describe function extended min;\r\nFunction: min\r\nClass: org.apache.spark.sql.catalyst.expressions.aggregate.Min\r\nUsage: min(expr) - Returns the minimum value of `expr`.\r\nExtended Usage:\r\n    Arguments:\r\n      expr - an expression of any type.\r\n```\r\n\r\n**When `Usage` is already in multiple lines**\r\n\r\n```sql\r\nspark-sql> describe function extended percentile_approx;\r\nFunction: percentile_approx\r\nClass: org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile\r\nUsage:\r\n    percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric\r\n      column `col` at the given percentage. The value of `percentage` must be between 0.0\r\n      and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which\r\n      controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields\r\n      better accuracy, `1.0/accuracy` is the relative error of the approximation.\r\n      When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0.\r\n\r\nExtended Usage:\r\n    Arguments:\r\n      col - a numeric expression.\r\n      percentage - a numeric literal or an array literal of numeric type that defines the\r\n        percentile. For example, 0.5 means 50-percentile.\r\n      accuracy - a numeric literal.\r\n\r\n    Examples:\r\n      > SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100);\r\n       [10.0,10.0,10.0]\r\n      > SELECT percentile_approx(10.0, 0.5, 100);\r\n       10.0\r\n```\r\n\r\n**When example/argument is missing**\r\n\r\n```sql\r\nspark-sql> describe function extended rank;\r\nFunction: rank\r\nClass: org.apache.spark.sql.catalyst.expressions.Rank\r\nUsage:\r\n    rank() - Computes the rank of a value in a group of values. The result is one plus the number\r\n      of rows preceding or equal to the current row in the ordering of the partition. The values\r\n      will produce gaps in the sequence.\r\n\r\nExtended Usage:\r\n    No example/argument for rank.\r\n```\r\n","created_at":"2016-10-29T00:36:24Z","updated_at":"2016-10-29T07:16:28Z","closed_at":null,"merged_at":null,"merge_commit_sha":"a389501e816e35e8e01277bed454cd929713b8f8","assignee":null,"assignees":[],"milestone":null,"commits_url":"https://api.github.com/repos/apache/spark/pulls/15677/commits","review_comments_url":"https://api.github.com/repos/apache/spark/pulls/15677/comments","review_comment_url":"https://api.github.com/repos/apache/spark/pulls/comments{/number}","comments_url":"https://api.github.com/repos/apache/spark/issues/15677/comments","statuses_url":"https://api.github.com/repos/apache/spark/statuses/2b437fe169080b53215f280c8987ff1d8e779df8","head":{"label":"HyukjinKwon:SPARK-17963-1","ref":"SPARK-17963-1","sha":"2b437fe169080b53215f280c8987ff1d8e779df8","user":{"login":"HyukjinKwon","id":6477701,"avatar_url":"https://avatars.githubusercontent.com/u/6477701?v=3","gravatar_id":"","url":"https://api.github.com/users/HyukjinKwon","html_url":"https://github.com/HyukjinKwon","followers_url":"https://api.github.com/users/HyukjinKwon/followers","following_url":"https://api.github.com/users/HyukjinKwon/following{/other_user}","gists_url":"https://api.github.com/users/HyukjinKwon/gists{/gist_id}","starred_url":"https://api.github.com/users/HyukjinKwon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/HyukjinKwon/subscriptions","organizations_url":"https://api.github.com/users/HyukjinKwon/orgs","repos_url":"https://api.github.com/users/HyukjinKwon/repos","events_url":"https://api.github.com/users/HyukjinKwon/events{/privacy}","received_events_url":"https://api.github.com/users/HyukjinKwon/received_events","type":"User","site_admin":false},"repo":{"id":42300713,"name":"spark","full_name":"HyukjinKwon/spark","owner":{"login":"HyukjinKwon","id":6477701,"avatar_url":"https://avatars.githubusercontent.com/u/6477701?v=3","gravatar_id":"","url":"https://api.github.com/users/HyukjinKwon","html_url":"https://github.com/HyukjinKwon","followers_url":"https://api.github.com/users/HyukjinKwon/followers","following_url":"https://api.github.com/users/HyukjinKwon/following{/other_user}","gists_url":"https://api.github.com/users/HyukjinKwon/gists{/gist_id}","starred_url":"https://api.github.com/users/HyukjinKwon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/HyukjinKwon/subscriptions","organizations_url":"https://api.github.com/users/HyukjinKwon/orgs","repos_url":"https://api.github.com/users/HyukjinKwon/repos","events_url":"https://api.github.com/users/HyukjinKwon/events{/privacy}","received_events_url":"https://api.github.com/users/HyukjinKwon/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/HyukjinKwon/spark","description":"Mirror 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