实时计算Flink版：Queries语句

Flink全托管兼容Apache Flink的Queries语句。以下BNF-grammar描述了支持的流批SQL特性的超集。

query:
    values
  | WITH withItem [ , withItem ]* query
  | {
        select
      | selectWithoutFrom
      | query UNION [ ALL ] query
      | query EXCEPT query
      | query INTERSECT query
    }
    [ ORDER BY orderItem [, orderItem ]* ]
    [ LIMIT { count | ALL } ]
    [ OFFSET start { ROW | ROWS } ]
    [ FETCH { FIRST | NEXT } [ count ] { ROW | ROWS } ONLY]

withItem:
    name
    [ '(' column [, column ]* ')' ]
    AS '(' query ')'

orderItem:
    expression [ ASC | DESC ]

select:
    SELECT [ ALL | DISTINCT ]
    { * | projectItem [, projectItem ]* }
    FROM tableExpression
    [ WHERE booleanExpression ]
    [ GROUP BY { groupItem [, groupItem ]* } ]
    [ HAVING booleanExpression ]
    [ WINDOW windowName AS windowSpec [, windowName AS windowSpec ]* ]

selectWithoutFrom:
    SELECT [ ALL | DISTINCT ]
    { * | projectItem [, projectItem ]* }

projectItem:
    expression [ [ AS ] columnAlias ]
  | tableAlias . *

tableExpression:
    tableReference [, tableReference ]*
  | tableExpression [ NATURAL ] [ LEFT | RIGHT | FULL ] JOIN tableExpression [ joinCondition ]

joinCondition:
    ON booleanExpression
  | USING '(' column [, column ]* ')'

tableReference:
    tablePrimary
    [ matchRecognize ]
    [ [ AS ] alias [ '(' columnAlias [, columnAlias ]* ')' ] ]

tablePrimary:
    [ TABLE ] tablePath [ dynamicTableOptions ] [systemTimePeriod] [[AS] correlationName]
  | LATERAL TABLE '(' functionName '(' expression [, expression ]* ')' ')'
  | [ LATERAL ] '(' query ')'
  | UNNEST '(' expression ')'

tablePath:
    [ [ catalogName . ] databaseName . ] tableName

systemTimePeriod:
    FOR SYSTEM_TIME AS OF dateTimeExpression

dynamicTableOptions:
    /*+ OPTIONS(key=val [, key=val]*) */

key:
    stringLiteral

val:
    stringLiteral

values:
    VALUES expression [, expression ]*

groupItem:
    expression
  | '(' ')'
  | '(' expression [, expression ]* ')'
  | CUBE '(' expression [, expression ]* ')'
  | ROLLUP '(' expression [, expression ]* ')'
  | GROUPING SETS '(' groupItem [, groupItem ]* ')'

windowRef:
    windowName
  | windowSpec

windowSpec:
    [ windowName ]
    '('
    [ ORDER BY orderItem [, orderItem ]* ]
    [ PARTITION BY expression [, expression ]* ]
    [
        RANGE numericOrIntervalExpression {PRECEDING}
      | ROWS numericExpression {PRECEDING}
    ]
    ')'

matchRecognize:
    MATCH_RECOGNIZE '('
    [ PARTITION BY expression [, expression ]* ]
    [ ORDER BY orderItem [, orderItem ]* ]
    [ MEASURES measureColumn [, measureColumn ]* ]
    [ ONE ROW PER MATCH ]
    [ AFTER MATCH
      ( SKIP TO NEXT ROW
      | SKIP PAST LAST ROW
      | SKIP TO FIRST variable
      | SKIP TO LAST variable
      | SKIP TO variable )
    ]
    PATTERN '(' pattern ')'
    [ WITHIN intervalLiteral ]
    DEFINE variable AS condition [, variable AS condition ]*
    ')'

measureColumn:
    expression AS alias

pattern:
    patternTerm [ '|' patternTerm ]*

patternTerm:
    patternFactor [ patternFactor ]*

patternFactor:
    variable [ patternQuantifier ]

patternQuantifier:
    '*'
  | '*?'
  | '+'
  | '+?'
  | '?'
  | '??'
  | '{' { [ minRepeat ], [ maxRepeat ] } '}' ['?']
  | '{' repeat '}'

标识符

对于标识符（表名，列名，函数名），Flink 采用了和Java相似的语法策略：

• 不管标识符是否被反引号标识，该标识符是大小写敏感的。

• 标识符的匹配是大小写敏感的。

和Java不同的是，Flink SQL支持标识符包含非英文或数字的字符，例如，以下是符合标准的。

SELECT a AS `my field` FROM t

字符串常量

Flink SQL使用单引号来表示字符串常量，而非使用双引号来表示，例如：

SELECT 'Hello World' 

为了在字符串表示单引号，您可以使用两个单引号来转义。例如：

Flink SQL> SELECT 'Hello World', 'It''s me';
+-------------+---------+
|      EXPR$0 |  EXPR$1 |
+-------------+---------+
| Hello World | It's me |
+-------------+---------+
1 row in set

Flink SQL支持在字符串常量中包含unicode值，您可以通过以下方式声明：

• 使用反斜杠作为默认转义符

  SELECT U&'\263A' 

• 使用自定义符号作为转义符

  SELECT U&'#263A' UESCAPE '#' -- 使用'#'作为转义符

Apache Flink V1.15 Queries语句详情如下表所示。

Query操作运行时信息说明

在流模式下，我们根据是否包含更新消息将处理的流数据分为更新流（包含更新消息）和非更新流（只包含INSERT类型消息的称为非更新流），例如CDC源就是Flink集成自外部的更新流，另外Query内部的一些操作也可能产生更新数据，如分组聚合（Group Aggregation）、Top-N计算等。能产生更新事件的操作通常会使用状态（State），我们一般将这类操作称为状态算子。值得注意的是，并非所有的状态算子都支持处理更新流，例如，Over聚合（Over Aggregation）和Interval Join目前还不支持将更新流作为输入。

以下表格信息基于VVR-6.0.x 及以上版本整理，包括了Query操作对应的运行时算子名称、算子是否使用了状态（State）、是否支持处理更新流、是否产生更新。