NULL 语义
描述
一个表由一组行组成,每行包含一组列。列与数据类型相关联,表示实体的特定属性(例如,age
是名为 person
的实体的列)。有时,特定于行的列的值在行创建时未知。在 SQL
中,此类值表示为 NULL
。本节详细介绍了在各种运算符、表达式和其他 SQL
结构中处理 NULL
值的语义。
- 比较运算符中的 NULL 处理
- 逻辑运算符中的 NULL 处理
- 表达式中的 NULL 处理
- WHERE、HAVING 和 JOIN 条件中的 NULL 处理
- GROUP BY 和 DISTINCT 中的 NULL 处理
- ORDER BY 中的 NULL 处理
- UNION、INTERSECT、EXCEPT 中的 NULL 处理
- EXISTS 和 NOT EXISTS 子查询中的 NULL 处理
- IN 和 NOT IN 子查询中的 NULL 处理
以下说明了名为 person
的表的模式布局和数据。数据在 age
列中包含 NULL
值,此表将在以下各节中的各种示例中使用。
表:person
Id | Name | Age |
---|---|---|
100 | Joe | 30 |
200 | Marry | NULL |
300 | Mike | 18 |
400 | Fred | 50 |
500 | Albert | NULL |
600 | Michelle | 30 |
700 | Dan | 50 |
比较运算符
Apache spark 支持标准比较运算符,如“>”、“>=”、“=”、“<”和“<=”。当一个或两个操作数未知或为 NULL
时,这些运算符的结果未知或为 NULL
。为了比较 NULL
值以确定是否相等,Spark 提供了一个空安全等于运算符(“<=>”),当一个操作数为 NULL
时,它返回 False
,当两个操作数都为 NULL
时,它返回“True。下表说明了当一个或两个操作数为
NULL` 时比较运算符的行为
左操作数 | 右操作数 | > | >= | = | < | <= | <=> |
---|---|---|---|---|---|---|---|
NULL | 任何值 | NULL | NULL | NULL | NULL | NULL | False |
任何值 | NULL | NULL | NULL | NULL | NULL | NULL | False |
NULL | NULL | NULL | NULL | NULL | NULL | NULL | True |
示例
-- Normal comparison operators return `NULL` when one of the operand is `NULL`.
SELECT 5 > null AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| null|
+-----------------+
-- Normal comparison operators return `NULL` when both the operands are `NULL`.
SELECT null = null AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| null|
+-----------------+
-- Null-safe equal operator return `False` when one of the operand is `NULL`
SELECT 5 <=> null AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| false|
+-----------------+
-- Null-safe equal operator return `True` when one of the operand is `NULL`
SELECT NULL <=> NULL;
+-----------------+
|expression_output|
+-----------------+
| true|
+-----------------+
逻辑运算符
Spark 支持标准逻辑运算符,如 AND
、OR
和 NOT
。这些运算符以 Boolean
表达式作为参数,并返回一个 Boolean
值。
下表说明了当一个或两个操作数为 NULL
时逻辑运算符的行为。
左操作数 | 右操作数 | OR | AND |
---|---|---|---|
True | NULL | True | NULL |
False | NULL | NULL | False |
NULL | True | True | NULL |
NULL | False | NULL | False |
NULL | NULL | NULL | NULL |
操作数 | NOT |
---|---|
NULL | NULL |
示例
-- Normal comparison operators return `NULL` when one of the operands is `NULL`.
SELECT (true OR null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| true|
+-----------------+
-- Normal comparison operators return `NULL` when both the operands are `NULL`.
SELECT (null OR false) AS expression_output
+-----------------+
|expression_output|
+-----------------+
| null|
+-----------------+
-- Null-safe equal operator returns `False` when one of the operands is `NULL`
SELECT NOT(null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| null|
+-----------------+
表达式
比较运算符和逻辑运算符在 Spark 中被视为表达式。除了这两种表达式之外,Spark 还支持其他形式的表达式,如函数表达式、强制转换表达式等。Spark 中的表达式可以大致分为
- 不接受 NULL 的表达式
- 可以处理
NULL
值操作数的表达式- 这些表达式的结果取决于表达式本身。
不接受 NULL 的表达式
当表达式的一个或多个参数为 NULL
时,不接受 NULL 的表达式返回 NULL
,大多数表达式都属于此类别。
示例
SELECT concat('John', null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| null|
+-----------------+
SELECT positive(null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| null|
+-----------------+
SELECT to_date(null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| null|
+-----------------+
可以处理 NULL 值操作数的表达式
此类表达式旨在处理 NULL
值。表达式的结果取决于表达式本身。例如,函数表达式 isnull
在 NULL 输入上返回 true
,在非 NULL 输入上返回 false
,而函数 coalesce
返回其操作数列表中的第一个非 NULL
值。但是,当所有操作数都为 NULL
时,coalesce
返回 NULL
。以下是不完整列表,其中包含此类表达式。
- COALESCE
- NULLIF
- IFNULL
- NVL
- NVL2
- ISNAN
- NANVL
- ISNULL
- ISNOTNULL
- ATLEASTNNONNULLS
- IN
示例
SELECT isnull(null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| true|
+-----------------+
-- Returns the first occurrence of non `NULL` value.
SELECT coalesce(null, null, 3, null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| 3|
+-----------------+
-- Returns `NULL` as all its operands are `NULL`.
SELECT coalesce(null, null, null, null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| null|
+-----------------+
SELECT isnan(null) AS expression_output;
+-----------------+
|expression_output|
+-----------------+
| false|
+-----------------+
内置聚合表达式
聚合函数通过处理一组输入行来计算单个结果。以下是聚合函数如何处理 NULL
值的规则。
NULL
值将被所有聚合函数忽略。- 此规则的唯一例外是 COUNT(*) 函数。
- 当所有输入值都为
NULL
或输入数据集为空时,某些聚合函数返回NULL
。
这些函数的列表如下:- MAX
- MIN
- SUM
- AVG
- EVERY
- ANY
- SOME
示例
-- `count(*)` does not skip `NULL` values.
SELECT count(*) FROM person;
+--------+
|count(1)|
+--------+
| 7|
+--------+
-- `NULL` values in column `age` are skipped from processing.
SELECT count(age) FROM person;
+----------+
|count(age)|
+----------+
| 5|
+----------+
-- `count(*)` on an empty input set returns 0. This is unlike the other
-- aggregate functions, such as `max`, which return `NULL`.
SELECT count(*) FROM person where 1 = 0;
+--------+
|count(1)|
+--------+
| 0|
+--------+
-- `NULL` values are excluded from computation of maximum value.
SELECT max(age) FROM person;
+--------+
|max(age)|
+--------+
| 50|
+--------+
-- `max` returns `NULL` on an empty input set.
SELECT max(age) FROM person where 1 = 0;
+--------+
|max(age)|
+--------+
| null|
+--------+
WHERE、HAVING 和 JOIN 子句中的条件表达式
WHERE
、HAVING
运算符根据用户指定的条件过滤行。JOIN
运算符用于根据连接条件组合来自两个表的行。对于所有三个运算符,条件表达式都是布尔表达式,可以返回 True、False 或 Unknown (NULL)
。如果条件的结果为 True
,则它们“满足”。
示例
-- Persons whose age is unknown (`NULL`) are filtered out from the result set.
SELECT * FROM person WHERE age > 0;
+--------+---+
| name|age|
+--------+---+
|Michelle| 30|
| Fred| 50|
| Mike| 18|
| Dan| 50|
| Joe| 30|
+--------+---+
-- `IS NULL` expression is used in disjunction to select the persons
-- with unknown (`NULL`) records.
SELECT * FROM person WHERE age > 0 OR age IS NULL;
+--------+----+
| name| age|
+--------+----+
| Albert|null|
|Michelle| 30|
| Fred| 50|
| Mike| 18|
| Dan| 50|
| Marry|null|
| Joe| 30|
+--------+----+
-- Person with unknown(`NULL`) ages are skipped from processing.
SELECT age, count(*) FROM person GROUP BY age HAVING max(age) > 18;
+---+--------+
|age|count(1)|
+---+--------+
| 50| 2|
| 30| 2|
+---+--------+
-- A self join case with a join condition `p1.age = p2.age AND p1.name = p2.name`.
-- The persons with unknown age (`NULL`) are filtered out by the join operator.
SELECT * FROM person p1, person p2
WHERE p1.age = p2.age
AND p1.name = p2.name;
+--------+---+--------+---+
| name|age| name|age|
+--------+---+--------+---+
|Michelle| 30|Michelle| 30|
| Fred| 50| Fred| 50|
| Mike| 18| Mike| 18|
| Dan| 50| Dan| 50|
| Joe| 30| Joe| 30|
+--------+---+--------+---+
-- The age column from both legs of join are compared using null-safe equal which
-- is why the persons with unknown age (`NULL`) are qualified by the join.
SELECT * FROM person p1, person p2
WHERE p1.age <=> p2.age
AND p1.name = p2.name;
+--------+----+--------+----+
| name| age| name| age|
+--------+----+--------+----+
| Albert|null| Albert|null|
|Michelle| 30|Michelle| 30|
| Fred| 50| Fred| 50|
| Mike| 18| Mike| 18|
| Dan| 50| Dan| 50|
| Marry|null| Marry|null|
| Joe| 30| Joe| 30|
+--------+----+--------+----+
聚合运算符 (GROUP BY、DISTINCT)
如上一节 比较运算符 中所述,两个 NULL
值不相等。但是,出于分组和 distinct 处理的目的,两个或多个具有 NULL 数据
的值将被分组到同一个桶中。此行为符合 SQL 标准以及其他企业数据库管理系统。
示例
-- `NULL` values are put in one bucket in `GROUP BY` processing.
SELECT age, count(*) FROM person GROUP BY age;
+----+--------+
| age|count(1)|
+----+--------+
|null| 2|
| 50| 2|
| 30| 2|
| 18| 1|
+----+--------+
-- All `NULL` ages are considered one distinct value in `DISTINCT` processing.
SELECT DISTINCT age FROM person;
+----+
| age|
+----+
|null|
| 50|
| 30|
| 18|
+----+
排序运算符 (ORDER BY 子句)
Spark SQL 支持 ORDER BY
子句中的 NULL 排序规范。Spark 通过将所有 NULL
值放在最前面或最后面来处理 ORDER BY
子句,具体取决于 NULL 排序规范。默认情况下,所有 NULL
值都放在最前面。
示例
-- `NULL` values are shown at first and other values
-- are sorted in ascending way.
SELECT age, name FROM person ORDER BY age;
+----+--------+
| age| name|
+----+--------+
|null| Marry|
|null| Albert|
| 18| Mike|
| 30|Michelle|
| 30| Joe|
| 50| Fred|
| 50| Dan|
+----+--------+
-- Column values other than `NULL` are sorted in ascending
-- way and `NULL` values are shown at the last.
SELECT age, name FROM person ORDER BY age NULLS LAST;
+----+--------+
| age| name|
+----+--------+
| 18| Mike|
| 30|Michelle|
| 30| Joe|
| 50| Dan|
| 50| Fred|
|null| Marry|
|null| Albert|
+----+--------+
-- Columns other than `NULL` values are sorted in descending
-- and `NULL` values are shown at the last.
SELECT age, name FROM person ORDER BY age DESC NULLS LAST;
+----+--------+
| age| name|
+----+--------+
| 50| Fred|
| 50| Dan|
| 30|Michelle|
| 30| Joe|
| 18| Mike|
|null| Marry|
|null| Albert|
+----+--------+
集合运算符 (UNION、INTERSECT、EXCEPT)
在集合运算的上下文中,NULL
值以空安全的方式进行比较以确定是否相等。这意味着在比较行时,两个 NULL
值被认为是相等的,这与常规的 EqualTo
(=
) 运算符不同。
示例
CREATE VIEW unknown_age SELECT * FROM person WHERE age IS NULL;
-- Only common rows between two legs of `INTERSECT` are in the
-- result set. The comparison between columns of the row are done
-- in a null-safe manner.
SELECT name, age FROM person
INTERSECT
SELECT name, age from unknown_age;
+------+----+
| name| age|
+------+----+
|Albert|null|
| Marry|null|
+------+----+
-- `NULL` values from two legs of the `EXCEPT` are not in output.
-- This basically shows that the comparison happens in a null-safe manner.
SELECT age, name FROM person
EXCEPT
SELECT age FROM unknown_age;
+---+--------+
|age| name|
+---+--------+
| 30| Joe|
| 50| Fred|
| 30|Michelle|
| 18| Mike|
| 50| Dan|
+---+--------+
-- Performs `UNION` operation between two sets of data.
-- The comparison between columns of the row ae done in
-- null-safe manner.
SELECT name, age FROM person
UNION
SELECT name, age FROM unknown_age;
+--------+----+
| name| age|
+--------+----+
| Albert|null|
| Joe| 30|
|Michelle| 30|
| Marry|null|
| Fred| 50|
| Mike| 18|
| Dan| 50|
+--------+----+
EXISTS/NOT EXISTS 子查询
在 Spark 中,EXISTS 和 NOT EXISTS 表达式允许在 WHERE 子句中使用。这些是布尔表达式,它们返回 TRUE
或 FALSE
。换句话说,EXISTS 是一个成员资格条件,当它引用的子查询返回一行或多行时,它返回 TRUE
。类似地,NOT EXISTS 是一个非成员资格条件,当子查询返回零行或没有行时,它返回 TRUE。
这两个表达式不受子查询结果中存在 NULL 值的影响。它们通常更快,因为它们可以转换为半连接/反半连接,而无需为 NULL 意识提供特殊规定。
示例
-- Even if subquery produces rows with `NULL` values, the `EXISTS` expression
-- evaluates to `TRUE` as the subquery produces 1 row.
SELECT * FROM person WHERE EXISTS (SELECT null);
+--------+----+
| name| age|
+--------+----+
| Albert|null|
|Michelle| 30|
| Fred| 50|
| Mike| 18|
| Dan| 50|
| Marry|null|
| Joe| 30|
+--------+----+
-- `NOT EXISTS` expression returns `FALSE`. It returns `TRUE` only when
-- subquery produces no rows. In this case, it returns 1 row.
SELECT * FROM person WHERE NOT EXISTS (SELECT null);
+----+---+
|name|age|
+----+---+
+----+---+
-- `NOT EXISTS` expression returns `TRUE`.
SELECT * FROM person WHERE NOT EXISTS (SELECT 1 WHERE 1 = 0);
+--------+----+
| name| age|
+--------+----+
| Albert|null|
|Michelle| 30|
| Fred| 50|
| Mike| 18|
| Dan| 50|
| Marry|null|
| Joe| 30|
+--------+----+
IN/NOT IN 子查询
在 Spark 中,IN
和 NOT IN
表达式允许在查询的 WHERE 子句中使用。与 EXISTS
表达式不同,IN
表达式可以返回 TRUE
、FALSE
或 UNKNOWN (NULL)
值。从概念上讲,IN
表达式在语义上等效于由析取运算符 (OR
) 分隔的一组等式条件。例如,c1 IN (1, 2, 3) 在语义上等效于 (C1 = 1 OR c1 = 2 OR c1 = 3)
。
就处理 NULL
值而言,语义可以从比较运算符 (=
) 和逻辑运算符 (OR
) 中的 NULL
值处理中推断出来。总而言之,以下是计算 IN
表达式结果的规则。
- 当列表中找到所讨论的非 NULL 值时,返回 TRUE
- 当列表中未找到非 NULL 值且列表不包含 NULL 值时,返回 FALSE
- 当值为
NULL
时,或者列表中未找到非 NULL 值且列表至少包含一个NULL
值时,返回 UNKNOWN
当列表包含 NULL
时,NOT IN 始终返回 UNKNOWN,无论输入值如何。这是因为如果值不在包含 NULL
的列表中,IN 会返回 UNKNOWN,并且因为 NOT UNKNOWN 再次是 UNKNOWN。
示例
-- The subquery has only `NULL` value in its result set. Therefore,
-- the result of `IN` predicate is UNKNOWN.
SELECT * FROM person WHERE age IN (SELECT null);
+----+---+
|name|age|
+----+---+
+----+---+
-- The subquery has `NULL` value in the result set as well as a valid
-- value `50`. Rows with age = 50 are returned.
SELECT * FROM person
WHERE age IN (SELECT age FROM VALUES (50), (null) sub(age));
+----+---+
|name|age|
+----+---+
|Fred| 50|
| Dan| 50|
+----+---+
-- Since subquery has `NULL` value in the result set, the `NOT IN`
-- predicate would return UNKNOWN. Hence, no rows are
-- qualified for this query.
SELECT * FROM person
WHERE age NOT IN (SELECT age FROM VALUES (50), (null) sub(age));
+----+---+
|name|age|
+----+---+
+----+---+