NULL 语义
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,表示实体的一个特定属性(例如,age
是一个名为 person
的实体的列)。有时,特定行的列值在行创建时是未知的。在 SQL 中,这些值表示为 NULL
。本节详细介绍了在各种运算符、表达式和其他 SQL 构造中处理 NULL
值的语义。
- 比较运算符
- 逻辑运算符
- 表达式
- WHERE、HAVING 和 JOIN 子句中的条件表达式
- 聚合运算符 (GROUP BY, DISTINCT)
- 排序运算符 (ORDER BY 子句)
- 集合运算符 (UNION, INTERSECT, EXCEPT)
- EXISTS/NOT EXISTS 子查询
- IN/NOT IN 子查询
下文展示了一个名为 person
的表的模式布局和数据。数据在 age
列中包含 NULL
值,该表将在以下各节的各种示例中使用。
表格: person
Id | 姓名 | 年龄 |
---|---|---|
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,例如 >
、>=
、=
、<
和 <=
。当一个操作数或两个操作数未知或为 NULL
时,这些运算符的结果为未知或 NULL
。为了比较 NULL
值的相等性,Spark 提供了一个 null-safe 等于运算符 (<=>
),当其中一个操作数为 NULL
时返回 False
,当两个操作数都为 NULL
时返回 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 | 任意值 | NULL | NULL | NULL | NULL | NULL | False |
任意值 | 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,例如 AND
、OR
和 NOT
。这些运算符接受布尔表达式作为参数并返回布尔值。
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,例如函数表达式、转换表达式等。Spark 中的表达式大致可以分为:
- 不容忍 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。
不容忍 NULL 的表达式
当表达式的一个或多个参数为 NULL
时,不容忍 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
这类表达式旨在处理 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 输入时返回 true
,在非 NULL 输入时返回 false
,而函数 coalesce
返回其操作数列表中第一个非 NULL
值。然而,当所有操作数都为 NULL
时,coalesce
返回 NULL
。下面是此类表达式的不完整列表。
- 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。以下是聚合函数处理 NULL
值的规则。
- 所有聚合函数在处理时都会忽略
NULL
值。- 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
和 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。对于这三个运算符,条件表达式是布尔表达式,可以返回 True
、False
或未知 (NULL
)。如果条件结果为 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)
如前一节 比较运算符 中所述,两个 NULL
值不相等。然而,为了分组和去重处理的目的,两个或多个包含 NULL
数据的值被分组到同一个桶中。此行为符合 SQL 标准和企业级数据库管理系统。
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 支持在 ORDER BY
子句中指定 NULL 排序。Spark 根据 NULL 排序规范将所有 NULL
值放在开头或末尾来处理 ORDER BY
子句。默认情况下,所有 NULL
值都放在开头。
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)
在集合操作的上下文中,NULL
值以 null-safe 的方式进行相等性比较。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 表达式允许出现在 WHERE 子句中。这些是布尔表达式,返回 TRUE
或 FALSE
。换句话说,EXISTS 是一个成员条件,当其引用的子查询返回一行或多行时返回 TRUE
。类似地,NOT EXISTS 是一个非成员条件,当子查询不返回任何行或返回零行时返回 TRUE。
这两个表达式不受子查询结果中 NULL 存在的影响。它们通常更快,因为可以将它们转换为半连接/反半连接,而无需特殊的 NULL 感知规定。
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,查询的 WHERE 子句中允许使用 IN
和 NOT IN
表达式。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
表达式在语义上等同于一组由析取运算符 (OR
) 分隔的相等条件。例如,c1 IN (1, 2, 3) 在语义上等同于 (C1 = 1 OR c1 = 2 OR c1 = 3)
。
至于 NULL
值的处理,其语义可以从比较运算符 (=
) 和逻辑运算符 (OR
) 中 NULL
值的处理方式推导出来。总而言之,以下是计算 IN
表达式结果的规则。
- 当列表中找到非 NULL 值时,返回 TRUE。
- 当列表中未找到非 NULL 值且列表中不包含 NULL 值时,返回 FALSE。
- 当值为
NULL
,或者列表中未找到非 NULL 值且列表包含至少一个NULL
值时,返回 UNKNOWN。
当列表中包含 NULL
时,无论输入值是什么,NOT IN 始终返回 UNKNOWN。这是因为如果值不在包含 NULL
的列表中,IN 返回 UNKNOWN,而 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|
+----+---+
+----+---+