大数据开发之Hive篇6-Hive函数详解

野性酷女 2022-12-30 12:00 420阅读 0赞

备注:  
Hive 版本 2.1.1

### 文章目录 ###

*  一.Hive函数概述
 *   *  1.1 Hive函数分类
     *  1.2 查看Hive函数
     *   *  1.2.1 show functions命令
         *  1.2.2 DESCRIBE 命令
     *  二.Hive的数值函数
     *  2.1 round函数
     *  2.2 floor函数
     *  2.3 ceil函数
     *  2.4 rand()
     *  2.5 power函数
     *  2.6 sqrt函数
     *  2.7 bin
     *  2.8 abs函数
     *  2.9 greatest函数
     *  2.10 least函数
 *  三.Hive的日期时间函数
 *   *  3.1 from\_unixtime和UNIX\_TIMESTAMP
     *  3.2 to\_date函数
     *  3.3 year/quarter/month/day/hour/minute/second/weekofyear
     *  3.4 weekofyear 函数
     *  3.5 extract 函数
     *  3.6 datediff 函数
     *  3.6 date\_add/date\_sub 函数
     *  3.7 from\_utc\_timestamp/to\_utc\_timestamp 函数
     *  3.8 current\_date/current\_timestamp
     *  3.9 add\_months 函数
     *  3.10 last\_day 函数
     *  3.10 next\_day函数
     *  3.11 trunc 函数
     *  3.12 months\_between 函数
     *  3.13 date\_format 函数
 *  四.条件函数
 *   *  4.1 nvl函数
     *  4.2 isnull/isnotnull 函数
     *  4.3 if函数
     *  4.4 COALESCE函数
     *  4.5 CASE 函数
 *  五.字符函数
 *   *  5.1 ascii值函数
     *  5.2 字符连接函数
     *  5.3 大小写函数
     *  5.4 字符填充函数
     *  5.5 空格函数
     *  5.6 字符长度函数
     *  5.7 字符截取函数
     *  5.8 字符替换函数
     *  5.9 其它函数
 *  六.UDTF
 *  参考

# 一.Hive函数概述 #

## 1.1 Hive函数分类 ##

1.  函数/UDF  
    输入一行记录，输出一行记录  
    示例：upper/lower/length
2.  聚集函数/UDAF  
    输入多行记录，输出一行记录  
    示例：sum/count/avg
3.  表生成函数/UDTF  
    输入一行记录，输出多行记录  
    示例：explode

## 1.2 查看Hive函数 ##

### 1.2.1 show functions命令 ###

show functions命令是查询当前hive支持哪些函数，可以看到hive总共支持248个函数，总体来说函数与MySQL的比较类似，但是也包含 to\_date、decode等Oracle独有的函数（函数用法与Oracle存在差别）。真的是给从关系型数据库转到大数据库开发的人提供了便利。

hive> SHOW FUNCTIONS;
    OK
    !
    !=
    $sum0
    %
    &
    *
    +
    -
    /
    <
    <=
    <=>
    <>
    =
    ==
    >
    >=
    ^
    abs
    acos
    add_months
    and
    array
    array_contains
    ascii
    asin
    assert_true
    atan
    avg
    base64
    between
    bin
    bround
    case
    cbrt
    ceil
    ceiling
    chr
    coalesce
    collect_list
    collect_set
    compute_stats
    concat
    concat_ws
    context_ngrams
    conv
    corr
    cos
    count
    covar_pop
    covar_samp
    crc32
    create_union
    cume_dist
    current_database
    current_date
    current_timestamp
    current_user
    date_add
    date_format
    date_sub
    datediff
    day
    dayofmonth
    dayofweek
    decode
    degrees
    dense_rank
    div
    e
    elt
    encode
    ewah_bitmap
    ewah_bitmap_and
    ewah_bitmap_empty
    ewah_bitmap_or
    exp
    explode
    factorial
    field
    find_in_set
    first_value
    floor
    floor_day
    floor_hour
    floor_minute
    floor_month
    floor_quarter
    floor_second
    floor_week
    floor_year
    format_number
    from_unixtime
    from_utc_timestamp
    get_json_object
    get_splits
    greatest
    grouping
    hash
    hex
    histogram_numeric
    hour
    if
    in
    in_file
    index
    initcap
    inline
    instr
    internal_interval
    isnotnull
    isnull
    java_method
    json_tuple
    lag
    last_day
    last_value
    lcase
    lead
    least
    length
    levenshtein
    like
    ln
    locate
    log
    log10
    log2
    logged_in_user
    lower
    lpad
    ltrim
    map
    map_keys
    map_values
    mask
    mask_first_n
    mask_hash
    mask_last_n
    mask_show_first_n
    mask_show_last_n
    matchpath
    max
    md5
    min
    minute
    month
    months_between
    named_struct
    negative
    next_day
    ngrams
    noop
    noopstreaming
    noopwithmap
    noopwithmapstreaming
    not
    ntile
    nvl
    or
    parse_url
    parse_url_tuple
    percent_rank
    percentile
    percentile_approx
    pi
    pmod
    posexplode
    positive
    pow
    power
    printf
    quarter
    radians
    rand
    rank
    reflect
    reflect2
    regexp
    regexp_extract
    regexp_replace
    repeat
    replace
    reverse
    rlike
    round
    row_number
    rpad
    rtrim
    second
    sentences
    sha
    sha1
    sha2
    shiftleft
    shiftright
    shiftrightunsigned
    sign
    sin
    size
    sort_array
    soundex
    space
    split
    sqrt
    stack
    std
    stddev
    stddev_pop
    stddev_samp
    str_to_map
    struct
    substr
    substring
    substring_index
    sum
    tan
    to_date
    to_unix_timestamp
    to_utc_timestamp
    translate
    trim
    trunc
    ucase
    unbase64
    unhex
    unix_timestamp
    upper
    uuid
    var_pop
    var_samp
    variance
    version
    weekofyear
    when
    windowingtablefunction
    xpath
    xpath_boolean
    xpath_double
    xpath_float
    xpath_int
    xpath_long
    xpath_number
    xpath_short
    xpath_string
    year
    |
    ~
    Time taken: 1.246 seconds, Fetched: 248 row(s)
    hive>

### 1.2.2 DESCRIBE 命令 ###

语法:

DESCRIBE FUNCTION <function_name>;
    DESCRIBE FUNCTION EXTENDED <function_name>;

测试记录:

hive> 
        > 
        > desc function decode;
    OK
    decode(bin, str) - Decode the first argument using the second argument character set
    Time taken: 0.023 seconds, Fetched: 1 row(s)
    hive> desc function extended decode;
    OK
    decode(bin, str) - Decode the first argument using the second argument character set
    Possible options for the character set are 'US-ASCII', 'ISO-8859-1',
    'UTF-8', 'UTF-16BE', 'UTF-16LE', and 'UTF-16'. If either argument
    is null, the result will also be null
    Time taken: 0.027 seconds, Fetched: 4 row(s)
    hive>

## 二.Hive的数值函数 ##

下列是Hive常见的数值函数:

<table> 
 <thead> 
  <tr> 
   <th>函数名</th> 
   <th>描述</th> 
   <th>返回值类型</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>round(DOUBLE a)<br>round(DOUBLE a, INT d)</td> 
   <td>四舍五入函数</td> 
   <td>DOUBLE</td> 
  </tr> 
  <tr> 
   <td>floor(DOUBLE a)</td> 
   <td>返回小于等于a的整数</td> 
   <td>BIGINT</td> 
  </tr> 
  <tr> 
   <td>ceil(DOUBLE a), ceiling(DOUBLE a)</td> 
   <td>返回大于等于a的整数</td> 
   <td>BIGINT</td> 
  </tr> 
  <tr> 
   <td>rand(), rand(INT seed)</td> 
   <td>返回0 到1 之间的随机数</td> 
   <td>DOUBLE</td> 
  </tr> 
  <tr> 
   <td>pow(DOUBLE a, DOUBLE p), power(DOUBLE a, DOUBLE p)</td> 
   <td>返回a的p次方</td> 
   <td>DOUBLE</td> 
  </tr> 
  <tr> 
   <td>sqrt(DOUBLE a), sqrt(DECIMAL a)</td> 
   <td>开方函数</td> 
   <td>DOUBLE</td> 
  </tr> 
  <tr> 
   <td>bin(BIGINT a)</td> 
   <td>返回二进制数</td> 
   <td>STRING</td> 
  </tr> 
  <tr> 
   <td>abs(DOUBLE a)</td> 
   <td>返回a的绝对值</td> 
   <td>DOUBLE</td> 
  </tr> 
  <tr> 
   <td>greatest(T v1, T v2, …)</td> 
   <td>返回最大值</td> 
   <td>T</td> 
  </tr> 
  <tr> 
   <td>least(T v1, T v2, …)</td> 
   <td>返回最小值</td> 
   <td>T</td> 
  </tr> 
 </tbody> 
</table>

## 2.1 round函数 ##

round函数，用于四舍五入。

测试记录:

hive> 
        > select round(5.1);
    OK
    5.0
    Time taken: 0.09 seconds, Fetched: 1 row(s)
    hive> select round(5.5);
    OK
    6.0
    Time taken: 0.07 seconds, Fetched: 1 row(s)
    hive> 
        > select round(5.5,1);
    OK
    5.5
    Time taken: 0.086 seconds, Fetched: 1 row(s)

## 2.2 floor函数 ##

floor向下取整

测试记录:

hive> 
        > 
        > select floor(5.4);
    OK
    5
    Time taken: 0.092 seconds, Fetched: 1 row(s)
    hive> select floor(5.5);
    OK
    5
    Time taken: 0.074 seconds, Fetched: 1 row(s)
    hive> select floor(5.9);
    OK
    5
    Time taken: 0.08 seconds, Fetched: 1 row(s)
    hive>

## 2.3 ceil函数 ##

ceil 向上取整

测试记录:

hive> 
        > select ceil(5.4);
    OK
    6
    Time taken: 0.094 seconds, Fetched: 1 row(s)
    hive> select ceil(5.1);
    OK
    6
    Time taken: 0.063 seconds, Fetched: 1 row(s)
    hive> select ceil(5.9);
    OK
    6
    Time taken: 0.08 seconds, Fetched: 1 row(s)

## 2.4 rand() ##

rand 随机数函数

测试记录:

-- 取值1-100之间的随机数
    hive> 
        > select round(100*rand());
    Query ID = root_20201208110850_c85b86e9-012e-449d-a1f5-0b158741315b
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0141, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0141/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0141
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-08 11:08:57,146 Stage-1 map = 0%,  reduce = 0%
    2020-12-08 11:09:03,404 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.04 sec
    MapReduce Total cumulative CPU time: 3 seconds 40 msec
    Ended Job = job_1606698967173_0141
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 3.04 sec   HDFS Read: 5300 HDFS Write: 104 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 3 seconds 40 msec
    OK
    62.0
    Time taken: 14.362 seconds, Fetched: 1 row(s)

## 2.5 power函数 ##

power平方函数

测试记录:

-- 求2的3次方
    hive> 
        > select power(2,3);
    OK
    8.0
    Time taken: 0.087 seconds, Fetched: 1 row(s)

## 2.6 sqrt函数 ##

sqrt开方函数

测试记录:

hive> 
        > select sqrt(9);
    OK
    3.0
    Time taken: 0.074 seconds, Fetched: 1 row(s)

## 2.7 bin ##

bin函数，返回二进制数

测试记录:

hive> 
        > 
        > select bin(10);
    OK
    1010
    Time taken: 0.085 seconds, Fetched: 1 row(s)
    hive> select bin(9);
    OK
    1001
    Time taken: 0.065 seconds, Fetched: 1 row(s)
    hive>

## 2.8 abs函数 ##

abc函数求绝对值

测试记录:

hive> 
        > select abs(10);
    OK
    10
    Time taken: 0.09 seconds, Fetched: 1 row(s)
    hive> select abs(-10);
    OK
    10
    Time taken: 0.084 seconds, Fetched: 1 row(s)

## 2.9 greatest函数 ##

greatest函数，求最大值函数，如果有参数为null，就会返回null

测试记录:

hive> 
        > select greatest(1,2,3);
    OK
    3
    Time taken: 0.094 seconds, Fetched: 1 row(s)
    hive> select greatest(1,2,3,1.5);
    OK
    3.0
    Time taken: 0.075 seconds, Fetched: 1 row(s)
    hive> 
        > select greatest(1,2,3,null);
    OK
    NULL
    Time taken: 0.084 seconds, Fetched: 1 row(s)

## 2.10 least函数 ##

least函数求最小值，如参数中有null值，则返回null

测试记录:

hive> 
        > 
        > select least(1,2,3);
    OK
    1
    Time taken: 0.094 seconds, Fetched: 1 row(s)
    hive> select least(1,2,3,-1);
    OK
    -1
    Time taken: 0.079 seconds, Fetched: 1 row(s)
    hive> select least(1,2,3,null);
    OK
    NULL
    Time taken: 0.059 seconds, Fetched: 1 row(s)
    hive>

# 三.Hive的日期时间函数 #

下列是Hive常见的日期时间函数:

<table> 
 <thead> 
  <tr> 
   <th>函数名</th> 
   <th>描述</th> 
   <th>返回值类型</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>from_unixtime(bigint unixtime[, string format])</td> 
   <td>与UNIX_TIMESTAMP()结合使用，数值转换成日期时间类型</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>unix_timestamp()<br>unix_timestamp(string date)<br>unix_timestamp(string date, string pattern)</td> 
   <td>将时间转成数字，与FROM_UNIXTIME相互对应</td> 
   <td>bigint</td> 
  </tr> 
  <tr> 
   <td>to_date(string timestamp)</td> 
   <td>返回时间戳类型的日期时间部分</td> 
   <td>date</td> 
  </tr> 
  <tr> 
   <td>year(string date)</td> 
   <td>返回年份</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>quarter(date/timestamp/string)</td> 
   <td>返回季度</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>month(string date)</td> 
   <td>返回月份</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>day(string date) dayofmonth(date)</td> 
   <td>返回日</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>hour(string date)</td> 
   <td>返回小时</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>minute(string date)</td> 
   <td>返回分钟</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>second(string date)</td> 
   <td>返回秒</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>weekofyear(string date)</td> 
   <td>返回当年的星期数</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>extract(field FROM source)</td> 
   <td>提取日期的部分</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>datediff(string enddate, string startdate)</td> 
   <td>返回两个日期间隔的天数</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>date_add(date/timestamp/string startdate, tinyint/smallint/int days)</td> 
   <td>增加日期天数</td> 
   <td>date</td> 
  </tr> 
  <tr> 
   <td>date_sub(date/timestamp/string startdate, tinyint/smallint/int days)</td> 
   <td>减去日期天数</td> 
   <td>date</td> 
  </tr> 
  <tr> 
   <td>from_utc_timestamp({any primitive type} ts, string timezone)</td> 
   <td>将UTC中的时间戳*转换为给定时区</td> 
   <td>timestamp</td> 
  </tr> 
  <tr> 
   <td>to_utc_timestamp({any primitive type} ts, string timezone)</td> 
   <td>将给定时区中的时间戳*转换为UTC</td> 
   <td>timestamp</td> 
  </tr> 
  <tr> 
   <td>current_date</td> 
   <td>返回当前日期</td> 
   <td>date</td> 
  </tr> 
  <tr> 
   <td>current_timestamp</td> 
   <td>返回当前时间</td> 
   <td>timestamp</td> 
  </tr> 
  <tr> 
   <td>add_months(string start_date, int num_months, output_date_format)</td> 
   <td>增加月份</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>last_day(string date)</td> 
   <td>求本月最后一天</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>next_day(string start_date, string day_of_week)</td> 
   <td>返回比start_date晚并命名为day_of_week的第一个日期</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>trunc(string date, string format)</td> 
   <td>返回格式指定的单元截断的日期</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>months_between(date1, date2)</td> 
   <td>两个日期之间间隔的月份</td> 
   <td>double</td> 
  </tr> 
  <tr> 
   <td>date_format(date/timestamp/string ts, string fmt)</td> 
   <td>格式日期指定</td> 
   <td>string</td> 
  </tr> 
 </tbody> 
</table>

## 3.1 from\_unixtime和UNIX\_TIMESTAMP ##

from\_unixtime 将数字转成时间，与UNIX\_TIMESTAMP相互对应  
UNIX\_TIMESTAMP将时间转成数字，与FROM\_UNIXTIME相互对应  
测试记录:

hive> 
        > select UNIX_TIMESTAMP('2020-12-09 23:50:59');
    OK
    1607529059
    Time taken: 0.101 seconds, Fetched: 1 row(s)
    hive> 
        > select from_unixtime(1607529059);
    OK
    2020-12-09 23:50:59
    Time taken: 0.084 seconds, Fetched: 1 row(s)

## 3.2 to\_date函数 ##

to\_date(string timestamp) 函数 (string timestamp)

hive> 
        > 
        > select to_date('2020-12-09 23:59:59 123456789');
    OK
    2020-12-09
    Time taken: 0.109 seconds, Fetched: 1 row(s)
    hive> select to_date('2020-12-09 23:59:59');
    OK
    2020-12-09
    Time taken: 0.086 seconds, Fetched: 1 row(s)

## 3.3 year/quarter/month/day/hour/minute/second/weekofyear ##

1.  year(string date) 返回年份
2.  quarter(date/timestamp/string) 返回季度
3.  month(string date) 返回月份
4.  day(string date) dayofmonth(date) 返回日
5.  hour(string date) 返回小时
6.  minute(string date) 返回分钟
7.  second(string date) 返回秒

测试记录:

hive> 
        > select year(dt) dt_year,
        >        quarter(dt) dt_quarter,
        >        month(dt) dt_month,
        >        day(dt) dt_day,
        >        hour(dt) dt_hour,
        >        minute(dt) dt_minute,
        >        second(dt) dt_second
        > from
        > (
        > select '2020-12-09 23:50:59' as dt
        > ) tmp;
    OK
    dt_year dt_quarter      dt_month        dt_day  dt_hour dt_minute       dt_second
    2020    4       12      9       23      50      59
    Time taken: 0.098 seconds, Fetched: 1 row(s)
    hive>

## 3.4 weekofyear 函数 ##

weekofyear(string date) 返回当年的星期数

测试记录:  
'2020-12-09’是本年的第50周

hive> 
        > select weekofyear('2020-12-09');
    OK
    _c0
    50
    Time taken: 0.073 seconds, Fetched: 1 row(s)

## 3.5 extract 函数 ##

extract(field FROM source) 提取日期的部分  
代码:

-- 提取年
    SELECT EXTRACT(YEAR FROM '2020-12-09');
    -- 提取月
    SELECT EXTRACT(MONTH FROM '2020-12-09');
    -- 提取日
    SELECT EXTRACT(DAY FROM '2020-12-09');
    -- 提取小时
    SELECT EXTRACT(HOUR FROM '2020-12-09 13:02:01');
    -- 提取分钟
    SELECT EXTRACT(MINUTE FROM '2020-12-09 13:02:01');
    -- 提取秒
    SELECT EXTRACT(SECOND FROM '2020-12-09 13:02:01');

测试记录:

hive> 
        > SELECT EXTRACT(YEAR FROM '2020-12-09');
    OK
    _c0
    2020
    Time taken: 1.85 seconds, Fetched: 1 row(s)
    hive> SELECT EXTRACT(MONTH FROM '2020-12-09');
    OK
    _c0
    12
    Time taken: 0.227 seconds, Fetched: 1 row(s)
    hive> SELECT EXTRACT(DAY FROM '2020-12-09');
    OK
    _c0
    9
    Time taken: 0.089 seconds, Fetched: 1 row(s)
    hive> SELECT EXTRACT(HOUR FROM '2020-12-09 13:02:01');
    OK
    _c0
    13
    Time taken: 0.097 seconds, Fetched: 1 row(s)
    hive> SELECT EXTRACT(MINUTE FROM '2020-12-09 13:02:01');
    OK
    _c0
    2
    Time taken: 0.081 seconds, Fetched: 1 row(s)
    hive> SELECT EXTRACT(SECOND FROM '2020-12-09 13:02:01');
    OK
    _c0
    1

## 3.6 datediff 函数 ##

datediff(string enddate, string startdate) 返回两个日期间隔的天数

测试记录:

hive> 
        > 
        > select datediff('2020-12-09','2020-11-09');
    OK
    _c0
    30
    Time taken: 0.101 seconds, Fetched: 1 row(s)
    hive> select datediff('2020-12-09 23:50:59','2020-11-09');
    OK
    _c0
    30
    Time taken: 0.113 seconds, Fetched: 1 row(s)

## 3.6 date\_add/date\_sub 函数 ##

date\_add(date/timestamp/string startdate, tinyint/smallint/int days) 增加日期间隔，默认为天  
date\_sub(date/timestamp/string startdate, tinyint/smallint/int days) 减去日期间隔，默认为天

测试记录:

hive> 
        > 
        > SELECT DATE_ADD('2020-01-01', 31);
    OK
    _c0
    2020-02-01
    Time taken: 0.098 seconds, Fetched: 1 row(s)
    hive> SELECT DATE_ADD('2020-01-01', -31);
    OK
    _c0
    2019-12-01
    Time taken: 0.075 seconds, Fetched: 1 row(s)
    hive> 
        > SELECT DATE_SUB('2020-01-01', 31);
    OK
    _c0
    2019-12-01
    Time taken: 0.075 seconds, Fetched: 1 row(s)
    hive> SELECT DATE_SUB('2020-01-01', -31);
    OK
    _c0
    2020-02-01
    Time taken: 0.071 seconds, Fetched: 1 row(s)

## 3.7 from\_utc\_timestamp/to\_utc\_timestamp 函数 ##

from\_utc\_timestamp(\{any primitive type\} ts, string timezone) 将UTC中的时间戳*转换为给定时区  
to\_utc\_timestamp(\{any primitive type\} ts, string timezone) 将给定时区中的时间戳*转换为UTC

测试记录:  
CST是美国时间简写  
UTC的

hive> 
        > select from_utc_timestamp('2020-12-09 23:59:59','CST');
    OK
    _c0
    2020-12-09 17:59:59
    Time taken: 0.078 seconds, Fetched: 1 row(s)
    hive> 
        > select to_utc_timestamp('2020-12-09 23:59:59','CST');
    OK
    _c0
    2020-12-10 05:59:59
    Time taken: 0.085 seconds, Fetched: 1 row(s)

## 3.8 current\_date/current\_timestamp ##

current\_date 返回当前日期  
current\_timestamp 返回当前时间

测试记录:

hive> 
        > select current_date() cur_date,current_timestamp() cur_timestamp;
    OK
    cur_date        cur_timestamp
    2020-12-09      2020-12-09 14:58:44.535
    Time taken: 0.079 seconds, Fetched: 1 row(s)

## 3.9 add\_months 函数 ##

add\_months(string start\_date, int num\_months, output\_date\_format) 增加月份

测试记录:

hive> 
        > 
        > select add_months('2020-12-31 23:59:59',2,'YYYY-MM-dd HH:mm:ss');
    OK
    _c0
    2021-02-28 23:59:59
    Time taken: 0.097 seconds, Fetched: 1 row(s)

## 3.10 last\_day 函数 ##

last\_day(string date) 求本月最后一天

测试记录:

hive> 
        > select last_day('2020-12-09');
    OK
    _c0
    2020-12-31
    Time taken: 0.104 seconds, Fetched: 1 row(s)
    hive> select last_day('2020-12-09 23:59:59');
    OK
    _c0
    2020-12-31
    Time taken: 0.077 seconds, Fetched: 1 row(s)

## 3.10 next\_day函数 ##

next\_day(string start\_date, string day\_of\_week) 返回比start\_date晚并命名为day\_of\_week的第一个日期

测试记录:  
求下一个礼拜二

hive> 
        > 
        > select next_day('2020-12-09','TU');
    OK
    _c0
    2020-12-15
    Time taken: 0.086 seconds, Fetched: 1 row(s)

## 3.11 trunc 函数 ##

trunc(string date, string format) 返回格式指定的单元截断的日期  
支持格式:MONTH/MON/MM, YEAR/YYYY/YY

测试记录:

hive> 
        > select trunc('2020-12-09 23:59:59','YYYY');
    OK
    _c0
    2020-01-01
    hive> 
        > select trunc('2020-12-09 23:59:59','MM');
    OK
    _c0
    2020-12-01
    Time taken: 0.063 seconds, Fetched: 1 row(s)

## 3.12 months\_between 函数 ##

months\_between(date1, date2) 两个日期之间间隔的月份

date1和date2之间的月份间隔，会有小数的存在，另外，如果date1小于date2，那么就会出现负数

测试记录:

hive> 
        > 
        > select months_between('2020-12-09','2020-01-01');
    OK
    _c0
    11.25806452
    Time taken: 0.09 seconds, Fetched: 1 row(s)
    hive> 
        > select months_between('2020-12-09','2021-01-01');
    OK
    _c0
    -0.74193548
    Time taken: 0.083 seconds, Fetched: 1 row(s)
    hive>

## 3.13 date\_format 函数 ##

date\_format(date/timestamp/string ts, string fmt) 格式日期指定

测试记录:

hive> SELECT date_format('2020-12-09 23:59:59', 'yyyy.MM.dd HH:mm:ss');
    OK
    _c0
    2020.12.09 23:59:59
    Time taken: 0.071 seconds, Fetched: 1 row(s)

# 四.条件函数 #

下列是Hive常见的条件函数:

<table> 
 <thead> 
  <tr> 
   <th>函数名</th> 
   <th>描述</th> 
   <th>返回值类型</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>nvl(a,b)</td> 
   <td>当a为null的时候返回b，否则返回a</td> 
   <td>T</td> 
  </tr> 
  <tr> 
   <td>isnull( a )</td> 
   <td>当a为null的时候返回true，否则返回false</td> 
   <td>boolean</td> 
  </tr> 
  <tr> 
   <td>isnotnull ( a )</td> 
   <td>当a为null的时候返回false，否则返回true</td> 
   <td>boolean</td> 
  </tr> 
  <tr> 
   <td>if(boolean testCondition, T valueTrue, T valueFalseOrNull)</td> 
   <td>当testCondition为true 返回 valueTrue，否则返回 valueFalseOrNull)</td> 
   <td>T</td> 
  </tr> 
  <tr> 
   <td>COALESCE(T v1, T v2, …)</td> 
   <td>返回第一个不为null，当所有的都是null，就返回null</td> 
   <td>T</td> 
  </tr> 
  <tr> 
   <td>CASE WHEN a THEN b [WHEN c THEN d]* [ELSE e] END</td> 
   <td>case条件判断语句</td> 
   <td>T</td> 
  </tr> 
  <tr> 
   <td>CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END</td> 
   <td>case值判断语句</td> 
   <td>T</td> 
  </tr> 
 </tbody> 
</table>

## 4.1 nvl函数 ##

nvl(a,b) 当a为null的时候返回b，否则返回a

测试记录:

hive> 
        > select nvl(null,123);
    OK
    _c0
    123
    hive> 
        > select nvl('345',123);
    OK
    _c0
    345
    Time taken: 0.067 seconds, Fetched: 1 row(s)

## 4.2 isnull/isnotnull 函数 ##

isnull( a ) 当a为null的时候返回true，否则返回false  
isnotnull ( a ) 当a为null的时候返回false，否则返回true

测试记录:

hive> 
        > select isnull(1);
    OK
    _c0
    false
    Time taken: 0.083 seconds, Fetched: 1 row(s)
    hive> select isnull(null);
    OK
    _c0
    true
    Time taken: 0.072 seconds, Fetched: 1 row(s)
    hive> select isnull('');
    OK
    _c0
    false
    Time taken: 0.076 seconds, Fetched: 1 row(s)
    hive> 
        > select isnull(' ');
    OK
    _c0
    false
    Time taken: 0.08 seconds, Fetched: 1 row(s)
    hive> 
        > 
        > select isnotnull(1);
    OK
    _c0
    true
    Time taken: 0.081 seconds, Fetched: 1 row(s)
    hive> 
        > select isnotnull(null);
    OK
    _c0
    false
    Time taken: 0.065 seconds, Fetched: 1 row(s)
    hive> 
        > select isnotnull('');
    OK
    _c0
    true
    Time taken: 0.071 seconds, Fetched: 1 row(s)
    hive> 
        > select isnotnull(' ');
    OK
    _c0
    true
    Time taken: 0.086 seconds, Fetched: 1 row(s)
    hive>

## 4.3 if函数 ##

if(boolean testCondition, T valueTrue, T valueFalseOrNull) 当testCondition为true 返回 valueTrue，否则返回 valueFalseOrNull)

测试记录:

hive> 
        > select if(1>2,'abc','def');
    OK
    _c0
    def
    Time taken: 0.134 seconds, Fetched: 1 row(s)
    hive> 
        > select if(1<2,'abc','def');
    OK
    _c0
    abc
    Time taken: 0.1 seconds, Fetched: 1 row(s)

## 4.4 COALESCE函数 ##

COALESCE(T v1, T v2, …) 返回第一个不为null，当所有的都是null，就返回null

测试记录:

hive> 
        > select coalesce('a','b',null);
    OK
    _c0
    a
    Time taken: 0.081 seconds, Fetched: 1 row(s)
    hive> 
        > select coalesce('null','b','c');
    OK
    _c0
    null
    Time taken: 0.073 seconds, Fetched: 1 row(s)
    hive> 
        > select coalesce('null',null,null);
    OK
    _c0
    null
    Time taken: 0.073 seconds, Fetched: 1 row(s)

## 4.5 CASE 函数 ##

CASE WHEN a THEN b \[WHEN c THEN d\]\* \[ELSE e\] END case条件判断语句  
CASE a WHEN b THEN c \[WHEN d THEN e\]\* \[ELSE f\] END case值判断语句

代码:

-- 值判断，根据职位英文名判断职位中文名
    select e.empno,
         e.job,
         case when e.job = 'SALESMAN' then '销售员'
              when e.job = 'MANAGER' then '管理人员'
              else '未知' end  as job_china
    from emp e;
    
    -- 条件判断,小于某个时间点入职的是老员工，大于等于的是新员工
    select e.empno,
         e.hiredate,
         case e.job when  'SALESMAN' then '销售员'
                    when  'MANAGER' then '管理人员'
                     else '未知' 
         end  as job_china
    from emp e;
    
    -- 没有else语句且判断不成功的时候，会返回为空
    SELECT CASE 'c'
        WHEN 'a' THEN 1 WHEN 'b' THEN 2 END is_c;

测试记录:

hive> 
        > -- 值判断，根据职位英文名判断职位中文名
        > select e.empno,
        >      e.job,
        >      case when e.job = 'SALESMAN' then '销售员'
        >           when e.job = 'MANAGER' then '管理人员'
        >           else '未知' end  as job_china
        > from emp e;
    Query ID = root_20201209163913_c6290550-dfa0-4f82-891e-24673ce9c704
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0142, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0142/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0142
    Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 0
    2020-12-09 16:39:21,669 Stage-1 map = 0%,  reduce = 0%
    2020-12-09 16:39:27,895 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 6.68 sec
    MapReduce Total cumulative CPU time: 6 seconds 680 msec
    Ended Job = job_1606698967173_0142
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 2   Cumulative CPU: 6.68 sec   HDFS Read: 12384 HDFS Write: 764 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 6 seconds 680 msec
    OK
    e.empno e.job   job_china
    7499    SALESMAN        销售员
    7876    CLERK   未知
    7934    CLERK   未知
    7521    SALESMAN        销售员
    7654    SALESMAN        销售员
    7698    MANAGER 管理人员
    7788    ANALYST 未知
    7369    CLERK   未知
    7900    CLERK   未知
    7902    ANALYST 未知
    7566    MANAGER 管理人员
    7782    MANAGER 管理人员
    7839    PRESIDENT       未知
    7844    SALESMAN        销售员
    Time taken: 15.553 seconds, Fetched: 14 row(s)
    hive> 
        > -- 条件判断,小于某个时间点入职的是老员工，大于等于的是新员工
        > select e.empno,
        >      e.hiredate,
        >      case e.job when  'SALESMAN' then '销售员'
        >                 when  'MANAGER' then '管理人员'
        >                  else '未知' 
        >      end  as job_china
        > from emp e;
    Query ID = root_20201209164150_7030d7f3-686c-4bb8-85e7-4f4e1db2dfef
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0143, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0143/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0143
    Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 0
    2020-12-09 16:41:57,812 Stage-1 map = 0%,  reduce = 0%
    2020-12-09 16:42:05,052 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 6.23 sec
    MapReduce Total cumulative CPU time: 6 seconds 230 msec
    Ended Job = job_1606698967173_0143
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 2   Cumulative CPU: 6.23 sec   HDFS Read: 12252 HDFS Write: 808 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 6 seconds 230 msec
    OK
    e.empno e.hiredate      job_china
    7499    1981-02-20      销售员
    7876    1987-06-13      未知
    7934    1982-01-23      未知
    7521    1981-02-22      销售员
    7654    1981-09-28      销售员
    7698    1981-05-01      管理人员
    7788    1987-06-13      未知
    7369    1980-12-17      未知
    7900    1981-12-03      未知
    7902    1981-12-03      未知
    7566    1981-04-02      管理人员
    7782    1981-06-09      管理人员
    7839    1981-11-17      未知
    7844    1981-09-08      销售员
    Time taken: 15.383 seconds, Fetched: 14 row(s)
    hive> -- 没有else语句且判断不成功的时候，会返回为空
    hive> SELECT CASE 'c'
        >     WHEN 'a' THEN 1 WHEN 'b' THEN 2 END is_c;
    OK
    is_c
    NULL
    Time taken: 0.082 seconds, Fetched: 1 row(s)
    hive>

# 五.字符函数 #

下列是Hive常见的字符函数:

<table> 
 <thead> 
  <tr> 
   <th>函数名</th> 
   <th>描述</th> 
   <th>返回值类型</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>ascii(string str)</td> 
   <td>返回字符的ascii值</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>chr(bigint or double A)</td> 
   <td>通过ascii值返回对应字符</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>concat(string or binary A, string or binary B…)</td> 
   <td>字符连接函数</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>concat_ws(string SEP, string A, string B…)</td> 
   <td>字符连接函数，可以加连接符</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>decode(binary bin, string charset)</td> 
   <td>将二进制文件转字符集</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>elt(N int,str1 string,str2 string,str3 string,…)</td> 
   <td>返回指定的字符</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>field(val T,val1 T,val2 T,val3 T,…)</td> 
   <td>返回字符在参数中出现的位置</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>find_in_set(string str, string strList)</td> 
   <td>返回字符在字符串中出现的位置</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>instr(string str, string substr)</td> 
   <td>返回substr在str中出现的位置</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>length(string A)</td> 
   <td>字符串长度长度函数</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>locate(string substr, string str[, int pos])</td> 
   <td>返回在位置pos后的str中substr第一次出现的位置。</td> 
   <td>int</td> 
  </tr> 
  <tr> 
   <td>lower(string A) lcase(string A)</td> 
   <td>将字符转为小写</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>lpad(string str, int len, string pad)</td> 
   <td>字符填充函数，向左填充</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>ltrim(string A)</td> 
   <td>截断字符串左边的空格</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>repeat(string str, int n)</td> 
   <td>字符str重复n次</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>replace(string A, string OLD, string NEW)</td> 
   <td>字符串替换函数</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>reverse(string A)</td> 
   <td>字符反转函数</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>rpad(string str, int len, string pad)</td> 
   <td>字符填充函数，从右边填充</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>rtrim(string A)</td> 
   <td>截断字符串右边的空格</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>space(int n)</td> 
   <td>空格函数，返回n个空格</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>substr(string or binary A, int start, int len)<br>substring(string or binary A, int start, int len)</td> 
   <td>字符截取函数</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>translate(string or char or varchar input, string or char or varchar from, string or char or varchar to)</td> 
   <td>同postgresql的translate函数</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>trim(string A)</td> 
   <td>截断字符串左边和右边的空格</td> 
   <td>string</td> 
  </tr> 
  <tr> 
   <td>upper(string A) ucase(string A)</td> 
   <td>字符串大写函数</td> 
   <td>string</td> 
  </tr> 
 </tbody> 
</table>

## 5.1 ascii值函数 ##

ascii(string str) 返回字符的ascii值  
chr(bigint or double A) 通过ascii值返回对应字符

这两个函数刚好是相对应的。

测试记录:

hive> 
        > select ascii('a') ascii_a,ascii('A') ascii_a;
    OK
    ascii_a ascii_a
    97      65
    Time taken: 0.104 seconds, Fetched: 1 row(s)
    hive> 
        > select chr(97) chr_97,chr(65) chr_65;
    OK
    chr_97  chr_65
    a       A
    Time taken: 0.075 seconds, Fetched: 1 row(s)

## 5.2 字符连接函数 ##

concat(string or binary A, string or binary B…) 字符连接函数  
concat\_ws(string SEP, string A, string B…) 字符连接函数，可以加连接符

测试记录:

hive> 
        > 
        > select concat('A','b','DEF');
    OK
    _c0
    AbDEF
    Time taken: 0.214 seconds, Fetched: 1 row(s)
    hive> 
        > select concat_ws('-','A','b','DEF');
    OK
    _c0
    A-b-DEF
    Time taken: 0.063 seconds, Fetched: 1 row(s)
    hive>

## 5.3 大小写函数 ##

lower(string A) lcase(string A) 将字符转为小写  
upper(string A) ucase(string A) 字符串大写函数

测试记录:

hive> 
        > select lower('AbCdE'),upper('AbCdE');
    OK
    _c0     _c1
    abcde   ABCDE
    Time taken: 0.085 seconds, Fetched: 1 row(s)

## 5.4 字符填充函数 ##

lpad(string str, int len, string pad) 字符填充函数，向左填充  
rpad(string str, int len, string pad) 字符填充函数，从右边填充

测试记录:

hive> 
        > SELECT LPAD('hi',4,'??');
    OK
    _c0
    ??hi
    Time taken: 0.065 seconds, Fetched: 1 row(s)
    hive> SELECT LPAD('hi',1,'??');
    OK
    _c0
    h
    Time taken: 0.072 seconds, Fetched: 1 row(s)
    hive> SELECT RPAD('hi',4,'??');
    OK
    _c0
    hi??
    Time taken: 0.064 seconds, Fetched: 1 row(s)
    hive> SELECT RPAD('hi',1,'??');
    OK
    _c0
    h
    Time taken: 0.08 seconds, Fetched: 1 row(s)

## 5.5 空格函数 ##

LTRIM() 左边去掉空格  
TRIM() 去掉空格  
RTRIM() 右边去掉空格  
SPACE() 返回指定长度的空格

如果中间有空格需要去掉，可以使用replace

代码:

-- 去掉左边、两端、右边的空格
    select ltrim(' abc def '),trim(' abc def '),rtrim(' abc def ');
    -- 返回10个空格
    select SPACE(10);

测试记录:

hive> select ltrim(' abc def '),trim(' abc def '),rtrim(' abc def ');
    OK
    _c0     _c1     _c2
    abc def         abc def  abc def
    Time taken: 0.077 seconds, Fetched: 1 row(s)
    hive> select SPACE(10);
    OK
    _c0
              
    Time taken: 0.062 seconds, Fetched: 1 row(s)

## 5.6 字符长度函数 ##

length(string A) 字符串长度长度函数

测试记录:

hive> 
        > select length('aaaaaabbbbcccc');
    OK
    _c0
    14
    Time taken: 0.081 seconds, Fetched: 1 row(s)

## 5.7 字符截取函数 ##

SUBSTR() 截取字符  
SUBSTRING\_INDEX() 截取字符  
INSTR() 字符出现位置函数

语法:  
substr(string or binary A, int start, int len)  
substring(string or binary A, int start, int len)  
INSTR(str,substr)  
locate(string substr, string str\[, int pos\])

代码:

-- 字符截取,从第5位开始截，len没有值，表示截取到最后
    select substr('abcdefghi',5);
    
    -- 字符截取,从第5位开始截，截取2个字符
    select substr('abcdefghi',5,2);
    
    -- 字符截取,从第-5位开始截，截取2个字符
    select substr('abcdefghi',-5,2);
    
    
    
    -- '.'第一次出现的地方，保留左边的
    select substring_index('www.mysql.com','.',1);
    
    -- '.'最后一次出现的地方，保留右边的
    select substring_index('www.mysql.com','.',-1);
    
    -- '.'出现的位置
    select instr('www.mysql.com','.');
    
    -- '.'在第6个字符之后第一次出现的位置
    select locate('.','hive.apache.org',6);

测试记录:

hive> 
        > 
        > select substr('abcdefghi',5);
    OK
    _c0
    efghi
    Time taken: 0.091 seconds, Fetched: 1 row(s)
    hive> select substr('abcdefghi',5,2);
    OK
    _c0
    ef
    Time taken: 0.066 seconds, Fetched: 1 row(s)
    hive> select substr('abcdefghi',-5,2);
    OK
    _c0
    ef
    Time taken: 0.064 seconds, Fetched: 1 row(s)
    hive> select substring_index('www.mysql.com','.',1);
    OK
    _c0
    www
    Time taken: 0.062 seconds, Fetched: 1 row(s)
    hive> select substring_index('www.mysql.com','.',-1);
    OK
    _c0
    com
    Time taken: 0.062 seconds, Fetched: 1 row(s)
    hive> select instr('www.mysql.com','.');
    OK
    _c0
    4
    Time taken: 0.062 seconds, Fetched: 1 row(s)
    hive> select locate('.','hive.apache.org',6);
    OK
    _c0
    12
    Time taken: 0.067 seconds, Fetched: 1 row(s)
    hive>

## 5.8 字符替换函数 ##

replace(string A, string OLD, string NEW) 字符串替换函数  
translate(string or char or varchar input, string or char or varchar from, string or char or varchar to) 同postgresql的translate函数

replace 会把string OLD当做一个整体来进行替换  
translate 会把string or char or varchar from拆分成一个一个的字符，与string to一个一个对应进行替换

translate比replace功能更强大  
translate(‘123abc’,‘2dc’,‘4e’): 将会把2->4,d->e,c-’’  
translate(‘123abc’,‘2’,‘4e’): 将会把2->4

测试记录:

hive> 
        > SELECT REPLACE('acdd','cd','ef');
    OK
    _c0
    aefd
    Time taken: 0.093 seconds, Fetched: 1 row(s)
    hive> SELECT TRANSLATE('acdd','cd','ef');
    OK
    _c0
    aeff
    Time taken: 0.072 seconds, Fetched: 1 row(s)
    hive> select translate('123abc','2dc','4e');
    OK
    _c0
    143ab
    Time taken: 0.073 seconds, Fetched: 1 row(s)
    hive> select translate('123abc','2','4e');
    OK
    _c0
    143abc
    Time taken: 0.075 seconds, Fetched: 1 row(s)
    hive>

## 5.9 其它函数 ##

decode(binary bin, string charset) 将二进制文件转字符集  
elt(N int,str1 string,str2 string,str3 string,…) 返回指定的字符  
field(val T,val1 T,val2 T,val3 T,…) 返回字符在参数中出现的位置  
repeat(string str, int n) 字符str重复n次  
reverse(string A) 字符反转函数

从Oracle开发转过来的同学看到decode这个函数会觉得特别亲切，不过还是得把Hive的decode语法看一下，这两者完全不是一回事，这个decode是转字符集的。

测试记录:

hive> 
        > select reflect('java.net.URLDecoder', 'decode','1' , "GBK");
    Query ID = root_20201209172130_cafe84ad-3e66-4e4a-92a0-3868efd52957
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator Starting Job = job_1606698967173_0145, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0145/ Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job -kill job_1606698967173_0145 Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0 2020-12-09 17:21:39,319 Stage-1 map = 0%, reduce = 0% 2020-12-09 17:21:45,570 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.88 sec MapReduce Total cumulative CPU time: 2 seconds 880 msec Ended Job = job_1606698967173_0145 MapReduce Jobs Launched: Stage-Stage-1: Map: 1 Cumulative CPU: 2.88 sec HDFS Read: 4726 HDFS Write: 101 HDFS EC Read: 0 SUCCESS Total MapReduce CPU Time Spent: 2 seconds 880 msec OK _c0 1 Time taken: 15.797 seconds, Fetched: 1 row(s) hive> > select elt(3,'abc','aaa','def','test'); OK _c0 def Time taken: 0.084 seconds, Fetched: 1 row(s) hive> > select field('ab','abc','ba','ab','dab'); OK _c0 3 hive> > > select repeat('abc',10); OK _c0 abcabcabcabcabcabcabcabcabcabc Time taken: 0.085 seconds, Fetched: 1 row(s) hive> > select reverse('abc');
    OK
    _c0
    cba
    Time taken: 0.078 seconds, Fetched: 1 row(s)

# 六.UDTF #

UDTF 是表生成函数

这点与关系型数据库的区别比较大,下面通过几个例子来看看

代码:

-- explode (array)
    select explode(array('A','B','C'));
    select explode(array('A','B','C')) as col;
    select tf.* from (select 0) t lateral view explode(array('A','B','C')) tf;
    select tf.* from (select 0) t lateral view explode(array('A','B','C')) tf as col;
    -- explode (map)
    select explode(map('A',10,'B',20,'C',30));
    select explode(map('A',10,'B',20,'C',30)) as (key,value);
    select tf.* from (select 0) t lateral view explode(map('A',10,'B',20,'C',30)) tf;
    select tf.* from (select 0) t lateral view explode(map('A',10,'B',20,'C',30)) tf as key,value;

测试记录:

hive> 
        > 
        > 
        > select explode(array('A','B','C'));
    Query ID = root_20201210161223_3527486d-76e5-4306-b3aa-8521f89774d9
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0150, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0150/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0150
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-10 16:12:30,620 Stage-1 map = 0%,  reduce = 0%
    2020-12-10 16:12:37,839 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.92 sec
    MapReduce Total cumulative CPU time: 2 seconds 920 msec
    Ended Job = job_1606698967173_0150
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 2.92 sec   HDFS Read: 5007 HDFS Write: 129 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 920 msec
    OK
    col
    A
    B
    C
    Time taken: 15.401 seconds, Fetched: 3 row(s)
    hive> select explode(array('A','B','C')) as col;
    Query ID = root_20201210161249_acc85662-e686-4176-a5e8-032ed0992a45
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0151, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0151/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0151
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-10 16:12:56,243 Stage-1 map = 0%,  reduce = 0%
    2020-12-10 16:13:02,434 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.8 sec
    MapReduce Total cumulative CPU time: 2 seconds 800 msec
    Ended Job = job_1606698967173_0151
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 2.8 sec   HDFS Read: 5007 HDFS Write: 129 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 800 msec
    OK
    col
    A
    B
    C
    Time taken: 14.35 seconds, Fetched: 3 row(s)
    hive> select tf.* from (select 0) t lateral view explode(array('A','B','C')) tf;
    Query ID = root_20201210161305_5012fe10-4599-4ffe-838f-b867d212a10a
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0152, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0152/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0152
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-10 16:13:14,529 Stage-1 map = 0%,  reduce = 0%
    2020-12-10 16:13:20,716 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.86 sec
    MapReduce Total cumulative CPU time: 2 seconds 860 msec
    Ended Job = job_1606698967173_0152
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 2.86 sec   HDFS Read: 5764 HDFS Write: 129 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 860 msec
    OK
    tf.col
    A
    B
    C
    Time taken: 16.663 seconds, Fetched: 3 row(s)
    hive> select tf.* from (select 0) t lateral view explode(array('A','B','C')) tf as col;
    Query ID = root_20201210161324_8f1bcaa5-931d-49c1-8dbd-5da936bfa67f
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0153, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0153/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0153
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-10 16:13:32,968 Stage-1 map = 0%,  reduce = 0%
    2020-12-10 16:13:40,179 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.99 sec
    MapReduce Total cumulative CPU time: 2 seconds 990 msec
    Ended Job = job_1606698967173_0153
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 2.99 sec   HDFS Read: 5778 HDFS Write: 129 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 990 msec
    OK
    tf.col
    A
    B
    C
    Time taken: 17.102 seconds, Fetched: 3 row(s)
    hive> select explode(map('A',10,'B',20,'C',30));
    Query ID = root_20201210161354_1c522046-cac8-4182-9e28-9789a17c1a98
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0154, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0154/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0154
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-10 16:14:00,978 Stage-1 map = 0%,  reduce = 0%
    2020-12-10 16:14:08,199 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.96 sec
    MapReduce Total cumulative CPU time: 2 seconds 960 msec
    Ended Job = job_1606698967173_0154
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 2.96 sec   HDFS Read: 5144 HDFS Write: 138 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 960 msec
    OK
    key     value
    A       10
    B       20
    C       30
    Time taken: 15.114 seconds, Fetched: 3 row(s)
    hive> select explode(map('A',10,'B',20,'C',30)) as (key,value);
    Query ID = root_20201210161411_59b615ac-2733-40c2-bec4-1a17f593140f
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0155, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0155/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0155
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-10 16:14:19,606 Stage-1 map = 0%,  reduce = 0%
    2020-12-10 16:14:25,805 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.95 sec
    MapReduce Total cumulative CPU time: 2 seconds 950 msec
    Ended Job = job_1606698967173_0155
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 2.95 sec   HDFS Read: 5144 HDFS Write: 138 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 950 msec
    OK
    key     value
    A       10
    B       20
    C       30
    Time taken: 15.767 seconds, Fetched: 3 row(s)
    hive> select tf.* from (select 0) t lateral view explode(map('A',10,'B',20,'C',30)) tf;
    Query ID = root_20201210161428_5b45482b-39c0-4a6f-9e67-ec640e03d816
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0156, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0156/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0156
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-10 16:14:38,792 Stage-1 map = 0%,  reduce = 0%
    2020-12-10 16:14:46,098 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.92 sec
    MapReduce Total cumulative CPU time: 2 seconds 920 msec
    Ended Job = job_1606698967173_0156
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 2.92 sec   HDFS Read: 5987 HDFS Write: 138 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 920 msec
    OK
    tf.key  tf.value
    A       10
    B       20
    C       30
    Time taken: 18.456 seconds, Fetched: 3 row(s)
    hive> select tf.* from (select 0) t lateral view explode(map('A',10,'B',20,'C',30)) tf;
    Query ID = root_20201210161448_4ebe123f-57b7-4869-9e0e-557cccb27a10
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1606698967173_0157, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0157/
    Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0157
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2020-12-10 16:14:57,112 Stage-1 map = 0%,  reduce = 0%
    2020-12-10 16:15:03,299 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.74 sec
    MapReduce Total cumulative CPU time: 2 seconds 740 msec
    Ended Job = job_1606698967173_0157
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 2.74 sec   HDFS Read: 5987 HDFS Write: 138 HDFS EC Read: 0 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 740 msec
    OK
    tf.key  tf.value
    A       10
    B       20
    C       30
    Time taken: 16.725 seconds, Fetched: 3 row(s)
    hive>

# 参考 #

1.https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF\#LanguageManualUDF-MathematicalFunctions