• Sparksql的内置函数的使用以及案例


    开发环境:spark:2.2.0

         工具:IDEA

         OS:Windows

    数据文件:

    001E8CB5AB11,ASUSTek,2018-07-12 14:00:57,2018-07-12 14:00:57,2018-07-12 14:00:57,未知,僵尸屏,0
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    20EE28579BA6,其他,2018-07-12 14:01:30,2018-07-12 14:01:30,2018-07-12 14:01:30,北京市-朝阳区,国贸CBD小屏组,0
    20EE28881E8B,其他,2018-07-12 13:58:52,2018-07-12 13:58:52,2018-07-12 14:05:46,北京市-朝阳区,国贸CBD小屏组,414
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    286C0722AB5B,Xiaomi,2018-07-12 13:59:01,2018-07-12 13:59:01,2018-07-12 14:06:42,未知,僵尸屏,461
    286C07A9E76D,Xiaomi,2018-07-12 13:58:54,2018-07-12 13:58:54,2018-07-12 14:06:23,未知,僵尸屏,449
    28D244480987,其他,2018-07-12 14:05:01,2018-07-12 14:05:01,2018-07-12 14:05:01,未知,僵尸屏,0
    28D244F7FB6D,其他,2018-07-12 14:04:38,2018-07-12 14:04:38,2018-07-12 14:04:38,未知,僵尸屏,0
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    2C200BCF44CE,Apple,2018-07-12 13:59:15,2018-07-12 13:59:15,2018-07-12 13:59:15,未知,僵尸屏,0
    2C27D71ED30F,其他,2018-07-12 14:06:03,2018-07-12 14:06:03,2018-07-12 14:06:03,未知,僵尸屏,0
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    300ED5EE5DD5,HonHai,2018-07-12 13:58:52,2018-07-12 13:58:52,2018-07-12 14:06:43,未知,僵尸屏,471
    300ED5EE5E90,HonHai,2018-07-12 13:58:55,2018-07-12 13:58:55,2018-07-12 14:06:43,未知,僵尸屏,468
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    300ED5EE5F73,HonHai,2018-07-12 13:58:53,2018-07-12 13:58:53,2018-07-12 14:06:37,未知,僵尸屏,464
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    300ED5EE5FEB,HonHai,2018-07-12 13:58:53,2018-07-12 13:58:53,2018-07-12 14:06:35,未知,僵尸屏,462
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    333300000001,其他,2018-07-12 13:59:04,2018-07-12 13:59:04,2018-07-12 14:02:35,未知,僵尸屏,211
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    3333FF4FD8E3,其他,2018-07-12 14:01:07,2018-07-12 14:01:07,2018-07-12 14:01:07,未知,僵尸屏,0
    3423870149D0,HonHai,2018-07-12 13:58:59,2018-07-12 13:58:59,2018-07-12 14:06:41,北京市-海定区,中关村鼎好电子大厦屏,462
    342387458657,HonHai,2018-07-12 13:59:01,2018-07-12 13:59:01,2018-07-12 14:05:55,四川省-成都市,人民南路跳伞塔数码大厦屏,414
    34CE00365F52,Xiaomi,2018-07-12 13:59:04,2018-07-12 13:59:04,2018-07-12 14:06:42,未知,僵尸屏,458
    34CE00365F53,Xiaomi,2018-07-12 14:02:45,2018-07-12 14:02:45,2018-07-12 14:02:45,未知,僵尸屏,0
    34CE008C161A,Xiaomi,2018-07-12 14:02:32,2018-07-12 14:02:32,2018-07-12 14:05:41,未知,僵尸屏,189
    34CE00A5F9E0,Xiaomi,2018-07-12 13:59:11,2018-07-12 13:59:11,2018-07-12 14:06:29,未知,僵尸屏,438
    34F39A7E5833,其他,2018-07-12 14:02:41,2018-07-12 14:02:41,2018-07-12 14:06:41,北京市-朝阳区,国贸CBD小屏组,240
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    5CE0C519E74A,其他,2018-07-12 13:58:56,2018-07-12 13:58:56,2018-07-12 14:06:41,未知,僵尸屏,465
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    617461010802,其他,2018-07-12 13:59:53,2018-07-12 13:59:53,2018-07-12 13:59:53,未知,僵尸屏,0
    626126E68717,其他,2018-07-12 14:04:59,2018-07-12 14:04:59,2018-07-12 14:04:59,未知-未知,广播,0
    62A0A77197ED,其他,2018-07-12 14:01:37,2018-07-12 14:01:37,2018-07-12 14:01:37,未知-未知,广播,0
    64A2F924BBF5,其他,2018-07-12 13:58:54,2018-07-12 13:58:54,2018-07-12 14:04:57,北京市-朝阳区,国贸CBD小屏组,363
    64B0A61B5116,Apple,2018-07-12 13:59:03,2018-07-12 13:59:03,2018-07-12 14:06:21,北京市-朝阳区,国贸CBD小屏组,438
    66499E6E806C,其他,2018-07-12 13:59:04,2018-07-12 13:59:04,2018-07-12 13:59:04,未知-未知,广播,0
    66FACD823429,其他,2018-07-12 14:04:03,2018-07-12 14:04:03,2018-07-12 14:04:04,未知-未知,广播,1
    68AB1E1E476B,其他,2018-07-12 14:03:27,2018-07-12 14:03:27,2018-07-12 14:03:28,未知-未知,广播,1
    68DBCA4B2398,Apple,2018-07-12 14:01:09,2018-07-12 14:01:09,2018-07-12 14:01:09,北京市-朝阳区,国贸CBD小屏组,0
    68F728099B93,其他,2018-07-12 13:58:57,2018-07-12 13:58:57,2018-07-12 14:04:29,未知,僵尸屏,332
    68F72809A006,其他,2018-07-12 14:01:56,2018-07-12 14:01:56,2018-07-12 14:01:56,未知,僵尸屏,0
    68F7281D8047,其他,2018-07-12 13:58:54,2018-07-12 13:58:54,2018-07-12 14:04:19,未知,僵尸屏,325
    68F7283626B2,其他,2018-07-12 13:58:54,2018-07-12 13:58:54,2018-07-12 14:06:39,未知,僵尸屏,465
    68F7288D4ECE,其他,2018-07-12 14:04:16,2018-07-12 14:04:16,2018-07-12 14:04:22,未知,僵尸屏,6
    68F728A1D100,其他,2018-07-12 13:59:30,2018-07-12 13:59:30,2018-07-12 14:04:29,未知,僵尸屏,299
    68F728BA1A3F,其他,2018-07-12 13:59:07,2018-07-12 13:59:07,2018-07-12 14:05:50,未知,僵尸屏,403
    68F728C18AFE,其他,2018-07-12 13:59:10,2018-07-12 13:59:10,2018-07-12 13:59:10,未知,僵尸屏,0
    68F728C23AC3,其他,2018-07-12 14:02:45,2018-07-12 14:02:45,2018-07-12 14:06:35,未知,僵尸屏,230
    6A2B0D59EED7,其他,2018-07-12 14:04:28,2018-07-12 14:04:28,2018-07-12 14:04:28,未知-未知,广播,0
    6AB4554CF30C,其他,2018-07-12 14:02:38,2018-07-12 14:02:38,2018-07-12 14:02:38,未知-未知,广播,0
    6C72E73965AC,Apple,2018-07-12 13:59:15,2018-07-12 13:59:15,2018-07-12 14:00:50,未知-未知,广播,95
    6C72E7594DEB,Apple,2018-07-12 14:02:32,2018-07-12 14:02:32,2018-07-12 14:02:34,北京市-朝阳区,国贸CBD小屏组,2
    6CC217226B22,其他,2018-07-12 14:00:25,2018-07-12 14:00:25,2018-07-12 14:02:54,未知,僵尸屏,149
    6EC677CF6B23,其他,2018-07-12 14:01:45,2018-07-12 14:01:45,2018-07-12 14:01:45,未知-未知,广播,0
    70480F1BE7A8,Apple,2018-07-12 13:58:59,2018-07-12 13:58:59,2018-07-12 14:05:43,未知-未知,广播,404
    70480F476EC1,Apple,2018-07-12 14:01:24,2018-07-12 14:01:24,2018-07-12 14:06:29,北京市-朝阳区,国贸CBD小屏组,305
    70F08783BECD,Apple,2018-07-12 13:58:54,2018-07-12 13:58:54,2018-07-12 14:06:38,北京市-朝阳区,国贸CBD小屏组,464
    727D7D76FEB1,其他,2018-07-12 14:02:22,2018-07-12 14:02:22,2018-07-12 14:02:23,未知-未知,广播,1
    74A528BCAABD,HUAWEI,2018-07-12 13:58:55,2018-07-12 13:58:55,2018-07-12 14:04:32,北京市-朝阳区,国贸CBD小屏组,337
    7673F615363F,其他,2018-07-12 14:02:11,2018-07-12 14:02:11,2018-07-12 14:02:11,未知-未知,广播,0
    7811DC483347,Xiaomi,2018-07-12 13:59:14,2018-07-12 13:59:14,2018-07-12 14:01:04,未知,僵尸屏,110
    784F43BD2CE3,Apple,2018-07-12 13:59:24,2018-07-12 13:59:24,2018-07-12 14:01:18,未知,僵尸屏,114
    784F43C1618D,Apple,2018-07-12 13:59:25,2018-07-12 13:59:25,2018-07-12 14:05:55,北京市-朝阳区,国贸CBD小屏组,390
    78ACC0B47587,其他,2018-07-12 13:59:00,2018-07-12 13:59:00,2018-07-12 14:05:27,未知,僵尸屏,387
    78E3B5D20296,其他,2018-07-12 13:59:20,2018-07-12 13:59:20,2018-07-12 13:59:20,未知,僵尸屏,0
    78E7D1C2B47E,其他,2018-07-12 14:04:19,2018-07-12 14:04:19,2018-07-12 14:04:19,未知,僵尸屏,0
    78E7D1C58D0E,其他,2018-07-12 14:02:48,2018-07-12 14:02:48,2018-07-12 14:02:48,未知,僵尸屏,0
    78E7D1C58E24,其他,2018-07-12 14:01:23,2018-07-12 14:01:23,2018-07-12 14:01:23,未知,僵尸屏,0
    78E7D1D92219,其他,2018-07-12 14:01:40,2018-07-12 14:01:40,2018-07-12 14:03:53,未知,僵尸屏,133
    78E7D1D92235,其他,2018-07-12 14:02:38,2018-07-12 14:02:38,2018-07-12 14:02:38,未知,僵尸屏,0
    7A8210E5CA96,其他,2018-07-12 14:01:38,2018-07-12 14:01:38,2018-07-12 14:01:39,未知-未知,广播,1
    7AB3CDEF05D4,其他,2018-07-12 14:04:48,2018-07-12 14:04:48,2018-07-12 14:05:10,未知-未知,广播,22
    7E35C9726EBF,其他,2018-07-12 14:03:33,2018-07-12 14:03:33,2018-07-12 14:03:33,未知-未知,广播,0
    7E4AE2267C39,其他,2018-07-12 14:05:54,2018-07-12 14:05:54,2018-07-12 14:05:55,未知-未知,广播,1
    7EA6AEC5D494,其他,2018-07-12 14:03:16,2018-07-12 14:03:16,2018-07-12 14:03:22,未知-未知,广播,6
    7EFEA9FDAD1D,其他,2018-07-12 13:58:52,2018-07-12 13:58:52,2018-07-12 13:58:58,未知-未知,广播,6
    80717A5D00E7,HUAWEI,2018-07-12 14:00:16,2018-07-12 14:00:16,2018-07-12 14:06:01,未知,僵尸屏,345
    8243455B963D,其他,2018-07-12 14:02:35,2018-07-12 14:02:35,2018-07-12 14:02:35,未知-未知,广播,0
    826AD7ABB8A8,其他,2018-07-12 14:02:18,2018-07-12 14:02:18,2018-07-12 14:02:19,未知-未知,广播,1
    8441678BFAA3,其他,2018-07-12 14:03:35,2018-07-12 14:03:35,2018-07-12 14:03:41,北京市-朝阳区,国贸CBD小屏组,6
    865CAED88A76,其他,2018-07-12 14:06:04,2018-07-12 14:06:04,2018-07-12 14:06:05,未知-未知,广播,1
    86714590D43F,其他,2018-07-12 14:05:58,2018-07-12 14:05:58,2018-07-12 14:05:58,未知-未知,广播,0
    86814B2B907F,其他,2018-07-12 14:03:34,2018-07-12 14:03:34,2018-07-12 14:03:34,未知-未知,广播,0
    86E5DF74D12A,其他,2018-07-12 14:01:10,2018-07-12 14:01:10,2018-07-12 14:01:45,未知-未知,广播,35
    8AF35338839D,其他,2018-07-12 14:03:43,2018-07-12 14:03:43,2018-07-12 14:03:43,未知-未知,广播,0
    8C34FD58DAEA,HUAWEI,2018-07-12 13:59:49,2018-07-12 13:59:49,2018-07-12 14:04:24,北京市-朝阳区,国贸CBD小屏组,275
    8CA9823E26DC,其他,2018-07-12 13:59:02,2018-07-12 13:59:02,2018-07-12 14:06:42,未知,僵尸屏,460
    8CDCD42DDE4D,其他,2018-07-12 13:59:10,2018-07-12 13:59:10,2018-07-12 14:05:30,未知,僵尸屏,380
    8CDCD42DDE61,其他,2018-07-12 14:04:32,2018-07-12 14:04:32,2018-07-12 14:04:32,未知,僵尸屏,0
    8CDCD4301844,其他,2018-07-12 14:00:57,2018-07-12 14:00:57,2018-07-12 14:00:57,未知,僵尸屏,0
    8CDCD4301AB3,其他,2018-07-12 14:02:12,2018-07-12 14:02:12,2018-07-12 14:04:22,未知,僵尸屏,130
    8CDCD4301B1D,其他,2018-07-12 14:02:12,2018-07-12 14:02:12,2018-07-12 14:02:25,未知,僵尸屏,13
    8CDCD4301B2E,其他,2018-07-12 14:00:48,2018-07-12 14:00:48,2018-07-12 14:06:19,未知,僵尸屏,331
    8CDCD4311C78,其他,2018-07-12 13:59:17,2018-07-12 13:59:17,2018-07-12 13:59:17,未知,僵尸屏,0
    8CDCD4334EB8,其他,2018-07-12 14:02:35,2018-07-12 14:02:35,2018-07-12 14:02:35,未知,僵尸屏,0
    8CDCD4334F37,其他,2018-07-12 14:00:35,2018-07-12 14:00:35,2018-07-12 14:05:27,未知,僵尸屏,292
    8CDCD4334F6A,其他,2018-07-12 13:59:07,2018-07-12 13:59:07,2018-07-12 14:05:11,未知,僵尸屏,364
    8E26F8DC15EE,其他,2018-07-12 13:59:08,2018-07-12 13:59:08,2018-07-12 13:59:08,未知-未知,广播,0
    8E5F2226CD50,其他,2018-07-12 14:01:46,2018-07-12 14:01:46,2018-07-12 14:01:46,未知-未知,广播,0
    9202D3982BA3,其他,2018-07-12 14:03:27,2018-07-12 14:03:27,2018-07-12 14:03:40,未知-未知,广播,13
    963075096988,其他,2018-07-12 14:05:06,2018-07-12 14:05:06,2018-07-12 14:05:06,未知-未知,广播,0
    985AEBC6B4DF,Apple,2018-07-12 14:00:09,2018-07-12 14:00:09,2018-07-12 14:00:09,未知,僵尸屏,0
    9C2EA1FADCD1,其他,2018-07-12 13:59:28,2018-07-12 13:59:28,2018-07-12 14:06:31,未知-未知,广播,423
    9C443D1952FA,其他,2018-07-12 13:59:07,2018-07-12 13:59:07,2018-07-12 14:06:33,未知,僵尸屏,446
    9CA5C0BD6F6D,vivo,2018-07-12 14:00:00,2018-07-12 14:00:00,2018-07-12 14:00:45,四川省-成都市,人民南路跳伞塔数码大厦屏,45
    9CB6D00D99FD,其他,2018-07-12 13:58:58,2018-07-12 13:58:58,2018-07-12 14:06:37,未知-未知,广播,459
    9CE33F33F576,其他,2018-07-12 14:03:32,2018-07-12 14:03:32,2018-07-12 14:03:49,北京市-朝阳区,国贸CBD小屏组,17
    9CF48E3335F5,Apple,2018-07-12 14:01:13,2018-07-12 14:01:13,2018-07-12 14:06:33,北京市-朝阳区,国贸CBD小屏组,320
    9CF48E43079E,Apple,2018-07-12 13:59:01,2018-07-12 13:59:01,2018-07-12 14:06:07,未知,僵尸屏,426
    9CFC01682E78,Apple,2018-07-12 14:03:18,2018-07-12 14:03:18,2018-07-12 14:03:18,北京市-朝阳区,国贸CBD小屏组,0
    9E7EE62C840B,其他,2018-07-12 14:06:13,2018-07-12 14:06:13,2018-07-12 14:06:14,未知-未知,广播,1
    9E850CB1C147,其他,2018-07-12 14:05:22,2018-07-12 14:05:22,2018-07-12 14:05:22,未知-未知,广播,0
    9ED082F4539F,其他,2018-07-12 13:59:15,2018-07-12 13:59:15,2018-07-12 13:59:15,未知-未知,广播,0
    A0086F310909,HUAWEI,2018-07-12 13:59:47,2018-07-12 13:59:47,2018-07-12 14:05:54,北京市-朝阳区,国贸CBD小屏组,367
    A0086F31090A,HUAWEI,2018-07-12 13:58:56,2018-07-12 13:58:56,2018-07-12 14:06:37,北京市-海定区,中关村鼎好电子大厦屏,461
    A0086F31090E,HUAWEI,2018-07-12 13:58:53,2018-07-12 13:58:53,2018-07-12 14:06:09,北京市-朝阳区,国贸CBD小屏组,436
    A057E36FD3E6,其他,2018-07-12 13:58:54,2018-07-12 13:58:54,2018-07-12 14:05:55,北京市-朝阳区,国贸CBD小屏组,421
    A20D43A7B97D,其他,2018-07-12 14:03:12,2018-07-12 14:03:12,2018-07-12 14:05:36,未知-未知,广播,144
    A211DDAB06FB,其他,2018-07-12 13:59:02,2018-07-12 13:59:02,2018-07-12 13:59:02,未知-未知,广播,0
    A2578286415A,其他,2018-07-12 14:02:38,2018-07-12 14:02:38,2018-07-12 14:02:38,未知-未知,广播,0
    A25FA9400829,其他,2018-07-12 14:06:43,2018-07-12 14:06:43,2018-07-12 14:06:44,未知-未知,广播,1
    A41731F375EB,HonHai,2018-07-12 14:03:03,2018-07-12 14:03:03,2018-07-12 14:03:03,未知-未知,广播,0
    A493FE3F6F08,其他,2018-07-12 13:58:52,2018-07-12 13:58:52,2018-07-12 14:06:43,未知,僵尸屏,471
    A4CAA00A81C7,HUAWEI,2018-07-12 14:05:27,2018-07-12 14:05:27,2018-07-12 14:05:27,未知,僵尸屏,0
    A679D542B052,其他,2018-07-12 14:02:35,2018-07-12 14:02:35,2018-07-12 14:02:35,未知-未知,广播,0
    A67E6B8751A9,其他,2018-07-12 14:06:35,2018-07-12 14:06:35,2018-07-12 14:06:35,未知-未知,广播,0
    A6FFFFFF404A,其他,2018-07-12 14:04:32,2018-07-12 14:04:32,2018-07-12 14:04:32,北京市-朝阳区,国贸CBD小屏组,0
    A8667F624EE6,Apple,2018-07-12 14:00:33,2018-07-12 14:00:33,2018-07-12 14:00:33,北京市-朝阳区,国贸CBD小屏组,0
    A8FFFFFF404A,其他,2018-07-12 14:00:01,2018-07-12 14:00:01,2018-07-12 14:00:01,北京市-海定区,中关村鼎好电子大厦屏,0
    A9FFFFFF6F08,其他,2018-07-12 14:01:43,2018-07-12 14:01:43,2018-07-12 14:01:43,北京市-朝阳区,国贸CBD小屏组,0
    AACCBD34BAD6,其他,2018-07-12 14:04:01,2018-07-12 14:04:01,2018-07-12 14:04:01,未知-未知,广播,0
    AAE7B712600A,其他,2018-07-12 14:03:17,2018-07-12 14:03:17,2018-07-12 14:03:20,未知-未知,广播,3
    AAFFFFFF6F08,其他,2018-07-12 14:02:00,2018-07-12 14:02:00,2018-07-12 14:03:26,北京市-朝阳区,国贸CBD小屏组,86
    ABFFFFFF6F08,其他,2018-07-12 14:01:04,2018-07-12 14:01:04,2018-07-12 14:02:24,北京市-朝阳区,国贸CBD小屏组,80
    ACFFFFFF404A,其他,2018-07-12 14:00:36,2018-07-12 14:00:36,2018-07-12 14:00:36,未知,僵尸屏,0
    ADFFFFFF6F08,其他,2018-07-12 14:01:45,2018-07-12 14:01:45,2018-07-12 14:01:45,北京市-朝阳区,国贸CBD小屏组,0
    AE3DA8C9C025,其他,2018-07-12 14:03:24,2018-07-12 14:03:24,2018-07-12 14:03:40,未知-未知,广播,16
    AE862332942D,其他,2018-07-12 14:06:33,2018-07-12 14:06:33,2018-07-12 14:06:34,未知-未知,广播,1
    AE8AD572A19E,其他,2018-07-12 14:04:15,2018-07-12 14:04:15,2018-07-12 14:04:15,未知-未知,广播,0
    AEFFFFFF404A,其他,2018-07-12 14:02:50,2018-07-12 14:02:50,2018-07-12 14:02:50,北京市-海定区,中关村鼎好电子大厦屏,0
    AEFFFFFF6F08,其他,2018-07-12 14:01:17,2018-07-12 14:01:17,2018-07-12 14:05:44,北京市-朝阳区,国贸CBD小屏组,267
    AFFFFFFF404A,其他,2018-07-12 14:04:21,2018-07-12 14:04:21,2018-07-12 14:04:21,北京市-朝阳区,国贸CBD小屏组,0
    AFFFFFFF6F08,其他,2018-07-12 14:00:13,2018-07-12 14:00:13,2018-07-12 14:06:26,北京市-朝阳区,国贸CBD小屏组,373
    B0FFFFFF404A,其他,2018-07-12 13:59:22,2018-07-12 13:59:22,2018-07-12 14:06:15,北京市-海定区,中关村鼎好电子大厦屏,413
    B0FFFFFF6F08,其他,2018-07-12 14:00:14,2018-07-12 14:00:14,2018-07-12 14:05:21,北京市-海定区,中关村鼎好电子大厦屏,307
    B1FFFFFF404A,其他,2018-07-12 14:00:51,2018-07-12 14:00:51,2018-07-12 14:00:51,北京市-朝阳区,国贸CBD小屏组,0
    B1FFFFFF6F08,其他,2018-07-12 14:00:24,2018-07-12 14:00:24,2018-07-12 14:02:11,未知,僵尸屏,107
    B279695FAE3C,其他,2018-07-12 14:02:44,2018-07-12 14:02:44,2018-07-12 14:02:44,未知-未知,广播,0
    B29508D7A6F8,其他,2018-07-12 14:04:04,2018-07-12 14:04:04,2018-07-12 14:04:04,未知-未知,广播,0
    B2C08E9AD237,其他,2018-07-12 14:02:46,2018-07-12 14:02:46,2018-07-12 14:02:46,未知-未知,广播,0
    B2F2FC1243C0,其他,2018-07-12 14:02:31,2018-07-12 14:02:31,2018-07-12 14:02:31,未知-未知,广播,0
    B2FBB64DCF85,其他,2018-07-12 14:00:31,2018-07-12 14:00:31,2018-07-12 14:00:31,未知-未知,广播,0
    B2FFFFFF404A,其他,2018-07-12 14:03:52,2018-07-12 14:03:52,2018-07-12 14:06:41,北京市-海定区,中关村鼎好电子大厦屏,169
    B3FFFFFF404A,其他,2018-07-12 14:02:47,2018-07-12 14:02:47,2018-07-12 14:02:47,未知,僵尸屏,0
    B3FFFFFF6F08,其他,2018-07-12 14:00:25,2018-07-12 14:00:25,2018-07-12 14:00:25,北京市-朝阳区,国贸CBD小屏组,0
    B4E62D2FBC25,其他,2018-07-12 13:59:28,2018-07-12 13:59:28,2018-07-12 13:59:30,未知-未知,广播,2
    B4FFFFFF404A,其他,2018-07-12 14:00:52,2018-07-12 14:00:52,2018-07-12 14:00:52,未知,僵尸屏,0
    B4FFFFFF6F08,其他,2018-07-12 14:03:22,2018-07-12 14:03:22,2018-07-12 14:03:22,未知,僵尸屏,0
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    DAA119FE7089,其他,2018-07-12 14:01:21,2018-07-12 14:01:21,2018-07-12 14:01:22,未知-未知,广播,1
    DC53604C66C0,其他,2018-07-12 13:59:16,2018-07-12 13:59:16,2018-07-12 14:06:33,未知-未知,广播,437
    DE2E36FEE82E,其他,2018-07-12 14:00:38,2018-07-12 14:00:38,2018-07-12 14:00:56,未知-未知,广播,18
    DEF19D23F154,其他,2018-07-12 14:02:38,2018-07-12 14:02:38,2018-07-12 14:02:38,未知-未知,广播,0
    E006E6CE8F97,HonHai,2018-07-12 14:00:50,2018-07-12 14:00:50,2018-07-12 14:05:50,未知-未知,广播,300
    E006E6CE9A0E,HonHai,2018-07-12 13:58:58,2018-07-12 13:58:58,2018-07-12 14:06:33,未知,僵尸屏,455
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    E6F613C30991,其他,2018-07-12 13:59:05,2018-07-12 13:59:05,2018-07-12 13:59:05,未知-未知,广播,0
    E8802EF16712,Apple,2018-07-12 14:01:12,2018-07-12 14:01:12,2018-07-12 14:05:36,未知,僵尸屏,264
    E8B1FC4CA239,其他,2018-07-12 13:59:06,2018-07-12 13:59:06,2018-07-12 14:05:48,未知-未知,广播,402
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    E8B1FC5AC445,其他,2018-07-12 14:03:26,2018-07-12 14:03:26,2018-07-12 14:05:03,北京市-朝阳区,国贸CBD小屏组,97
    E8B1FCAD1F4C,其他,2018-07-12 13:58:53,2018-07-12 13:58:53,2018-07-12 14:05:32,未知,僵尸屏,399
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    EE170A33F722,其他,2018-07-12 14:01:48,2018-07-12 14:01:48,2018-07-12 14:01:48,未知-未知,广播,0
    F0B4290F5DEC,Xiaomi,2018-07-12 14:01:56,2018-07-12 14:01:56,2018-07-12 14:04:32,未知,僵尸屏,156
    F0B429CDDA00,Xiaomi,2018-07-12 13:59:14,2018-07-12 13:59:14,2018-07-12 14:06:33,未知,僵尸屏,439
    F2231510B8B8,其他,2018-07-12 14:03:23,2018-07-12 14:03:23,2018-07-12 14:03:23,未知-未知,广播,0
    F242B220867E,其他,2018-07-12 13:59:00,2018-07-12 13:59:00,2018-07-12 13:59:00,未知-未知,广播,0
    F48C500B6F6C,其他,2018-07-12 13:59:38,2018-07-12 13:59:38,2018-07-12 14:01:37,未知,僵尸屏,119
    F60F8D888CED,其他,2018-07-12 13:59:16,2018-07-12 13:59:16,2018-07-12 13:59:16,未知-未知,广播,0
    F6E4DA3E9024,其他,2018-07-12 14:06:05,2018-07-12 14:06:05,2018-07-12 14:06:05,未知-未知,广播,0
    F6F3B944683D,其他,2018-07-12 14:02:04,2018-07-12 14:02:04,2018-07-12 14:02:04,未知-未知,广播,0
    FA5360AD50CC,其他,2018-07-12 14:02:15,2018-07-12 14:02:15,2018-07-12 14:02:15,未知-未知,广播,0
    FA8B4C2A31A4,其他,2018-07-12 14:05:52,2018-07-12 14:05:52,2018-07-12 14:05:53,未知-未知,广播,1
    FCD848C807B5,Apple,2018-07-12 14:00:41,2018-07-12 14:00:41,2018-07-12 14:06:32,北京市-朝阳区,国贸CBD小屏组,351
    FE305C782386,其他,2018-07-12 14:02:50,2018-07-12 14:02:50,2018-07-12 14:02:50,未知-未知,广播,0
    FE519EDD25D9,其他,2018-07-12 14:03:31,2018-07-12 14:03:31,2018-07-12 14:03:31,未知-未知,广播,0
    FEC0608F30DC,其他,2018-07-12 14:02:51,2018-07-12 14:02:51,2018-07-12 14:02:51,未知-未知,广播,0
    FEDE7582DCAF,其他,2018-07-12 14:01:12,2018-07-12 14:01:12,2018-07-12 14:01:12,未知-未知,广播,0
    FEEBB23B99E6,其他,2018-07-12 14:01:56,2018-07-12 14:01:56,2018-07-12 14:01:56,未知-未知,广播,0
    

    本次所有示例的函数有:

      1 // misc non-aggregate functions
      2     expression[Abs]("abs"),
      3     expression[Coalesce]("coalesce"),
      4     expression[Explode]("explode"),
      5     expression[Greatest]("greatest"),
      6     expression[If]("if"),
      7     expression[Inline]("inline"),
      8     expression[IsNaN]("isnan"),
      9     expression[IfNull]("ifnull"),
     10     expression[IsNull]("isnull"),
     11     expression[IsNotNull]("isnotnull"),
     12     expression[Least]("least"),
     13     expression[NaNvl]("nanvl"),
     14     expression[NullIf]("nullif"),
     15     expression[Nvl]("nvl"),
     16     expression[Nvl2]("nvl2"),
     17     expression[PosExplode]("posexplode"),
     18     expression[Rand]("rand"),
     19     expression[Randn]("randn"),
     20     expression[Stack]("stack"),
     21     expression[CaseWhen]("when"),
     22 
     23     // math functions
     24     expression[Acos]("acos"),
     25     expression[Asin]("asin"),
     26     expression[Atan]("atan"),
     27     expression[Atan2]("atan2"),
     28     expression[Bin]("bin"),
     29     expression[BRound]("bround"),
     30     expression[Cbrt]("cbrt"),
     31     expression[Ceil]("ceil"),
     32     expression[Ceil]("ceiling"),
     33     expression[Cos]("cos"),
     34     expression[Cosh]("cosh"),
     35     expression[Conv]("conv"),
     36     expression[ToDegrees]("degrees"),
     37     expression[EulerNumber]("e"),
     38     expression[Exp]("exp"),
     39     expression[Expm1]("expm1"),
     40     expression[Floor]("floor"),
     41     expression[Factorial]("factorial"),
     42     expression[Hex]("hex"),
     43     expression[Hypot]("hypot"),
     44     expression[Logarithm]("log"),
     45     expression[Log10]("log10"),
     46     expression[Log1p]("log1p"),
     47     expression[Log2]("log2"),
     48     expression[Log]("ln"),
     49     expression[UnaryMinus]("negative"),
     50     expression[Pi]("pi"),
     51     expression[Pmod]("pmod"),
     52     expression[UnaryPositive]("positive"),
     53     expression[Pow]("pow"),
     54     expression[Pow]("power"),
     55     expression[ToRadians]("radians"),
     56     expression[Rint]("rint"),
     57     expression[Round]("round"),
     58     expression[ShiftLeft]("shiftleft"),
     59     expression[ShiftRight]("shiftright"),
     60     expression[ShiftRightUnsigned]("shiftrightunsigned"),
     61     expression[Signum]("sign"),
     62     expression[Signum]("signum"),
     63     expression[Sin]("sin"),
     64     expression[Sinh]("sinh"),
     65     expression[StringToMap]("str_to_map"),
     66     expression[Sqrt]("sqrt"),
     67     expression[Tan]("tan"),
     68     expression[Tanh]("tanh"),
     69 
     70     expression[Add]("+"),
     71     expression[Subtract]("-"),
     72     expression[Multiply]("*"),
     73     expression[Divide]("/"),
     74     expression[Remainder]("%"),
     75 
     76     // aggregate functions
     77     expression[HyperLogLogPlusPlus]("approx_count_distinct"),
     78     expression[Average]("avg"),
     79     expression[Corr]("corr"),
     80     expression[Count]("count"),
     81     expression[CovPopulation]("covar_pop"),
     82     expression[CovSample]("covar_samp"),
     83     expression[First]("first"),
     84     expression[First]("first_value"),
     85     expression[Kurtosis]("kurtosis"),
     86     expression[Last]("last"),
     87     expression[Last]("last_value"),
     88     expression[Max]("max"),
     89     expression[Average]("mean"),
     90     expression[Min]("min"),
     91     expression[Percentile]("percentile"),
     92     expression[Skewness]("skewness"),
     93     expression[ApproximatePercentile]("percentile_approx"),
     94     expression[ApproximatePercentile]("approx_percentile"),
     95     expression[StddevSamp]("std"),
     96     expression[StddevSamp]("stddev"),
     97     expression[StddevPop]("stddev_pop"),
     98     expression[StddevSamp]("stddev_samp"),
     99     expression[Sum]("sum"),
    100     expression[VarianceSamp]("variance"),
    101     expression[VariancePop]("var_pop"),
    102     expression[VarianceSamp]("var_samp"),
    103     expression[CollectList]("collect_list"),
    104     expression[CollectSet]("collect_set"),
    105 
    106     // string functions
    107     expression[Ascii]("ascii"),
    108     expression[Base64]("base64"),
    109     expression[Concat]("concat"),
    110     expression[ConcatWs]("concat_ws"),
    111     expression[Decode]("decode"),
    112     expression[Elt]("elt"),
    113     expression[Encode]("encode"),
    114     expression[FindInSet]("find_in_set"),
    115     expression[FormatNumber]("format_number"),
    116     expression[FormatString]("format_string"),
    117     expression[GetJsonObject]("get_json_object"),
    118     expression[InitCap]("initcap"),
    119     expression[StringInstr]("instr"),
    120     expression[Lower]("lcase"),
    121     expression[Length]("length"),
    122     expression[Levenshtein]("levenshtein"),
    123     expression[Like]("like"),
    124     expression[Lower]("lower"),
    125     expression[StringLocate]("locate"),
    126     expression[StringLPad]("lpad"),
    127     expression[StringTrimLeft]("ltrim"),
    128     expression[JsonTuple]("json_tuple"),
    129     expression[ParseUrl]("parse_url"),
    130     expression[FormatString]("printf"),
    131     expression[RegExpExtract]("regexp_extract"),
    132     expression[RegExpReplace]("regexp_replace"),
    133     expression[StringRepeat]("repeat"),
    134     expression[StringReverse]("reverse"),
    135     expression[RLike]("rlike"),
    136     expression[StringRPad]("rpad"),
    137     expression[StringTrimRight]("rtrim"),
    138     expression[Sentences]("sentences"),
    139     expression[SoundEx]("soundex"),
    140     expression[StringSpace]("space"),
    141     expression[StringSplit]("split"),
    142     expression[Substring]("substr"),
    143     expression[Substring]("substring"),
    144     expression[SubstringIndex]("substring_index"),
    145     expression[StringTranslate]("translate"),
    146     expression[StringTrim]("trim"),
    147     expression[Upper]("ucase"),
    148     expression[UnBase64]("unbase64"),
    149     expression[Unhex]("unhex"),
    150     expression[Upper]("upper"),
    151     expression[XPathList]("xpath"),
    152     expression[XPathBoolean]("xpath_boolean"),
    153     expression[XPathDouble]("xpath_double"),
    154     expression[XPathDouble]("xpath_number"),
    155     expression[XPathFloat]("xpath_float"),
    156     expression[XPathInt]("xpath_int"),
    157     expression[XPathLong]("xpath_long"),
    158     expression[XPathShort]("xpath_short"),
    159     expression[XPathString]("xpath_string"),
    160 
    161     // datetime functions
    162     expression[AddMonths]("add_months"),
    163     expression[CurrentDate]("current_date"),
    164     expression[CurrentTimestamp]("current_timestamp"),
    165     expression[DateDiff]("datediff"),
    166     expression[DateAdd]("date_add"),
    167     expression[DateFormatClass]("date_format"),
    168     expression[DateSub]("date_sub"),
    169     expression[DayOfMonth]("day"),
    170     expression[DayOfYear]("dayofyear"),
    171     expression[DayOfMonth]("dayofmonth"),
    172     expression[FromUnixTime]("from_unixtime"),
    173     expression[FromUTCTimestamp]("from_utc_timestamp"),
    174     expression[Hour]("hour"),
    175     expression[LastDay]("last_day"),
    176     expression[Minute]("minute"),
    177     expression[Month]("month"),
    178     expression[MonthsBetween]("months_between"),
    179     expression[NextDay]("next_day"),
    180     expression[CurrentTimestamp]("now"),
    181     expression[Quarter]("quarter"),
    182     expression[Second]("second"),
    183     expression[ToDate]("to_date"),
    184     expression[ToUnixTimestamp]("to_unix_timestamp"),
    185     expression[ToUTCTimestamp]("to_utc_timestamp"),
    186     expression[TruncDate]("trunc"),
    187     expression[UnixTimestamp]("unix_timestamp"),
    188     expression[WeekOfYear]("weekofyear"),
    189     expression[Year]("year"),
    190     expression[TimeWindow]("window"),
    191 
    192     // collection functions
    193     expression[CreateArray]("array"),
    194     expression[ArrayContains]("array_contains"),
    195     expression[CreateMap]("map"),
    196     expression[CreateNamedStruct]("named_struct"),
    197     expression[MapKeys]("map_keys"),
    198     expression[MapValues]("map_values"),
    199     expression[Size]("size"),
    200     expression[SortArray]("sort_array"),
    201     CreateStruct.registryEntry,
    202 
    203     // misc functions
    204     expression[AssertTrue]("assert_true"),
    205     expression[Crc32]("crc32"),
    206     expression[Md5]("md5"),
    207     expression[Murmur3Hash]("hash"),
    208     expression[Sha1]("sha"),
    209     expression[Sha1]("sha1"),
    210     expression[Sha2]("sha2"),
    211     expression[SparkPartitionID]("spark_partition_id"),
    212     expression[InputFileName]("input_file_name"),
    213     expression[MonotonicallyIncreasingID]("monotonically_increasing_id"),
    214     expression[CurrentDatabase]("current_database"),
    215     expression[CallMethodViaReflection]("reflect"),
    216     expression[CallMethodViaReflection]("java_method"),
    217 
    218     // grouping sets
    219     expression[Cube]("cube"),
    220     expression[Rollup]("rollup"),
    221     expression[Grouping]("grouping"),
    222     expression[GroupingID]("grouping_id"),
    223 
    224     // window functions
    225     expression[Lead]("lead"),
    226     expression[Lag]("lag"),
    227     expression[RowNumber]("row_number"),
    228     expression[CumeDist]("cume_dist"),
    229     expression[NTile]("ntile"),
    230     expression[Rank]("rank"),
    231     expression[DenseRank]("dense_rank"),
    232     expression[PercentRank]("percent_rank"),
    233 
    234     // predicates
    235     expression[And]("and"),
    236     expression[In]("in"),
    237     expression[Not]("not"),
    238     expression[Or]("or"),
    239 
    240     // comparison operators
    241     expression[EqualNullSafe]("<=>"),
    242     expression[EqualTo]("="),
    243     expression[EqualTo]("=="),
    244     expression[GreaterThan](">"),
    245     expression[GreaterThanOrEqual](">="),
    246     expression[LessThan]("<"),
    247     expression[LessThanOrEqual]("<="),
    248     expression[Not]("!"),
    249 
    250     // bitwise
    251     expression[BitwiseAnd]("&"),
    252     expression[BitwiseNot]("~"),
    253     expression[BitwiseOr]("|"),
    254     expression[BitwiseXor]("^"),
    255 
    256     // Cast aliases (SPARK-16730)
    257     castAlias("boolean", BooleanType),
    258     castAlias("tinyint", ByteType),
    259     castAlias("smallint", ShortType),
    260     castAlias("int", IntegerType),
    261     castAlias("bigint", LongType),
    262     castAlias("float", FloatType),
    263     castAlias("double", DoubleType),
    264     castAlias("decimal", DecimalType.USER_DEFAULT),
    265     castAlias("date", DateType),
    266     castAlias("timestamp", TimestampType),
    267     castAlias("binary", BinaryType),
    268     castAlias("string", StringType)

    Start

    import org.apache.spark.sql
    import org.apache.spark.sql.functions._

    object Query { val ssc
    = new sql.SparkSession .Builder() .appName("query") .master("local[2]") .getOrCreate() ssc.conf.set("spark.sql.shuffle.partitions", 6) ssc.conf.set("spark.executor.memory", "6g") ssc.sparkContext.setLogLevel("error")
    def main(args:Array[String]):Unit = {
    //读入文件并将其转换为DF val fileName = "file:///E:\\工作\\WifiData\\data\\" + args(0) + "visit.txt" val df_1 = ssc.read.option("headler", "false").option("inferschema", "true").csv(fileName) .toDF("mac", "phone_brand", "enter_time", "first_time", "last_time", "region", "screen", "stay_long")
    }

    注:在使用函数的时候最好要导入org.apache.spark.sql.functions._这个包

      import ssc.implicits._(这个包在实际写sql的时候将其导入)

    一、混合非聚合函数(misc non-aggregate functions)

      1、abs:绝对值 

    import ssc.implicits._
    df_1.selectExpr("abs(stay_long) as res_abs").show(5)
    

        

      2、coalesce:返回第一列部位空的列值,源码解释如下:

    /**Returns the first column that is not null, or null if all inputs are null.
    * For example, `coalesce(a, b, c)` will return a if a is not null,
    * or b if a is null and b is not null, or c if both a and b are null but c is not null.
    import ssc.implicits._
    df_1.selectExpr("coalesce(mac,screen,stay_long) as res_colesce").show(5)//手动将第一条记录的第一个字段置为空,则显示第二个字段值 

       

      3、explode:Creates a new row for each element in the given array or map column.

      3.1、有些时候我们需要使用sparksql来解析一些json文件,对于常规的无嵌套的json文件来说,比如:

    {"name":"Michael"}
    {"name":"Andy","age":30}
    {"name":"Justin","age":19}

      

    val fileName = "E:\\工作\\WifiData\\data\\" + startDate + "visit.txt"
    val df_numFile = ssc.read.json("file:///" + fileName)
    df_numFile.select("*").show()
    

      

      

      我们直接使用ssc.read.json("path")的方式进行读取,之后可以直接进行相关的sql进行查询。

      但是对于嵌套类型的json文件,就感觉到有点吃力了,比如:

    {"name":"Michael", "age":25,"myScore":[{"score1":19,"score2":23},{"score1":58,"score2":50}]}
    {"name":"Andy", "age":30,"myScore":[{"score1":29,"score2":33},{"score1":38,"score2":52},{"score1":88,"score2":71}]}
    {"name":"Justin", "age":19,"myScore":[{"score1":39,"score2":43},{"score1":28,"score2":53}]}
    

      

    val fileName = "E:\\工作\\WifiData\\data\\" + startDate + "visit.txt"
    val df_numFile = ssc.read.json("file:///" + fileName)
    df_numFile.select("*").show()
    

      

    然而,这并不是我们想要的,这里explode函数有有用处了,将上面的代码修改一下

    val fileName = "E:\\工作\\WifiData\\data\\" + startDate + "visit.txt"
    val df_numFile = ssc.read.json("file:///" + fileName)
    
    val df_score = df_numFile.select(df_numFile("name"),explode(df_numFile("myScore"))).toDF("name","myScore")
    val dfMyScore = df_score.select("name","myScore.score1","myScore.score2") 

      

    咦?好像少点啥,age字段呢,给丢了,加上,一个都不能少不是:

    val fileName = "E:\\工作\\WifiData\\data\\" + startDate + "visit.txt"
    val df_numFile = ssc.read.json("file:///" + fileName)
    
    val df_score = df_numFile.select(df_numFile("name"),df_numFile("age"),explode(df_numFile("myScore"))).toDF("name","myScore")
    val dfMyScore = df_score.select("name","age","myScore.score1","myScore.score2")
    dfMyScore.select("*").show()
    //这会应该全了吧,试试...

      

    什么情况?报错了,列名数量不匹配,在来修改

    val fileName = "E:\\工作\\WifiData\\data\\" + startDate + "visit.txt"
    val df_numFile = ssc.read.json("file:///" + fileName)
    
    val df_score = df_numFile.select(df_numFile("name"),df_numFile("age"),explode(df_numFile("myScore"))).toDF("name","age","myScore")
    val dfMyScore = df_score.select("name","age","myScore.score1","myScore.score2")
    dfMyScore.select("*").show()
    //再试一下...

      

    这会得到了正确的结果。

      3.2、explode还可以将单列扩展成多行

      参数可以是Array,可以是Map 

    ssc.sql(
            """
              |select explode(Array("a","b","c","d"))
              |from d1
            """.stripMargin).show(4, false)

      

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select explode(Map("a","b"))
              |from d1
            """.stripMargin).show(4, false)

      

    总结:explode该函数将指定 字段转换成DF的时候,是对应的整个表的列而不是该函数里面的字段对应里面的列数,这句话听起来比较绕口,把我自己都绕蒙了,还是得根据实例来理解

      4、greatest:求列表中的最大值

    源码解释:(Returns the greatest value of the list of values, skipping null values.This function takes at least 2 parameters. It will return null iff all parameters are null.)

    注意:必须是同类型的才可以比较

    df_1.select(greatest("enter_time","first_time","last_time") as("greatest")).show(3)
    

       

       5、if:用于条件判断

    源码解释:Returns valueTrue when testCondition is true, returns valueFalseOrNull otherwise.

    df_1.createOrReplaceTempView("d1")
    ssc.sql(
         """
         |select enter_time,first_time,last_time,if(stay_long = 0,'x','y') as type from d1
         """.stripMargin).show(3)
    

      

       6、inline:

       7、isnan:判断字段值是否为空

     df_1.createOrReplaceTempView("d1")
     ssc.sql(
         """
            |select isnan(screen) as isnull,first_time,last_time,if(isnan(mac),1,2) as type from d1
         """.stripMargin).show(3)
    

      

      8、json_tuple():获取json中指定字段的值  

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,json_tuple('{"a":"lihua","b":"wangming"}','a','b')
              |from d1
              |
            """.stripMargin).show(4, false)

      如果需要对查询出的结果起别名,可以使用嵌套外层循环即可

      

      9、get_json_object():获取指定json路径的指定字符串的值,使用$.的方式 

      [JSON Path介绍](http://blog.csdn.net/koflance/article/details/63262484)

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,get_json_object('{"a":"lihua","b":"wangming"}','$.a') as valueOfJson
              |from d1
              |
            """.stripMargin).show(4, false)

      

       10、from_json:解析json字符串为StructType或ArrayType

      

    二、字符函数

      1、ascii(string str):返回str中第一个字符的ascii值

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,ascii(mac) as after_convert
              |from d1
            """.stripMargin).show(3, false)
    

      

      2、base64(column str):将str列进行base64编码作为字符串返回,与unbase64对应

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,base64(mac) as after_convert
              |from d1
            """.stripMargin).show(3, false)
    

      

      3、concat(string str1,string str2...):将多个字符串拼接为一个字符串

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,concat(mac,first_time) as after_convert
              |from d1
            """.stripMargin).show(3, false)
    

      

      4、concat_ws:使用指定的连接符将多个字符拼接为一个字符

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,first_time,concat_ws('-',mac,first_time) as after_convert
              |from d1
            """.stripMargin).show(3, false)
    

      

      5、encode(value:column,charset:string):转码,character支持的格式有:US-ASCII,ISO-8859-1,UTF-8,UTF-16BE,UTF-16LE,UTF-16

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,first_time,encode(mac,'ISO-8859-1') as after_convert
              |from d1
            """.stripMargin).show(3, false)

      

      6、decode(value:column,charset:string):使用charset编码格式进行解码,支持的格式有:US-ASCII,ISO-8859-1,UTF-8,UTF-16BE,UTF-16LE,UTF-16

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,first_time,decode(encode(mac,'iso-8859-1'),'iso-8859-1') as after_convert
              |from d1
            """.stripMargin).show(3, false)

      

      7、format_number(value:column,Int:d):实现对数字进行格式化,例如四舍五入保留d位小数,column必须是数组,不能是字符串

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,format_number(3.1415926,3) as after_convert
              |from d1
            """.stripMargin).show(3, false)

       

      8、format_string(format:string,argument:column*):将column按照format进行格式化

       目前我还不知道有么用处

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,format_string('%s',mac) as after_convert
              |from d1
            """.stripMargin).show(3, false)

      

      9、get_json_object():

      10、initcap(column:str):将str字段的首个字母大写

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,initcap('asdfSDFcasdfASDf') as after_convert 
          |from d1
         """.stripMargin).show(3, false)

      

      11、lower,upper:转大写,转小写 

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,lower(mac) as lower,upper(mac) as upper
              |from d1
            """.stripMargin).show(3, false)

      

      12、instr(str:column,substring:String):返回substring在str中第一次出现的位置(索引从1开始)

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,instr(mac,'D') as after_convert
              |from d1
            """.stripMargin).show(3, false)

      

     13、length:返回字符串的长度

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,length(mac) as after_convert
              |from d1
            """.stripMargin).show(3, false)

       

      14、 levenshtein(l:column,r:column):计算两个字符串之间的编辑距离 

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,first_time,levenshtein(mac,first_time) as after_convert
              |from d1
            """.stripMargin).show(3, false)

      

      15、locate(substr:string,str:column,pos:Int):

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,first_time,locate('E',mac) as after_convert_1,locate('D',mac,8) as after_convert_2
              |from d1
            """.stripMargin).show(3, false)

      

      16、lpad:左补齐

        rpad:右补齐    

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,lpad(mac,20,'--') as left,rpad(mac,20,'--') as right
              |from d1
            """.stripMargin).show(3, false)

      

      17、ltrim、ltrim、trim:去除做空格,去除有空格,去除左右两边的空格  

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,ltrim(mac) as ltrim,trim(mac) as rtrim,trim(mac) as trim
              |from d1
            """.stripMargin).show(4, false)

      

      18、json_tuple():

      19、parse_url(string url,string par):按照par规则抽取url中的字段 

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select mac,'http://facebook.com/path/p1.php?query=1#Ref' as url,
              |parse_url('http://facebook.com/path/p1.php?query=1#Ref','HOST') as host,
              |parse_url('http://facebook.com/path/p1.php?query=1#Ref','PATH') as path,
              |parse_url('http://facebook.com/path/p1.php?query=1#Ref','QUERY') as query,
              |parse_url('http://facebook.com/path/p1.php?query=1#Ref','PROTOCOL') as protocol,
              |parse_url('http://facebook.com/path/p1.php?query=1#引用','REF') as ref
              |from d1
            """.stripMargin).show(4, false)

      

      20、repeat(column str,int n):将str重复n次

        reverse(column str):将str反转打印 

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,reverse(mac) as reverse,repeat(mac,2) as repeat_mac
              |from d1
            """.stripMargin).show(4, false)

      

      21、regextp_extract(column:col,exp:string,groupId:int):正则提取匹配的组

      

      22、soundex (column:col):计算桑迪克斯代码(soundex code)PS:用于按英语发音来索引姓名,发音相同但拼写不同的单词,会映射成同一个码。 

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,(first_time) as soundex
              |from d1
            """.stripMargin).show(4, false)

      

      23、rlike:可以通过正则进行where条件匹配 

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,screen
              |from d1 where screen rlike '僵.*'
            """.stripMargin).show(4, false)

      

      24、sentence():

      25、split(column:col,string:str):按照str作为分隔符对col进行分割

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,screen,split(first_time,' ')[0] as split
              |from d1
            """.stripMargin).show(4, false)

      

      26、substr(column:col,start:int,end:int):截取col中[start,end]之间的字符

        substr(column:col,stat:int):截取start开始到结尾的字符

        substring(column:col,pos:int,len:int):从col的第pos个字符开始截取长度为len个字符,注意,索引从1开始 

        substring_index(column:col,delimit:string,count:int):对col使用delimit进行分割,返回分割后count个元素,如果count>0,则从左->右开始算起,如果count<0 则从右—>左开始算起,并拼接起来   

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,
              |substr(first_time,4) as sunstr_1,
              |substr(first_time,4,10) as substr_2,
              |substring(first_time,1,11) as substring,
          |substring_index('http://facebook.com/path/p1.php?query=1#Ref','/','3')
    |from d1 """.stripMargin).show(4, false)

      

      27、translate(src:string,match:string,replace:string):将src中的match全部替换为replace 

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,
              |translate(first_time,'2018','0000') as translate
              |from d1
            """.stripMargin).show(4, false)

      

      28、

    三、日期函数

      1、add_months:将指定时间字段的月份加指定的月份数

    df_1.createOrReplaceTempView("d1")
    ssc.sql(
      """
        |select first_time,add_months(first_time,3) as after_add from d1
      """.stripMargin).show(3)
    

      

      2、current_date:获取当前的日期

    df_1.createOrReplaceTempView("d1")
    ssc.sql(
      """
        |select first_time,current_date() as current from d1
      """.stripMargin).show(3)
    

      

      3、current_timestamp:获取当前的时间

    df_1.createOrReplaceTempView("d1")
    ssc.sql(
      """
       |select first_time,current_timestamp() as current_time from d1
      """.stripMargin).show(3,false)
    

      

      4、datediff(a,b):返回日期a与日期b的时间差,结果是天数

    df_1.createOrReplaceTempView("d1")
    ssc.sql(
      """
        |select first_time,current_timestamp() as current_time,datediff(first_time,current_timestamp) as diff from d1
      """.stripMargin).show(3,false)
    

      

      5、date_add:指定天数之后的某几天

    df_1.createOrReplaceTempView("d1")
    ssc.sql(
      """
        |select first_time,date_add(first_time,3) as after_add from d1
      """.stripMargin).show(3,false)
    

      

      6、date_format:将日期字段(timestamp类型)格式化为想要的格式

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,
              |date_format(first_time,'yyyy-MM-dd') as date,
              |date_format(first_time,'yyyy') as year,
              |date_format(first_time,'MM') as month,
              |date_format(first_time,'dd') as day,
              |date_format(first_time,'HH:mm:ss') as time,
              |date_format(first_time,'HH') as hour,
              |date_format(first_time,'mm') as minute,
              |date_format(first_time,'ss') as seconds
              |from d1
            """.stripMargin).show(3,false)
    

      

      7、date_sub:返回日期的前几天

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,date_sub(first_time,3) as sub
              |from d1
            """.stripMargin).show(3,false)
    

      

      8、day:返回日期中的天

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,day(first_time) as sub
              |from d1
            """.stripMargin).show(3,false)
        }
    

      

      9、dayofyear:返回给定时间中day是这一年中的第几天,返回一个数字

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,dayofyear(first_time) as day
              |from d1
            """.stripMargin).show(3,false)
    

      

      10、dayofmonth:返回给定时间中的天是该月的第几天

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,dayofmonth(first_time) as month
              |from d1
            """.stripMargin).show(3,false)
    

     

      11、from_unixtime:

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,from_unixtime(tm,'yyyy-MM-dd HH:mm:ss') as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      注:这里的tm必须是时间秒数,例如(1250111000)

      12、from_utc_timestamp :如果给定的时间戳并非UTC,则将其转化成指定时区下的时间戳

     df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,from_utc_timestamp(first_time,'PST') as after_convert  //将first_time(默认是UTC时区时间)转换为PST时区的时间戳
              |from d1
            """.stripMargin).show(3,false)
    

      

      13、hour:返回指定时间戳的小时

     df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,hour(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      14、last_day:返回给定时间戳的当月的最后一天的日期

     df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,last_day(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      15、minute:返回指定时间戳的分钟数

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,minute(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      16、month:返回指定时间戳的月

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,month(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

       17、months_between:返回两个时间戳月份的时间差

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select enter_time,first_time,months_between(enter_time,first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      18、next_day:返回指定时间戳的一周之后的这天

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,next_day(first_time,,'TU') as after_convert //返回first_time开始,下周的星期二的日期
              |from d1
            """.stripMargin).show(3,false)
    

      

      19、now:放回当前的时间戳

      

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,now() as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      20、quarter:返回指定时间戳是在该年中的第几个季度

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,quarter(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      21、second:返回给定时间戳的秒数

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,second(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      22、to_date:返回时间字符串的日期部分

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,to_date(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      23、to_unix_timestamp:根据提供的字符串格式转换成Unix时间

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,to_unix_timestamp('16/Mar/2017:12:25:01 +0800','dd/MMM/yyyy:HH:mm:ss Z') as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,to_unix_timestamp(first_time,'yyyy-MM-dd HH:mm:ss') as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      24、to_utc_timestamp:将指定的时间戳转换为UTC下的时间戳

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,to_utc_timestamp(first_time,'yyyy-MM-dd HH:mm:ss') as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      25、trunc:返回指定时间最开始的年份或是月份(只支持MONTH/MON/MM,YEAR/YYYY/YY)

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,trunc(first_time,'MM') as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      26、unix_timestamp:转换时间字符串格式yyyy-MM-dd HH:mm:dd 到unix时间(秒),如果不加参数的话,则获取的是当前的时间的unix时间

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,unix_timestamp(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      27、weekofyear:返回指定的时间戳是该年中第几周

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,weekofyear(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      28、year:返回指定时间戳中的年

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select first_time,year(first_time) as after_convert
              |from d1
            """.stripMargin).show(3,false)
    

      

      29、window:

    四、聚合函数(尽量结合分组函数group/groupBy使用)

      1、count():求总条数

        avg():求平均值

        sum():累加求和

        mean():求平均值

        max():求最大值

        min():求最小值    

    df_1.createOrReplaceTempView("d1")
          ssc.sql(
            """
              |select phone_brand,
              |count(phone_brand) as count,
              |avg(stay_long) as avg,
              |mean(stay_long) as mean,
              |min(stay_long) as min,
              |max(stay_long) as max,
              |sum(stay_long) as sum
              |from d1 group by phone_brand
            """.stripMargin).show(4, false)

      

      2、var_pop:求总体方差(方差:) 

         stddev_pop:求总体的标准差(标准差:方差开方既得)

         skenwness:偏度

         kurtosis:峰态值

    df_1.createOrReplaceTempView("d1")
    ssc.sql(
    """
    |select phone_brand,
    |var_pop(stay_long) as fc,
    |stddev_pop(stay_long) as bzc,
    |skewness(stay_long) as pd,
    |kurtosis(stay_long) as ftz
    |from d1 group by phone_brand
    """.stripMargin).show(4, false)

      

    五、数学函数

    六、类型转换函数

      

     

      

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  • 原文地址:https://www.cnblogs.com/Gxiaobai/p/9678218.html
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