高动态数据集分析

今天的数据是动态的和由应用程序驱动的。一个新的数据类型和新的数据模型,生成数据集,是商业应用的发展趋势,比如网络,社交,移动和互联网的新时代的增长。这些应用程序被快速迭代,这些数据模型和半结构化,无 Schema 结合,并快速发展。半结构化数据模型可以是复杂的/嵌套的,无 Schema 的,并且能够在每一行中具有不同的字段,并不断地发展,以满足业务需求的不断增加和删除。

本教程向你展示如何来查询动态数据,例如 JSON,并在几分钟之内从任何数据类型中,得到结果。在本例中使用的数据集是从 Yelp 的签到数据,它具有以下结构:

check-in
{
    'type': 'checkin',
    'business_id': (encrypted business id),
    'checkin_info': {
        '0-0': (number of checkins from 00:00 to 01:00 on all Sundays),
        '1-0': (number of checkins from 01:00 to 02:00 on all Sundays),
        ...
        '14-4': (number of checkins from 14:00 to 15:00 on all Thursdays),
        ...
        '23-6': (number of checkins from 23:00 to 00:00 on all Saturdays)
    }, # if there was no checkin for a hour-day block it will not be in the dataset
}

值得在这段底部重复评论:

# if there was no checkin for a hour-day block it will not be in the dataset.

元素的名称,在 checkin_info 看到预期的结果之前可以每行的变化。数据,虽然简单,但是是高动态的数据。在你会在 Hadoop 上使用任何 SQL 技术,对数据进行分析时,无需首先在数据集上建立一个扁平的关系结构。

第一步:首先下载 Drill,如果你还没有下载,那么请先在你的机器上下载。

http://drill.apache.org/download/
tar -xvf apache-drill-1.4.0.tar

安装 Drill 在你的本机上(或者是本机的虚拟机上)。你不需要 Hadoop。

第二步:启动 Drill Shell。

bin/drill-embedded

第三步:使用 SQL 去分析数据

首先,让我们先看看数据集:

0: jdbc:drill:zk=local> SELECT * FROM dfs.`/users/nrentachintala/Downloads/yelp/yelp_academic_dataset_checkin.json` limit 2;
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+------------------------+
|                                                                 checkin_info                                                                                                                                                             |    type    |      business_id       |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+------------------------+
| {"3-4":1,"13-5":1,"6-6":1,"14-5":1,"14-6":1,"14-2":1,"14-3":1,"19-0":1,"11-5":1,"13-2":1,"11-6":2,"11-3":1,"12-6":1,"6-5":1,"5-5":1,"9-2":1,"9-5":1,"9-6":1,"5-2":1,"7-6":1,"7-5":1,"7-4":1,"17-5":1,"8-5":1,"10-2":1,"10-5":1,"10-6":1} | checkin    | JwUE5GmEO-sH1FuwJgKBlQ |
| {"6-6":2,"6-5":1,"7-6":1,"7-5":1,"8-5":2,"10-5":1,"9-3":1,"12-5":1,"15-3":1,"15-5":1,"15-6":1,"16-3":1,"10-0":1,"15-4":1,"10-4":1,"8-2":1}                                                                                               | checkin    | uGykseHzyS5xAMWoN6YUqA |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+------------------------+

注意:该文档输出只是一个示例,Drill 的输出结果并不是对齐的。

你可以直接查询 JSON 文件。Schema 的定义在 Hive 中并不是必需的。checkin_info 列在第一行和第二行是不同的。

Drill 提供了一个函数叫 KVGEN(Key 和 Value 的生成器),它用于工作在复杂数据,在包含动态的 maps 集合和未知的元素名称,比如 checkin_info。KVGEN 将动态的 Map 转化为键值对,键代表动态元素名称的数组。

让我们应用 KVGEN 到 checkin_info 元素中去生成键值对。

0: jdbc:drill:zk=local> SELECT KVGEN(checkin_info) checkins FROM dfs.`/users/nrentachintala/Downloads/yelp/yelp_academic_dataset_checkin.json` LIMIT 2;
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|                                                                    checkins                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| [{"key":"3-4","value":1},{"key":"13-5","value":1},{"key":"6-6","value":1},{"key":"14-5","value":1},{"key":"14-6","value":1},{"key":"14-2","value":1},{"key":"14-3","value":1},{"key":"19-0","value":1},{"key":"11-5","value":1},{"key":"13-2","value":1},{"key":"11-6","value":2},{"key":"11-3","value":1},{"key":"12-6","value":1},{"key":"6-5","value":1},{"key":"5-5","value":1},{"key":"9-2","value":1},{"key":"9-5","value":1},{"key":"9-6","value":1},{"key":"5-2","value":1},{"key":"7-6","value":1},{"key":"7-5","value":1},{"key":"7-4","value":1},{"key":"17-5","value":1},{"key":"8-5","value":1},{"key":"10-2","value":1},{"key":"10-5","value":1},{"key":"10-6","value":1}] |
| [{"key":"6-6","value":2},{"key":"6-5","value":1},{"key":"7-6","value":1},{"key":"7-5","value":1},{"key":"8-5","value":2},{"key":"10-5","value":1},{"key":"9-3","value":1},{"key":"12-5","value":1},{"key":"15-3","value":1},{"key":"15-5","value":1},{"key":"15-6","value":1},{"key":"16-3","value":1},{"key":"10-0","value":1},{"key":"15-4","value":1},{"key":"10-4","value":1},{"key":"8-2","value":1}]                                                                                                                                                                                                                                                                               |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Drill 还提供了其他的函数去操作复杂的数据,该函数叫做 “Flatten”,它用于拆分键值对集合从 KVGEN 中返回的结果当中。

0: jdbc:drill:zk=local> SELECT FLATTEN(KVGEN(checkin_info)) checkins FROM dfs.`/users/nrentachintala/Downloads/yelp/yelp_academic_dataset_checkin.json` LIMIT 20;
+--------------------------+
|         checkins         |
+--------------------------+
| {"key":"3-4","value":1}  |
| {"key":"13-5","value":1} |
| {"key":"6-6","value":1}  |
| {"key":"14-5","value":1} |
| {"key":"14-6","value":1} |
| {"key":"14-2","value":1} |
| {"key":"14-3","value":1} |
| {"key":"19-0","value":1} |
| {"key":"11-5","value":1} |
| {"key":"13-2","value":1} |
| {"key":"11-6","value":2} |
| {"key":"11-3","value":1} |
| {"key":"12-6","value":1} |
| {"key":"6-5","value":1}  |
| {"key":"5-5","value":1}  |
| {"key":"9-2","value":1}  |
| {"key":"9-5","value":1}  |
| {"key":"9-6","value":1}  |
| {"key":"5-2","value":1}  |
| {"key":"7-6","value":1}  |
+--------------------------+

你可以快速的获取数据通过应用 KVGEN 和 FLATTEN 函数于数据集上,不需要花时间去定义 Schema 和数据存储的中间格式。

在输出的扁平数据中,你使用标准的 SQL 功能,例如 filters,aggregates 和 sort。让我们先看一些例子。

获取 Yelp 数据集中的总评论中,从 checkin-ins 记录中

0: jdbc:drill:zk=local> SELECT SUM(checkintbl.checkins.`value`) AS TotalCheckins FROM (
. . . . . . . . . . . >  SELECT FLATTEN(KVGEN(checkin_info)) checkins FROM dfs.`/users/nrentachintala/Downloads/yelp/yelp_academic_dataset_checkin.json` ) checkintbl
. . . . . . . . . . . >  ;
+---------------+
| TotalCheckins |
+---------------+
| 4713811       |
+---------------+

获取星期天晚上的数据

0: jdbc:drill:zk=local> SELECT SUM(checkintbl.checkins.`value`) AS SundayMidnightCheckins FROM (
. . . . . . . . . . . >  SELECT FLATTEN(KVGEN(checkin_info)) checkins FROM dfs.`/users/nrentachintala/Downloads/yelp/yelp_academic_dataset_checkin.json` ) checkintbl WHERE checkintbl.checkins.key='23-0';
+------------------------+
| SundayMidnightCheckins |
+------------------------+
| 8575                   |
+------------------------+

获取一周中每天的数据

0: jdbc:drill:zk=local> SELECT `right`(checkintbl.checkins.key,1) WeekDay,sum(checkintbl.checkins.`value`) TotalCheckins from (
. . . . . . . . . . . >  select flatten(kvgen(checkin_info)) checkins FROM dfs.`/users/nrentachintala/Downloads/yelp/yelp_academic_dataset_checkin.json`  ) checkintbl GROUP BY `right`(checkintbl.checkins.key,1) ORDER BY TotalCheckins;
+------------+---------------+
|  WeekDay   | TotalCheckins |
+------------+---------------+
| 1          | 545626        |
| 0          | 555038        |
| 2          | 555747        |
| 3          | 596296        |
| 6          | 735830        |
| 4          | 788073        |
| 5          | 937201        |
+------------+---------------+

获取一天中每小时的数据

0: jdbc:drill:zk=local> SELECT SUBSTR(checkintbl.checkins.key,1,strpos(checkintbl.checkins.key,'-')-1) AS HourOfTheDay ,SUM(checkintbl.checkins.`value`) TotalCheckins FROM (
. . . . . . . . . . . >  SELECT FLATTEN(KVGEN(checkin_info)) checkins FROM dfs.`/users/nrentachintala/Downloads/yelp/yelp_academic_dataset_checkin.json` ) checkintbl GROUP BY SUBSTR(checkintbl.checkins.key,1,strpos(checkintbl.checkins.key,'-')-1) ORDER BY TotalCheckins;
+--------------+---------------+
| HourOfTheDay | TotalCheckins |
+--------------+---------------+
| 3            | 20357         |
| 4            | 21076         |
| 2            | 28116         |
| 5            | 33842         |
| 1            | 45467         |
| 6            | 54174         |
| 0            | 74127         |
| 7            | 96329         |
| 23           | 102009        |
| 8            | 130091        |
| 22           | 140338        |
| 9            | 162913        |
| 21           | 211949        |
| 10           | 220687        |
| 15           | 261384        |
| 14           | 276188        |
| 16           | 292547        |
| 20           | 293783        |
| 13           | 328373        |
| 11           | 338675        |
| 17           | 374186        |
| 19           | 385381        |
| 12           | 399797        |
| 18           | 422022        |
+--------------+---------------+

总结

本教程中,你浏览结构化和半结构化的数据时,没有任何的 Schema 管理和 ETL。

Copyright © smartloli 2016 all right reserved,powered by GitbookModify: 2016-08-26 06:59:28