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Hive综合案例分析之不正常订单状态统计

时间：2014-08-27 16:20:08      阅读：3508      评论：0      收藏：0      [点我收藏+]

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需求

订单有5个状态：创建、捡货、发送、送达、取消

统计：创建和捡货之间不能操作2小时，创建到发送时间不能操作4小时，创建到送达之间不能超过48小时。

 

知识点

1）external table

2）desc formatted的使用

3）virtual column

4）Alter FILEFORMAT

5）COALESCE、unix_timestamp的使用

6）PARQUET

 

实现

外部表

订单创建表：

CREATE EXTERNAL TABLE order_created (
    orderNumber STRING
  , event_time  STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘;

加载表数据

load data local inpath ‘/home/spark/software/data/order_created.txt‘ overwrite into table order_created;

导入数据另一种方式：在创建表的时候通过location指定文件目录来导入数据 

1）在创建表时location指定的目录下已经存在文件

CREATE  TABLE test_load1 (
    id STRING
  , name  STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘
LOCATION ‘/student‘;

2）在创建表时location指定的目录下还不存在文件

CREATE  TABLE test_load2 (
    id STRING
  , name  STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘
LOCATION ‘/load‘;

将文件数据上传到指定目录下：

hadoop fs -put /home/spark/software/data/student.txt /load/

查询数据

select * from order_created;

10703007267488  2014-05-01 06:01:12.334+01
10101043505096  2014-05-01 07:28:12.342+01
10103043509747  2014-05-01 07:50:12.33+01
10103043501575  2014-05-01 09:27:12.33+01
10104043514061  2014-05-01 09:03:12.324+01

 

静态分区表

CREATE TABLE order_created_partition (
    orderNumber STRING
  , event_time  STRING
)
PARTITIONED BY (event_month string)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘;

分区表加载数据方式一：

load data local inpath ‘/home/spark/software/data/order_created.txt‘ overwrite into table order_created_partition PARTITION(event_month=‘2014-05‘);

数据查询

select * from order_created_partition where event_month=‘2014-05‘;   #不跑mapreduce

10703007267488  2014-05-01 06:01:12.334+01      2014-05
10101043505096  2014-05-01 07:28:12.342+01      2014-05
10103043509747  2014-05-01 07:50:12.33+01       2014-05
10103043501575  2014-05-01 09:27:12.33+01       2014-05
10104043514061  2014-05-01 09:03:12.324+01      2014-05

分区表加载数据方式二：

第一步：创建hdfs目录：在hdfs目录：/user/hive/warehouse/order_created_partition目录下创建event_month=2014-06

hadoop fs -mkdir /user/hive/warehouse/order_created_partition/event_month=2014-06

第二步：拷贝数据到新创建的目录下：

hadoop fs -put /home/spark/software/data/order_created.txt /user/hive/warehouse/order_created_partition/event_month=2014-06

第三步：添加新分区数据到元数据信息中

msck repair table order_created_partition;

执行日志信息：

Partitions not in metastore:    order_created_partition:event_month=2014-06
Repair: Added partition to metastore order_created_partition:event_month=2014-06

查询event_month=2014-06分区的数据：

select * from order_created_partition where event_month=‘2014-06‘; 

10703007267488  2014-05-01 06:01:12.334+01      2014-06
10101043505096  2014-05-01 07:28:12.342+01      2014-06
10103043509747  2014-05-01 07:50:12.33+01       2014-06
10103043501575  2014-05-01 09:27:12.33+01       2014-06
10104043514061  2014-05-01 09:03:12.324+01      2014-06

查看分区表已有的所有分区：

show partitions order_created_partition;

查看分区表已有的指定分区：

SHOW PARTITIONS order_created_partition PARTITION(event_month=‘2014-06‘);

查看表字段信息：

desc order_created_partition;
desc extended order_created_partition;
desc formatted order_created_partition;
desc formatted order_created_partition partition(event_month=‘2014-05‘);

 

动态分区表

CREATE TABLE order_created_dynamic_partition (
    orderNumber STRING
  , event_time  STRING
)
PARTITIONED BY (event_month string);

加载数据：

insert into table order_created_dynamic_partition PARTITION (event_month)
select orderNumber, event_time, substr(event_time, 1, 7) as event_month from order_created;

报错：

FAILED: SemanticException [Error 10096]: Dynamic partition strict mode requires at least one static partition column. 
To turn this off set hive.exec.dynamic.partition.mode=nonstrict

解决方案：

set hive.exec.dynamic.partition.mode=nonstrict;

重新执行：

insert into table order_created_dynamic_partition PARTITION (event_month)
select orderNumber, event_time, substr(event_time, 1, 7) as event_month from order_created;

查询数据：

select * from order_created_dynamic_partition;

10703007267488  2014-05-01 06:01:12.334+01      2014-05
10101043505096  2014-05-01 07:28:12.342+01      2014-05
10103043509747  2014-05-01 07:50:12.33+01       2014-05
10103043501575  2014-05-01 09:27:12.33+01       2014-05
10104043514061  2014-05-01 09:03:12.324+01      2014-05

 

Parquet类型表以及ALTER FILEFORMAT

创建存储方式为parquet类型的分区表

CREATE TABLE order_created_dynamic_partition_parquet (
    orderNumber STRING
  , event_time  STRING
)
PARTITIONED BY (event_month string)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘
STORED AS parquet;

查看表信息：需要重点关注下SerDe Library/InputFormat/OutputFormat三个属性的区别

desc formatted order_created_dynamic_partition_parquet;
SerDe Library:          org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe      
InputFormat:            org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat    
OutputFormat:           org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat

desc formatted order_created_dynamic_partition;
SerDe Library:          org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe       
InputFormat:            org.apache.hadoop.mapred.TextInputFormat         
OutputFormat:           org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat

插入数据：

insert into table order_created_dynamic_partition_parquet PARTITION (event_month)
select orderNumber, event_time, substr(event_time, 1, 7) as event_month from order_created;

查询数据：

select * from order_created_dynamic_partition_parquet;

10703007267488  2014-05-01 06:01:12.334+01      2014-05
10101043505096  2014-05-01 07:28:12.342+01      2014-05
10103043509747  2014-05-01 07:50:12.33+01       2014-05
10103043501575  2014-05-01 09:27:12.33+01       2014-05
10104043514061  2014-05-01 09:03:12.324+01      2014-05

关注下hdfs上存储的文件：

hadoop fs -text /user/hive/warehouse/order_created_dynamic_partition_parquet/event_month=2014-05/000000_0

查看发现是“乱码”的，文件内容不能直接肉眼识别。

注意：如下的操作是将textfile的文件拷贝到parquet类型的表中

在hdfs目录：/user/hive/warehouse/order_created_dynamic_partition_parquet目录下创建event_month=2014-06

hadoop fs -mkdir /user/hive/warehouse/order_created_dynamic_partition_parquet/event_month=2014-06

拷贝数据到新创建的目录下：

hadoop fs -put /home/spark/software/data/order_created.txt /user/hive/warehouse/order_created_dynamic_partition_parquet/event_month=2014-06

添加新分区数据到元数据信息中：

msck repair table order_created_dynamic_partition_parquet;

查询数据：

select * from order_created_dynamic_partition_parquet;

报错，信息如下：

Failed with exception java.io.IOException:java.lang.RuntimeException: 
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition_parquet/event_month=2014-06/order_created.txt 
is not a Parquet file. expected magic number at tail [80, 65, 82, 49] but found [52, 43, 48, 49]

报错原因：因为event_month=2014-06不是parquet文件，而是普通的文本类型文件

而读取parquet分区的数据是正常的，比如：

select * from order_created_dynamic_partition_parquet where event_month=‘2014-05‘;

解决方案：

hive0.12版本以及以下版本的解决方案

ALTER TABLE order_created_dynamic_partition_parquet PARTITION (event_month=‘2014-06‘) SET SERDE ‘org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe‘;
ALTER TABLE order_created_dynamic_partition_parquet PARTITION (event_month=‘2014-06‘) SET FILEFORMAT textfile;

必须要同时修改SERDE又要修改FILEFORMAT才能起作用；

手工设置以后就可以进行正常的查询操作了。

注意查看SerDe LazySimpleSerDe/INPUTFORMAT/OUTPUTFORMAT：

查看event_month=‘2014-06‘分区的Storage Information：

desc formatted order_created_dynamic_partition_parquet partition(event_month=‘2014-06‘);
SerDe Library:          org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe       
InputFormat:            org.apache.hadoop.mapred.TextInputFormat         
OutputFormat:           org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat

查看event_month=‘2014-05‘分区的Storage Information：

desc formatted order_created_dynamic_partition_parquet partition(event_month=‘2014-05‘);
SerDe Library:          org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe      
InputFormat:            org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat    
OutputFormat:           org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat

查看整个表的Storage Information：

desc formatted order_created_dynamic_partition_parquet;
SerDe Library:          org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe      
InputFormat:            org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat    
OutputFormat:           org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat

如上操作后得出结论：

1）order_created_dynamic_partition_parquet中的不同分区可以有不同的存储类型；

2）表的存储类型还是创建时指定的存储类型；

hive0.13版本以及以上版本的解决方案：

ALTER TABLE order_created_dynamic_partition_parquet PARTITION (event_month=‘2014-06‘) SET FILEFORMAT parquet;

insert数据到parquet表

insert into table order_created_dynamic_partition_parquet PARTITION (event_month=‘2014-06‘) select orderNumber, event_time from order_created;

查看order_created_dynamic_partition_parquet目录下的文件发现一个partition中有两种不同类型的FILEFORMAT了

hadoop fs -ls /user/hive/warehouse/order_created_dynamic_partition_parquet/event_month=2014-06
/user/hive/warehouse/order_created_dynamic_partition_parquet/event_month=2014-06/000000_0      #Parquet类型
/user/hive/warehouse/order_created_dynamic_partition_parquet/event_month=2014-06/order_created.txt   #TEXTFILE类型

因为原先的event_month=‘2014-06‘已经被我们手工修改成TEXTFILE类型了，而insert into进去的是Parquet类型(insert into进去的存储类型和创建表时指定的存储类型一致)。

select * from order_created_dynamic_partition_parquet; 查询报错

查看表信息：

desc formatted order_created_dynamic_partition_parquet partition(event_month=‘2014-06‘);
SerDe Library:          org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe      
InputFormat:            org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat    
OutputFormat:           org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat

发现SerDe Library是Parquet类型了，原先手工设置的就无效了，变成创建表时指定的存储类型了。需要重新设置：

ALTER TABLE order_created_dynamic_partition_parquet PARTITION (event_month=‘2014-06‘) SET SERDE ‘org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe‘;
ALTER TABLE order_created_dynamic_partition_parquet PARTITION (event_month=‘2014-06‘) SET FILEFORMAT textfile;

select * from order_created_dynamic_partition_parquet;

能查询出部分数据，其中有些是Parquet格式的就显示不出来。这可能是hive的一个bug

 

Hive虚拟列

INPUT__FILE__NAME: 输入文件的文件名

BLOCK__OFFSET__INSIDE__FILE: 这一行数据所在的文件中的偏移量

select INPUT__FILE__NAME, ordernumber, event_time, BLOCK__OFFSET__INSIDE__FILE  from order_created_dynamic_partition;
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10703007267488 2014-05-01 06:01:12.334+01      0
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10101043505096 2014-05-01 07:28:12.342+01      42
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10103043509747 2014-05-01 07:50:12.33+01       84
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10103043501575 2014-05-01 09:27:12.33+01       125
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10104043514061 2014-05-01 09:03:12.324+01      166

求这一行在文件的第几行

select INPUT__FILE__NAME, ordernumber, event_time, BLOCK__OFFSET__INSIDE__FILE / (length(ordernumber) + length(event_time) + 2) + 1 from order_created_dynamic_partition;
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10703007267488 2014-05-01 06:01:12.334+01      1
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10101043505096 2014-05-01 07:28:12.342+01      2
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10103043509747 2014-05-01 07:50:12.33+01       3.0487804878
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10103043501575 2014-05-01 09:27:12.33+01       4.0487804878
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10104043514061 2014-05-01 09:03:12.324+01      4.95238095238
select INPUT__FILE__NAME, ordernumber, event_time, round(BLOCK__OFFSET__INSIDE__FILE / (length(ordernumber) + length(event_time) + 2) + 1) from order_created_dynamic_partition;
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10703007267488 2014-05-01 06:01:12.334+01      1
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10101043505096 2014-05-01 07:28:12.342+01      2
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10103043509747 2014-05-01 07:50:12.33+01       3
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10103043501575 2014-05-01 09:27:12.33+01       4
hdfs://hadoop000:8020/user/hive/warehouse/order_created_dynamic_partition/event_month=2014-05/000000_0  10104043514061 2014-05-01 09:03:12.324+01      5

订单捡货表

创建表：

CREATE TABLE order_picked (
    orderNumber STRING
  , event_time  STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘;

加载数据：

load data local inpath ‘/home/spark/software/data/order_picked.txt‘ overwrite into table order_picked;

查询数据：

select * from order_picked;
10703007267488  2014-05-01 07:02:12.334+01
10101043505096  2014-05-01 08:29:12.342+01
10103043509747  2014-05-01 10:55:12.33+01

订单发货表

创建表：

CREATE TABLE order_shipped (
    orderNumber STRING
  , event_time  STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘;

加载数据：

load data local inpath ‘/home/spark/software/data/order_shipped.txt‘ overwrite into table order_shipped;

查询数据：

select * from order_shipped;
10703007267488  2014-05-01 10:00:12.334+01
10101043505096  2014-05-01 18:39:12.342+01

订单收货表

创建表：

CREATE TABLE order_received (
    orderNumber STRING
  , event_time  STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘;

加载数据：

load data local inpath ‘/home/spark/software/data/order_received.txt‘ overwrite into table order_received;

查询数据：

select * from order_received;
10703007267488  2014-05-02 12:12:12.334+01

订单取消表

创建表：

CREATE TABLE order_cancelled (
    orderNumber STRING
  , event_time  STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t‘;

加载数据：

load data local inpath ‘/home/spark/software/data/order_cancelled.txt‘ overwrite into table order_cancelled;

查询数据：

select * from order_cancelled;
10103043501575  2014-05-01 12:12:12.334+01

行列互换操作：

方法一：采用union all

CREATE TABLE order_tracking AS
SELECT orderNumber
     , max(CASE WHEN type_id="order_created"   THEN event_time ELSE ‘0‘ END) AS order_created_ts
     , max(CASE WHEN type_id="order_picked"    THEN event_time ELSE ‘0‘ END) AS order_picked_ts
     , max(CASE WHEN type_id="order_shipped"   THEN event_time ELSE ‘0‘ END) AS order_shipped_ts
     , max(CASE WHEN type_id="order_received"  THEN event_time ELSE ‘0‘ END) AS order_received_ts
     , max(CASE WHEN type_id="order_cancelled" THEN event_time ELSE ‘0‘ END) AS order_cancelled_ts
FROM (
    select orderNumber, "order_created"   as type_id, event_time FROM order_created
  UNION ALL
    select orderNumber, "order_picked"    as type_id, event_time FROM order_picked
  UNION ALL
    select orderNumber, "order_shipped"   as type_id, event_time FROM order_shipped
  UNION ALL
    select orderNumber, "order_received"  as type_id, event_time FROM order_received
  UNION ALL
    select orderNumber, "order_cancelled" as type_id, event_time FROM order_cancelled
) u
group by orderNumber;
select * from order_tracking order by order_created_ts limit 5;
10703007267488  2014-05-01 06:01:12.334+01      2014-05-01 07:02:12.334+01      2014-05-01 10:00:12.334+01    2014-05-02 12:12:12.334+01       0
10101043505096  2014-05-01 07:28:12.342+01      2014-05-01 08:29:12.342+01      2014-05-01 18:39:12.342+01    0
10103043509747  2014-05-01 07:50:12.33+01       2014-05-01 10:55:12.33+01       0       0       0
10104043514061  2014-05-01 09:03:12.324+01      0       0       0       0
10103043501575  2014-05-01 09:27:12.33+01       0       0       0       2014-05-01 12:12:12.334+01

方法二：采用join

CREATE TABLE order_tracking_join AS
select t1.orderNumber
     , t1.event_time as order_created_ts
     , t2.event_time as order_picked_ts
     , t3.event_time as order_shipped_ts
     , t4.event_time as order_received_ts
     , t5.event_time as order_cancelled_ts
from (
  select ordernumber, max(event_time) as event_time from order_created group by ordernumber
) t1
left outer join (
  select ordernumber, max(event_time) as event_time from order_picked group by ordernumber
) t2
on t1.ordernumber = t2.ordernumber
left outer join (
  select ordernumber, max(event_time) as event_time from order_shipped group by ordernumber
) t3
on t1.ordernumber = t3.ordernumber
left outer join (
  select ordernumber, max(event_time) as event_time from order_received group by ordernumber
) t4
on t1.ordernumber = t4.ordernumber
left outer join (
  select ordernumber, max(event_time) as event_time from order_cancelled group by ordernumber
) t5
on t1.ordernumber = t5.ordernumber;
select * from order_tracking_join order by order_created_ts limit 5;
10703007267488  2014-05-01 06:01:12.334+01      2014-05-01 07:02:12.334+01      2014-05-01 10:00:12.334+01    2014-05-02 12:12:12.334+01       NULL
10101043505096  2014-05-01 07:28:12.342+01      2014-05-01 08:29:12.342+01      2014-05-01 18:39:12.342+01    NULL     NULL
10103043509747  2014-05-01 07:50:12.33+01       2014-05-01 10:55:12.33+01       NULL    NULL    NULL
10104043514061  2014-05-01 09:03:12.324+01      NULL    NULL    NULL    NULL
10103043501575  2014-05-01 09:27:12.33+01       NULL    NULL    NULL    2014-05-01 12:12:12.334+01

最终的统计操作：

COALESCE(unix_timestamp(order_picked_ts, ‘yyyy-MM-dd HH:mm:ss.S‘), 0)使用说明：如果第一个参数为null的话就采用第二个参数作为第一个参数的值

方法一：采用采用union all的结果表

select orderNumber
     , order_created_ts
     , order_picked_ts
     , order_shipped_ts
     , order_received_ts
     , order_cancelled_ts
  from order_tracking
 WHERE order_created_ts != ‘0‘ AND order_cancelled_ts = ‘0‘
   AND (
    COALESCE(unix_timestamp(order_picked_ts, ‘yyyy-MM-dd HH:mm:ss.S‘), 0) - unix_timestamp(order_created_ts, ‘yyyy-MM-dd HH:mm:ss.S‘) > 2 * 60 * 60
    OR
    COALESCE(unix_timestamp(order_shipped_ts, ‘yyyy-MM-dd HH:mm:ss.S‘), 0) - unix_timestamp(order_created_ts, ‘yyyy-MM-dd HH:mm:ss.S‘) > 4 * 60 * 60
    OR
    COALESCE(unix_timestamp(order_received_ts, ‘yyyy-MM-dd HH:mm:ss.S‘), 0) - unix_timestamp(order_created_ts, ‘yyyy-MM-dd HH:mm:ss.S‘) > 48 * 60 * 60
   )
;

10101043505096  2014-05-01 07:28:12.342+01      2014-05-01 08:29:12.342+01      2014-05-01 18:39:12.342+01    0
10103043509747  2014-05-01 07:50:12.33+01       2014-05-01 10:55:12.33+01       0       0       0

方法二：采用join的结果表

select orderNumber
     , order_created_ts
     , order_picked_ts
     , order_shipped_ts
     , order_received_ts
     , order_cancelled_ts
  from order_tracking_join
 WHERE order_created_ts IS NOT NULL AND order_cancelled_ts IS NULL
   AND (
    COALESCE(unix_timestamp(order_picked_ts, ‘yyyy-MM-dd HH:mm:ss.S‘), 0) - unix_timestamp(order_created_ts, ‘yyyy-MM-dd HH:mm:ss.S‘) > 2 * 60 * 60
    OR
    COALESCE(unix_timestamp(order_shipped_ts, ‘yyyy-MM-dd HH:mm:ss.S‘), 0) - unix_timestamp(order_created_ts, ‘yyyy-MM-dd HH:mm:ss.S‘) > 4 * 60 * 60
    OR
    COALESCE(unix_timestamp(order_received_ts, ‘yyyy-MM-dd HH:mm:ss.S‘), 0) - unix_timestamp(order_created_ts, ‘yyyy-MM-dd HH:mm:ss.S‘) > 48 * 60 * 60
   )
;

10101043505096  2014-05-01 07:28:12.342+01      2014-05-01 08:29:12.342+01      2014-05-01 18:39:12.342+01    NULL     NULL
10103043509747  2014-05-01 07:50:12.33+01       2014-05-01 10:55:12.33+01       NULL    NULL    NULL

 

Hive综合案例分析之不正常订单状态统计

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原文地址：http://www.cnblogs.com/luogankun/p/3939533.html
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