预备知识：
了解hive窗口函数：LAG 和 LEAD
数据准备：
cookie1,2015-04-10 10:00:02,url2
cookie1,2015-04-10 10:00:00,url1
cookie1,2015-04-10 10:03:04,1url3
cookie1,2015-04-10 10:50:05,url6
cookie1,2015-04-10 11:00:00,url7
cookie1,2015-04-10 10:10:00,url4
cookie1,2015-04-10 10:50:01,url5
cookie2,2015-04-10 10:00:02,url22
cookie2,2015-04-10 10:00:00,url11
cookie2,2015-04-10 10:03:04,1url33
cookie2,2015-04-10 10:50:05,url66
cookie2,2015-04-10 11:00:00,url77
cookie2,2015-04-10 10:10:00,url44
cookie2,2015-04-10 10:50:01,url55
CREATE EXTERNAL TABLE lxw1234 (
cookieid string,
createtime string, –页面访问时间
url STRING –被访问页面
) ROW FORMAT DELIMITED
FIELDS TERMINATED BY ‘,’
stored as textfile location ‘/tmp/lxw11/’;
hive> select * from lxw1234;
OK
cookie1 2015-04-10 10:00:02 url2
cookie1 2015-04-10 10:00:00 url1
cookie1 2015-04-10 10:03:04 1url3
cookie1 2015-04-10 10:50:05 url6
cookie1 2015-04-10 11:00:00 url7
cookie1 2015-04-10 10:10:00 url4
cookie1 2015-04-10 10:50:01 url5
cookie2 2015-04-10 10:00:02 url22
cookie2 2015-04-10 10:00:00 url11
cookie2 2015-04-10 10:03:04 1url33
cookie2 2015-04-10 10:50:05 url66
cookie2 2015-04-10 11:00:00 url77
cookie2 2015-04-10 10:10:00 url44
cookie2 2015-04-10 10:50:01 url55
LAG
LAG(col,n,DEFAULT) 用于统计窗口内往上第n行值
第一个参数为列名，第二个参数为往上第n行（可选，默认为1），第三个参数为默认值（当往上第n行为NULL时候，取默认值，如不指定，则为NULL）
SELECT cookieid,
createtime,
url,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
LAG(createtime,1,’1970-01-01 00:00:00′) OVER(PARTITION BY cookieid ORDER BY createtime) AS last_1_time,
LAG(createtime,2) OVER(PARTITION BY cookieid ORDER BY createtime) AS last_2_time
FROM lxw1234;
cookieid createtime url rn last_1_time last_2_time
——————————————————————————————-
cookie1 2015-04-10 10:00:00 url1 1 1970-01-01 00:00:00 NULL
cookie1 2015-04-10 10:00:02 url2 2 2015-04-10 10:00:00 NULL
cookie1 2015-04-10 10:03:04 1url3 3 2015-04-10 10:00:02 2015-04-10 10:00:00
cookie1 2015-04-10 10:10:00 url4 4 2015-04-10 10:03:04 2015-04-10 10:00:02
cookie1 2015-04-10 10:50:01 url5 5 2015-04-10 10:10:00 2015-04-10 10:03:04
cookie1 2015-04-10 10:50:05 url6 6 2015-04-10 10:50:01 2015-04-10 10:10:00
cookie1 2015-04-10 11:00:00 url7 7 2015-04-10 10:50:05 2015-04-10 10:50:01
cookie2 2015-04-10 10:00:00 url11 1 1970-01-01 00:00:00 NULL
cookie2 2015-04-10 10:00:02 url22 2 2015-04-10 10:00:00 NULL
cookie2 2015-04-10 10:03:04 1url33 3 2015-04-10 10:00:02 2015-04-10 10:00:00
cookie2 2015-04-10 10:10:00 url44 4 2015-04-10 10:03:04 2015-04-10 10:00:02
cookie2 2015-04-10 10:50:01 url55 5 2015-04-10 10:10:00 2015-04-10 10:03:04
cookie2 2015-04-10 10:50:05 url66 6 2015-04-10 10:50:01 2015-04-10 10:10:00
cookie2 2015-04-10 11:00:00 url77 7 2015-04-10 10:50:05 2015-04-10 10:50:01
last_1_time: 指定了往上第1行的值，default为’1970-01-01 00:00:00′
cookie1第一行，往上1行为NULL,因此取默认值 1970-01-01 00:00:00
cookie1第三行，往上1行值为第二行值，2015-04-10 10:00:02
cookie1第六行，往上1行值为第五行值，2015-04-10 10:50:01
last_2_time: 指定了往上第2行的值，为指定默认值
cookie1第一行，往上2行为NULL
cookie1第二行，往上2行为NULL
cookie1第四行，往上2行为第二行值，2015-04-10 10:00:02
cookie1第七行，往上2行为第五行值，2015-04-10 10:50:01
LEAD
与LAG相反
LEAD(col,n,DEFAULT) 用于统计窗口内往下第n行值
第一个参数为列名，第二个参数为往下第n行（可选，默认为1），第三个参数为默认值（当往下第n行为NULL时候，取默认值，如不指定，则为NULL）
SELECT cookieid,
createtime,
url,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
LEAD(createtime,1,’1970-01-01 00:00:00′) OVER(PARTITION BY cookieid ORDER BY createtime) AS next_1_time,
LEAD(createtime,2) OVER(PARTITION BY cookieid ORDER BY createtime) AS next_2_time
FROM lxw1234;
cookieid createtime url rn next_1_time next_2_time
——————————————————————————————-
cookie1 2015-04-10 10:00:00 url1 1 2015-04-10 10:00:02 2015-04-10 10:03:04
cookie1 2015-04-10 10:00:02 url2 2 2015-04-10 10:03:04 2015-04-10 10:10:00
cookie1 2015-04-10 10:03:04 1url3 3 2015-04-10 10:10:00 2015-04-10 10:50:01
cookie1 2015-04-10 10:10:00 url4 4 2015-04-10 10:50:01 2015-04-10 10:50:05
cookie1 2015-04-10 10:50:01 url5 5 2015-04-10 10:50:05 2015-04-10 11:00:00
cookie1 2015-04-10 10:50:05 url6 6 2015-04-10 11:00:00 NULL
cookie1 2015-04-10 11:00:00 url7 7 1970-01-01 00:00:00 NULL
cookie2 2015-04-10 10:00:00 url11 1 2015-04-10 10:00:02 2015-04-10 10:03:04
cookie2 2015-04-10 10:00:02 url22 2 2015-04-10 10:03:04 2015-04-10 10:10:00
cookie2 2015-04-10 10:03:04 1url33 3 2015-04-10 10:10:00 2015-04-10 10:50:01
cookie2 2015-04-10 10:10:00 url44 4 2015-04-10 10:50:01 2015-04-10 10:50:05
cookie2 2015-04-10 10:50:01 url55 5 2015-04-10 10:50:05 2015-04-10 11:00:00
cookie2 2015-04-10 10:50:05 url66 6 2015-04-10 11:00:00 NULL
cookie2 2015-04-10 11:00:00 url77 7 1970-01-01 00:00:00 NULL
–逻辑与LAG一样，只不过LAG是往上，LEAD是往下。
进入主题，hive实现拉链表示例：
—–目标表
create external table existing_time_series_table
(
primary_key string, —业务主键（字段个数不限）
effective_dt bigint, —-开始日期
expired_dt bigint, —-失效日期
event_value string—-业务员度量值
)
stored as parquet
location
‘hdfs://nameservice/it/ods/erp/existing_time_series_table’;
—-增量结果集
create external table new_time_series_table
(
primary_key string,—业务主键（字段个数不限）
effective_dt bigint, —-开始日期
event_value string—-业务员度量值
)
stored as parquet
location
‘hdfs://nameservice/it/ods/erp/new_time_series_table’;
—–逻辑实现：lead函数实现了取下个日期作为本记录的失效日期
insert overwrite table existing_time_series_table
select primary_key,
effective_dt,
case
when lead(effective_dt, 1, null)
over(partition by primary_key order by effective_dt) is null then
null
else
lead(effective_dt, 1, null)
over(partition by primary_key order by effective_dt)
end as expired_dt,
event_value
from (select primary_key, effective_dt, event_value
from existing_time_series_table
where expired_dt is null
union all
select primary_key, effective_dt, event_value
from new_time_series_table) sub_1
union all
—–历史已经失效的记录
select primary_key, effective_dt, expired_dt, event_value
from existing_time_series_table
where expired_dt is not null
