spark 累加历史主要用到了窗口函数，而进行全部统计，则需要用到rollup函数
1 应用场景：
1、我们需要统计用户的总使用时长（累加历史）
2、前台展现页面需要对多个维度进行查询，如：产品、地区等等
2 原始数据：
product_code |event_date |duration | -------------|-----------|---------| 1438 |2016-05-13 |165 | 1438 |2016-05-14 |595 | 1438 |2016-05-15 |105 | 1629 |2016-05-13 |12340 | 1629 |2016-05-14 |13850 | 1629 |2016-05-15 |227 |
3 业务场景实现
3.1 业务场景1：累加历史：
如数据源所示：我们已经有当天用户的使用时长，我们期望在进行统计的时候，14号能累加13号的，15号能累加14、13号的，以此类推
3.1.1 spark-sql实现
//spark sql 使用窗口函数累加历史数据 sqlContext.sql( """ select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc) as sum_duration from userlogs_date """).show +-----+----------+------------+ |pcode|event_date|sum_duration| +-----+----------+------------+ | 1438|2016-05-13| 165| | 1438|2016-05-14| 760| | 1438|2016-05-15| 865| | 1629|2016-05-13| 12340| | 1629|2016-05-14| 26190| | 1629|2016-05-15| 26417| +-----+----------+------------+
3.1.2 dataframe实现
//使用Column提供的over 函数，传入窗口操作
import org.apache.spark.sql.expressions._
val first_2_now_window = Window.partitionBy("pcode").orderBy("event_date")
df_userlogs_date.select(
$"pcode",
$"event_date",
sum($"duration").over(first_2_now_window).as("sum_duration")
).show
+-----+----------+------------+
|pcode|event_date|sum_duration|
+-----+----------+------------+
| 1438|2016-05-13| 165|
| 1438|2016-05-14| 760|
| 1438|2016-05-15| 865|
| 1629|2016-05-13| 12340|
| 1629|2016-05-14| 26190|
| 1629|2016-05-15| 26417|
+-----+----------+------------+
3.1.3 扩展 累加一段时间范围内
实际业务中的累加逻辑远比上面复杂，比如，累加之前N天，累加前N天到后N天等等。以下我们来实现：
3.1.3.1 累加历史所有：
select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc) as sum_duration from userlogs_dateselect pcode,event_date,sum(duration) over (partition by pcode order by event_date asc rows between unbounded preceding and current row) as sum_duration from userlogs_dateWindow.partitionBy("pcode").orderBy("event_date").rowsBetween(Long.MinValue,0)Window.partitionBy("pcode").orderBy("event_date")上边四种写法完全相等
3.1.3.2 累加N天之前，假设N=3
select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc rows between 3 preceding and current row) as sum_duration from userlogs_dateWindow.partitionBy("pcode").orderBy("event_date").rowsBetween(-3,0)
3.1.3.3 累加前N天，后M天: 假设N=3 M=5
select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc rows between 3 preceding and 5 following ) as sum_duration from userlogs_dateWindow.partitionBy("pcode").orderBy("event_date").rowsBetween(-3,5)3.1.3.4 累加该分区内所有行
select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc rows between unbounded preceding and unbounded following ) as sum_duration from userlogs_date
Window.partitionBy("pcode").orderBy("event_date").rowsBetween(Long.MinValue,Long.MaxValue)
总结如下：
preceding：用于累加前N行（分区之内）。若是从分区第一行头开始，则为 unbounded。 N为：相对当前行向前的偏移量following ：与preceding相反，累加后N行（分区之内）。若是累加到该分区结束，则为 unbounded。N为：相对当前行向后的偏移量current row：顾名思义，当前行，偏移量为0
说明：上边的前N，后M，以及current row均会累加该偏移量所在行3.1.3.4 实测结果
累加历史：分区内当天及之前所有 写法1：select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc) as sum_duration from userlogs_date
+-----+----------+------------+ |pcode|event_date|sum_duration| +-----+----------+------------+ | 1438|2016-05-13| 165| | 1438|2016-05-14| 760| | 1438|2016-05-15| 865| | 1629|2016-05-13| 12340| | 1629|2016-05-14| 26190| | 1629|2016-05-15| 26417| +-----+----------+------------+
累加历史：分区内当天及之前所有 写法2：select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc rows between unbounded preceding and current row) as sum_duration from userlogs_date
+-----+----------+------------+ |pcode|event_date|sum_duration| +-----+----------+------------+ | 1438|2016-05-13| 165| | 1438|2016-05-14| 760| | 1438|2016-05-15| 865| | 1629|2016-05-13| 12340| | 1629|2016-05-14| 26190| | 1629|2016-05-15| 26417| +-----+----------+------------+
累加当日和昨天：select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc rows between 1 preceding and current row) as sum_duration from userlogs_date
+-----+----------+------------+ |pcode|event_date|sum_duration| +-----+----------+------------+ | 1438|2016-05-13| 165| | 1438|2016-05-14| 760| | 1438|2016-05-15| 700| | 1629|2016-05-13| 12340| | 1629|2016-05-14| 26190| | 1629|2016-05-15| 14077| +-----+----------+------------+
累加当日、昨日、明日：select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc rows between 1 preceding and 1 following ) as sum_duration from userlogs_date
+-----+----------+------------+ |pcode|event_date|sum_duration| +-----+----------+------------+ | 1438|2016-05-13| 760| | 1438|2016-05-14| 865| | 1438|2016-05-15| 700| | 1629|2016-05-13| 26190| | 1629|2016-05-14| 26417| | 1629|2016-05-15| 14077| +-----+----------+------------+
累加分区内所有：当天和之前之后所有：select pcode,event_date,sum(duration) over (partition by pcode order by event_date asc rows between unbounded preceding and unbounded following ) as sum_duration from userlogs_date
+-----+----------+------------+ |pcode|event_date|sum_duration| +-----+----------+------------+ | 1438|2016-05-13| 865| | 1438|2016-05-14| 865| | 1438|2016-05-15| 865| | 1629|2016-05-13| 26417| | 1629|2016-05-14| 26417| | 1629|2016-05-15| 26417| +-----+----------+------------+
3.2 业务场景2：统计全部
3.2.1 spark sql实现
//spark sql 使用rollup添加all统计 sqlContext.sql( """ select pcode,event_date,sum(duration) as sum_duration from userlogs_date_1 group by pcode,event_date with rollup order by pcode,event_date """).show() +-----+----------+------------+ |pcode|event_date|sum_duration| +-----+----------+------------+ | null| null| 27282| | 1438| null| 865| | 1438|2016-05-13| 165| | 1438|2016-05-14| 595| | 1438|2016-05-15| 105| | 1629| null| 26417| | 1629|2016-05-13| 12340| | 1629|2016-05-14| 13850| | 1629|2016-05-15| 227| +-----+----------+------------+
3.2.2 dataframe函数实现
//使用dataframe提供的rollup函数，进行多维度all统计 df_userlogs_date.rollup($"pcode", $"event_date").agg(sum($"duration")).orderBy($"pcode", $"event_date") +-----+----------+-------------+ |pcode|event_date|sum(duration)| +-----+----------+-------------+ | null| null| 27282| | 1438| null| 865| | 1438|2016-05-13| 165| | 1438|2016-05-14| 595| | 1438|2016-05-15| 105| | 1629| null| 26417| | 1629|2016-05-13| 12340| | 1629|2016-05-14| 13850| | 1629|2016-05-15| 227| +-----+----------+-------------+
附录
下面是这两个函数的官方api说明：
org.apache.spark.sql.scala
def rollup(col1: String, cols: String*): GroupedData
Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them. See GroupedData for all the available aggregate functions.
This is a variant of rollup that can only group by existing columns using column names (i.e. cannot construct expressions).
// Compute the average for all numeric columns rolluped by department and group.
df.rollup("department", "group").avg()
// Compute the max age and average salary, rolluped by department and gender.
df.rollup($"department", $"gender").agg(Map(
"salary" -> "avg",
"age" -> "max"
))
def rollup(cols: Column*): GroupedData Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them. See GroupedData for all the available aggregate functions. df.rollup($"department", $"group").avg() // Compute the max age and average salary, rolluped by department and gender. df.rollup($"department", $"gender").agg(Map( "salary" -> "avg", "age" -> "max" ))
org.apache.spark.sql.Column.scala
def over(window: WindowSpec): Column
Define a windowing column.
val w = Window.partitionBy("name").orderBy("id")
df.select(
sum("price").over(w.rangeBetween(Long.MinValue, 2)),
avg("price").over(w.rowsBetween(0, 4))
)
时间： 2024-08-06 15:55:33