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
3、需要展现的表格头如： 产品、2015-04、2015-05、2015-06
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_date
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
Window.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_date
Window.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_date
Window.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", $"event_date") +-----+----------+-------------+ |pcode|event_date|sum(duration)| +-----+----------+-------------+ | null| null| 27282| | 1438| null| 865| | 1438|2016-05-13| 165| | 1438|2016-05-14| 595| | 1438|2016-05-15| 105| | 1629| null| 26417| | 1629|2016-05-13| 12340| | 1629|2016-05-14| 13850| | 1629|2016-05-15| 227| +-----+----------+-------------+
3.3 行转列 ->pivot
pivot目前还没有sql语法，先用df语法吧
val userlogs_date_all = sqlContext.sql("select dcode, pcode,event_date,sum(duration) as duration from userlogs group by dognum, pcode,event_date ") userlogs_date_all.registerTempTable("userlogs_date_all") val dates = userlogs_date_all.select($"event_date").map(row => row.getAs[String]("event_date")).distinct().collect().toList userlogs_date_all.groupBy($"dcode", $"pcode").pivot("event_date", dates).sum("duration").na.fill(0).show +-----------------+-----+----------+----------+----------+----------+ | dcode|pcode|2016-05-26|2016-05-13|2016-05-14|2016-05-15| +-----------------+-----+----------+----------+----------+----------+ | F2429186| 1438| 0| 0| 227| 0| | AI2342441| 1438| 0| 0| 0| 345| | A320018711| 1438| 0| 939| 0| 0| | H2635817| 1438| 0| 522| 0| 0| | D0288196| 1438| 0| 101| 0| 0| | Y0242218| 1438| 0| 1036| 0| 0| | H2392574| 1438| 0| 0| 689| 0| | D2245588| 1438| 0| 0| 1| 0| | Y2514906| 1438| 0| 0| 118| 4| | H2540419| 1438| 0| 465| 242| 5| | R2231926| 1438| 0| 0| 305| 0| | H2684591| 1438| 0| 136| 0| 0| | A2548470| 1438| 0| 412| 0| 0| | GH000309| 1438| 0| 0| 0| 4| | H2293216| 1438| 0| 0| 0| 534| | R2170601| 1438| 0| 0| 0| 0| |B2365238;B2559538| 1438| 0| 0| 0| 0| | BQ005465| 1438| 0| 0| 642| 78| | AH2180324| 1438| 0| 608| 146| 36| | H0279306| 1438| 0| 490| 0| 0| +-----------------+-----+----------+----------+----------+----------+
附录
下面是这两个函数的官方api说明：
org.apache.spark.sql.scala
def rollup(col1: String, cols: String*): GroupedData
Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them. See GroupedData for all the available aggregate functions. This is a variant of rollup that can only group by existing columns using column names (i.e. cannot construct expressions). // Compute the average for all numeric columns rolluped by department and group. df.rollup("department", "group").avg() // Compute the max age and average salary, rolluped by department and gender. df.rollup($"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)) )