问题描述
我有这样的数据:
userid,eventtime,location_point
4e191908,2017-06-04 03:00:00,18685891
4e191908,2017-06-04 03:04:00,18685891
3136afcb,2017-06-04 03:03:00,18382821
661212dd,2017-06-04 03:06:00,80831484
40e8a7c3,2017-06-04 03:12:00,18825769
如果在同一 location_point 的 5 分钟窗口内有 2 个或更多userid，我想添加一个新的布尔列，该列标记为 true.我有一个想法，使用 lag 函数来查找由 userid 划分的窗口以及当前时间戳和接下来 5 分钟之间的范围:
I would like to add a new boolean column that marks true if there are 2 or moreuserid within a 5 minutes window in the same location_point. I had an idea of using lag function to lookup over a window partitioned by the userid and with the range between the current timestamp and the next 5 minutes:
from pyspark.sql import functions as F
from pyspark.sql import Window as W
from pyspark.sql.functions import col
days = lambda i: i * 60*5
windowSpec = W.partitionBy(col("userid")).orderBy(col("eventtime").cast("timestamp").cast("long")).rangeBetween(0, days(5))
lastURN = F.lag(col("location_point"), 1).over(windowSpec)
visitCheck = (last_location_point == output.location_pont)
output.withColumn("visit_check", visitCheck).select("userid","eventtime", "location_pont", "visit_check")
当我使用 RangeBetween 函数时，这段代码给了我一个分析异常:
This code is giving me an analysis exception when I use the RangeBetween function:
AnalysisException:当前行和 1500 之间的 u'Window Frame RANGEFOLLOWING 必须匹配所需的帧 ROWS BETWEEN 1 PRECEDING AND 1前面;
你知道解决这个问题的方法吗?
Do you know any way to tackle this problem?
推荐答案
鉴于您的数据:
让我们添加一个以秒为单位的时间戳列:
Let's add a column with a timestamp in seconds:
df = df.withColumn('timestamp',df_taf.eventtime.astype('Timestamp').cast("long"))
df.show()
+--------+-------------------+--------------+----------+
| userid| eventtime|location_point| timestamp|
+--------+-------------------+--------------+----------+
|4e191908|2017-06-04 03:00:00| 18685891|1496545200|
|4e191908|2017-06-04 03:04:00| 18685891|1496545440|
|3136afcb|2017-06-04 03:03:00| 18382821|1496545380|
|661212dd|2017-06-04 03:06:00| 80831484|1496545560|
|40e8a7c3|2017-06-04 03:12:00| 18825769|1496545920|
|4e191908|2017-06-04 03:11:30| 18685891|1496545890|
+--------+-------------------+--------------+----------+
现在，让我们定义一个窗口函数，按位置点划分，按时间戳排序，范围在 -300 秒和当前时间之间.我们可以计算这个窗口中元素的数量，并将这些数据放在名为occurences in_5_min"的列中:
Now, let's define a window function, with a partition by location_point, an order by timestamp and a range between -300s and current time. We can count the number of elements in this window and put these data in a column named 'occurences in_5_min':
w = Window.partitionBy('location_point').orderBy('timestamp').rangeBetween(-60*5,0)
df = df.withColumn('occurrences_in_5_min',F.count('timestamp').over(w))
df.show()
+--------+-------------------+--------------+----------+--------------------+
| userid| eventtime|location_point| timestamp|occurrences_in_5_min|
+--------+-------------------+--------------+----------+--------------------+
|40e8a7c3|2017-06-04 03:12:00| 18825769|1496545920| 1|
|3136afcb|2017-06-04 03:03:00| 18382821|1496545380| 1|
|661212dd|2017-06-04 03:06:00| 80831484|1496545560| 1|
|4e191908|2017-06-04 03:00:00| 18685891|1496545200| 1|
|4e191908|2017-06-04 03:04:00| 18685891|1496545440| 2|
|4e191908|2017-06-04 03:11:30| 18685891|1496545890| 1|
+--------+-------------------+--------------+----------+--------------------+
现在，如果特定位置在过去 5 分钟内出现的次数严格超过 1，您可以使用 True 添加所需的列:
Now you can add the desired column with True if the number of occurences is strictly more than 1 in the last 5 minutes on a particular location:
add_bool = udf(lambda col : True if col>1 else False, BooleanType())
df = df.withColumn('already_occured',add_bool('occurrences_in_5_min'))
df.show()
+--------+-------------------+--------------+----------+--------------------+---------------+
| userid| eventtime|location_point| timestamp|occurrences_in_5_min|already_occured|
+--------+-------------------+--------------+----------+--------------------+---------------+
|40e8a7c3|2017-06-04 03:12:00| 18825769|1496545920| 1| false|
|3136afcb|2017-06-04 03:03:00| 18382821|1496545380| 1| false|
|661212dd|2017-06-04 03:06:00| 80831484|1496545560| 1| false|
|4e191908|2017-06-04 03:00:00| 18685891|1496545200| 1| false|
|4e191908|2017-06-04 03:04:00| 18685891|1496545440| 2| true|
|4e191908|2017-06-04 03:11:30| 18685891|1496545890| 1| false|
+--------+-------------------+--------------+----------+--------------------+---------------+
这篇关于如何在时间戳值上使用 lag 和 rangeBetween 函数?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！