问题
I have the following sample Spark dataframe
import pandas as pd
import pyspark
import pyspark.sql.functions as fn
from pyspark.sql.window import Window
raw_df = pd.DataFrame([
(1115, dt.datetime(2019,8,5,18,20), dt.datetime(2019,8,5,18,40)),
(484, dt.datetime(2019,8,5,18,30), dt.datetime(2019,8,9,18,40)),
(484, dt.datetime(2019,8,4,18,30), dt.datetime(2019,8,6,18,40)),
(484, dt.datetime(2019,8,2,18,30), dt.datetime(2019,8,3,18,40)),
(484, dt.datetime(2019,8,7,18,50), dt.datetime(2019,8,9,18,50)),
(1115, dt.datetime(2019,8,6,18,20), dt.datetime(2019,8,6,18,40)),
], columns=['server_id', 'start_time', 'end_time'])
df = spark.createDataFrame(raw_df)
which result in
+---------+-------------------+-------------------+
|server_id| start_time| end_time|
+---------+-------------------+-------------------+
| 1115|2019-08-05 18:20:00|2019-08-05 18:40:00|
| 484|2019-08-05 18:30:00|2019-08-09 18:40:00|
| 484|2019-08-04 18:30:00|2019-08-06 18:40:00|
| 484|2019-08-02 18:30:00|2019-08-03 18:40:00|
| 484|2019-08-07 18:50:00|2019-08-09 18:50:00|
| 1115|2019-08-06 18:20:00|2019-08-06 18:40:00|
+---------+-------------------+-------------------+
This indicates the usage date ranges of each server. I want to convert this into a time series of non-overlapping dates.
I would like to achieve this without using UDFs.
This is what I'm doing now, which is wrong
w = Window().orderBy(fn.lit('A'))
# Separate start/end date of usage into rows
df = (df.withColumn('start_end_time', fn.array('start_time', 'end_time'))
.withColumn('event_dt', fn.explode('start_end_time'))
.withColumn('row_num', fn.row_number().over(w)))
# Indicate start/end date of the usage (start date will always be on odd rows)
df = (df.withColumn('is_start', fn.when(fn.col('row_num')%2 == 0, 0).otherwise(1))
.select('server_id', 'event_dt', 'is_start'))
which gives
+---------+-------------------+--------+
|server_id| event_dt|is_start|
+---------+-------------------+--------+
| 1115|2019-08-05 18:20:00| 1|
| 1115|2019-08-05 18:40:00| 0|
| 484|2019-08-05 18:30:00| 1|
| 484|2019-08-09 18:40:00| 0|
| 484|2019-08-04 18:30:00| 1|
| 484|2019-08-06 18:40:00| 0|
| 484|2019-08-02 18:30:00| 1|
| 484|2019-08-03 18:40:00| 0|
| 484|2019-08-07 18:50:00| 1|
| 484|2019-08-09 18:50:00| 0|
| 1115|2019-08-06 18:20:00| 1|
| 1115|2019-08-06 18:40:00| 0|
+---------+-------------------+--------+
But the end result I would like to achieve is the following:
+---------+-------------------+--------+
|server_id| event_dt|is_start|
+---------+-------------------+--------+
| 1115|2019-08-05 18:20:00| 1|
| 1115|2019-08-05 18:40:00| 0|
| 1115|2019-08-06 18:20:00| 1|
| 1115|2019-08-06 18:40:00| 0|
| 484|2019-08-02 18:30:00| 1|
| 484|2019-08-03 18:40:00| 0|
| 484|2019-08-04 18:30:00| 1|
| 484|2019-08-09 18:50:00| 0|
+---------+-------------------+--------+
So for server_id
484 I have the actual start and end dates without all the noise in between.
Do you have any suggestion on how to achieve that without using UDFs?
Thanks
回答1:
IIUC, this is one of the problems which can be resolved by using Window lag(), sum() function to add a sub-group label for ordered consecutive rows which match some specific conditions. Similar to what we do in Pandas using shift()+cumsum().
Set up the Window Spec
w1
:w1 = Window.partitionBy('server_id').orderBy('start_time')
and calculate the following:
- max('end_time'): the max
end_time
before the current row over window-w1
- lag('end_time'): the previous
end_time
- sum('prev_end_time < current_start_time ? 1 : 0'): the flag to identify the sub-group
The above three items can be corresponding to Pandas cummax(), shift() and cumsum().
- max('end_time'): the max
Calculate df1 by updating df.end_time with
max(end_time).over(w1)
and setting up the sub-group label g, then doinggroupby(server_id, g)
to calculate themin(start_time)
andmax(end_time)
df1 = df.withColumn('end_time', fn.max('end_time').over(w1)) \ .withColumn('g', fn.sum(fn.when(fn.lag('end_time').over(w1) < fn.col('start_time'),1).otherwise(0)).over(w1)) \ .groupby('server_id', 'g') \ .agg(fn.min('start_time').alias('start_time'), fn.max('end_time').alias('end_time')) df1.show() +---------+---+-------------------+-------------------+ |server_id| g| start_time| end_time| +---------+---+-------------------+-------------------+ | 1115| 0|2019-08-05 18:20:00|2019-08-05 18:40:00| | 1115| 1|2019-08-06 18:20:00|2019-08-06 18:40:00| | 484| 0|2019-08-02 18:30:00|2019-08-03 18:40:00| | 484| 1|2019-08-04 18:30:00|2019-08-09 18:50:00| +---------+---+-------------------+-------------------+
After we have df1, we can split the data using two selects and then union the resultset:
df_new = df1.selectExpr('server_id', 'start_time as event_dt', '1 as is_start').union( df1.selectExpr('server_id', 'end_time as event_dt', '0 as is_start') ) df_new.orderBy('server_id', 'event_dt').show() +---------+-------------------+--------+ |server_id| event_dt|is_start| +---------+-------------------+--------+ | 484|2019-08-02 18:30:00| 1| | 484|2019-08-03 18:40:00| 0| | 484|2019-08-04 18:30:00| 1| | 484|2019-08-09 18:50:00| 0| | 1115|2019-08-05 18:20:00| 1| | 1115|2019-08-05 18:40:00| 0| | 1115|2019-08-06 18:20:00| 1| | 1115|2019-08-06 18:40:00| 0| +---------+-------------------+--------+
来源:https://stackoverflow.com/questions/57737035/pyspark-and-time-series-data-how-to-smartly-avoid-overlapping-dates