0 
+-------------------+
|           Dev_time|
+-------------------+
|2015-09-18 05:00:20|
|2015-09-18 05:00:21|
|2015-09-18 05:00:22|
|2015-09-18 05:00:23|
|2015-09-18 05:00:24|
|2015-09-18 05:00:25|
|2015-09-18 05:00:26|
|2015-09-18 05:00:27|
|2015-09-18 05:00:37|
|2015-09-18 05:00:37|
|2015-09-18 05:00:37|
|2015-09-18 05:00:38|
|2015-09-18 05:00:39|
+-------------------+

对于 spark 的数据框，我想计算日期时间的差异，就像在numpy.diff(array)

4

1 回答 1

1

一般来说，使用 Spark 没有有效的方法来实现这一点DataFrames。更不用说像订单这样的事情在分布式设置中变得非常棘手。理论上你可以使用lag如下函数：

from pyspark.sql.functions import lag, col, unix_timestamp
from pyspark.sql.window import Window

dev_time = (unix_timestamp(col("dev_time")) * 1000).cast("timestamp")

df = sc.parallelize([
    ("2015-09-18 05:00:20", ), ("2015-09-18 05:00:21", ),
    ("2015-09-18 05:00:22", ), ("2015-09-18 05:00:23", ),
    ("2015-09-18 05:00:24", ), ("2015-09-18 05:00:25", ),
    ("2015-09-18 05:00:26", ), ("2015-09-18 05:00:27", ),
    ("2015-09-18 05:00:37", ), ("2015-09-18 05:00:37", ),
    ("2015-09-18 05:00:37", ), ("2015-09-18 05:00:38", ),
    ("2015-09-18 05:00:39", )
]).toDF(["dev_time"]).withColumn("dev_time", dev_time)

w = Window.orderBy("dev_time")
lag_dev_time = lag("dev_time").over(w).cast("integer")

diff = df.select((col("dev_time").cast("integer") - lag_dev_time).alias("diff"))

## diff.show()
## +----+
## |diff|
## +----+
## |null|
## |   1|
## |   1|
## |   1|
## |   1|
## |   1|
## |   1|
## |   1|
## |  10|
## ...

但效率极低（对于窗口函数，如果没有PARTITION BY提供子句，则将所有数据移动到单个分区）。sliding在实践中，在 RDD (Scala) 上使用方法或实现自己的滑动窗口 (Python)更有意义。看：

于 2015-11-27T00:38:41.547 回答