假设您的数据已经解析：
df1
# # Source: table<df1> [?? x 4]
# # Database: spark_connection
# id timefrom timeto value
# <int> <dttm> <dttm> <int>
# 1 10 2017-06-06 08:30:00 2017-06-06 08:31:00 50
# 2 10 2017-06-06 08:31:00 2017-06-06 08:32:00 80
# 3 10 2017-06-06 08:32:00 2017-06-06 08:33:00 20
# 4 22 2017-06-06 08:33:00 2017-06-06 08:34:00 30
# 5 22 2017-06-06 08:34:00 2017-06-06 08:35:00 50
# 6 22 2017-06-06 08:35:00 2017-06-06 08:36:00 50
df2
# # Source: table<df2> [?? x 4]
# # Database: spark_connection
# id timefrom timeto value
# <int> <dttm> <dttm> <int>
# 1 10 2017-06-06 08:30:00 2017-06-06 08:33:00 30
# 2 22 2017-06-06 08:33:00 2017-06-06 08:36:00 67
# 3 32 2017-06-06 08:36:00 2017-06-06 08:39:00 28
# 4 14 2017-06-06 08:39:00 2017-06-06 08:42:00 30
# 5 27 2017-06-06 08:42:00 2017-06-06 08:55:00 90
您可以使用window功能 https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.functions%24@window(timeColumn:org.apache.spark.sql.Column,windowDuration:String,slideDuration:String,startTime:String):org.apache.spark.sql.Column:
exprs <- list(
"id", "value as value2",
# window generates structure struct<start: timestamp, end: timestamp>
# we use dot syntax to access nested fields
"window.start as timefrom", "window.end as timeto")
df1_agg <- df1 %>%
mutate(window = window(timefrom, "3 minutes")) %>%
group_by(id, window) %>%
summarise(value = avg(value)) %>%
# As far as I am aware there is no sparklyr syntax
# for accessing struct fields, so we'll use simple SQL expression
spark_dataframe() %>%
invoke("selectExpr", exprs) %>%
sdf_register() %>%
print()
# Source: table<sparklyr_tmp_472ee8ba244> [?? x 4]
# Database: spark_connection
id value2 timefrom timeto
<int> <dbl> <dttm> <dttm>
1 22 43.3 2017-06-06 08:33:00 2017-06-06 08:36:00
2 10 50.0 2017-06-06 08:30:00 2017-06-06 08:33:00
然后你就可以通过id和时间戳列：
df2 %>% inner_join(df1_agg, by = c("id", "timefrom", "timeto"))
# # Source: lazy query [?? x 5]
# # Database: spark_connection
# id timefrom timeto value value2
# <int> <dttm> <dttm> <int> <dbl>
# 1 10 2017-06-06 08:30:00 2017-06-06 08:33:00 30 50.0
# 2 22 2017-06-06 08:33:00 2017-06-06 08:36:00 67 43.3
