我有两个数据帧,我想基于一列连接它们,但需要注意的是,该列是一个时间戳,并且该时间戳必须在一定的偏移量(5 秒)内才能连接记录。更具体地说,记录在dates_df
with date=1/3/2015:00:00:00
应该加入events_df
with time=1/3/2015:00:00:01
因为两个时间戳相差不超过 5 秒。
我试图让这个逻辑与 python Spark 一起工作,这是非常痛苦的。人们如何在 Spark 中进行这样的连接?
我的方法是添加两个额外的列dates_df
这将决定lower_timestamp
and upper_timestamp
以 5 秒偏移为边界,并执行条件连接。这就是它失败的地方,更具体地说:
joined_df = dates_df.join(events_df,
dates_df.lower_timestamp < events_df.time < dates_df.upper_timestamp)
joined_df.explain()
仅捕获查询的最后部分:
Filter (time#6 < upper_timestamp#4)
CartesianProduct
....
它给了我一个错误的结果。
我真的必须对每个不等式进行完整的笛卡尔连接,并在进行过程中删除重复项吗?
这是完整的代码:
from datetime import datetime, timedelta
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql.functions import udf
master = 'local[*]'
app_name = 'stackoverflow_join'
conf = SparkConf().setAppName(app_name).setMaster(master)
sc = SparkContext(conf=conf)
sqlContext = SQLContext(sc)
def lower_range_func(x, offset=5):
return x - timedelta(seconds=offset)
def upper_range_func(x, offset=5):
return x + timedelta(seconds=offset)
lower_range = udf(lower_range_func, TimestampType())
upper_range = udf(upper_range_func, TimestampType())
dates_fields = [StructField("name", StringType(), True), StructField("date", TimestampType(), True)]
dates_schema = StructType(dates_fields)
dates = [('day_%s' % x, datetime(year=2015, day=x, month=1)) for x in range(1,5)]
dates_df = sqlContext.createDataFrame(dates, dates_schema)
dates_df.show()
# extend dates_df with time ranges
dates_df = dates_df.withColumn('lower_timestamp', lower_range(dates_df['date'])).\
withColumn('upper_timestamp', upper_range(dates_df['date']))
event_fields = [StructField("time", TimestampType(), True), StructField("event", StringType(), True)]
event_schema = StructType(event_fields)
events = [(datetime(year=2015, day=3, month=1, second=3), 'meeting')]
events_df = sqlContext.createDataFrame(events, event_schema)
events_df.show()
# finally, join the data
joined_df = dates_df.join(events_df,
dates_df.lower_timestamp < events_df.time < dates_df.upper_timestamp)
joined_df.show()
我得到以下输出:
+-----+--------------------+
| name| date|
+-----+--------------------+
|day_1|2015-01-01 00:00:...|
|day_2|2015-01-02 00:00:...|
|day_3|2015-01-03 00:00:...|
|day_4|2015-01-04 00:00:...|
+-----+--------------------+
+--------------------+-------+
| time| event|
+--------------------+-------+
|2015-01-03 00:00:...|meeting|
+--------------------+-------+
+-----+--------------------+--------------------+--------------------+--------------------+-------+
| name| date| lower_timestamp| upper_timestamp| time| event|
+-----+--------------------+--------------------+--------------------+--------------------+-------+
|day_3|2015-01-03 00:00:...|2015-01-02 23:59:...|2015-01-03 00:00:...|2015-01-03 00:00:...|meeting|
|day_4|2015-01-04 00:00:...|2015-01-03 23:59:...|2015-01-04 00:00:...|2015-01-03 00:00:...|meeting|
+-----+--------------------+--------------------+--------------------+--------------------+-------+