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Vinay Kolar
Vinay Kolar

Reputation: 983

pyspark's "between" function: range search on timestamps is not inclusive

pyspark's 'between' function is not inclusive for timestamp input.

For example, if we want all rows between two dates, say, '2017-04-13' and '2017-04-14', then it performs an "exclusive" search when the dates are passed as strings. i.e., it omits the '2017-04-14 00:00:00' fields

However, the document seem to hint that it is inclusive (no reference on timestamp though)

Of course, one way is to add a microsecond from the upper bound and pass it to the function. However, not a great fix. Any clean way of doing inclusive search?

Example:

import pandas as pd
from pyspark.sql import functions as F
... sql_context creation ...
test_pd=pd.DataFrame([{"start":'2017-04-13 12:00:00', "value":1.0},{"start":'2017-04-14 00:00:00', "value":1.1}])
test_df = sql_context.createDataFrame(test_pd).withColumn("start", F.col("start").cast('timestamp'))
test_df.show()

+--------------------+-----+
|               start|value|
+--------------------+-----+
|2017-04-13 12:00:...|  1.0|
|2017-04-14 00:00:...|  1.1|
+--------------------+-----+

test_df.filter(F.col("start").between('2017-04-13','2017-04-14')).show()

+--------------------+-----+
|               start|value|
+--------------------+-----+
|2017-04-13 12:00:...|  1.0|
+--------------------+-----+

Upvotes: 29

Views: 116039

Answers (3)

dkdlfls26
dkdlfls26

Reputation: 171

Just to be clear, if you want to get data from a single date it's better to specify the exact time

ex) Retrieve data only on a single day (2017-04-13)

test_df.filter(F.col("start").between('2017-04-13 00:00:00','2017-04-13 23:59:59.59')

cf) if you set the date as between '2017-04-13', '2017-04-14' this will include 2017-04-14 00:00:00 data also, which technically isn't the data you want to pull out since it's 2017-04-14 data.

Upvotes: 4

Anna K.
Anna K.

Reputation: 1530

.between() method is always inclusive. The problem in your example is that when you pass string to .between() method, it treats your data as strings as well. For string comparison, '2017-04-14 00:00:00' is strictly greater than '2017-04-14' because the former is a longer string than the latter, this is why the second date is filtered out in your example. To avoid the "inconsistency", you should pass arguments in datetime format to .between() as follows:

filtered_df = (test_df.filter(F.col("start")
                .between(dt.strptime('2017-04-13 12:00:00', '%Y-%m-%d %H:%M:%S'), 
                         dt.strptime('2017-04-14 00:00:00', '%Y-%m-%d %H:%M:%S'))))

This will produce the expected result:

+--------------------+-----+
|               start|value|
+--------------------+-----+
|2017-04-13 12:00:...|  1.0|
|2017-04-14 00:00:...|  1.1|
+--------------------+-----+

Upvotes: 12

Vinay Kolar
Vinay Kolar

Reputation: 983

Found out the answer. pyspark's "between" function is inconsistent in handling timestamp inputs.

  1. If you provide the the input in string format without time, it performs an exclusive search (Not what we expect from the documentation linked above).
  2. If you provide the input as datetime object or with exact time (e.g., '2017-04-14 00:00:00', then it performs an inclusive search.

For the above example, here is the output for exclusive search (use pd.to_datetime):

test_df.filter(F.col("start").between(pd.to_datetime('2017-04-13'),pd.to_datetime('2017-04-14'))).show()

+--------------------+-----+
|               start|value|
+--------------------+-----+
|2017-04-13 12:00:...|  1.0|
|2017-04-14 00:00:...|  1.1|
+--------------------+-----+

Similarly, if we provide in the date AND time in string format, it seems to perform an inclusive search:

test_df.filter(F.col("start").between('2017-04-13 12:00:00','2017-04-14 00:00:00')).show()

+--------------------+-----+
|               start|value|
+--------------------+-----+
|2017-04-13 12:00:...|  1.0|
|2017-04-14 00:00:...|  1.1|
+--------------------+-----+

Upvotes: 32

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