Pyspark: filter dataframe by regex with string formatting?

Question

I've read several posts on using the "like" operator to filter a spark dataframe by the condition of containing a string/expression, but was wondering if the following is a "best-practice" on using %s in the desired condition as follows:

input_path = <s3_location_str>
my_expr = "Arizona.*hot"  # a regex expression
dx = sqlContext.read.parquet(input_path)  # "keyword" is a field in dx

# is the following correct?
substr = "'%%%s%%'" %my_keyword  # escape % via %% to get "%"
dk = dx.filter("keyword like %s" %substr)

# dk should contain rows with keyword values such as "Arizona is hot."

Note

I'm trying to get all rows in dx that contain the expression my_keyword. Otherwise, for exact matches we wouldn't need surrounding percent signs '%'.

Solution 1

From neeraj's hint, it seems like the correct way to do this in pyspark is:

expr = "Arizona.*hot"
dk = dx.filter(dx["keyword"].rlike(expr))

Note that dx.filter($"keyword" ...) did not work since (my version) of pyspark didn't seem to support the $ nomenclature out of the box.

Solution 2

Try rlike function as mentioned below.

df.filter(<column_name> rlike "<regex_pattern>")

for example.

dk = dx.filter($"keyword" rlike "<pattern>")

Solution 3

I used the following for the timestamp regex

expression = r'[0-9]{4}-(0[1-9]|1[0-2])-(0[1-9]|[1-2][0-9]|3[0-1]) (2[0-3]|[01][0-9]):[0-5][0-9]:[0-5][0-9]'
df1 = df.filter(df['eta'].rlike(expression))