Multi row range filters?

[alpuy]

I have the following data table:
columns = {
"meas_point_key": {"cf": "rowkey", "col": "key1", "type": "string", "length":"8"},
"date_key": {"cf": "rowkey", "col": "key2", "type": "string", "length":"14"},
"magnitude_key": {"cf": "rowkey", "col": "key3", "type": "string", "length":"2"},
"meas_int_key": {"cf": "rowkey", "col": "key4", "type": "string", "length":"1"},
"source_key": {"cf": "rowkey", "col": "key5", "type": "string"},
"date": {"cf": "IV", "col": "D", "type": "bigint"},
"file": {"cf": "IV", "col": "F", "type": "string"},
"last_update_date": {"cf": "IV", "col": "L", "type": "bigint"},
"magnitude": {"cf": "IV", "col": "M", "type": "bigint"},
"meas_int": {"cf": "IV", "col": "MI", "type": "bigint"},
"meas_point": {"cf": "IV", "col": "MP", "type": "bigint"},
"source": {"cf": "IV", "col": "S", "type": "bigint"},
"value": {"cf": "IV", "col": "V", "type": "double"},
"last_update_val": {"cf": "IV", "col": "LAV", "type": "bigint"},
"val_det": {"cf": "IV", "col": "VD", "type": "string"},
"val_res": {"cf": "IV", "col": "VR", "type": "string"}
}

and i want to scan based on the rowkey with the following filters:

df = df1.where((df1.meas_point_key.isin(meter_list_B.value) ) & (df1.magnitude_key == "13") & (df1.date_key >= '01588302000000') & (df1.date_key <= '01593572400000') & (df1.meas_int_key == '1'))

where meter_list_B is a broadcasted list of string values, this list contains about 15000 values.

Is this query optimal? because i think that because of the time it is taking it is not an optimal scan.

Are MultiRowRangeFilters used in shc?