pyspark.sql.functions.count_min_sketch#

pyspark.sql.functions.count_min_sketch(col, eps, confidence, seed=None)[source]#

Returns a count-min sketch of a column with the given esp, confidence and seed. The result is an array of bytes, which can be deserialized to a CountMinSketch before usage. Count-min sketch is a probabilistic data structure used for cardinality estimation using sub-linear space.

New in version 3.5.0.

Parameters

colColumn or str: target column to compute on.
epsColumn or float: relative error, must be positive

Changed in version 4.0.0: eps now accepts float value.
confidenceColumn or float: confidence, must be positive and less than 1.0

Changed in version 4.0.0: confidence now accepts float value.
seedColumn or int, optional: random seed

Changed in version 4.0.0: seed now accepts int value.

Returns

Column: count-min sketch of the column

Examples

Example 1: Using columns as arguments

>>> from pyspark.sql import functions as sf
>>> spark.range(100).select(
...     sf.hex(sf.count_min_sketch(sf.col("id"), sf.lit(3.0), sf.lit(0.1), sf.lit(1)))
... ).show(truncate=False)
+------------------------------------------------------------------------+
|hex(count_min_sketch(id, 3.0, 0.1, 1))                                  |
+------------------------------------------------------------------------+
|0000000100000000000000640000000100000001000000005D8D6AB90000000000000064|
+------------------------------------------------------------------------+

Example 2: Using numbers as arguments

>>> from pyspark.sql import functions as sf
>>> spark.range(100).select(
...     sf.hex(sf.count_min_sketch("id", 1.0, 0.3, 2))
... ).show(truncate=False)
+----------------------------------------------------------------------------------------+
|hex(count_min_sketch(id, 1.0, 0.3, 2))                                                  |
+----------------------------------------------------------------------------------------+
|0000000100000000000000640000000100000002000000005D96391C00000000000000320000000000000032|
+----------------------------------------------------------------------------------------+

Example 3: Using a random seed

>>> from pyspark.sql import functions as sf
>>> spark.range(100).select(
...     sf.hex(sf.count_min_sketch("id", sf.lit(1.5), 0.6))
... ).show(truncate=False) 
+----------------------------------------------------------------------------------------------------------------------------------------+
|hex(count_min_sketch(id, 1.5, 0.6, 2120704260))                                                                                         |
+----------------------------------------------------------------------------------------------------------------------------------------+
|0000000100000000000000640000000200000002000000005ADECCEE00000000153EBE090000000000000033000000000000003100000000000000320000000000000032|
+----------------------------------------------------------------------------------------------------------------------------------------+