Github user BryanCutler commented on the issue:
https://github.com/apache/spark/pull/18659
The following was used to test performance locally
```python
spark = SparkSession.builder.appName("vectorized_udfs").getOrCreate()
vectorize = True
if vectorize:
from numpy import log, exp
spark.conf.set("spark.sql.execution.arrow.vectorizeUDFs", "true")
else:
from math import log, exp
spark.conf.set("spark.sql.execution.arrow.vectorizeUDFs", "false")
def my_func(p1, p2):
w = 0.5
return exp(log(p1) + log(p2) - log(w))
df = spark.range(1 << 24, numPartitions=16).toDF("id") \
.withColumn("p1", rand()).withColumn("p2", rand())
my_udf = udf(my_func, DoubleType())
df.withColumn("p", my_udf(col("p1"), col("p2")))
```
**Non-Vectorized** ~ 6.127449s
**Vectorized** ~ 2.867868s
Speedup of 2.14x
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