Using broadcasted dataframe in pyspark UDF - apache-spark

Question

Is it possible to use a broadcasted data frame in the UDF of a pyspark SQl application.
My Code calls the broadcasted Dataframe inside a pyspark dataframe like below.
fact_ent_df_data =
sparkSession.sparkContext.broadcast(fact_ent_df.collect())
def generate_lookup_code(col1,col2,col3):
fact_ent_df_count=fact_ent_df_data.
select(fact_ent_df_br.TheDate.between(col1,col2),
fact_ent_df_br.Ent.isin('col3')).count()
return fact_ent_df_count
sparkSession.udf.register("generate_lookup_code" , generate_lookup_code )
sparkSession.sql('select sample4,generate_lookup_code(sample1,sample2,sample 3) as count_hol from table_t')
I am getting local variable used before assignment error when i use the broadcasted df_bc. Any help is appreciated
And the Error i am getting is
Traceback (most recent call last):
File "C:/Users/Vignesh/PycharmProjects/gettingstarted/aramex_transit/spark_driver.py", line 46, in <module>
sparkSession.udf.register("generate_lookup_code" , generate_lookup_code )
File "D:\spark-2.3.2-bin-hadoop2.6\spark-2.3.2-bin-hadoop2.6\python\pyspark\sql\udf.py", line 323, in register
self.sparkSession._jsparkSession.udf().registerPython(name, register_udf._judf)
File "D:\spark-2.3.2-bin-hadoop2.6\spark-2.3.2-bin-hadoop2.6\python\pyspark\sql\udf.py", line 148, in _judf
self._judf_placeholder = self._create_judf()
File "D:\spark-2.3.2-bin-hadoop2.6\spark-2.3.2-bin-hadoop2.6\python\pyspark\sql\udf.py", line 157, in _create_judf
wrapped_func = _wrap_function(sc, self.func, self.returnType)
File "D:\spark-2.3.2-bin-hadoop2.6\spark-2.3.2-bin-hadoop2.6\python\pyspark\sql\udf.py", line 33, in _wrap_function
pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command)
File "D:\spark-2.3.2-bin-hadoop2.6\spark-2.3.2-bin-hadoop2.6\python\pyspark\rdd.py", line 2391, in _prepare_for_python_RDD
pickled_command = ser.dumps(command)
File "D:\spark-2.3.2-bin-hadoop2.6\spark-2.3.2-bin-hadoop2.6\python\pyspark\serializers.py", line 575, in dumps
return cloudpickle.dumps(obj, 2)
File "D:\spark-2.3.2-bin-hadoop2.6\spark-2.3.2-bin-hadoop2.6\python\pyspark\cloudpickle.py", line 918, in dumps
cp.dump(obj)
File "D:\spark-2.3.2-bin-hadoop2.6\spark-2.3.2-bin-hadoop2.6\python\pyspark\cloudpickle.py", line 249, in dump
raise pickle.PicklingError(msg)
pickle.PicklingError: Could not serialize object: Py4JError: An error occurred while calling o24.__getnewargs__. Trace:
py4j.Py4JException: Method __getnewargs__([]) does not exist
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326)
at py4j.Gateway.invoke(Gateway.java:274)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)

Answer 1

Think about Spark Broadcast variable as a Python simple data type like list, So the problem is how to pass a variable to the UDF functions. Here is an example:
Suppose we have ages list d and a data frame with columns name and age. So we want to check if the age of each person is in ages list.
from pyspark.sql.functions import udf, col
l = [13, 21, 34] # ages list
d = [('Alice', 10), ('bob', 21)] # data frame rows
rdd = sc.parallelize(l)
b_rdd = sc.broadcast(rdd.collect()) # define broadcast variable
df = spark.createDataFrame(d , ["name", "age"])
def check_age (age, age_list):
if age in l:
return "true"
return "false"
def udf_check_age(age_list):
return udf(lambda x : check_age(x, age_list))
df.withColumn("is_age_in_list", udf_check_age(b_rdd.value)(col("age"))).show()
Output:
+-----+---+--------------+
| name|age|is_age_in_list|
+-----+---+--------------+
|Alice| 10| false|
| bob| 21| true|
+-----+---+--------------+

Answer 2

Just trying to contribute with a simpler example based on Soheil's answer.
from pyspark.sql.functions import udf, col
def check_age (_age):
return _age > 18
dict_source = {"alice": 10, "bob": 21}
broadcast_dict = sc.broadcast(dict_source) # define broadcast variable
rdd = sc.parallelize(list(dict_source.keys()))
result = rdd.map(
lambda _name: check_age(broadcast_dict.value.get(_name)) # Here you specify the broadcasted var `.value`
)
print(result.collect())