How can use sqlContext in spark udf - pyspark

Question

I'm trying to load a json file in spark UDF and use it to query something. What i need to do is to do use a column value (storeId) from a data frame and use it in
But, i get a pickle error. If I try coding without the sqlContext then it work.
Is there any workaround or is this not possible.
def get_id_udf (storeId,sqlContext):
df = sqlContext.read.json("file_url_s3")
if storeId == None:
return None
return None
from pyspark.sql.functions import udf, col
from pyspark.sql.types import IntegerType
desc_udf = udf(lambda storeId : get_id_udf(storeId,sqlContext), IntegerType())
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-b5c4070c110e> in <module>()
1 from pyspark.sql.functions import udf, col
2 from pyspark.sql.types import IntegerType
----> 3 desc_udf = udf(lambda storeId : get_cluster_id_udf(storeId,sqlContext), IntegerType())
/usr/lib/spark/python/pyspark/sql/functions.py in udf(f, returnType)
1799 [Row(slen=5), Row(slen=3)]
1800 """
-> 1801 return UserDefinedFunction(f, returnType)
1802
1803 blacklist = ['map', 'since', 'ignore_unicode_prefix']
/usr/lib/spark/python/pyspark/sql/functions.py in __init__(self, func, returnType, name)
1758 self.returnType = returnType
1759 self._broadcast = None
-> 1760 self._judf = self._create_judf(name)
1761
1762 def _create_judf(self, name):
/usr/lib/spark/python/pyspark/sql/functions.py in _create_judf(self, name)
1763 from pyspark.sql import SQLContext
1764 sc = SparkContext.getOrCreate()
-> 1765 wrapped_func = _wrap_function(sc, self.func, self.returnType)
1766 ctx = SQLContext.getOrCreate(sc)
1767 jdt = ctx._ssql_ctx.parseDataType(self.returnType.json())
/usr/lib/spark/python/pyspark/sql/functions.py in _wrap_function(sc, func, returnType)
1743 def _wrap_function(sc, func, returnType):
1744 command = (func, returnType)
-> 1745 pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command)
1746 return sc._jvm.PythonFunction(bytearray(pickled_command), env, includes,
sc.pythonExec,
1747 sc.pythonVer, broadcast_vars, sc._javaAccumulator)
/usr/lib/spark/python/pyspark/rdd.py in _prepare_for_python_RDD(sc, command)
2313 # the serialized command will be compressed by broadcast
2314 ser = CloudPickleSerializer()
-> 2315 pickled_command = ser.dumps(command)
2316 if len(pickled_command) > (1 << 20): # 1M
2317 # The broadcast will have same life cycle as created PythonRDD
/usr/lib/spark/python/pyspark/serializers.py in dumps(self, obj)
426
427 def dumps(self, obj):
--> 428 return cloudpickle.dumps(obj, 2)
429
430
/usr/lib/spark/python/pyspark/cloudpickle.py in dumps(obj, protocol)
655
656 cp = CloudPickler(file,protocol)
--> 657 cp.dump(obj)
658
659 return file.getvalue()
/usr/lib/spark/python/pyspark/cloudpickle.py in dump(self, obj)
105 self.inject_addons()
106 try:
--> 107 return Pickler.dump(self, obj)
108 except RuntimeError as e:
109 if 'recursion' in e.args[0]:
/usr/lib64/python2.7/pickle.pyc in dump(self, obj)
222 if self.proto >= 2:
223 self.write(PROTO + chr(self.proto))
--> 224 self.save(obj)
225 self.write(STOP)
226
/usr/lib64/python2.7/pickle.pyc in save(self, obj)
284 f = self.dispatch.get(t)
285 if f:
--> 286 f(self, obj) # Call unbound method with explicit self
287 return
288
/usr/lib64/python2.7/pickle.pyc in save_dict(self, obj)
653
654 self.memoize(obj)
--> 655 self._batch_setitems(obj.iteritems())
656
657 dispatch[DictionaryType] = save_dict
/usr/lib64/python2.7/pickle.pyc in _batch_setitems(self, items)
685 for k, v in tmp:
686 save(k)
--> 687 save(v)
688 write(SETITEMS)
689 elif n:
/usr/lib64/python2.7/pickle.pyc in save(self, obj)
304 reduce = getattr(obj, "__reduce_ex__", None)
305 if reduce:
--> 306 rv = reduce(self.proto)
307 else:
308 reduce = getattr(obj, "__reduce__", None)
TypeError: 'JavaPackage' object is not callable