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author | Wenchen Fan <wenchen@databricks.com> | 2016-02-24 12:44:54 -0800 |
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committer | Davies Liu <davies.liu@gmail.com> | 2016-02-24 12:44:54 -0800 |
commit | a60f91284ceee64de13f04559ec19c13a820a133 (patch) | |
tree | 68d7d84620835d5e66cc3f94771a11655c4cbe2b /python/pyspark/sql | |
parent | f92f53faeea020d80638a06752d69ca7a949cdeb (diff) | |
download | spark-a60f91284ceee64de13f04559ec19c13a820a133.tar.gz spark-a60f91284ceee64de13f04559ec19c13a820a133.tar.bz2 spark-a60f91284ceee64de13f04559ec19c13a820a133.zip |
[SPARK-13467] [PYSPARK] abstract python function to simplify pyspark code
## What changes were proposed in this pull request?
When we pass a Python function to JVM side, we also need to send its context, e.g. `envVars`, `pythonIncludes`, `pythonExec`, etc. However, it's annoying to pass around so many parameters at many places. This PR abstract python function along with its context, to simplify some pyspark code and make the logic more clear.
## How was the this patch tested?
by existing unit tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #11342 from cloud-fan/python-clean.
Diffstat (limited to 'python/pyspark/sql')
-rw-r--r-- | python/pyspark/sql/context.py | 2 | ||||
-rw-r--r-- | python/pyspark/sql/functions.py | 8 |
2 files changed, 4 insertions, 6 deletions
diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index 89bf1443a6..87e32c04ea 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -29,7 +29,7 @@ else: from py4j.protocol import Py4JError from pyspark import since -from pyspark.rdd import RDD, _prepare_for_python_RDD, ignore_unicode_prefix +from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.serializers import AutoBatchedSerializer, PickleSerializer from pyspark.sql.types import Row, StringType, StructType, _verify_type, \ _infer_schema, _has_nulltype, _merge_type, _create_converter diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 6894c27338..b30cc6799e 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -25,7 +25,7 @@ if sys.version < "3": from itertools import imap as map from pyspark import since, SparkContext -from pyspark.rdd import _prepare_for_python_RDD, ignore_unicode_prefix +from pyspark.rdd import _wrap_function, ignore_unicode_prefix from pyspark.serializers import PickleSerializer, AutoBatchedSerializer from pyspark.sql.types import StringType from pyspark.sql.column import Column, _to_java_column, _to_seq @@ -1645,16 +1645,14 @@ class UserDefinedFunction(object): f, returnType = self.func, self.returnType # put them in closure `func` func = lambda _, it: map(lambda x: returnType.toInternal(f(*x)), it) ser = AutoBatchedSerializer(PickleSerializer()) - command = (func, None, ser, ser) sc = SparkContext.getOrCreate() - pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self) + wrapped_func = _wrap_function(sc, func, ser, ser) ctx = SQLContext.getOrCreate(sc) jdt = ctx._ssql_ctx.parseDataType(self.returnType.json()) if name is None: name = f.__name__ if hasattr(f, '__name__') else f.__class__.__name__ judf = sc._jvm.org.apache.spark.sql.execution.python.UserDefinedPythonFunction( - name, bytearray(pickled_command), env, includes, sc.pythonExec, sc.pythonVer, - broadcast_vars, sc._javaAccumulator, jdt) + name, wrapped_func, jdt) return judf def __del__(self): |