Source code for pyspark.util

# -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
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#    http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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import copy
import functools
import itertools
import os
import platform
import re
import sys
import threading
import traceback
import typing
import socket
import warnings
from types import TracebackType
from typing import Any, Callable, IO, Iterator, List, Optional, TextIO, Tuple, Union

from pyspark.errors import PySparkRuntimeError
from pyspark.serializers import (
    write_int,
    read_int,
    write_with_length,
    SpecialLengths,
    UTF8Deserializer,
)

__all__: List[str] = []

if typing.TYPE_CHECKING:
    import io

    from py4j.java_collections import JavaArray
    from py4j.java_gateway import JavaObject

    from pyspark._typing import NonUDFType
    from pyspark.sql.pandas._typing import (
        PandasScalarUDFType,
        PandasGroupedMapUDFType,
        PandasGroupedAggUDFType,
        PandasWindowAggUDFType,
        PandasScalarIterUDFType,
        PandasMapIterUDFType,
        PandasCogroupedMapUDFType,
        ArrowMapIterUDFType,
        PandasGroupedMapUDFWithStateType,
        ArrowGroupedMapUDFType,
        ArrowCogroupedMapUDFType,
        PandasGroupedMapUDFTransformWithStateType,
        PandasGroupedMapUDFTransformWithStateInitStateType,
    )
    from pyspark.sql._typing import (
        SQLArrowBatchedUDFType,
        SQLArrowTableUDFType,
        SQLBatchedUDFType,
        SQLTableUDFType,
    )
    from pyspark.serializers import Serializer
    from pyspark.sql import SparkSession


JVM_BYTE_MIN: int = -(1 << 7)
JVM_BYTE_MAX: int = (1 << 7) - 1
JVM_SHORT_MIN: int = -(1 << 15)
JVM_SHORT_MAX: int = (1 << 15) - 1
JVM_INT_MIN: int = -(1 << 31)
JVM_INT_MAX: int = (1 << 31) - 1
JVM_LONG_MIN: int = -(1 << 63)
JVM_LONG_MAX: int = (1 << 63) - 1


def print_exec(stream: TextIO) -> None:
    ei = sys.exc_info()
    traceback.print_exception(ei[0], ei[1], ei[2], None, stream)


[docs]class VersionUtils: """ Provides utility method to determine Spark versions with given input string. """
[docs] @staticmethod def majorMinorVersion(sparkVersion: str) -> Tuple[int, int]: """ Given a Spark version string, return the (major version number, minor version number). E.g., for 2.0.1-SNAPSHOT, return (2, 0). Examples -------- >>> sparkVersion = "2.4.0" >>> VersionUtils.majorMinorVersion(sparkVersion) (2, 4) >>> sparkVersion = "2.3.0-SNAPSHOT" >>> VersionUtils.majorMinorVersion(sparkVersion) (2, 3) """ m = re.search(r"^(\d+)\.(\d+)(\..*)?$", sparkVersion) if m is not None: return (int(m.group(1)), int(m.group(2))) else: raise ValueError( "Spark tried to parse '%s' as a Spark" % sparkVersion + " version string, but it could not find the major and minor" + " version numbers." )
def fail_on_stopiteration(f: Callable) -> Callable: """ Wraps the input function to fail on 'StopIteration' by raising a 'RuntimeError' prevents silent loss of data when 'f' is used in a for loop in Spark code """ def wrapper(*args: Any, **kwargs: Any) -> Any: try: return f(*args, **kwargs) except StopIteration as exc: raise PySparkRuntimeError( errorClass="STOP_ITERATION_OCCURRED", messageParameters={ "exc": str(exc), }, ) return wrapper def walk_tb(tb: Optional[TracebackType]) -> Iterator[TracebackType]: while tb is not None: yield tb tb = tb.tb_next def try_simplify_traceback(tb: TracebackType) -> Optional[TracebackType]: """ Simplify the traceback. It removes the tracebacks in the current package, and only shows the traceback that is related to the thirdparty and user-specified codes. Returns ------- TracebackType or None Simplified traceback instance. It returns None if it fails to simplify. Notes ----- This keeps the tracebacks once it sees they are from a different file even though the following tracebacks are from the current package. Examples -------- >>> import importlib >>> import sys >>> import traceback >>> import tempfile >>> with tempfile.TemporaryDirectory(prefix="try_simplify_traceback") as tmp_dir: ... with open("%s/dummy_module.py" % tmp_dir, "w") as f: ... _ = f.write( ... 'def raise_stop_iteration():\\n' ... ' raise StopIteration()\\n\\n' ... 'def simple_wrapper(f):\\n' ... ' def wrapper(*a, **k):\\n' ... ' return f(*a, **k)\\n' ... ' return wrapper\\n') ... f.flush() ... spec = importlib.util.spec_from_file_location( ... "dummy_module", "%s/dummy_module.py" % tmp_dir) ... dummy_module = importlib.util.module_from_spec(spec) ... spec.loader.exec_module(dummy_module) >>> def skip_doctest_traceback(tb): ... import pyspark ... root = os.path.dirname(pyspark.__file__) ... pairs = zip(walk_tb(tb), traceback.extract_tb(tb)) ... for cur_tb, cur_frame in pairs: ... if cur_frame.filename.startswith(root): ... return cur_tb Regular exceptions should show the file name of the current package as below. >>> exc_info = None >>> try: ... fail_on_stopiteration(dummy_module.raise_stop_iteration)() ... except Exception as e: ... tb = sys.exc_info()[-1] ... e.__cause__ = None ... exc_info = "".join( ... traceback.format_exception(type(e), e, tb)) >>> print(exc_info) # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS Traceback (most recent call last): File ... ... File "/.../pyspark/util.py", line ... ... pyspark.errors.exceptions.base.PySparkRuntimeError: ... >>> "pyspark/util.py" in exc_info True If the traceback is simplified with this method, it hides the current package file name: >>> exc_info = None >>> try: ... fail_on_stopiteration(dummy_module.raise_stop_iteration)() ... except Exception as e: ... tb = try_simplify_traceback(sys.exc_info()[-1]) ... e.__cause__ = None ... exc_info = "".join( ... traceback.format_exception( ... type(e), e, try_simplify_traceback(skip_doctest_traceback(tb)))) >>> print(exc_info) # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS pyspark.errors.exceptions.base.PySparkRuntimeError: ... >>> "pyspark/util.py" in exc_info False In the case below, the traceback contains the current package in the middle. In this case, it just hides the top occurrence only. >>> exc_info = None >>> try: ... fail_on_stopiteration(dummy_module.simple_wrapper( ... fail_on_stopiteration(dummy_module.raise_stop_iteration)))() ... except Exception as e: ... tb = sys.exc_info()[-1] ... e.__cause__ = None ... exc_info_a = "".join( ... traceback.format_exception(type(e), e, tb)) ... exc_info_b = "".join( ... traceback.format_exception( ... type(e), e, try_simplify_traceback(skip_doctest_traceback(tb)))) >>> exc_info_a.count("pyspark/util.py") 2 >>> exc_info_b.count("pyspark/util.py") 1 """ if "pypy" in platform.python_implementation().lower(): # Traceback modification is not supported with PyPy in PySpark. return None import pyspark root = os.path.dirname(pyspark.__file__) tb_next = None new_tb = None pairs = zip(walk_tb(tb), traceback.extract_tb(tb)) last_seen = [] for cur_tb, cur_frame in pairs: if not cur_frame.filename.startswith(root): # Filter the stacktrace from the PySpark source itself. last_seen = [(cur_tb, cur_frame)] break for cur_tb, cur_frame in reversed(list(itertools.chain(last_seen, pairs))): # Once we have seen the file names outside, don't skip. new_tb = TracebackType( tb_next=tb_next, tb_frame=cur_tb.tb_frame, tb_lasti=cur_tb.tb_frame.f_lasti, tb_lineno=cur_tb.tb_frame.f_lineno if cur_tb.tb_frame.f_lineno is not None else -1, ) tb_next = new_tb return new_tb def _print_missing_jar(lib_name: str, pkg_name: str, jar_name: str, spark_version: str) -> None: print( """ ________________________________________________________________________________________________ Spark %(lib_name)s libraries not found in class path. Try one of the following. 1. Include the %(lib_name)s library and its dependencies with in the spark-submit command as $ bin/spark-submit --packages org.apache.spark:spark-%(pkg_name)s:%(spark_version)s ... 2. Download the JAR of the artifact from Maven Central http://search.maven.org/, Group Id = org.apache.spark, Artifact Id = spark-%(jar_name)s, Version = %(spark_version)s. Then, include the jar in the spark-submit command as $ bin/spark-submit --jars <spark-%(jar_name)s.jar> ... ________________________________________________________________________________________________ """ % { "lib_name": lib_name, "pkg_name": pkg_name, "jar_name": jar_name, "spark_version": spark_version, } ) def _parse_memory(s: str) -> int: """ Parse a memory string in the format supported by Java (e.g. 1g, 200m) and return the value in MiB Examples -------- >>> _parse_memory("256m") 256 >>> _parse_memory("2g") 2048 """ units = {"g": 1024, "m": 1, "t": 1 << 20, "k": 1.0 / 1024} if s[-1].lower() not in units: raise ValueError("invalid format: " + s) return int(float(s[:-1]) * units[s[-1].lower()])
[docs]def inheritable_thread_target(f: Optional[Union[Callable, "SparkSession"]] = None) -> Callable: """ Return thread target wrapper which is recommended to be used in PySpark when the pinned thread mode is enabled. The wrapper function, before calling original thread target, it inherits the inheritable properties specific to JVM thread such as ``InheritableThreadLocal``, or thread local such as tags with Spark Connect. When the pinned thread mode is off, it return the original ``f``. .. versionadded:: 3.2.0 .. versionchanged:: 3.5.0 Supports Spark Connect. Parameters ---------- f : function, or :class:`SparkSession` the original thread target, or :class:`SparkSession` if Spark Connect is being used. See the examples below. Notes ----- This API is experimental. It is important to know that it captures the local properties or tags when you decorate it whereas :class:`InheritableThread` captures when the thread is started. Therefore, it is encouraged to decorate it when you want to capture the local properties. For example, the local properties or tags from the current Spark context or Spark session is captured when you define a function here instead of the invocation: >>> @inheritable_thread_target ... def target_func(): ... pass # your codes. If you have any updates on local properties or tags afterwards, it would not be reflected to the Spark context in ``target_func()``. The example below mimics the behavior of JVM threads as close as possible: >>> Thread(target=inheritable_thread_target(target_func)).start() # doctest: +SKIP If you're using Spark Connect or if you want to inherit the tags properly, you should explicitly provide Spark session as follows: >>> @inheritable_thread_target(session) # doctest: +SKIP ... def target_func(): ... pass # your codes. >>> Thread(target=inheritable_thread_target(session)(target_func)).start() # doctest: +SKIP """ from pyspark.sql import is_remote # Spark Connect if is_remote(): session = f assert session is not None, "Spark Connect session must be provided." def outer(ff: Callable) -> Callable: thread_local = session.client.thread_local # type: ignore[union-attr, operator] session_client_thread_local_attrs = [ (attr, copy.deepcopy(value)) for ( attr, value, ) in thread_local.__dict__.items() ] @functools.wraps(ff) def inner(*args: Any, **kwargs: Any) -> Any: # Set thread locals in child thread. for attr, value in session_client_thread_local_attrs: setattr( session.client.thread_local, # type: ignore[union-attr, operator] attr, value, ) return ff(*args, **kwargs) return inner return outer # Non Spark Connect with SparkSession or Callable from pyspark.sql import SparkSession from pyspark import SparkContext from py4j.clientserver import ClientServer if isinstance(SparkContext._gateway, ClientServer): # Here's when the pinned-thread mode (PYSPARK_PIN_THREAD) is on. if isinstance(f, SparkSession): session = f assert session is not None tags = set(session.getTags()) # Local properties are copied when wrapping the function. assert SparkContext._active_spark_context is not None properties = SparkContext._active_spark_context._jsc.sc().getLocalProperties().clone() def outer(ff: Callable) -> Callable: @functools.wraps(ff) def wrapped(*args: Any, **kwargs: Any) -> Any: # Apply properties and tags in the child thread. assert SparkContext._active_spark_context is not None SparkContext._active_spark_context._jsc.sc().setLocalProperties(properties) for tag in tags: session.addTag(tag) # type: ignore[union-attr] return ff(*args, **kwargs) return wrapped return outer warnings.warn( "Spark session is not provided. Tags will not be inherited.", UserWarning, ) # NOTICE the internal difference vs `InheritableThread`. `InheritableThread` # copies local properties when the thread starts but `inheritable_thread_target` # copies when the function is wrapped. assert SparkContext._active_spark_context is not None properties = SparkContext._active_spark_context._jsc.sc().getLocalProperties().clone() assert callable(f) @functools.wraps(f) def wrapped(*args: Any, **kwargs: Any) -> Any: # Set local properties in child thread. assert SparkContext._active_spark_context is not None SparkContext._active_spark_context._jsc.sc().setLocalProperties(properties) return f(*args, **kwargs) # type: ignore[misc, operator] return wrapped else: return f # type: ignore[return-value]
def handle_worker_exception(e: BaseException, outfile: IO) -> None: """ Handles exception for Python worker which writes SpecialLengths.PYTHON_EXCEPTION_THROWN (-2) and exception traceback info to outfile. JVM could then read from the outfile and perform exception handling there. """ try: exc_info = None if os.environ.get("SPARK_SIMPLIFIED_TRACEBACK", False): tb = try_simplify_traceback(sys.exc_info()[-1]) # type: ignore[arg-type] if tb is not None: e.__cause__ = None exc_info = "".join(traceback.format_exception(type(e), e, tb)) if exc_info is None: exc_info = traceback.format_exc() write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile) write_with_length(exc_info.encode("utf-8"), outfile) except IOError: # JVM close the socket pass except BaseException: # Write the error to stderr if it happened while serializing print("PySpark worker failed with exception:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
[docs]class InheritableThread(threading.Thread): """ Thread that is recommended to be used in PySpark when the pinned thread mode is enabled. The wrapper function, before calling original thread target, it inherits the inheritable properties specific to JVM thread such as ``InheritableThreadLocal``, or thread local such as tags with Spark Connect. When the pinned thread mode is off, this works as :class:`threading.Thread`. .. versionadded:: 3.1.0 .. versionchanged:: 3.5.0 Supports Spark Connect. Notes ----- This API is experimental. """ _props: "JavaObject" def __init__( self, target: Callable, *args: Any, session: Optional["SparkSession"] = None, **kwargs: Any ): from pyspark.sql import is_remote # Spark Connect if is_remote(): assert session is not None, "Spark Connect must be provided." self._session = session def copy_local_properties(*a: Any, **k: Any) -> Any: # Set tags in child thread. assert hasattr(self, "_tags") thread_local = session.client.thread_local # type: ignore[union-attr, operator] thread_local.tags = self._tags # type: ignore[has-type] return target(*a, **k) super(InheritableThread, self).__init__( target=copy_local_properties, *args, **kwargs # type: ignore[misc] ) else: # Non Spark Connect from pyspark import SparkContext from py4j.clientserver import ClientServer self._session = session # type: ignore[assignment] if isinstance(SparkContext._gateway, ClientServer): # Here's when the pinned-thread mode (PYSPARK_PIN_THREAD) is on. def copy_local_properties(*a: Any, **k: Any) -> Any: # self._props is set before starting the thread to match the behavior with JVM. assert hasattr(self, "_props") if hasattr(self, "_tags"): for tag in self._tags: # type: ignore[has-type] self._session.addTag(tag) assert SparkContext._active_spark_context is not None SparkContext._active_spark_context._jsc.sc().setLocalProperties(self._props) return target(*a, **k) super(InheritableThread, self).__init__( target=copy_local_properties, *args, **kwargs # type: ignore[misc] ) else: super(InheritableThread, self).__init__( target=target, *args, **kwargs # type: ignore[misc] ) def start(self) -> None: from pyspark.sql import is_remote if is_remote(): # Spark Connect assert hasattr(self, "_session") thread_local = self._session.client.thread_local # type: ignore[union-attr, operator] if not hasattr(thread_local, "tags"): thread_local.tags = set() self._tags = set(thread_local.tags) else: # Non Spark Connect from pyspark import SparkContext from py4j.clientserver import ClientServer if isinstance(SparkContext._gateway, ClientServer): # Here's when the pinned-thread mode (PYSPARK_PIN_THREAD) is on. # Local property copy should happen in Thread.start to mimic JVM's behavior. assert SparkContext._active_spark_context is not None self._props = ( SparkContext._active_spark_context._jsc.sc().getLocalProperties().clone() ) if self._session is not None: self._tags = self._session.getTags() return super(InheritableThread, self).start()
class PythonEvalType: """ Evaluation type of python rdd. These values are internal to PySpark. These values should match values in org.apache.spark.api.python.PythonEvalType. """ NON_UDF: "NonUDFType" = 0 SQL_BATCHED_UDF: "SQLBatchedUDFType" = 100 SQL_ARROW_BATCHED_UDF: "SQLArrowBatchedUDFType" = 101 SQL_SCALAR_PANDAS_UDF: "PandasScalarUDFType" = 200 SQL_GROUPED_MAP_PANDAS_UDF: "PandasGroupedMapUDFType" = 201 SQL_GROUPED_AGG_PANDAS_UDF: "PandasGroupedAggUDFType" = 202 SQL_WINDOW_AGG_PANDAS_UDF: "PandasWindowAggUDFType" = 203 SQL_SCALAR_PANDAS_ITER_UDF: "PandasScalarIterUDFType" = 204 SQL_MAP_PANDAS_ITER_UDF: "PandasMapIterUDFType" = 205 SQL_COGROUPED_MAP_PANDAS_UDF: "PandasCogroupedMapUDFType" = 206 SQL_MAP_ARROW_ITER_UDF: "ArrowMapIterUDFType" = 207 SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE: "PandasGroupedMapUDFWithStateType" = 208 SQL_GROUPED_MAP_ARROW_UDF: "ArrowGroupedMapUDFType" = 209 SQL_COGROUPED_MAP_ARROW_UDF: "ArrowCogroupedMapUDFType" = 210 SQL_TRANSFORM_WITH_STATE_PANDAS_UDF: "PandasGroupedMapUDFTransformWithStateType" = 211 SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF: "PandasGroupedMapUDFTransformWithStateInitStateType" = ( # noqa: E501 212 ) SQL_TABLE_UDF: "SQLTableUDFType" = 300 SQL_ARROW_TABLE_UDF: "SQLArrowTableUDFType" = 301 def _create_local_socket(sock_info: "JavaArray") -> "io.BufferedRWPair": """ Create a local socket that can be used to load deserialized data from the JVM Parameters ---------- sock_info : tuple Tuple containing port number and authentication secret for a local socket. Returns ------- sockfile file descriptor of the local socket """ sockfile: "io.BufferedRWPair" sock: "socket.socket" port: int = sock_info[0] auth_secret: str = sock_info[1] sockfile, sock = local_connect_and_auth(port, auth_secret) # The RDD materialization time is unpredictable, if we set a timeout for socket reading # operation, it will very possibly fail. See SPARK-18281. sock.settimeout(None) return sockfile def _load_from_socket(sock_info: "JavaArray", serializer: "Serializer") -> Iterator[Any]: """ Connect to a local socket described by sock_info and use the given serializer to yield data Parameters ---------- sock_info : tuple Tuple containing port number and authentication secret for a local socket. serializer : class:`Serializer` The PySpark serializer to use Returns ------- result of meth:`Serializer.load_stream`, usually a generator that yields deserialized data """ sockfile = _create_local_socket(sock_info) # The socket will be automatically closed when garbage-collected. return serializer.load_stream(sockfile) def _local_iterator_from_socket(sock_info: "JavaArray", serializer: "Serializer") -> Iterator[Any]: class PyLocalIterable: """Create a synchronous local iterable over a socket""" def __init__(self, _sock_info: "JavaArray", _serializer: "Serializer"): port: int auth_secret: str jsocket_auth_server: "JavaObject" port, auth_secret, self.jsocket_auth_server = _sock_info self._sockfile = _create_local_socket((port, auth_secret)) self._serializer = _serializer self._read_iter: Iterator[Any] = iter([]) # Initialize as empty iterator self._read_status = 1 def __iter__(self) -> Iterator[Any]: while self._read_status == 1: # Request next partition data from Java write_int(1, self._sockfile) self._sockfile.flush() # If response is 1 then there is a partition to read, if 0 then fully consumed self._read_status = read_int(self._sockfile) if self._read_status == 1: # Load the partition data as a stream and read each item self._read_iter = self._serializer.load_stream(self._sockfile) for item in self._read_iter: yield item # An error occurred, join serving thread and raise any exceptions from the JVM elif self._read_status == -1: self.jsocket_auth_server.getResult() def __del__(self) -> None: # If local iterator is not fully consumed, if self._read_status == 1: try: # Finish consuming partition data stream for _ in self._read_iter: pass # Tell Java to stop sending data and close connection write_int(0, self._sockfile) self._sockfile.flush() except Exception: # Ignore any errors, socket is automatically closed when garbage-collected pass return iter(PyLocalIterable(sock_info, serializer)) def local_connect_and_auth(port: Optional[Union[str, int]], auth_secret: str) -> Tuple: """ Connect to local host, authenticate with it, and return a (sockfile,sock) for that connection. Handles IPV4 & IPV6, does some error handling. Parameters ---------- port : str or int, optional auth_secret : str Returns ------- tuple with (sockfile, sock) """ sock = None errors = [] # Support for both IPv4 and IPv6. addr = "127.0.0.1" if os.environ.get("SPARK_PREFER_IPV6", "false").lower() == "true": addr = "::1" for res in socket.getaddrinfo(addr, port, socket.AF_UNSPEC, socket.SOCK_STREAM): af, socktype, proto, _, sa = res try: sock = socket.socket(af, socktype, proto) sock.settimeout(int(os.environ.get("SPARK_AUTH_SOCKET_TIMEOUT", 15))) sock.connect(sa) sockfile = sock.makefile("rwb", int(os.environ.get("SPARK_BUFFER_SIZE", 65536))) _do_server_auth(sockfile, auth_secret) return (sockfile, sock) except socket.error as e: emsg = str(e) errors.append("tried to connect to %s, but an error occurred: %s" % (sa, emsg)) if sock is not None: sock.close() sock = None raise PySparkRuntimeError( errorClass="CANNOT_OPEN_SOCKET", messageParameters={ "errors": str(errors), }, ) def _do_server_auth(conn: "io.IOBase", auth_secret: str) -> None: """ Performs the authentication protocol defined by the SocketAuthHelper class on the given file-like object 'conn'. """ write_with_length(auth_secret.encode("utf-8"), conn) conn.flush() reply = UTF8Deserializer().loads(conn) if reply != "ok": conn.close() raise PySparkRuntimeError( errorClass="UNEXPECTED_RESPONSE_FROM_SERVER", messageParameters={}, ) _is_remote_only = None def is_remote_only() -> bool: """ Returns if the current running environment is only for Spark Connect. If users install pyspark-connect alone, RDD API does not exist. .. versionadded:: 4.0.0 Notes ----- This will only return ``True`` if installed PySpark is only for Spark Connect. Otherwise, it returns ``False``. This API is unstable, and for developers. Returns ------- bool Examples -------- >>> from pyspark.sql import is_remote >>> is_remote() False """ global _is_remote_only if "SPARK_SKIP_CONNECT_COMPAT_TESTS" in os.environ: return True if _is_remote_only is not None: return _is_remote_only try: from pyspark import core # noqa: F401 _is_remote_only = False return _is_remote_only except ImportError: _is_remote_only = True return _is_remote_only if __name__ == "__main__": if "pypy" not in platform.python_implementation().lower() and sys.version_info[:2] >= (3, 9): import doctest import pyspark.util from pyspark.core.context import SparkContext globs = pyspark.util.__dict__.copy() globs["sc"] = SparkContext("local[4]", "PythonTest") (failure_count, test_count) = doctest.testmod(pyspark.util, globs=globs) globs["sc"].stop() if failure_count: sys.exit(-1)