Source code for pyspark.util

# -*- coding: utf-8 -*-
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import functools
import itertools
import os
import platform
import re
import sys
import threading
import traceback
from types import TracebackType
from typing import Any, Callable, Iterator, List, Optional, TextIO, Tuple

from pyspark.errors import PySparkRuntimeError

from py4j.clientserver import ClientServer

__all__: List[str] = []

from py4j.java_gateway import JavaObject


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( error_class="STOP_ITERATION_OCCURRED", message_parameters={ "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() 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 if sys.version_info[:2] < (3, 7): # Traceback creation is not supported Python < 3.7. # See https://bugs.python.org/issue30579. 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: Callable) -> 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``. Also, note that pinned thread mode does not close the connection from Python to JVM when the thread is finished in the Python side. With this wrapper, Python garbage-collects the Python thread instance and also closes the connection which finishes JVM thread correctly. When the pinned thread mode is off, it return the original ``f``. .. versionadded:: 3.2.0 Parameters ---------- f : function the original thread target. Notes ----- This API is experimental. It is important to know that it captures the local properties 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 from the current Spark context 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 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 """ from pyspark import SparkContext if isinstance(SparkContext._gateway, ClientServer): # Here's when the pinned-thread mode (PYSPARK_PIN_THREAD) is on. # 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() @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) return wrapped else: return f
[docs]class InheritableThread(threading.Thread): """ Thread that is recommended to be used in PySpark instead of :class:`threading.Thread` when the pinned thread mode is enabled. The usage of this class is exactly same as :class:`threading.Thread` but correctly inherits the inheritable properties specific to JVM thread such as ``InheritableThreadLocal``. Also, note that pinned thread mode does not close the connection from Python to JVM when the thread is finished in the Python side. With this class, Python garbage-collects the Python thread instance and also closes the connection which finishes JVM thread correctly. When the pinned thread mode is off, this works as :class:`threading.Thread`. .. versionadded:: 3.1.0 Notes ----- This API is experimental. """ _props: JavaObject def __init__(self, target: Callable, *args: Any, **kwargs: Any): from pyspark import SparkContext 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") 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 import SparkContext 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() return super(InheritableThread, self).start()
if __name__ == "__main__": if "pypy" not in platform.python_implementation().lower() and sys.version_info[:2] >= (3, 7): import doctest import pyspark.util from pyspark.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)