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Posted to commits@spark.apache.org by gu...@apache.org on 2020/07/27 11:12:42 UTC
[spark] branch master updated: [SPARK-32435][PYTHON] Remove heapq3
port from Python 3
This is an automated email from the ASF dual-hosted git repository.
gurwls223 pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/spark.git
The following commit(s) were added to refs/heads/master by this push:
new a82aee0 [SPARK-32435][PYTHON] Remove heapq3 port from Python 3
a82aee0 is described below
commit a82aee044127825ffefa0ed09b0ae5b987b9dd21
Author: HyukjinKwon <gu...@apache.org>
AuthorDate: Mon Jul 27 20:10:13 2020 +0900
[SPARK-32435][PYTHON] Remove heapq3 port from Python 3
### What changes were proposed in this pull request?
This PR removes the manual port of `heapq3.py` introduced from SPARK-3073. The main reason of this was to support Python 2.6 and 2.7 because Python 2's `heapq.merge()` doesn't not support `key` and `reverse`.
See
- https://docs.python.org/2/library/heapq.html#heapq.merge in Python 2
- https://docs.python.org/3.8/library/heapq.html#heapq.merge in Python 3
Since we dropped the Python 2 at SPARK-32138, we can remove this away.
### Why are the changes needed?
To remove unnecessary codes. Also, we can leverage bug fixes made in Python 3.x at `heapq`.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Existing tests should cover. I locally ran and verified:
```bash
./python/run-tests --python-executable=python3 --testname="pyspark.tests.test_shuffle"
./python/run-tests --python-executable=python3 --testname="pyspark.shuffle ExternalSorter"
./python/run-tests --python-executable=python3 --testname="pyspark.tests.test_rdd RDDTests.test_external_group_by_key"
```
Closes #29229 from HyukjinKwon/SPARK-32435.
Authored-by: HyukjinKwon <gu...@apache.org>
Signed-off-by: HyukjinKwon <gu...@apache.org>
---
LICENSE | 1 -
LICENSE-binary | 6 -
dev/.rat-excludes | 1 -
dev/tox.ini | 2 +-
licenses-binary/LICENSE-heapq.txt | 280 ------------
licenses/LICENSE-heapq.txt | 49 ---
python/pylintrc | 2 +-
python/pyspark/heapq3.py | 890 --------------------------------------
python/pyspark/shuffle.py | 6 +-
9 files changed, 5 insertions(+), 1232 deletions(-)
diff --git a/LICENSE b/LICENSE
index 8cec4f5..df6bed1 100644
--- a/LICENSE
+++ b/LICENSE
@@ -222,7 +222,6 @@ external/spark-ganglia-lgpl/src/main/java/com/codahale/metrics/ganglia/GangliaRe
Python Software Foundation License
----------------------------------
-pyspark/heapq3.py
python/docs/source/_static/copybutton.js
BSD 3-Clause
diff --git a/LICENSE-binary b/LICENSE-binary
index b50da6b..d363661 100644
--- a/LICENSE-binary
+++ b/LICENSE-binary
@@ -557,12 +557,6 @@ jakarta.ws.rs:jakarta.ws.rs-api https://github.com/eclipse-ee4j/jaxrs-api
org.glassfish.hk2.external:jakarta.inject
-Python Software Foundation License
-----------------------------------
-
-pyspark/heapq3.py
-
-
Public Domain
-------------
diff --git a/dev/.rat-excludes b/dev/.rat-excludes
index db6a4ce..3889dc9 100644
--- a/dev/.rat-excludes
+++ b/dev/.rat-excludes
@@ -49,7 +49,6 @@ jsonFormatter.min.js
.*log
pyspark-coverage-site/*
cloudpickle/*
-heapq3.py
join.py
SparkExprTyper.scala
SparkILoop.scala
diff --git a/dev/tox.ini b/dev/tox.ini
index e25595a..5bf27d1 100644
--- a/dev/tox.ini
+++ b/dev/tox.ini
@@ -16,4 +16,4 @@
[pycodestyle]
ignore=E226,E241,E305,E402,E722,E731,E741,W503,W504
max-line-length=100
-exclude=python/pyspark/cloudpickle/*.py,heapq3.py,shared.py,python/docs/source/conf.py,work/*/*.py,python/.eggs/*,dist/*,.git/*
+exclude=python/pyspark/cloudpickle/*.py,shared.py,python/docs/source/conf.py,work/*/*.py,python/.eggs/*,dist/*,.git/*
diff --git a/licenses-binary/LICENSE-heapq.txt b/licenses-binary/LICENSE-heapq.txt
deleted file mode 100644
index 0c4c4b9..0000000
--- a/licenses-binary/LICENSE-heapq.txt
+++ /dev/null
@@ -1,280 +0,0 @@
-
-# A. HISTORY OF THE SOFTWARE
-# ==========================
-#
-# Python was created in the early 1990s by Guido van Rossum at Stichting
-# Mathematisch Centrum (CWI, see http://www.cwi.nl) in the Netherlands
-# as a successor of a language called ABC. Guido remains Python's
-# principal author, although it includes many contributions from others.
-#
-# In 1995, Guido continued his work on Python at the Corporation for
-# National Research Initiatives (CNRI, see http://www.cnri.reston.va.us)
-# in Reston, Virginia where he released several versions of the
-# software.
-#
-# In May 2000, Guido and the Python core development team moved to
-# BeOpen.com to form the BeOpen PythonLabs team. In October of the same
-# year, the PythonLabs team moved to Digital Creations (now Zope
-# Corporation, see http://www.zope.com). In 2001, the Python Software
-# Foundation (PSF, see http://www.python.org/psf/) was formed, a
-# non-profit organization created specifically to own Python-related
-# Intellectual Property. Zope Corporation is a sponsoring member of
-# the PSF.
-#
-# All Python releases are Open Source (see http://www.opensource.org for
-# the Open Source Definition). Historically, most, but not all, Python
-# releases have also been GPL-compatible; the table below summarizes
-# the various releases.
-#
-# Release Derived Year Owner GPL-
-# from compatible? (1)
-#
-# 0.9.0 thru 1.2 1991-1995 CWI yes
-# 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes
-# 1.6 1.5.2 2000 CNRI no
-# 2.0 1.6 2000 BeOpen.com no
-# 1.6.1 1.6 2001 CNRI yes (2)
-# 2.1 2.0+1.6.1 2001 PSF no
-# 2.0.1 2.0+1.6.1 2001 PSF yes
-# 2.1.1 2.1+2.0.1 2001 PSF yes
-# 2.2 2.1.1 2001 PSF yes
-# 2.1.2 2.1.1 2002 PSF yes
-# 2.1.3 2.1.2 2002 PSF yes
-# 2.2.1 2.2 2002 PSF yes
-# 2.2.2 2.2.1 2002 PSF yes
-# 2.2.3 2.2.2 2003 PSF yes
-# 2.3 2.2.2 2002-2003 PSF yes
-# 2.3.1 2.3 2002-2003 PSF yes
-# 2.3.2 2.3.1 2002-2003 PSF yes
-# 2.3.3 2.3.2 2002-2003 PSF yes
-# 2.3.4 2.3.3 2004 PSF yes
-# 2.3.5 2.3.4 2005 PSF yes
-# 2.4 2.3 2004 PSF yes
-# 2.4.1 2.4 2005 PSF yes
-# 2.4.2 2.4.1 2005 PSF yes
-# 2.4.3 2.4.2 2006 PSF yes
-# 2.4.4 2.4.3 2006 PSF yes
-# 2.5 2.4 2006 PSF yes
-# 2.5.1 2.5 2007 PSF yes
-# 2.5.2 2.5.1 2008 PSF yes
-# 2.5.3 2.5.2 2008 PSF yes
-# 2.6 2.5 2008 PSF yes
-# 2.6.1 2.6 2008 PSF yes
-# 2.6.2 2.6.1 2009 PSF yes
-# 2.6.3 2.6.2 2009 PSF yes
-# 2.6.4 2.6.3 2009 PSF yes
-# 2.6.5 2.6.4 2010 PSF yes
-# 2.7 2.6 2010 PSF yes
-#
-# Footnotes:
-#
-# (1) GPL-compatible doesn't mean that we're distributing Python under
-# the GPL. All Python licenses, unlike the GPL, let you distribute
-# a modified version without making your changes open source. The
-# GPL-compatible licenses make it possible to combine Python with
-# other software that is released under the GPL; the others don't.
-#
-# (2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
-# because its license has a choice of law clause. According to
-# CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
-# is "not incompatible" with the GPL.
-#
-# Thanks to the many outside volunteers who have worked under Guido's
-# direction to make these releases possible.
-#
-#
-# B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
-# ===============================================================
-#
-# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
-# --------------------------------------------
-#
-# 1. This LICENSE AGREEMENT is between the Python Software Foundation
-# ("PSF"), and the Individual or Organization ("Licensee") accessing and
-# otherwise using this software ("Python") in source or binary form and
-# its associated documentation.
-#
-# 2. Subject to the terms and conditions of this License Agreement, PSF hereby
-# grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
-# analyze, test, perform and/or display publicly, prepare derivative works,
-# distribute, and otherwise use Python alone or in any derivative version,
-# provided, however, that PSF's License Agreement and PSF's notice of copyright,
-# i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
-# 2011, 2012, 2013 Python Software Foundation; All Rights Reserved" are retained
-# in Python alone or in any derivative version prepared by Licensee.
-#
-# 3. In the event Licensee prepares a derivative work that is based on
-# or incorporates Python or any part thereof, and wants to make
-# the derivative work available to others as provided herein, then
-# Licensee hereby agrees to include in any such work a brief summary of
-# the changes made to Python.
-#
-# 4. PSF is making Python available to Licensee on an "AS IS"
-# basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
-# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
-# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
-# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
-# INFRINGE ANY THIRD PARTY RIGHTS.
-#
-# 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
-# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
-# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
-# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
-#
-# 6. This License Agreement will automatically terminate upon a material
-# breach of its terms and conditions.
-#
-# 7. Nothing in this License Agreement shall be deemed to create any
-# relationship of agency, partnership, or joint venture between PSF and
-# Licensee. This License Agreement does not grant permission to use PSF
-# trademarks or trade name in a trademark sense to endorse or promote
-# products or services of Licensee, or any third party.
-#
-# 8. By copying, installing or otherwise using Python, Licensee
-# agrees to be bound by the terms and conditions of this License
-# Agreement.
-#
-#
-# BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
-# -------------------------------------------
-#
-# BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
-#
-# 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
-# office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
-# Individual or Organization ("Licensee") accessing and otherwise using
-# this software in source or binary form and its associated
-# documentation ("the Software").
-#
-# 2. Subject to the terms and conditions of this BeOpen Python License
-# Agreement, BeOpen hereby grants Licensee a non-exclusive,
-# royalty-free, world-wide license to reproduce, analyze, test, perform
-# and/or display publicly, prepare derivative works, distribute, and
-# otherwise use the Software alone or in any derivative version,
-# provided, however, that the BeOpen Python License is retained in the
-# Software, alone or in any derivative version prepared by Licensee.
-#
-# 3. BeOpen is making the Software available to Licensee on an "AS IS"
-# basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
-# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
-# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
-# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
-# INFRINGE ANY THIRD PARTY RIGHTS.
-#
-# 4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
-# SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
-# AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
-# DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
-#
-# 5. This License Agreement will automatically terminate upon a material
-# breach of its terms and conditions.
-#
-# 6. This License Agreement shall be governed by and interpreted in all
-# respects by the law of the State of California, excluding conflict of
-# law provisions. Nothing in this License Agreement shall be deemed to
-# create any relationship of agency, partnership, or joint venture
-# between BeOpen and Licensee. This License Agreement does not grant
-# permission to use BeOpen trademarks or trade names in a trademark
-# sense to endorse or promote products or services of Licensee, or any
-# third party. As an exception, the "BeOpen Python" logos available at
-# http://www.pythonlabs.com/logos.html may be used according to the
-# permissions granted on that web page.
-#
-# 7. By copying, installing or otherwise using the software, Licensee
-# agrees to be bound by the terms and conditions of this License
-# Agreement.
-#
-#
-# CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
-# ---------------------------------------
-#
-# 1. This LICENSE AGREEMENT is between the Corporation for National
-# Research Initiatives, having an office at 1895 Preston White Drive,
-# Reston, VA 20191 ("CNRI"), and the Individual or Organization
-# ("Licensee") accessing and otherwise using Python 1.6.1 software in
-# source or binary form and its associated documentation.
-#
-# 2. Subject to the terms and conditions of this License Agreement, CNRI
-# hereby grants Licensee a nonexclusive, royalty-free, world-wide
-# license to reproduce, analyze, test, perform and/or display publicly,
-# prepare derivative works, distribute, and otherwise use Python 1.6.1
-# alone or in any derivative version, provided, however, that CNRI's
-# License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
-# 1995-2001 Corporation for National Research Initiatives; All Rights
-# Reserved" are retained in Python 1.6.1 alone or in any derivative
-# version prepared by Licensee. Alternately, in lieu of CNRI's License
-# Agreement, Licensee may substitute the following text (omitting the
-# quotes): "Python 1.6.1 is made available subject to the terms and
-# conditions in CNRI's License Agreement. This Agreement together with
-# Python 1.6.1 may be located on the Internet using the following
-# unique, persistent identifier (known as a handle): 1895.22/1013. This
-# Agreement may also be obtained from a proxy server on the Internet
-# using the following URL: http://hdl.handle.net/1895.22/1013".
-#
-# 3. In the event Licensee prepares a derivative work that is based on
-# or incorporates Python 1.6.1 or any part thereof, and wants to make
-# the derivative work available to others as provided herein, then
-# Licensee hereby agrees to include in any such work a brief summary of
-# the changes made to Python 1.6.1.
-#
-# 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
-# basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
-# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND
-# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
-# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT
-# INFRINGE ANY THIRD PARTY RIGHTS.
-#
-# 5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
-# 1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
-# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1,
-# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
-#
-# 6. This License Agreement will automatically terminate upon a material
-# breach of its terms and conditions.
-#
-# 7. This License Agreement shall be governed by the federal
-# intellectual property law of the United States, including without
-# limitation the federal copyright law, and, to the extent such
-# U.S. federal law does not apply, by the law of the Commonwealth of
-# Virginia, excluding Virginia's conflict of law provisions.
-# Notwithstanding the foregoing, with regard to derivative works based
-# on Python 1.6.1 that incorporate non-separable material that was
-# previously distributed under the GNU General Public License (GPL), the
-# law of the Commonwealth of Virginia shall govern this License
-# Agreement only as to issues arising under or with respect to
-# Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this
-# License Agreement shall be deemed to create any relationship of
-# agency, partnership, or joint venture between CNRI and Licensee. This
-# License Agreement does not grant permission to use CNRI trademarks or
-# trade name in a trademark sense to endorse or promote products or
-# services of Licensee, or any third party.
-#
-# 8. By clicking on the "ACCEPT" button where indicated, or by copying,
-# installing or otherwise using Python 1.6.1, Licensee agrees to be
-# bound by the terms and conditions of this License Agreement.
-#
-# ACCEPT
-#
-#
-# CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
-# --------------------------------------------------
-#
-# Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
-# The Netherlands. All rights reserved.
-#
-# Permission to use, copy, modify, and distribute this software and its
-# documentation for any purpose and without fee is hereby granted,
-# provided that the above copyright notice appear in all copies and that
-# both that copyright notice and this permission notice appear in
-# supporting documentation, and that the name of Stichting Mathematisch
-# Centrum or CWI not be used in advertising or publicity pertaining to
-# distribution of the software without specific, written prior
-# permission.
-#
-# STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
-# THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
-# FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
-# FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
-# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
-# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
-# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
\ No newline at end of file
diff --git a/licenses/LICENSE-heapq.txt b/licenses/LICENSE-heapq.txt
deleted file mode 100644
index 45be6b8..0000000
--- a/licenses/LICENSE-heapq.txt
+++ /dev/null
@@ -1,49 +0,0 @@
-PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
---------------------------------------------
-
-1. This LICENSE AGREEMENT is between the Python Software Foundation
-("PSF"), and the Individual or Organization ("Licensee") accessing and
-otherwise using this software ("Python") in source or binary form and
-its associated documentation.
-
-2. Subject to the terms and conditions of this License Agreement, PSF hereby
-grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
-analyze, test, perform and/or display publicly, prepare derivative works,
-distribute, and otherwise use Python alone or in any derivative version,
-provided, however, that PSF's License Agreement and PSF's notice of copyright,
-i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
-2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 Python Software Foundation;
-All Rights Reserved" are retained in Python alone or in any derivative version
-prepared by Licensee.
-
-3. In the event Licensee prepares a derivative work that is based on
-or incorporates Python or any part thereof, and wants to make
-the derivative work available to others as provided herein, then
-Licensee hereby agrees to include in any such work a brief summary of
-the changes made to Python.
-
-4. PSF is making Python available to Licensee on an "AS IS"
-basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
-IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
-DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
-FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
-INFRINGE ANY THIRD PARTY RIGHTS.
-
-5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
-FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
-A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
-OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
-
-6. This License Agreement will automatically terminate upon a material
-breach of its terms and conditions.
-
-7. Nothing in this License Agreement shall be deemed to create any
-relationship of agency, partnership, or joint venture between PSF and
-Licensee. This License Agreement does not grant permission to use PSF
-trademarks or trade name in a trademark sense to endorse or promote
-products or services of Licensee, or any third party.
-
-8. By copying, installing or otherwise using Python, Licensee
-agrees to be bound by the terms and conditions of this License
-Agreement.
-
diff --git a/python/pylintrc b/python/pylintrc
index 26d2741..5483774 100644
--- a/python/pylintrc
+++ b/python/pylintrc
@@ -29,7 +29,7 @@ profile=no
# Add files or directories to the ignoreList. They should be base names, not
# paths.
-ignore=pyspark.heapq3
+#ignore=
# Pickle collected data for later comparisons.
persistent=yes
diff --git a/python/pyspark/heapq3.py b/python/pyspark/heapq3.py
deleted file mode 100644
index 37a2914..0000000
--- a/python/pyspark/heapq3.py
+++ /dev/null
@@ -1,890 +0,0 @@
-# -*- encoding: utf-8 -*-
-# back ported from CPython 3
-# A. HISTORY OF THE SOFTWARE
-# ==========================
-#
-# Python was created in the early 1990s by Guido van Rossum at Stichting
-# Mathematisch Centrum (CWI, see http://www.cwi.nl) in the Netherlands
-# as a successor of a language called ABC. Guido remains Python's
-# principal author, although it includes many contributions from others.
-#
-# In 1995, Guido continued his work on Python at the Corporation for
-# National Research Initiatives (CNRI, see http://www.cnri.reston.va.us)
-# in Reston, Virginia where he released several versions of the
-# software.
-#
-# In May 2000, Guido and the Python core development team moved to
-# BeOpen.com to form the BeOpen PythonLabs team. In October of the same
-# year, the PythonLabs team moved to Digital Creations (now Zope
-# Corporation, see http://www.zope.com). In 2001, the Python Software
-# Foundation (PSF, see http://www.python.org/psf/) was formed, a
-# non-profit organization created specifically to own Python-related
-# Intellectual Property. Zope Corporation is a sponsoring member of
-# the PSF.
-#
-# All Python releases are Open Source (see http://www.opensource.org for
-# the Open Source Definition). Historically, most, but not all, Python
-# releases have also been GPL-compatible; the table below summarizes
-# the various releases.
-#
-# Release Derived Year Owner GPL-
-# from compatible? (1)
-#
-# 0.9.0 thru 1.2 1991-1995 CWI yes
-# 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes
-# 1.6 1.5.2 2000 CNRI no
-# 2.0 1.6 2000 BeOpen.com no
-# 1.6.1 1.6 2001 CNRI yes (2)
-# 2.1 2.0+1.6.1 2001 PSF no
-# 2.0.1 2.0+1.6.1 2001 PSF yes
-# 2.1.1 2.1+2.0.1 2001 PSF yes
-# 2.2 2.1.1 2001 PSF yes
-# 2.1.2 2.1.1 2002 PSF yes
-# 2.1.3 2.1.2 2002 PSF yes
-# 2.2.1 2.2 2002 PSF yes
-# 2.2.2 2.2.1 2002 PSF yes
-# 2.2.3 2.2.2 2003 PSF yes
-# 2.3 2.2.2 2002-2003 PSF yes
-# 2.3.1 2.3 2002-2003 PSF yes
-# 2.3.2 2.3.1 2002-2003 PSF yes
-# 2.3.3 2.3.2 2002-2003 PSF yes
-# 2.3.4 2.3.3 2004 PSF yes
-# 2.3.5 2.3.4 2005 PSF yes
-# 2.4 2.3 2004 PSF yes
-# 2.4.1 2.4 2005 PSF yes
-# 2.4.2 2.4.1 2005 PSF yes
-# 2.4.3 2.4.2 2006 PSF yes
-# 2.4.4 2.4.3 2006 PSF yes
-# 2.5 2.4 2006 PSF yes
-# 2.5.1 2.5 2007 PSF yes
-# 2.5.2 2.5.1 2008 PSF yes
-# 2.5.3 2.5.2 2008 PSF yes
-# 2.6 2.5 2008 PSF yes
-# 2.6.1 2.6 2008 PSF yes
-# 2.6.2 2.6.1 2009 PSF yes
-# 2.6.3 2.6.2 2009 PSF yes
-# 2.6.4 2.6.3 2009 PSF yes
-# 2.6.5 2.6.4 2010 PSF yes
-# 2.7 2.6 2010 PSF yes
-#
-# Footnotes:
-#
-# (1) GPL-compatible doesn't mean that we're distributing Python under
-# the GPL. All Python licenses, unlike the GPL, let you distribute
-# a modified version without making your changes open source. The
-# GPL-compatible licenses make it possible to combine Python with
-# other software that is released under the GPL; the others don't.
-#
-# (2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
-# because its license has a choice of law clause. According to
-# CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
-# is "not incompatible" with the GPL.
-#
-# Thanks to the many outside volunteers who have worked under Guido's
-# direction to make these releases possible.
-#
-#
-# B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
-# ===============================================================
-#
-# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
-# --------------------------------------------
-#
-# 1. This LICENSE AGREEMENT is between the Python Software Foundation
-# ("PSF"), and the Individual or Organization ("Licensee") accessing and
-# otherwise using this software ("Python") in source or binary form and
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-"""Heap queue algorithm (a.k.a. priority queue).
-
-Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
-all k, counting elements from 0. For the sake of comparison,
-non-existing elements are considered to be infinite. The interesting
-property of a heap is that a[0] is always its smallest element.
-
-Usage:
-
-heap = [] # creates an empty heap
-heappush(heap, item) # pushes a new item on the heap
-item = heappop(heap) # pops the smallest item from the heap
-item = heap[0] # smallest item on the heap without popping it
-heapify(x) # transforms list into a heap, in-place, in linear time
-item = heapreplace(heap, item) # pops and returns smallest item, and adds
- # new item; the heap size is unchanged
-
-Our API differs from textbook heap algorithms as follows:
-
-- We use 0-based indexing. This makes the relationship between the
- index for a node and the indexes for its children slightly less
- obvious, but is more suitable since Python uses 0-based indexing.
-
-- Our heappop() method returns the smallest item, not the largest.
-
-These two make it possible to view the heap as a regular Python list
-without surprises: heap[0] is the smallest item, and heap.sort()
-maintains the heap invariant!
-"""
-
-# Original code by Kevin O'Connor, augmented by Tim Peters and Raymond Hettinger
-
-__about__ = """Heap queues
-
-[explanation by François Pinard]
-
-Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
-all k, counting elements from 0. For the sake of comparison,
-non-existing elements are considered to be infinite. The interesting
-property of a heap is that a[0] is always its smallest element.
-
-The strange invariant above is meant to be an efficient memory
-representation for a tournament. The numbers below are `k', not a[k]:
-
- 0
-
- 1 2
-
- 3 4 5 6
-
- 7 8 9 10 11 12 13 14
-
- 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
-
-
-In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In
-an usual binary tournament we see in sports, each cell is the winner
-over the two cells it tops, and we can trace the winner down the tree
-to see all opponents s/he had. However, in many computer applications
-of such tournaments, we do not need to trace the history of a winner.
-To be more memory efficient, when a winner is promoted, we try to
-replace it by something else at a lower level, and the rule becomes
-that a cell and the two cells it tops contain three different items,
-but the top cell "wins" over the two topped cells.
-
-If this heap invariant is protected at all time, index 0 is clearly
-the overall winner. The simplest algorithmic way to remove it and
-find the "next" winner is to move some loser (let's say cell 30 in the
-diagram above) into the 0 position, and then percolate this new 0 down
-the tree, exchanging values, until the invariant is re-established.
-This is clearly logarithmic on the total number of items in the tree.
-By iterating over all items, you get an O(n ln n) sort.
-
-A nice feature of this sort is that you can efficiently insert new
-items while the sort is going on, provided that the inserted items are
-not "better" than the last 0'th element you extracted. This is
-especially useful in simulation contexts, where the tree holds all
-incoming events, and the "win" condition means the smallest scheduled
-time. When an event schedule other events for execution, they are
-scheduled into the future, so they can easily go into the heap. So, a
-heap is a good structure for implementing schedulers (this is what I
-used for my MIDI sequencer :-).
-
-Various structures for implementing schedulers have been extensively
-studied, and heaps are good for this, as they are reasonably speedy,
-the speed is almost constant, and the worst case is not much different
-than the average case. However, there are other representations which
-are more efficient overall, yet the worst cases might be terrible.
-
-Heaps are also very useful in big disk sorts. You most probably all
-know that a big sort implies producing "runs" (which are pre-sorted
-sequences, which size is usually related to the amount of CPU memory),
-followed by a merging passes for these runs, which merging is often
-very cleverly organised[1]. It is very important that the initial
-sort produces the longest runs possible. Tournaments are a good way
-to that. If, using all the memory available to hold a tournament, you
-replace and percolate items that happen to fit the current run, you'll
-produce runs which are twice the size of the memory for random input,
-and much better for input fuzzily ordered.
-
-Moreover, if you output the 0'th item on disk and get an input which
-may not fit in the current tournament (because the value "wins" over
-the last output value), it cannot fit in the heap, so the size of the
-heap decreases. The freed memory could be cleverly reused immediately
-for progressively building a second heap, which grows at exactly the
-same rate the first heap is melting. When the first heap completely
-vanishes, you switch heaps and start a new run. Clever and quite
-effective!
-
-In a word, heaps are useful memory structures to know. I use them in
-a few applications, and I think it is good to keep a `heap' module
-around. :-)
-
---------------------
-[1] The disk balancing algorithms which are current, nowadays, are
-more annoying than clever, and this is a consequence of the seeking
-capabilities of the disks. On devices which cannot seek, like big
-tape drives, the story was quite different, and one had to be very
-clever to ensure (far in advance) that each tape movement will be the
-most effective possible (that is, will best participate at
-"progressing" the merge). Some tapes were even able to read
-backwards, and this was also used to avoid the rewinding time.
-Believe me, real good tape sorts were quite spectacular to watch!
-From all times, sorting has always been a Great Art! :-)
-"""
-
-__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace', 'merge',
- 'nlargest', 'nsmallest', 'heappushpop']
-
-def heappush(heap, item):
- """Push item onto heap, maintaining the heap invariant."""
- heap.append(item)
- _siftdown(heap, 0, len(heap)-1)
-
-def heappop(heap):
- """Pop the smallest item off the heap, maintaining the heap invariant."""
- lastelt = heap.pop() # raises appropriate IndexError if heap is empty
- if heap:
- returnitem = heap[0]
- heap[0] = lastelt
- _siftup(heap, 0)
- return returnitem
- return lastelt
-
-def heapreplace(heap, item):
- """Pop and return the current smallest value, and add the new item.
-
- This is more efficient than heappop() followed by heappush(), and can be
- more appropriate when using a fixed-size heap. Note that the value
- returned may be larger than item! That constrains reasonable uses of
- this routine unless written as part of a conditional replacement:
-
- if item > heap[0]:
- item = heapreplace(heap, item)
- """
- returnitem = heap[0] # raises appropriate IndexError if heap is empty
- heap[0] = item
- _siftup(heap, 0)
- return returnitem
-
-def heappushpop(heap, item):
- """Fast version of a heappush followed by a heappop."""
- if heap and heap[0] < item:
- item, heap[0] = heap[0], item
- _siftup(heap, 0)
- return item
-
-def heapify(x):
- """Transform list into a heap, in-place, in O(len(x)) time."""
- n = len(x)
- # Transform bottom-up. The largest index there's any point to looking at
- # is the largest with a child index in-range, so must have 2*i + 1 < n,
- # or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so
- # j-1 is the largest, which is n//2 - 1. If n is odd = 2*j+1, this is
- # (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1.
- for i in reversed(range(n//2)):
- _siftup(x, i)
-
-def _heappop_max(heap):
- """Maxheap version of a heappop."""
- lastelt = heap.pop() # raises appropriate IndexError if heap is empty
- if heap:
- returnitem = heap[0]
- heap[0] = lastelt
- _siftup_max(heap, 0)
- return returnitem
- return lastelt
-
-def _heapreplace_max(heap, item):
- """Maxheap version of a heappop followed by a heappush."""
- returnitem = heap[0] # raises appropriate IndexError if heap is empty
- heap[0] = item
- _siftup_max(heap, 0)
- return returnitem
-
-def _heapify_max(x):
- """Transform list into a maxheap, in-place, in O(len(x)) time."""
- n = len(x)
- for i in reversed(range(n//2)):
- _siftup_max(x, i)
-
-# 'heap' is a heap at all indices >= startpos, except possibly for pos. pos
-# is the index of a leaf with a possibly out-of-order value. Restore the
-# heap invariant.
-def _siftdown(heap, startpos, pos):
- newitem = heap[pos]
- # Follow the path to the root, moving parents down until finding a place
- # newitem fits.
- while pos > startpos:
- parentpos = (pos - 1) >> 1
- parent = heap[parentpos]
- if newitem < parent:
- heap[pos] = parent
- pos = parentpos
- continue
- break
- heap[pos] = newitem
-
-# The child indices of heap index pos are already heaps, and we want to make
-# a heap at index pos too. We do this by bubbling the smaller child of
-# pos up (and so on with that child's children, etc) until hitting a leaf,
-# then using _siftdown to move the oddball originally at index pos into place.
-#
-# We *could* break out of the loop as soon as we find a pos where newitem <=
-# both its children, but turns out that's not a good idea, and despite that
-# many books write the algorithm that way. During a heap pop, the last array
-# element is sifted in, and that tends to be large, so that comparing it
-# against values starting from the root usually doesn't pay (= usually doesn't
-# get us out of the loop early). See Knuth, Volume 3, where this is
-# explained and quantified in an exercise.
-#
-# Cutting the # of comparisons is important, since these routines have no
-# way to extract "the priority" from an array element, so that intelligence
-# is likely to be hiding in custom comparison methods, or in array elements
-# storing (priority, record) tuples. Comparisons are thus potentially
-# expensive.
-#
-# On random arrays of length 1000, making this change cut the number of
-# comparisons made by heapify() a little, and those made by exhaustive
-# heappop() a lot, in accord with theory. Here are typical results from 3
-# runs (3 just to demonstrate how small the variance is):
-#
-# Compares needed by heapify Compares needed by 1000 heappops
-# -------------------------- --------------------------------
-# 1837 cut to 1663 14996 cut to 8680
-# 1855 cut to 1659 14966 cut to 8678
-# 1847 cut to 1660 15024 cut to 8703
-#
-# Building the heap by using heappush() 1000 times instead required
-# 2198, 2148, and 2219 compares: heapify() is more efficient, when
-# you can use it.
-#
-# The total compares needed by list.sort() on the same lists were 8627,
-# 8627, and 8632 (this should be compared to the sum of heapify() and
-# heappop() compares): list.sort() is (unsurprisingly!) more efficient
-# for sorting.
-
-def _siftup(heap, pos):
- endpos = len(heap)
- startpos = pos
- newitem = heap[pos]
- # Bubble up the smaller child until hitting a leaf.
- childpos = 2*pos + 1 # leftmost child position
- while childpos < endpos:
- # Set childpos to index of smaller child.
- rightpos = childpos + 1
- if rightpos < endpos and not heap[childpos] < heap[rightpos]:
- childpos = rightpos
- # Move the smaller child up.
- heap[pos] = heap[childpos]
- pos = childpos
- childpos = 2*pos + 1
- # The leaf at pos is empty now. Put newitem there, and bubble it up
- # to its final resting place (by sifting its parents down).
- heap[pos] = newitem
- _siftdown(heap, startpos, pos)
-
-def _siftdown_max(heap, startpos, pos):
- 'Maxheap variant of _siftdown'
- newitem = heap[pos]
- # Follow the path to the root, moving parents down until finding a place
- # newitem fits.
- while pos > startpos:
- parentpos = (pos - 1) >> 1
- parent = heap[parentpos]
- if parent < newitem:
- heap[pos] = parent
- pos = parentpos
- continue
- break
- heap[pos] = newitem
-
-def _siftup_max(heap, pos):
- 'Maxheap variant of _siftup'
- endpos = len(heap)
- startpos = pos
- newitem = heap[pos]
- # Bubble up the larger child until hitting a leaf.
- childpos = 2*pos + 1 # leftmost child position
- while childpos < endpos:
- # Set childpos to index of larger child.
- rightpos = childpos + 1
- if rightpos < endpos and not heap[rightpos] < heap[childpos]:
- childpos = rightpos
- # Move the larger child up.
- heap[pos] = heap[childpos]
- pos = childpos
- childpos = 2*pos + 1
- # The leaf at pos is empty now. Put newitem there, and bubble it up
- # to its final resting place (by sifting its parents down).
- heap[pos] = newitem
- _siftdown_max(heap, startpos, pos)
-
-def merge(iterables, key=None, reverse=False):
- '''Merge multiple sorted inputs into a single sorted output.
-
- Similar to sorted(itertools.chain(*iterables)) but returns a generator,
- does not pull the data into memory all at once, and assumes that each of
- the input streams is already sorted (smallest to largest).
-
- >>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25]))
- [0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]
-
- If *key* is not None, applies a key function to each element to determine
- its sort order.
-
- >>> list(merge(['dog', 'horse'], ['cat', 'fish', 'kangaroo'], key=len))
- ['dog', 'cat', 'fish', 'horse', 'kangaroo']
-
- '''
-
- h = []
- h_append = h.append
-
- if reverse:
- _heapify = _heapify_max
- _heappop = _heappop_max
- _heapreplace = _heapreplace_max
- direction = -1
- else:
- _heapify = heapify
- _heappop = heappop
- _heapreplace = heapreplace
- direction = 1
-
- if key is None:
- for order, it in enumerate(map(iter, iterables)):
- try:
- h_append([next(it), order * direction, it])
- except StopIteration:
- pass
- _heapify(h)
- while len(h) > 1:
- try:
- while True:
- value, order, it = s = h[0]
- yield value
- s[0] = next(it) # raises StopIteration when exhausted
- _heapreplace(h, s) # restore heap condition
- except StopIteration:
- _heappop(h) # remove empty iterator
- if h:
- # fast case when only a single iterator remains
- value, order, it = h[0]
- yield value
- for value in it:
- yield value
- return
-
- for order, it in enumerate(map(iter, iterables)):
- try:
- value = next(it)
- h_append([key(value), order * direction, value, it])
- except StopIteration:
- pass
- _heapify(h)
- while len(h) > 1:
- try:
- while True:
- key_value, order, value, it = s = h[0]
- yield value
- value = next(it)
- s[0] = key(value)
- s[2] = value
- _heapreplace(h, s)
- except StopIteration:
- _heappop(h)
- if h:
- key_value, order, value, it = h[0]
- yield value
- for value in it:
- yield value
-
-
-# Algorithm notes for nlargest() and nsmallest()
-# ==============================================
-#
-# Make a single pass over the data while keeping the k most extreme values
-# in a heap. Memory consumption is limited to keeping k values in a list.
-#
-# Measured performance for random inputs:
-#
-# number of comparisons
-# n inputs k-extreme values (average of 5 trials) % more than min()
-# ------------- ---------------- --------------------- -----------------
-# 1,000 100 3,317 231.7%
-# 10,000 100 14,046 40.5%
-# 100,000 100 105,749 5.7%
-# 1,000,000 100 1,007,751 0.8%
-# 10,000,000 100 10,009,401 0.1%
-#
-# Theoretical number of comparisons for k smallest of n random inputs:
-#
-# Step Comparisons Action
-# ---- -------------------------- ---------------------------
-# 1 1.66 * k heapify the first k-inputs
-# 2 n - k compare remaining elements to top of heap
-# 3 k * (1 + lg2(k)) * ln(n/k) replace the topmost value on the heap
-# 4 k * lg2(k) - (k/2) final sort of the k most extreme values
-#
-# Combining and simplifying for a rough estimate gives:
-#
-# comparisons = n + k * (log(k, 2) * log(n/k) + log(k, 2) + log(n/k))
-#
-# Computing the number of comparisons for step 3:
-# -----------------------------------------------
-# * For the i-th new value from the iterable, the probability of being in the
-# k most extreme values is k/i. For example, the probability of the 101st
-# value seen being in the 100 most extreme values is 100/101.
-# * If the value is a new extreme value, the cost of inserting it into the
-# heap is 1 + log(k, 2).
-# * The probability times the cost gives:
-# (k/i) * (1 + log(k, 2))
-# * Summing across the remaining n-k elements gives:
-# sum((k/i) * (1 + log(k, 2)) for i in range(k+1, n+1))
-# * This reduces to:
-# (H(n) - H(k)) * k * (1 + log(k, 2))
-# * Where H(n) is the n-th harmonic number estimated by:
-# gamma = 0.5772156649
-# H(n) = log(n, e) + gamma + 1 / (2 * n)
-# http://en.wikipedia.org/wiki/Harmonic_series_(mathematics)#Rate_of_divergence
-# * Substituting the H(n) formula:
-# comparisons = k * (1 + log(k, 2)) * (log(n/k, e) + (1/n - 1/k) / 2)
-#
-# Worst-case for step 3:
-# ----------------------
-# In the worst case, the input data is reversed sorted so that every new element
-# must be inserted in the heap:
-#
-# comparisons = 1.66 * k + log(k, 2) * (n - k)
-#
-# Alternative Algorithms
-# ----------------------
-# Other algorithms were not used because they:
-# 1) Took much more auxiliary memory,
-# 2) Made multiple passes over the data.
-# 3) Made more comparisons in common cases (small k, large n, semi-random input).
-# See the more detailed comparison of approach at:
-# http://code.activestate.com/recipes/577573-compare-algorithms-for-heapqsmallest
-
-def nsmallest(n, iterable, key=None):
- """Find the n smallest elements in a dataset.
-
- Equivalent to: sorted(iterable, key=key)[:n]
- """
-
- # Short-cut for n==1 is to use min()
- if n == 1:
- it = iter(iterable)
- sentinel = object()
- if key is None:
- result = min(it, default=sentinel)
- else:
- result = min(it, default=sentinel, key=key)
- return [] if result is sentinel else [result]
-
- # When n>=size, it's faster to use sorted()
- try:
- size = len(iterable)
- except (TypeError, AttributeError):
- pass
- else:
- if n >= size:
- return sorted(iterable, key=key)[:n]
-
- # When key is none, use simpler decoration
- if key is None:
- it = iter(iterable)
- # put the range(n) first so that zip() doesn't
- # consume one too many elements from the iterator
- result = [(elem, i) for i, elem in zip(range(n), it)]
- if not result:
- return result
- _heapify_max(result)
- top = result[0][0]
- order = n
- _heapreplace = _heapreplace_max
- for elem in it:
- if elem < top:
- _heapreplace(result, (elem, order))
- top = result[0][0]
- order += 1
- result.sort()
- return [r[0] for r in result]
-
- # General case, slowest method
- it = iter(iterable)
- result = [(key(elem), i, elem) for i, elem in zip(range(n), it)]
- if not result:
- return result
- _heapify_max(result)
- top = result[0][0]
- order = n
- _heapreplace = _heapreplace_max
- for elem in it:
- k = key(elem)
- if k < top:
- _heapreplace(result, (k, order, elem))
- top = result[0][0]
- order += 1
- result.sort()
- return [r[2] for r in result]
-
-def nlargest(n, iterable, key=None):
- """Find the n largest elements in a dataset.
-
- Equivalent to: sorted(iterable, key=key, reverse=True)[:n]
- """
-
- # Short-cut for n==1 is to use max()
- if n == 1:
- it = iter(iterable)
- sentinel = object()
- if key is None:
- result = max(it, default=sentinel)
- else:
- result = max(it, default=sentinel, key=key)
- return [] if result is sentinel else [result]
-
- # When n>=size, it's faster to use sorted()
- try:
- size = len(iterable)
- except (TypeError, AttributeError):
- pass
- else:
- if n >= size:
- return sorted(iterable, key=key, reverse=True)[:n]
-
- # When key is none, use simpler decoration
- if key is None:
- it = iter(iterable)
- result = [(elem, i) for i, elem in zip(range(0, -n, -1), it)]
- if not result:
- return result
- heapify(result)
- top = result[0][0]
- order = -n
- _heapreplace = heapreplace
- for elem in it:
- if top < elem:
- _heapreplace(result, (elem, order))
- top = result[0][0]
- order -= 1
- result.sort(reverse=True)
- return [r[0] for r in result]
-
- # General case, slowest method
- it = iter(iterable)
- result = [(key(elem), i, elem) for i, elem in zip(range(0, -n, -1), it)]
- if not result:
- return result
- heapify(result)
- top = result[0][0]
- order = -n
- _heapreplace = heapreplace
- for elem in it:
- k = key(elem)
- if top < k:
- _heapreplace(result, (k, order, elem))
- top = result[0][0]
- order -= 1
- result.sort(reverse=True)
- return [r[2] for r in result]
-
-# If available, use C implementation
-try:
- from _heapq import *
-except ImportError:
- pass
-try:
- from _heapq import _heapreplace_max
-except ImportError:
- pass
-try:
- from _heapq import _heapify_max
-except ImportError:
- pass
-try:
- from _heapq import _heappop_max
-except ImportError:
- pass
-
-
-if __name__ == "__main__":
- import doctest
- import sys
- (failure_count, test_count) = doctest.testmod()
- if failure_count:
- sys.exit(-1)
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
index 5d2d638..308305e 100644
--- a/python/pyspark/shuffle.py
+++ b/python/pyspark/shuffle.py
@@ -25,7 +25,7 @@ import operator
import random
import sys
-import pyspark.heapq3 as heapq
+import heapq
from pyspark.serializers import BatchedSerializer, PickleSerializer, FlattenedValuesSerializer, \
CompressedSerializer, AutoBatchedSerializer
from pyspark.util import fail_on_stopiteration
@@ -498,7 +498,7 @@ class ExternalSorter(object):
if current_chunk:
chunks.append(iter(current_chunk))
- return heapq.merge(chunks, key=key, reverse=reverse)
+ return heapq.merge(*chunks, key=key, reverse=reverse)
class ExternalList(object):
@@ -796,7 +796,7 @@ class ExternalGroupBy(ExternalMerger):
if self._sorted:
# all the partitions are already sorted
- sorted_items = heapq.merge(disk_items, key=operator.itemgetter(0))
+ sorted_items = heapq.merge(*disk_items, key=operator.itemgetter(0))
else:
# Flatten the combined values, so it will not consume huge
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