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From ma...@apache.org
Subject git commit: [SPARK-3073] [PySpark] use external sort in sortBy() and sortByKey()
Date Tue, 26 Aug 2014 23:57:47 GMT
Repository: spark
Updated Branches:
  refs/heads/master c4787a369 -> f1e71d4c3


[SPARK-3073] [PySpark] use external sort in sortBy() and sortByKey()

Using external sort to support sort large datasets in reduce stage.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1978 from davies/sort and squashes the following commits:

bbcd9ba [Davies Liu] check spilled bytes in tests
b125d2f [Davies Liu] add test for external sort in rdd
eae0176 [Davies Liu] choose different disks from different processes and instances
1f075ed [Davies Liu] Merge branch 'master' into sort
eb53ca6 [Davies Liu] Merge branch 'master' into sort
644abaf [Davies Liu] add license in LICENSE
19f7873 [Davies Liu] improve tests
55602ee [Davies Liu] use external sort in sortBy() and sortByKey()


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/f1e71d4c
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/f1e71d4c
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/f1e71d4c

Branch: refs/heads/master
Commit: f1e71d4c3ba678fc108effb05cf2d6101dadc0ce
Parents: c4787a3
Author: Davies Liu <davies.liu@gmail.com>
Authored: Tue Aug 26 16:57:40 2014 -0700
Committer: Matei Zaharia <matei@databricks.com>
Committed: Tue Aug 26 16:57:40 2014 -0700

----------------------------------------------------------------------
 .rat-excludes             |   1 +
 LICENSE                   | 283 +++++++++++++
 python/pyspark/heapq3.py  | 890 +++++++++++++++++++++++++++++++++++++++++
 python/pyspark/rdd.py     |   9 +-
 python/pyspark/shuffle.py |  91 ++++-
 python/pyspark/tests.py   |  42 +-
 tox.ini                   |   2 +-
 7 files changed, 1306 insertions(+), 12 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/f1e71d4c/.rat-excludes
----------------------------------------------------------------------
diff --git a/.rat-excludes b/.rat-excludes
index eaefef1..fb6323d 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -31,6 +31,7 @@ sorttable.js
 .*data
 .*log
 cloudpickle.py
+heapq3.py
 join.py
 SparkExprTyper.scala
 SparkILoop.scala

http://git-wip-us.apache.org/repos/asf/spark/blob/f1e71d4c/LICENSE
----------------------------------------------------------------------
diff --git a/LICENSE b/LICENSE
index e9a1153..a7eee04 100644
--- a/LICENSE
+++ b/LICENSE
@@ -338,6 +338,289 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
+========================================================================
+For heapq (pyspark/heapq3.py):
+========================================================================
+
+# 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.
 
 ========================================================================
 For sorttable (core/src/main/resources/org/apache/spark/ui/static/sorttable.js):

http://git-wip-us.apache.org/repos/asf/spark/blob/f1e71d4c/python/pyspark/heapq3.py
----------------------------------------------------------------------
diff --git a/python/pyspark/heapq3.py b/python/pyspark/heapq3.py
new file mode 100644
index 0000000..bc441f1
--- /dev/null
+++ b/python/pyspark/heapq3.py
@@ -0,0 +1,890 @@
+# -*- 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
+# 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
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+# ACCEPT
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+#
+# 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
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+#
+# STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
+# THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
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+# 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.
+"""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 Fran├žois 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:
+                next = it.next
+                h_append([next(), order * direction, next])
+            except StopIteration:
+                pass
+        _heapify(h)
+        while len(h) > 1:
+            try:
+                while True:
+                    value, order, next = s = h[0]
+                    yield value
+                    s[0] = next()           # 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, next = h[0]
+            yield value
+            for value in next.__self__:
+                yield value
+        return
+
+    for order, it in enumerate(map(iter, iterables)):
+        try:
+            next = it.next
+            value = next()
+            h_append([key(value), order * direction, value, next])
+        except StopIteration:
+            pass
+    _heapify(h)
+    while len(h) > 1:
+        try:
+            while True:
+                key_value, order, value, next = s = h[0]
+                yield value
+                value = next()
+                s[0] = key(value)
+                s[2] = value
+                _heapreplace(h, s)
+        except StopIteration:
+            _heappop(h)
+    if h:
+        key_value, order, value, next = h[0]
+        yield value
+        for value in next.__self__:
+            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 probabilty 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
+    print(doctest.testmod())

http://git-wip-us.apache.org/repos/asf/spark/blob/f1e71d4c/python/pyspark/rdd.py
----------------------------------------------------------------------
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 3a2e764..3191974 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -44,7 +44,7 @@ from pyspark.rddsampler import RDDSampler, RDDStratifiedSampler
 from pyspark.storagelevel import StorageLevel
 from pyspark.resultiterable import ResultIterable
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, \
-    get_used_memory
+    get_used_memory, ExternalSorter
 
 from py4j.java_collections import ListConverter, MapConverter
 
@@ -605,8 +605,13 @@ class RDD(object):
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
 
+        spill = (self.ctx._conf.get("spark.shuffle.spill", 'True').lower() == 'true')
+        memory = _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m"))
+        serializer = self._jrdd_deserializer
+
         def sortPartition(iterator):
-            return iter(sorted(iterator, key=lambda (k, v): keyfunc(k), reverse=not ascending))
+            sort = ExternalSorter(memory * 0.9, serializer).sorted if spill else sorted
+            return iter(sort(iterator, key=lambda (k, v): keyfunc(k), reverse=(not ascending)))
 
         if numPartitions == 1:
             if self.getNumPartitions() > 1:

http://git-wip-us.apache.org/repos/asf/spark/blob/f1e71d4c/python/pyspark/shuffle.py
----------------------------------------------------------------------
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
index 1ebe7df..49829f5 100644
--- a/python/pyspark/shuffle.py
+++ b/python/pyspark/shuffle.py
@@ -21,7 +21,10 @@ import platform
 import shutil
 import warnings
 import gc
+import itertools
+import random
 
+import pyspark.heapq3 as heapq
 from pyspark.serializers import BatchedSerializer, PickleSerializer
 
 try:
@@ -54,6 +57,17 @@ except ImportError:
         return 0
 
 
+def _get_local_dirs(sub):
+    """ Get all the directories """
+    path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
+    dirs = path.split(",")
+    if len(dirs) > 1:
+        # different order in different processes and instances
+        rnd = random.Random(os.getpid() + id(dirs))
+        random.shuffle(dirs, rnd.random)
+    return [os.path.join(d, "python", str(os.getpid()), sub) for d in dirs]
+
+
 class Aggregator(object):
 
     """
@@ -196,7 +210,7 @@ class ExternalMerger(Merger):
         # default serializer is only used for tests
         self.serializer = serializer or \
             BatchedSerializer(PickleSerializer(), 1024)
-        self.localdirs = localdirs or self._get_dirs()
+        self.localdirs = localdirs or _get_local_dirs(str(id(self)))
         # number of partitions when spill data into disks
         self.partitions = partitions
         # check the memory after # of items merged
@@ -212,13 +226,6 @@ class ExternalMerger(Merger):
         # randomize the hash of key, id(o) is the address of o (aligned by 8)
         self._seed = id(self) + 7
 
-    def _get_dirs(self):
-        """ Get all the directories """
-        path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
-        dirs = path.split(",")
-        return [os.path.join(d, "python", str(os.getpid()), str(id(self)))
-                for d in dirs]
-
     def _get_spill_dir(self, n):
         """ Choose one directory for spill by number n """
         return os.path.join(self.localdirs[n % len(self.localdirs)], str(n))
@@ -434,6 +441,74 @@ class ExternalMerger(Merger):
                 os.remove(os.path.join(path, str(i)))
 
 
+class ExternalSorter(object):
+    """
+    ExtenalSorter will divide the elements into chunks, sort them in
+    memory and dump them into disks, finally merge them back.
+
+    The spilling will only happen when the used memory goes above
+    the limit.
+
+    >>> sorter = ExternalSorter(1)  # 1M
+    >>> import random
+    >>> l = range(1024)
+    >>> random.shuffle(l)
+    >>> sorted(l) == list(sorter.sorted(l))
+    True
+    >>> sorted(l) == list(sorter.sorted(l, key=lambda x: -x, reverse=True))
+    True
+    """
+    def __init__(self, memory_limit, serializer=None):
+        self.memory_limit = memory_limit
+        self.local_dirs = _get_local_dirs("sort")
+        self.serializer = serializer or BatchedSerializer(PickleSerializer(), 1024)
+        self._spilled_bytes = 0
+
+    def _get_path(self, n):
+        """ Choose one directory for spill by number n """
+        d = self.local_dirs[n % len(self.local_dirs)]
+        if not os.path.exists(d):
+            os.makedirs(d)
+        return os.path.join(d, str(n))
+
+    def sorted(self, iterator, key=None, reverse=False):
+        """
+        Sort the elements in iterator, do external sort when the memory
+        goes above the limit.
+        """
+        batch = 10
+        chunks, current_chunk = [], []
+        iterator = iter(iterator)
+        while True:
+            # pick elements in batch
+            chunk = list(itertools.islice(iterator, batch))
+            current_chunk.extend(chunk)
+            if len(chunk) < batch:
+                break
+
+            if get_used_memory() > self.memory_limit:
+                # sort them inplace will save memory
+                current_chunk.sort(key=key, reverse=reverse)
+                path = self._get_path(len(chunks))
+                with open(path, 'w') as f:
+                    self.serializer.dump_stream(current_chunk, f)
+                self._spilled_bytes += os.path.getsize(path)
+                chunks.append(self.serializer.load_stream(open(path)))
+                current_chunk = []
+
+            elif not chunks:
+                batch = min(batch * 2, 10000)
+
+        current_chunk.sort(key=key, reverse=reverse)
+        if not chunks:
+            return current_chunk
+
+        if current_chunk:
+            chunks.append(iter(current_chunk))
+
+        return heapq.merge(chunks, key=key, reverse=reverse)
+
+
 if __name__ == "__main__":
     import doctest
     doctest.testmod()

http://git-wip-us.apache.org/repos/asf/spark/blob/f1e71d4c/python/pyspark/tests.py
----------------------------------------------------------------------
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 1db922f..3e7040e 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -30,6 +30,7 @@ import sys
 import tempfile
 import time
 import zipfile
+import random
 
 if sys.version_info[:2] <= (2, 6):
     import unittest2 as unittest
@@ -37,10 +38,11 @@ else:
     import unittest
 
 
+from pyspark.conf import SparkConf
 from pyspark.context import SparkContext
 from pyspark.files import SparkFiles
 from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer
-from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger
+from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter
 
 _have_scipy = False
 _have_numpy = False
@@ -117,6 +119,44 @@ class TestMerger(unittest.TestCase):
         m._cleanup()
 
 
+class TestSorter(unittest.TestCase):
+    def test_in_memory_sort(self):
+        l = range(1024)
+        random.shuffle(l)
+        sorter = ExternalSorter(1024)
+        self.assertEquals(sorted(l), list(sorter.sorted(l)))
+        self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True)))
+        self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x)))
+        self.assertEquals(sorted(l, key=lambda x: -x, reverse=True),
+                          list(sorter.sorted(l, key=lambda x: -x, reverse=True)))
+
+    def test_external_sort(self):
+        l = range(1024)
+        random.shuffle(l)
+        sorter = ExternalSorter(1)
+        self.assertEquals(sorted(l), list(sorter.sorted(l)))
+        self.assertGreater(sorter._spilled_bytes, 0)
+        last = sorter._spilled_bytes
+        self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True)))
+        self.assertGreater(sorter._spilled_bytes, last)
+        last = sorter._spilled_bytes
+        self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x)))
+        self.assertGreater(sorter._spilled_bytes, last)
+        last = sorter._spilled_bytes
+        self.assertEquals(sorted(l, key=lambda x: -x, reverse=True),
+                          list(sorter.sorted(l, key=lambda x: -x, reverse=True)))
+        self.assertGreater(sorter._spilled_bytes, last)
+
+    def test_external_sort_in_rdd(self):
+        conf = SparkConf().set("spark.python.worker.memory", "1m")
+        sc = SparkContext(conf=conf)
+        l = range(10240)
+        random.shuffle(l)
+        rdd = sc.parallelize(l, 10)
+        self.assertEquals(sorted(l), rdd.sortBy(lambda x: x).collect())
+        sc.stop()
+
+
 class SerializationTestCase(unittest.TestCase):
 
     def test_namedtuple(self):

http://git-wip-us.apache.org/repos/asf/spark/blob/f1e71d4c/tox.ini
----------------------------------------------------------------------
diff --git a/tox.ini b/tox.ini
index a1fefdd..b568029 100644
--- a/tox.ini
+++ b/tox.ini
@@ -15,4 +15,4 @@
 
 [pep8]
 max-line-length=100
-exclude=cloudpickle.py
+exclude=cloudpickle.py,heapq3.py


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