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Software developers rely on a fast and correct build system to compile their source code changes and produce modified deliverables for testing and deployment. The scale and complexity of the PostgreSQL build process makes build performance an important topic to discuss and address. In this talk, we will introduce a new build performance analysis technique that identifies "build hotspots", i.e., files that are slow to rebuild (by analyzing a build dependency graph), yet change often (by analyzing version control history). We will discuss the identified hotspots in the 9.2.4 release of PostgreSQL.
Citation preview
Identifying Hotspots in the PostgreSQL Build Processes
Shane McIntosh
Ahmed E. Hassan
Bram Adams
Meiyappan Nagappan
Source code
What is a build system?
!2
Source code
Deliverable
What is a build system?
!2
.tex
.c
.cc
.o
.o
.dvi
.a
.exe
.deb
Build systems describe how sources are translated into deliverables
!3
(4) Test
(1) Think
(2) Edit
(3) Build
The developer’s work cycle
All builds are equal…
!4
“
…But some builds are more equal than others
http://xkcd.com/303/
Why is the gtk build so abysmally slow? This hinders our ability to run gtk bots
and keep the gtk build green. :( ”!5
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.olibrary.a
del1.c del2.cutil1.o util2.o
util1.c util2.c
!6
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.olibrary.a
del1.c del2.cutil1.o util2.o
util1.c util2.c
!6
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.olibrary.a
del1.c del2.cutil1.o util2.o
util1.c util2.c
!6
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.olibrary.a
del1.c del2.cutil1.o util2.o
util1.c util2.c
!6
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.olibrary.a
del1.c del2.cutil1.o util2.o
util1.c util2.c
!6
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.olibrary.a
del1.c del2.cutil1.o util2.o
util1.c util2.c
!6
Before refactoring:!4 commands!
triggered
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.olibrary.a
del1.c del2.cutil1.o util2.o
util1.c util2.c
!6
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.o
del1.c del2.c
util1.o util2.o
util1.c util2.c
!7
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.o
del1.c del2.c
util1.o util2.o
util1.c util2.c
!7
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.o
del1.c del2.c
util1.o util2.o
util1.c util2.c
!7
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.o
del1.c del2.c
util1.o util2.o
util1.c util2.c
!7
Refactoring can improve build performance
deliverable1 deliverable2
del1.o del2.o
del1.c del2.c
util1.o util2.o
util1.c util2.c
!7
After refactoring:!2 commands!
triggered
…But where should we focus refactoring effort?
Files that rebuild the slowest?
!8
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
Graphanalysis results
(1)Dependency
Graph Construction (3)
Build Hotspot
DetectionQuadrant Plot
Build Activity
File
Chu
rn
(2)Dependency
GraphAnalysisDep. Graph
VersionControlSystem
An approach to detect slowly rebuilding files
!9
MAKAO tool extracts build dependency graphs
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
Design recovery and maintenance of build systems B. Adams, Herman Tromp, Kris De Schutter, Wolfgang De Meuter
[ICSM 2007]
!10
The cost of traversing an edge is derived by timing its build command
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
!11
Median of 10 repetitions
Open source case studies!!
!
!
!12
0 750,000 1,500,000 2,250,000 3,000,000
2,752,225
38,102
Build dependency graph properties
0 35,000 70,000 105,000 140,000
60,170
121,710
5,131
3,375
# Nodes# Edges
!13
Incremental build performance
!14
0 100 200 300 400 500 600
02
46
810
12
File ID
Build
Tim
e (s
)
0 100 200 300 400
050
100
150
File ID
Build
Tim
e (s
)
.c files .h files
Incremental build performance
!15
.c files .h files0 200 400 600 800 1000
0.0
0.5
1.0
1.5
2.0
File ID
Build
Tim
e (s
)
0 100 200 300 400 500 600
050
100
150
File ID
Build
Tim
e (s
)
Incremental build performance
!16
.c files .h files0 2000 4000 6000 8000 10000
050
100
150
File ID
Build
Tim
e (s
)
0 2000 4000 6000 8000 10000
020
0040
0060
0080
00
File ID
Build
Tim
e (s
)
So, refactoring should target the slow files!
!17
0 100 200 300 400 500 600
050
100
150
File ID
Build
Tim
e (s
)
So, refactoring should target the slow files!
!17
0 100 200 300 400 500 600
050
100
150
File ID
Build
Tim
e (s
)
So, refactoring should target the slow files!
!17
0 100 200 300 400 500 600
050
100
150
File ID
Build
Tim
e (s
)
So, refactoring should target the slow files!
!17
0 100 200 300 400 500 600
050
100
150
File ID
Build
Tim
e (s
)
!18
> Please answer the following questions for each source file listed above:!> (1) Is this source code file a performance bottleneck? Y/N!!For all of them no...
Let’s see what developers think!
!19
> Please answer the following questions for each source file listed above:!> (1) Is this source code file a performance bottleneck? Y/N!!For all of them no...!!> (2) Why do you believe this file is or is not a performance bottleneck?!!...because none of these headers change often.
Let’s see what developers think!
…But where should we focus refactoring effort?
Files that rebuild the slowest?
!20
…But where should we focus refactoring effort?
Files that rebuild the slowest?
…but they may only rarely change!
!20
…But where should we focus refactoring effort?
Files that rebuild the slowest?
Files that change the most often?
…but they may only rarely change!
!20
!21
Mining frequently changing files from version control
Terminal
$ git clone … … $ git checkout <some_tag> … $ git log --oneline <some_file>
!21
Mining frequently changing files from version control
Terminal
$ git clone … … $ git checkout <some_tag> … $ git log --oneline <some_file>
But many of these files already build quickly!
…But where should we focus refactoring effort?
Files that rebuild the slowest?
Files that change the most often?
…but they may only rarely change!
!22
…But where should we focus refactoring effort?
Files that rebuild the slowest?
Files that change the most often?
…but they may only rarely change!
…but they may already be optimal!
!22
…But where should we focus refactoring effort?
Files that rebuild the slowest?
Files that change the most often?
…but they may only rarely change!
…but they may already be optimal!
!22
Focus on build hotspots:
Files that rebuild slowly and change often
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
Graphanalysis results
(1)Dependency
Graph Construction (3)
Build Hotspot
DetectionQuadrant Plot
Build Activity
File
Chu
rn
(2)Dependency
GraphAnalysisDep. Graph
VersionControlSystem
An approach to detect slowly rebuilding files
!23
(3)Build
Hotspot Detection
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
Graphanalysis results
(1)Dependency
Graph Construction (3)
Build Hotspot
DetectionQuadrant Plot
Build Activity
File
Chu
rn
(2)Dependency
GraphAnalysisDep. Graph
VersionControlSystem
Hotspot detection approach
!24
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
(3)Build
Hotspot Detection
Quadrant plots highlight build hotspots
Rebuild cost
Num
ber o
f cha
nges
!25
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
(3)Build
Hotspot Detection
Quadrant plots highlight build hotspots
Rebuild cost
Num
ber o
f cha
nges
!25
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
(3)Build
Hotspot Detection
Quadrant plots highlight build hotspots
Rebuild cost
Num
ber o
f cha
nges
!25
(1)Dependency
Graph Construction
(2)Dependency
GraphAnalysis
(3)Build
Hotspot Detection
Quadrant plots highlight build hotspots
Rebuild cost
Num
ber o
f cha
nges
Refactor
these first!
!25
Open source case studies!!
!
!
Rebuild cost 90 seconds
Number of changes Median
Thresholds
!26
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0.25
0.50
0.75
1.00
0 50 100 150Build Time
Nor
mal
ized
File
Chu
rn
Rebuild cost (seconds)
Num
ber o
f cha
nges
(N
orm
aliz
ed)
65 hotspots!(7% of
source files)
!27
glib/glib.hglib/glib-object.h
Main culprits
Rebuild cost (seconds)
Num
ber o
f cha
nges
(N
orm
aliz
ed)
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!28
qtxmlpatterns
High hotspot!concentration
qtbase/…/corelib
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1.00
0 50 100 150 200Build Time (s)
Nor
mal
ized
File
Chu
rn27 hotspots!
(2% of source files)
!29
postgres.haccess/htup.h
Main culprits
access/genam.haccess/xact.hutils/builtins.h
hot.c
...1 2
hot.o
nn-1
!30
General trends Transitive property of build hotspots
hot.c
...1 2
hot.o
nn-1
!30
General trends Transitive property of build hotspots
x.h
hot.c
...1 2
hot.o
nn-1
!30
General trends Transitive property of build hotspots
x.h
hot.c
...1 2
hot.o
nn-1
!30
General trends Transitive property of build hotspots
x.h
hot.c
...1 2
hot.o
nn-1
General trends Transitive property of build hotspots
!31
x.h
hot.c
...1 2
hot.o
nn-1
General trends Transitive property of build hotspots
!31
hub.h
x.h
hot.c
...1 2
hot.o
nn-1
General trends Transitive property of build hotspots
!31
hub.h
x.h
hot.c
...1 2
hot.o
nn-1
a.h b.h y.h z.h
General trends Transitive property of build hotspots
!31
hub.h
x.h
hot.c
...1 2
hot.o
nn-1
a.h b.h y.h z.h
General trends Transitive property of build hotspots
!31
hub.h
x.h
hot.c
...1 2
hot.o
nn-1
a.h b.h y.h z.h
General trends Transitive property of build hotspots
!31
hub.h
x.h
hot.c
...1 2
hot.o
nn-1
Limit internal use of “header file hubs” as much as possible!
a.h b.h y.h z.h
General trends Transitive property of build hotspots
!31
RELENG 2014
2nd International Workshop on Release Engineering
http://releng.polymtl.ca
?
?
RELENG 2014
2nd International Workshop on Release Engineering
http://releng.polymtl.caDeadline: February 28, 2014
Event: April 11, 2014
?
talks on research
and practice
keynotes
poster session
and panel
?
RELENG 2014
2nd International Workshop on Release Engineering
http://releng.polymtl.caDeadline: February 28, 2014
Event: April 11, 2014
?
talks on research
and practice
keynotes
poster session
and panel
hosted in Mountainview (CA, US)
by?
RELENG 2014
2nd International Workshop on Release Engineering
http://releng.polymtl.caDeadline: February 28, 2014
Event: April 11, 2014
?
talks on research
and practice
keynotes
poster session
and panel
hosted in Mountainview (CA, US)
by
Dinah McNutt
(Google)
!34
Bonus!
!35
postgres.h
src/include/c.h src/include/port.h src/include/utils/elog.h src/include/utils/palloc.h src/include/pg_config_manual.h
!36
htup_details.h
src/include/access/htup.h src/include/storage/itemptr.h src/include/storage/itemid.h src/include/storage/bufpage.h src/include/access/tupdesc.h src/include/catalog/pg_attribute.h src/include/access/tupmacs.h
!37
genam.h
src/include/access/skey.h src/include/storage/lock.h src/include/storage/shmem.h src/include/utils/hsearch.h src/include/storage/lwlock.h src/include/utils/relcache.h
!38
src/include/access/xlog.h src/include/access/xlogdefs.h src/include/nodes/pg_list.h src/include/lib/stringinfo.h
xact.h
!39
src/include/fmgr.h src/include/nodes/parsenodes.h src/include/nodes/primnodes.h src/include/nodes/pg_list.h src/include/nodes/nodes.h
builtins.h
