1.
3/5/15
Challenge the future
Delft
University of
Technology
Understanding Software Performance
Regressions Using Differential Flame
Graphs
Cor-Paul Bezemer, Johan Pouwelse, Brendan Gregg

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2Understanding Software Performance Regressions Using DFGs
Regression Testing

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3Understanding Software Performance Regressions Using DFGs
Regression Testing Cycle
1)Write code
2)Deploy to continuous integration system
3)Test
4)Deploy to production code or return to 1)

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4Understanding Software Performance Regressions Using DFGs
Performance Regression Testing
• Functional testing is difficult, but:
• Functionality is either OK or broken
• Performance testing is even more difficult:
• Depends on requirements of application
• A library may be fast enough for one application, while it's too slow for
another
• How much performance regression is acceptable?

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5Understanding Software Performance Regressions Using DFGs

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6Understanding Software Performance Regressions Using DFGs
What to Measure
• Built-in performance metrics (or counters)
• Response time, throughput, queue lengths, utilization
• Usually system-wide
• Profilers
• Per-function info on execution time, bytes written, etc.
• Higher granularity == more expensive
• After measuring: tons of data! :) / :(

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7Understanding Software Performance Regressions Using DFGs

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8Understanding Software Performance Regressions Using DFGs
Detecting I/O Regression
• Previous work:
• “Detecting and analyzing I/O performance regressions”,
C. Bezemer, E. Milon, A. Zaidman, J. Pouwelse (Journal of Software:
Evolution and Process 26 (12), 1193-1212)
• Approach for finding out which function exhibits I/O write
regression over two software versions
• Results promising, but sometimes difficult to interpret

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9Understanding Software Performance Regressions Using DFGs
Work in Progress: Visualization

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10Understanding Software Performance Regressions Using DFGs
Flame Graphs (1)
def a ( ) :
if some condition :
b()
…
def b ( ) :
if some condition :
c ()
...
def c( ):
...
def d ( ) :
if some condition :
c ()
...
Stack trace, CPU time
a(), 100
a()→b(), 25
a()→b()→c(), 10
d(), 50
d()→c(), 10

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11Understanding Software Performance Regressions Using DFGs
Flame Graphs (2)

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12Understanding Software Performance Regressions Using DFGs
Flame Graphs (3)

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13Understanding Software Performance Regressions Using DFGs
Differential Flame Graphs (1)

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14Understanding Software Performance Regressions Using DFGs
Demo

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15Understanding Software Performance Regressions Using DFGs
Differential Flame Graphs (2)
• You can do this with performance profiles..
• But also with other data?
• Ideas:
• Website clickpaths
• Parallel/distributed computing
• ...
• Let us know! http://corpaul.github.io/flamegraphdiff/
• (they use this at Netflix, so it must be pretty cool!)

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16Understanding Software Performance Regressions Using DFGs
Discussion
• Data collection is difficult
• No out of the box solutions
• Expensive
• Data variation between executions can be considerable
• Especially for 'non-stable' metrics (CPU, memory)

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17Understanding Software Performance Regressions Using DFGs
c.bezemer@tudelft.nl
http://corpaul.github.io/flamegraphdiff/