tradeoffs, bad science, and polar bears: the world of java optimisation

Welcome to the Java optimisation jungle. Why can’t we “just make it go faster”? It turns out, in most cases, we need to first work out “faster for whom?” and “why do we want to go faster?” and “what even *is* faster?” This talk introduces the basic principles of optimisation, before bouncing through the pitfalls of optimisation; why the exact same techniques which make Quarkus rocket-fast used to be a terrible idea fifteen years ago, why fast benchmarks make for slow programs, and why even though it can be easy to get wrong, optimisation really really matters. Along the way we’ll talk about measuring things, bad advice, garbage collection, and climate change.

Transcript

1. 
   
   Holly Cummins
   IBM
   @holly_cummins
   tradeoffs, bad science, and polar bears:
   the world of java optimisation
   

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2. #Quarkus @holly_cummins
   why optimise?
   

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3. #Quarkus @holly_cummins
   why optimise?
   

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4. #Quarkus @holly_cummins
   why optimise?
   0.5s extra search
   page time
   

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5. #Quarkus @holly_cummins
   why optimise?
   0.5s extra search
   page time
   20% drop in
   traf
   fi
   c
   

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6. #Quarkus @holly_cummins
   why optimise?
   0.5s extra search
   page time
   20% drop in
   traf
   fi
   c
   100 ms latency on
   page load
   

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7. #Quarkus @holly_cummins
   why optimise?
   0.5s extra search
   page time
   20% drop in
   traf
   fi
   c
   100 ms latency on
   page load
   7% lower
   conversion rate
   

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8. #Quarkus @holly_cummins
   why optimise?
   0.5s extra search
   page time
   20% drop in
   traf
   fi
   c
   100 ms latency on
   page load
   7% lower
   conversion rate
   

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9. #Quarkus @holly_cummins
   why optimise?
   0.5s extra search
   page time
   20% drop in
   traf
   fi
   c
   10 ms delay in trading
   platform
   100 ms latency on
   page load
   7% lower
   conversion rate
   

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10. #Quarkus @holly_cummins
    why optimise?
    0.5s extra search
    page time
    20% drop in
    traf
    fi
    c
    10 ms delay in trading
    platform
    10% drop in
    revenue
    100 ms latency on
    page load
    7% lower
    conversion rate
    

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11. #Quarkus @holly_cummins
    what is optimising?
    

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12. #Quarkus @holly_cummins
    for whom?
    when?
    doing what?
    “make it go faster”
    

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13. #Quarkus @holly_cummins
    design thinking
    

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14. #Quarkus @holly_cummins
    

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15. #Quarkus @holly_cummins
    performance can be:
    

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16. #Quarkus @holly_cummins
    performance can be:
    throughput
    

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17. #Quarkus @holly_cummins
    performance can be:
    throughput transactions
    per second
    

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18. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    transactions
    per second
    

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19. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    transactions
    per second
    start-up time
    

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20. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    transactions
    per second
    response
    time start-up time
    

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21. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    transactions
    per second
    response
    time start-up time
    ramp-up
    time
    

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22. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    capacity
    transactions
    per second
    response
    time start-up time
    ramp-up
    time
    

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23. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    capacity
    transactions
    per second
    response
    time start-up time
    footprint
    ramp-up
    time
    

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24. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    capacity
    transactions
    per second
    response
    time start-up time
    CPU usage
    footprint
    ramp-up
    time
    

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25. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    capacity
    utilisation
    transactions
    per second
    response
    time start-up time
    CPU usage
    footprint
    ramp-up
    time
    

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26. #Quarkus @holly_cummins
    performance can be:
    throughput
    latency
    capacity
    utilisation
    …
    transactions
    per second
    response
    time start-up time
    CPU usage
    footprint
    ramp-up
    time
    

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27. #Quarkus @holly_cummins
    Never underestimate the bandwidth [throughput] of a
    station wagon full of tapes hurtling down the highway.
    –Andrew Tanenbaum, 1981
    

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28. #Quarkus @holly_cummins
    Never underestimate the bandwidth [throughput] of a
    station wagon full of tapes hurtling down the highway.
    –Andrew Tanenbaum, 1981
    but the latency is terrible …
    

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29. @holly_cummins
    requirements change
    

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30. @holly_cummins
    

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31. @holly_cummins
    

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32. @holly_cummins
    

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33. @holly_cummins
    

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34. @holly_cummins
    I am not
    designed for this.
    

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35. @holly_cummins
    the world changes
    

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36. #Quarkus @holly_cummins
    

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37. #Quarkus @holly_cummins
    -Xmx == $
    

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38. #Quarkus @holly_cummins
    -Xmx == $
    footprint
    

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39. #Quarkus @holly_cummins
    

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40. #Quarkus @holly_cummins
    which performs better?
    

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41. #Quarkus @holly_cummins
    quarkus
    trading-off
    fl
    exibility against
    startup speed and footprint
    

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42. #Quarkus @holly_cummins
    quarkus
    trading-off
    fl
    exibility against
    startup speed and footprint
    uhh … are you supposed
    to shut down applications
    after using them?
    

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43. #Quarkus @holly_cummins
    30% of VMs are zombies (antithesisgroup.com)
    behaviour at idle
    

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44. #Quarkus @holly_cummins
    how to optimise?
    

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45. #Quarkus @holly_cummins
    find the bottleneck.
    fi
    x it.
    

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46. #IBM @holly_cummins
    intuition
    pitfall 1
    

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47. #Quarkus @holly_cummins
    this is not the place for ideas
    

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48. #Quarkus @holly_cummins
    measure, don’t guess.
    

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49. #Quarkus @holly_cummins
    measure the right thing
    

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50. #Quarkus @holly_cummins
    measure the right thing
    what do your users care about?
    

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51. #IBM @holly_cummins
    numbers
    pitfall 2
    

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52. #Quarkus @holly_cummins
    

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53. #Quarkus @holly_cummins
    leading indicators
    

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54. #Quarkus @holly_cummins
    leading indicators lagging indicators
    

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55. #Quarkus @holly_cummins
    leading indicators
    we care about them
    lagging indicators
    

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56. #Quarkus @holly_cummins
    leading indicators
    we care about them
    easy to measure
    lagging indicators
    

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57. #Quarkus @holly_cummins
    leading indicators
    we care about them
    easy to measure
    hard to change
    lagging indicators
    

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58. #Quarkus @holly_cummins
    leading indicators
    we care about them
    easy to measure
    hard to change
    lagging indicators
    easy to change
    

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59. #Quarkus @holly_cummins
    leading indicators
    we care about them
    easy to measure
    hard to change
    lagging indicators
    predictive of a thing we care about
    easy to change
    

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60. #Quarkus @holly_cummins
    leading indicators
    we care about them
    easy to measure
    hard to change
    lagging indicators
    predictive of a thing we care about
    hard to identify
    easy to change
    

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61. #Quarkus @holly_cummins
    leading indicators
    we care about them
    easy to measure
    hard to change
    lagging indicators
    predictive of a thing we care about
    hard to identify
    easy to change
    

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62. #Quarkus @holly_cummins
    caution:
    performance experiments for entertainment purposes only.
    do not try these at home.
    

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63. #Quarkus @holly_cummins
    2007
    

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64. #Quarkus @holly_cummins
    bad-ish advice: “reduce time spent in garbage collection”
    

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65. #Quarkus @holly_cummins
    bad-ish advice: “reduce time spent in garbage collection”
    actually, garbage collection can make your application go faster
    

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66. #Quarkus @holly_cummins
    2007
    

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67. #Quarkus @holly_cummins
    2007
    

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68. #Quarkus @holly_cummins
    2021
    

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69. #Quarkus @holly_cummins
    2021
    

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70. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xcompactgc
    

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71. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    -Xcompactgc
    

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72. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    -Xmx110m
    -Xms110m
    -Xnocompactgc
    

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73. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    -Xmx160m
    -Xms160m
    -Xnocompactgc
    

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74. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    -Xmx300m
    -Xms300m
    -Xcompactgc
    why does the performance
    stay exactly the same no
    matter what gc settings I
    choose?
    

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75. #Quarkus @holly_cummins
    by the way, this is cheating.
    (remember the ‘bad science’?)
    

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76. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    

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77. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    tool: GCMV
    

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78. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    total GC time: 21.6s
    tool: GCMV
    

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79. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    total GC time: 21.6s
    4.1% of time in GC pause
    tool: GCMV
    

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80. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    total GC time: 21.6s
    4.1% of time in GC pause
    total GC time: 12.0s
    tool: GCMV
    

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81. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    total GC time: 21.6s
    4.1% of time in GC pause
    total GC time: 12.0s
    3.6% of time in GC pause
    tool: GCMV
    

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82. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    tool: GCMV
    

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83. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    tool: GCMV
    

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84. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    tool: GCMV
    

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85. #Quarkus @holly_cummins
    -verbose:gc
    -Xverbosegclog:gclog.xml
    -Xgcpolicy:optthruput
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    tool: GCMV
    

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86. #Quarkus @holly_cummins
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    

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87. #Quarkus @holly_cummins
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    

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88. #Quarkus @holly_cummins
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    leading indicator
    

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89. #Quarkus @holly_cummins
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    leading indicator
    

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90. #Quarkus @holly_cummins
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    leading indicator
    lagging indicator
    

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91. #Quarkus @holly_cummins
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    leading indicator
    lagging indicator
    ?
    

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92. #Quarkus @holly_cummins
    total GC time: 21.6s
    4.1% of time in GC pause
    23.9 GB garbage collected
    493 transactions/s
    total GC time: 12.0s
    3.6% of time in GC pause
    13.0 GB garbage collected
    260 transactions/s
    leading indicator
    lagging indicator
    ?
    ?
    

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93. #Quarkus @holly_cummins
    so wait, what changed to make the app faster?
    running jmeter on the same machine as the app gives a big speedup!
    

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94. #Quarkus @holly_cummins
    “Any improvements made anywhere
    besides the bottleneck are an
    illusion.”
    – Gene Kim
    

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95. #Quarkus @holly_cummins
    time kills all performance advice
    (even mine)
    

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96. #Quarkus @holly_cummins
    the takeaways:
    gc can improve performance by rearranging the heap
    find the bottleneck
    validate advice independently
    

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97. #IBM @holly_cummins
    advice
    pitfall 3
    

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98. #Quarkus @holly_cummins
    I read it on the
    internet!
    

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99. #IBM @holly_cummins
    noooooo!
    “make one big method because method dispatching is slow”
    

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100. #IBM @holly_cummins
     noooooo!
     “re-use your objects to help the garbage collector”
     

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101. #IBM @holly_cummins
     noooooo!
     “to tune your JVM, use this command-line:”
     -server -Xms1g -Xmx1g -XX:PermSize=1g -XX:MaxPermSize=256m -Xmn256m
     -Xss64k -XX:SurvivorRatio=30 -XX:+UseConcMarkSweepGC -XX:
     +CMSParallelRemarkEnabled -XX:+UseCMSInitiatingOccupancyOnly
     -XX:CMSInitiatingOccupancyFraction=10 -XX:+ScavengeBeforeFullGC -XX:
     +CMSScavengeBeforeRemark -XX:+PrintGCDateStamps -verbose:gc -XX:
     +PrintGCDetails -Dsun.net.inetaddr.ttl=5 -XX:+HeapDumpOnOutOfMemoryError
     -XX:HeapDumpPath=`date`.hprof -Dcom.sun.management.jmxremote.port=5616
     -Dcom.sun.management.jmxremote.authenticate=false
     -Dcom.sun.management.jmxremote.ssl=false -server -Xms2g -Xmx2g
     -XX:MaxPermSize=256m -XX:NewRatio=1 -XX:+UseConcMarkSweepGC
     

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102. #IBM @holly_cummins
     noooooo!
     use StringBuilder, never concatenate strings with +=
     

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103. #IBM @holly_cummins
     noooooo!
     use StringBuilder, never concatenate strings with +=
     wait, what?
     yes, right?
     

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104. #Quarkus @holly_cummins
     2 things ruin advice:
     • context
     • time
     

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105. #IBM @holly_cummins
     micro-optimisation
     pitfall 4
     

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106. #Quarkus @holly_cummins
     

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107. #Quarkus @holly_cummins
     static string beSlow()
     {
     string result = "";
     for (int i = 0; i < 314159; i++)
     {
     result += getStringData(i);
     }
     return result;
     }
     

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108. #Quarkus @holly_cummins
     @Override
     public String toString() {
     String ret = "\n\tMarket Summary at: " + getSummaryDate() + "\n\t\t TSIA:" + getTSIA() + "\n\t\t openTSIA:" + getOpenTSIA()
     + "\n\t\t gain:" + getGainPercent() + "\n\t\t volume:" + getVolume();
     if ((getTopGainers() == null) || (getTopLosers() == null)) {
     return ret;
     }
     ret += "\n\t\t Current Top Gainers:";
     Iterator it = getTopGainers().iterator();
     while (it.hasNext()) {
     QuoteDataBean quoteData = it.next();
     ret += ("\n\t\t\t" + quoteData.toString());
     }
     ret += "\n\t\t Current Top Losers:";
     it = getTopLosers().iterator();
     while (it.hasNext()) {
     QuoteDataBean quoteData = it.next();
     ret += ("\n\t\t\t" + quoteData.toString());
     }
     return ret;
     }
     

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109. #Quarkus @holly_cummins
     @Override
     public String toString() {
     String ret = "\n\tMarket Summary at: " + getSummaryDate() + "\n\t\t TSIA:" + getTSIA() + "\n\t\t openTSIA:" + getOpenTSIA()
     + "\n\t\t gain:" + getGainPercent() + "\n\t\t volume:" + getVolume();
     if ((getTopGainers() == null) || (getTopLosers() == null)) {
     return ret;
     }
     ret += "\n\t\t Current Top Gainers:";
     Iterator it = getTopGainers().iterator();
     while (it.hasNext()) {
     QuoteDataBean quoteData = it.next();
     ret += ("\n\t\t\t" + quoteData.toString());
     }
     ret += "\n\t\t Current Top Losers:";
     it = getTopLosers().iterator();
     while (it.hasNext()) {
     QuoteDataBean quoteData = it.next();
     ret += ("\n\t\t\t" + quoteData.toString());
     }
     return ret;
     }
     

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110. #Quarkus @holly_cummins
     @Override
     public String toString() {
     String ret = "\n\tMarket Summary at: " + getSummaryDate() + "\n\t\t TSIA:" + getTSIA() + "\n\t\t openTSIA:" + getOpenTSIA()
     + "\n\t\t gain:" + getGainPercent() + "\n\t\t volume:" + getVolume();
     if ((getTopGainers() == null) || (getTopLosers() == null)) {
     return ret;
     }
     ret += "\n\t\t Current Top Gainers:";
     Iterator it = getTopGainers().iterator();
     while (it.hasNext()) {
     QuoteDataBean quoteData = it.next();
     ret += ("\n\t\t\t" + quoteData.toString());
     }
     ret += "\n\t\t Current Top Losers:";
     it = getTopLosers().iterator();
     while (it.hasNext()) {
     QuoteDataBean quoteData = it.next();
     ret += ("\n\t\t\t" + quoteData.toString());
     }
     return ret;
     }
     

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111. #Quarkus @holly_cummins
     @Override
     public String toString() {
     String ret = "\n\tMarket Summary at: " + getSummaryDate() + "\n\t\t TSIA:" + getTSIA() + "\n\t\t openTSIA:" + getOpenTSIA()
     + "\n\t\t gain:" + getGainPercent() + "\n\t\t volume:" + getVolume();
     if ((getTopGainers() == null) || (getTopLosers() == null)) {
     return ret;
     }
     ret += "\n\t\t Current Top Gainers:";
     Iterator it = getTopGainers().iterator();
     while (it.hasNext()) {
     QuoteDataBean quoteData = it.next();
     ret += ("\n\t\t\t" + quoteData.toString());
     }
     ret += "\n\t\t Current Top Losers:";
     it = getTopLosers().iterator();
     while (it.hasNext()) {
     QuoteDataBean quoteData = it.next();
     ret += ("\n\t\t\t" + quoteData.toString());
     }
     return ret;
     }
     this never gets called
     

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112. #Quarkus @holly_cummins
     let’s make travel energy-efficient?
     

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113. #Quarkus @holly_cummins
     every little helps?
     

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114. #Quarkus @holly_cummins
     every little helps?
     every optimisation is another optimisation you aren’t doing
     

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115. #Quarkus @holly_cummins
     our platforms help
     

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116. #Quarkus @holly_cummins
     static string beSlow()
     {
     string result = "";
     for (int i = 0; i < 314159; i++)
     {
     result += getStringData(i);
     }
     return result;
     }
     

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117. #Quarkus @holly_cummins
     static string beSlow()
     {
     string result = “";
     result += getStringData(1);
     result += getStringData(2);
     result += getStringData(3);
     return result;
     }
     

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118. #Quarkus @holly_cummins
     static string beSlow()
     {
     string result = “";
     result += getStringData(1);
     result += getStringData(2);
     result += getStringData(3);
     return result;
     }
     this is fine
     

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119. #Quarkus @holly_cummins
     the JVM writers have far more time for optimising than you do
     clean, typical, code runs best
     

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120. #Quarkus @holly_cummins
     ok, but how to optimise?
     

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121. #Quarkus @holly_cummins
     tools
     

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122. #Quarkus @holly_cummins
     “What you can optimize is limited to
     what you can observe.”
     -Susie Xia, Netflix
     

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123. #Quarkus @holly_cummins
     observability
     

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124. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     

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125. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     VisualVM
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     

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126. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     VisualVM
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     

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127. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     VisualVM
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     IBM Health Center
     (for OpenJ9)
     

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128. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     VisualVM
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     IBM Health Center
     (for OpenJ9)
     

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129. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     GCMV
     VisualVM
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     IBM Health Center
     (for OpenJ9)
     

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130. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     GCMV
     VisualVM
     Eclipse MAT
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     IBM Health Center
     (for OpenJ9)
     

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131. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     GCMV
     VisualVM
     Eclipse MAT
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     GlowRoot
     IBM Health Center
     (for OpenJ9)
     

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132. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     GCMV
     New Relic*
     VisualVM
     Eclipse MAT
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     GlowRoot
     IBM Health Center
     (for OpenJ9)
     

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133. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     GCMV
     New Relic*
     AppDynamics*
     VisualVM
     Eclipse MAT
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     GlowRoot
     IBM Health Center
     (for OpenJ9)
     

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134. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     GCMV
     New Relic*
     AppDynamics*
     VisualVM
     Dynatrace*
     Eclipse MAT
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     GlowRoot
     IBM Health Center
     (for OpenJ9)
     

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135. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     GCMV
     New Relic*
     AppDynamics*
     Zipkin
     VisualVM
     Dynatrace*
     Eclipse MAT
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     GlowRoot
     IBM Health Center
     (for OpenJ9)
     

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136. #Quarkus @holly_cummins
     method profiler
     GC analysis
     heap analysis
     APM
     distributed tracing
     Mission Control
     fl
     ame graphs
     GCMV
     New Relic*
     AppDynamics*
     Jaeger
     Zipkin
     VisualVM
     Dynatrace*
     Eclipse MAT
     * not free
     this is an incomplete list, because there are a lot of tools out there, and many cost money
     GlowRoot
     IBM Health Center
     (for OpenJ9)
     

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137. #Quarkus @holly_cummins
     Net
     fl
     ix microservice
     architecture
     optimising a micro-service: is that micro-optimising?
     

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138. @holly_cummins
     you may need to know the whole
     system context to know what to
     optimise
     

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139. #Quarkus @holly_cummins
     “Nines don’t matter if your
     users aren’t happy.”
     – Charity Majors
     

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140. #Quarkus @holly_cummins
     don’t forget the edges
     queueing theory helps us understand
     where the disasters happen
     

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141. #Quarkus @holly_cummins
     “When it comes to IT performance, amateurs look
     at averages. Professionals look at distributions.”
     – Avishai Ish-Shalom
     

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142. #Quarkus @holly_cummins
     slow performance can turn into big cloud bills
     make cloud costs visible to engineers
     

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143. #Quarkus @holly_cummins
     ok, but you promised bears
     

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144. #Quarkus @holly_cummins
     if you leave the TV on when
     you’re not using it, you’re a
     polar bear murderer
     

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145. #Quarkus @holly_cummins
     there is a moral imperative to avoid waste
     

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146. #Quarkus @holly_cummins
     there is a moral imperative to avoid waste
     electricity
     hardware
     

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147. #Quarkus @holly_cummins
     data centres use 1-2% of
     the world’s electricity
     

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148. #Quarkus @holly_cummins
     fewer devices
     longer lifetime
     

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149. #Quarkus @holly_cummins
     fewer devices
     longer lifetime
     higher ef
     fi
     ciency
     

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150. #Quarkus @holly_cummins
     fewer devices
     longer lifetime
     higher ef
     fi
     ciency
     lower footprint
     

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151. #Quarkus @holly_cummins
     fewer devices
     longer lifetime
     higher ef
     fi
     ciency
     lower footprint
     more multitenancy
     

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152. #Quarkus @holly_cummins
     fewer devices
     longer lifetime
     higher ef
     fi
     ciency
     lower footprint
     more multitenancy
     optimise for longevity
     

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153. #Quarkus @holly_cummins
     fewer devices
     longer lifetime
     higher ef
     fi
     ciency
     lower footprint
     more multitenancy
     optimise for longevity
     the end of planned obsolescence?
     

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154. #Quarkus @holly_cummins
     sooo …
     you can optimise, and it can be fun
     measure, don’t guess
     only optimise what matters
     now for questions!
     

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