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

by Holly Cummins

Slide 1

Slide 1 text

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

Slide 2

Slide 2 text

#Quarkus @holly_cummins why optimise?

Slide 3

Slide 3 text

#Quarkus @holly_cummins why optimise?

Slide 4

Slide 4 text

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

Slide 5

Slide 5 text

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

Slide 6

Slide 6 text

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

Slide 7

Slide 7 text

#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

Slide 8

Slide 8 text

#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

Slide 9

Slide 9 text

#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

Slide 10

Slide 10 text

#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

Slide 11

Slide 11 text

#Quarkus @holly_cummins what is optimising?

Slide 12

Slide 12 text

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

Slide 13

Slide 13 text

#Quarkus @holly_cummins design thinking

Slide 14

Slide 14 text

#Quarkus @holly_cummins

Slide 15

Slide 15 text

#Quarkus @holly_cummins performance can be:

Slide 16

Slide 16 text

#Quarkus @holly_cummins performance can be: throughput

Slide 17

Slide 17 text

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

Slide 18

Slide 18 text

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

Slide 19

Slide 19 text

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

Slide 20

Slide 20 text

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

Slide 21

Slide 21 text

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

Slide 22

Slide 22 text

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

Slide 23

Slide 23 text

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

Slide 24

Slide 24 text

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

Slide 25

Slide 25 text

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

Slide 26

Slide 26 text

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

Slide 27

Slide 27 text

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

Slide 28

Slide 28 text

#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 …

Slide 29

Slide 29 text

@holly_cummins requirements change

Slide 30

Slide 30 text

@holly_cummins

Slide 31

Slide 31 text

@holly_cummins

Slide 32

Slide 32 text

@holly_cummins

Slide 33

Slide 33 text

@holly_cummins

Slide 34

Slide 34 text

@holly_cummins I am not designed for this.

Slide 35

Slide 35 text

@holly_cummins the world changes

Slide 36

Slide 36 text

#Quarkus @holly_cummins

Slide 37

Slide 37 text

#Quarkus @holly_cummins -Xmx == $

Slide 38

Slide 38 text

#Quarkus @holly_cummins -Xmx == $ footprint

Slide 39

Slide 39 text

#Quarkus @holly_cummins

Slide 40

Slide 40 text

#Quarkus @holly_cummins which performs better?

Slide 41

Slide 41 text

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

Slide 42

Slide 42 text

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

Slide 43

Slide 43 text

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

Slide 44

Slide 44 text

#Quarkus @holly_cummins how to optimise?

Slide 45

Slide 45 text

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

Slide 46

Slide 46 text

#IBM @holly_cummins intuition pitfall 1

Slide 47

Slide 47 text

#Quarkus @holly_cummins this is not the place for ideas

Slide 48

Slide 48 text

#Quarkus @holly_cummins measure, don’t guess.

Slide 49

Slide 49 text

#Quarkus @holly_cummins measure the right thing

Slide 50

Slide 50 text

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

Slide 51

Slide 51 text

#IBM @holly_cummins numbers pitfall 2

Slide 52

Slide 52 text

#Quarkus @holly_cummins

Slide 53

Slide 53 text

#Quarkus @holly_cummins leading indicators

Slide 54

Slide 54 text

#Quarkus @holly_cummins leading indicators lagging indicators

Slide 55

Slide 55 text

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

Slide 56

Slide 56 text

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

Slide 57

Slide 57 text

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

Slide 58

Slide 58 text

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

Slide 59

Slide 59 text

#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

Slide 60

Slide 60 text

#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

Slide 61

Slide 61 text

#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

Slide 62

Slide 62 text

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

Slide 63

Slide 63 text

#Quarkus @holly_cummins 2007

Slide 64

Slide 64 text

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

Slide 65

Slide 65 text

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

Slide 66

Slide 66 text

#Quarkus @holly_cummins 2007

Slide 67

Slide 67 text

#Quarkus @holly_cummins 2007

Slide 68

Slide 68 text

#Quarkus @holly_cummins 2021

Slide 69

Slide 69 text

#Quarkus @holly_cummins 2021

Slide 70

Slide 70 text

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

Slide 71

Slide 71 text

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

Slide 72

Slide 72 text

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

Slide 73

Slide 73 text

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

Slide 74

Slide 74 text

#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?

Slide 75

Slide 75 text

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

Slide 76

Slide 76 text

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

Slide 77

Slide 77 text

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

Slide 78

Slide 78 text

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

Slide 79

Slide 79 text

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

Slide 80

Slide 80 text

#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

Slide 81

Slide 81 text

#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

Slide 82

Slide 82 text

#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

Slide 83

Slide 83 text

#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

Slide 84

Slide 84 text

Slide 85

Slide 85 text

Slide 86

Slide 86 text

#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

Slide 87

Slide 87 text

Slide 88

Slide 88 text

Slide 89

Slide 89 text

Slide 90

Slide 90 text

Slide 91

Slide 91 text

Slide 92

Slide 92 text

Slide 93

Slide 93 text

#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!

Slide 94

Slide 94 text

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

Slide 95

Slide 95 text

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

Slide 96

Slide 96 text

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

Slide 97

Slide 97 text

#IBM @holly_cummins advice pitfall 3

Slide 98

Slide 98 text

#Quarkus @holly_cummins I read it on the internet!

Slide 99

Slide 99 text

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

Slide 100

Slide 100 text

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

Slide 101

Slide 101 text

#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

Slide 102

Slide 102 text

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

Slide 103

Slide 103 text

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

Slide 104

Slide 104 text

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

Slide 105

Slide 105 text

#IBM @holly_cummins micro-optimisation pitfall 4

Slide 106

Slide 106 text

#Quarkus @holly_cummins

Slide 107

Slide 107 text

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

Slide 108

Slide 108 text

#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; }

Slide 109

Slide 109 text

Slide 110

Slide 110 text

Slide 111

Slide 111 text

Slide 112

Slide 112 text

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

Slide 113

Slide 113 text

#Quarkus @holly_cummins every little helps?

Slide 114

Slide 114 text

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

Slide 115

Slide 115 text

#Quarkus @holly_cummins our platforms help

Slide 116

Slide 116 text

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

Slide 117

Slide 117 text

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

Slide 118

Slide 118 text

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

Slide 119

Slide 119 text

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

Slide 120

Slide 120 text

#Quarkus @holly_cummins ok, but how to optimise?

Slide 121

Slide 121 text

#Quarkus @holly_cummins tools

Slide 122

Slide 122 text

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

Slide 123

Slide 123 text

#Quarkus @holly_cummins observability

Slide 124

Slide 124 text

#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

Slide 125

Slide 125 text

#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

Slide 126

Slide 126 text

#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

Slide 127

Slide 127 text

Slide 128

Slide 128 text

#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)

Slide 129

Slide 129 text

#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)

Slide 130

Slide 130 text

#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)

Slide 131

Slide 131 text

Slide 132

Slide 132 text

#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)

Slide 133

Slide 133 text

#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)

Slide 134

Slide 134 text

#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)

Slide 135

Slide 135 text

#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)

Slide 136

Slide 136 text

#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)

Slide 137

Slide 137 text

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

Slide 138

Slide 138 text

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

Slide 139

Slide 139 text

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

Slide 140

Slide 140 text

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

Slide 141

Slide 141 text

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

Slide 142

Slide 142 text

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

Slide 143

Slide 143 text

#Quarkus @holly_cummins ok, but you promised bears

Slide 144

Slide 144 text

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

Slide 145

Slide 145 text

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

Slide 146

Slide 146 text

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

Slide 147

Slide 147 text

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

Slide 148

Slide 148 text

#Quarkus @holly_cummins fewer devices longer lifetime

Slide 149

Slide 149 text

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

Slide 150

Slide 150 text

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

Slide 151

Slide 151 text

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

Slide 152

Slide 152 text

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

Slide 153

Slide 153 text

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

Slide 154

Slide 154 text

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