A presentation at Accento by Holly Cummins
tradeoffs, bad science, and polar bears: the world of java optimisation Holly Cummins IBM @holly_cummins
why optimise? #IBM @holly_cummins
why optimise? #IBM @holly_cummins
0.5s extra search page time why optimise? #IBM @holly_cummins
0.5s extra search page time 20% drop in traf c why optimise? fi #IBM @holly_cummins
0.5s extra search page time 20% drop in traf c 100 ms latency on page load why optimise? fi #IBM @holly_cummins
0.5s extra search page time 20% drop in traf c 100 ms latency on page load 7% lower conversion rate why optimise? fi #IBM @holly_cummins
0.5s extra search page time 20% drop in traf c 100 ms latency on page load 7% lower conversion rate why optimise? fi #IBM @holly_cummins
0.5s extra search page time 20% drop in traf c 100 ms latency on page load 7% lower conversion rate 10 ms delay in trading platform fi #IBM why optimise? @holly_cummins
0.5s extra search page time 20% drop in traf c 100 ms latency on page load 7% lower conversion rate 10 ms delay in trading platform fi #IBM 10% drop in revenue why optimise? @holly_cummins
what is optimising? #IBM @holly_cummins
“make it go faster” for whom? when? doing what? #IBM @holly_cummins
design thinking #IBM @holly_cummins
#IBM @holly_cummins
performance can be: #IBM @holly_cummins
performance can be: throughput #IBM @holly_cummins
performance can be: throughput #IBM transactions per second @holly_cummins
performance can be: throughput transactions per second latency #IBM @holly_cummins
performance can be: throughput latency #IBM transactions per second start-up time @holly_cummins
performance can be: transactions per second throughput latency #IBM response time start-up time @holly_cummins
performance can be: transactions per second throughput latency #IBM response time ramp-up time start-up time @holly_cummins
performance can be: transactions per second throughput latency response time ramp-up time start-up time capacity #IBM @holly_cummins
performance can be: transactions per second throughput latency capacity #IBM ramp-up time response time start-up time footprint @holly_cummins
performance can be: transactions per second throughput latency capacity ramp-up time response time start-up time footprint CPU usage #IBM @holly_cummins
performance can be: transactions per second throughput latency capacity utilisation #IBM ramp-up time response time start-up time footprint CPU usage @holly_cummins
performance can be: transactions per second throughput latency capacity utilisation ramp-up time response time start-up time footprint CPU usage … #IBM @holly_cummins
Never underestimate the bandwidth [throughput] of a station wagon full of tapes hurtling down the highway. –Andrew Tanenbaum, 1981 #IBM @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 … #IBM @holly_cummins
requirements change #IBMGarage @holly_cummins
#IBMGarage @holly_cummins
#IBMGarage @holly_cummins
#IBMGarage @holly_cummins
#IBMGarage @holly_cummins
I am not designed for this. #IBMGarage @holly_cummins
the world changes #IBMGarage @holly_cummins
#IBM @holly_cummins
-Xmx == $ #IBM @holly_cummins
-Xmx == $ footprint #IBM @holly_cummins
#IBM @holly_cummins
which performs better? #IBM @holly_cummins
quarkus trading-off flexibility against startup speed and footprint #IBM @holly_cummins
quarkus trading-off flexibility against startup speed and footprint uhh … are you supposed to shut down applications after using them? #IBM @holly_cummins
behaviour at idle 30% of VMs are zombies (antithesisgroup.com) #IBM @holly_cummins
how to optimise? #IBM @holly_cummins
fi find the bottleneck. x it. #IBM @holly_cummins
pitfall 1 intuition #IBM @holly_cummins
this is not the place for ideas #IBM @holly_cummins
measure, don’t guess. #IBM @holly_cummins
measure the right thing #IBM @holly_cummins
measure the right thing what do your users care about? #IBM @holly_cummins
pitfall 2 numbers #IBM @holly_cummins
#IBM @holly_cummins
leading indicators #IBM @holly_cummins
leading indicators #IBM lagging indicators @holly_cummins
leading indicators lagging indicators we care about them #IBM @holly_cummins
leading indicators lagging indicators we care about them easy to measure #IBM @holly_cummins
leading indicators lagging indicators we care about them easy to measure hard to change #IBM @holly_cummins
#IBM leading indicators lagging indicators easy to change we care about them easy to measure hard to change @holly_cummins
leading indicators lagging indicators predictive of a thing we care about we care about them easy to measure hard to change easy to change #IBM @holly_cummins
#IBM leading indicators lagging indicators predictive of a thing we care about hard to identify easy to change we care about them easy to measure hard to change @holly_cummins
#IBM leading indicators lagging indicators predictive of a thing we care about hard to identify easy to change we care about them easy to measure hard to change @holly_cummins
caution: performance experiments for entertainment purposes only. do not try these at home. #IBM @holly_cummins
2007 #IBM @holly_cummins
bad-ish advice: “reduce time spent in garbage collection” #IBM @holly_cummins
bad-ish advice: “reduce time spent in garbage collection” actually, garbage collection can make your application go faster #IBM @holly_cummins
2007 #IBM @holly_cummins
2007 #IBM @holly_cummins
2021 #IBM @holly_cummins
2021 #IBM @holly_cummins
-verbose:gc -Xverbosegclog:gclog.xml -Xcompactgc #IBM @holly_cummins
-verbose:gc -Xverbosegclog:gclog.xml -Xgcpolicy:optthruput -Xcompactgc #IBM @holly_cummins
-verbose:gc -Xverbosegclog:gclog.xml -Xgcpolicy:optthruput -Xmx110m -Xms110m -Xnocompactgc #IBM @holly_cummins
-verbose:gc -Xverbosegclog:gclog.xml -Xgcpolicy:optthruput -Xmx160m -Xms160m -Xnocompactgc #IBM @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? #IBM @holly_cummins
by the way, this is cheating. (remember the ‘bad science’?) #IBM @holly_cummins
-verbose:gc #IBM @holly_cummins
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
total GC time: 21.6s 4.1% of time in GC pause 23.9 GB garbage collected 493 transactions/s #IBM total GC time: 12.0s 3.6% of time in GC pause 13.0 GB garbage collected 260 transactions/s @holly_cummins
leading indicator total GC time: 21.6s 4.1% of time in GC pause 23.9 GB garbage collected 493 transactions/s #IBM total GC time: 12.0s 3.6% of time in GC pause 13.0 GB garbage collected 260 transactions/s @holly_cummins
leading indicator total GC time: 21.6s 4.1% of time in GC pause 23.9 GB garbage collected 493 transactions/s #IBM total GC time: 12.0s 3.6% of time in GC pause 13.0 GB garbage collected 260 transactions/s @holly_cummins
lagging indicator leading indicator total GC time: 21.6s 4.1% of time in GC pause 23.9 GB garbage collected 493 transactions/s #IBM total GC time: 12.0s 3.6% of time in GC pause 13.0 GB garbage collected 260 transactions/s @holly_cummins
lagging indicator leading indicator ? total GC time: 21.6s 4.1% of time in GC pause 23.9 GB garbage collected 493 transactions/s #IBM total GC time: 12.0s 3.6% of time in GC pause 13.0 GB garbage collected 260 transactions/s @holly_cummins
lagging indicator ? leading indicator ? total GC time: 21.6s 4.1% of time in GC pause 23.9 GB garbage collected 493 transactions/s #IBM total GC time: 12.0s 3.6% of time in GC pause 13.0 GB garbage collected 260 transactions/s @holly_cummins
so wait, what changed to make the app faster? running jmeter on the same machine as the app gives a big speedup! #IBM @holly_cummins
“Any improvements made anywhere besides the bottleneck are an illusion.” – Gene Kim #IBM @holly_cummins
time kills all performance advice (even mine) #IBM @holly_cummins
the takeaways: gc can improve performance by rearranging the heap find the bottleneck validate advice independently #IBM @holly_cummins
pitfall 3 advice #IBM @holly_cummins
I read it on the internet! #IBM @holly_cummins
noooooo! “make one big method because method dispatching is slow” #IBM @holly_cummins
noooooo! “re-use your objects to help the garbage collector” #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 #IBM
@holly_cummins
noooooo! use StringBuilder, never concatenate strings with += #IBM @holly_cummins
noooooo! wait, what? yes, right? use StringBuilder, never concatenate strings with += #IBM @holly_cummins
2 things ruin advice: • context • time #IBM @holly_cummins
pitfall 4 micro-optimisation #IBM @holly_cummins
#IBM @holly_cummins
static string beSlow() { string result = “”; for (int i = 0; i < 314159; i++) { result += getStringData(i); } return result; } #IBM @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<QuoteDataBean> 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; } #IBM @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<QuoteDataBean> 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; } #IBM @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<QuoteDataBean> 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; } #IBM @holly_cummins
this never gets called @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<QuoteDataBean> 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; } #IBM @holly_cummins
let’s make travel energy-efficient? #IBM @holly_cummins
every little helps? #IBM @holly_cummins
every little helps? every optimisation is another optimisation you aren’t doing #IBM @holly_cummins
our platforms help #IBM @holly_cummins
static string beSlow() { string result = “”; for (int i = 0; i < 314159; i++) { result += getStringData(i); } return result; } #IBM @holly_cummins
static string beSlow() { string result = “”; result += getStringData(1); result += getStringData(2); result += getStringData(3); } #IBM return result; @holly_cummins
static string beSlow() { string result = “”; result += getStringData(1); result += getStringData(2); result += getStringData(3); } #IBM return result; this is fine @holly_cummins
the JVM writers have far more time for optimising than you do clean, typical, code runs best #IBM @holly_cummins
ok, but how to optimise? #IBM @holly_cummins
tools #IBM @holly_cummins
“What you can optimize is limited to what you can observe.” -Susie Xia, Netflix #IBM @holly_cummins
observability #IBM @holly_cummins
method profiler GC analysis heap analysis APM distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler VisualVM GC analysis heap analysis APM distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler VisualVM Mission Control GC analysis heap analysis APM distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler VisualVM Mission Control GC analysis IBM Health Center (for OpenJ9) heap analysis APM distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler flame graphs VisualVM Mission Control GC analysis IBM Health Center (for OpenJ9) heap analysis APM distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler flame graphs VisualVM Mission Control GC analysis IBM Health Center (for OpenJ9) GCMV heap analysis APM distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler flame graphs VisualVM Mission Control GC analysis IBM Health Center (for OpenJ9) GCMV heap analysis Eclipse MAT APM distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler flame graphs VisualVM Mission Control GC analysis GCMV heap analysis APM IBM Health Center (for OpenJ9) Eclipse MAT GlowRoot distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler flame graphs VisualVM Mission Control GC analysis GCMV heap analysis APM IBM Health Center (for OpenJ9) GlowRoot Eclipse MAT New Relic* distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler flame graphs VisualVM Mission Control GC analysis GCMV heap analysis APM IBM Health Center (for OpenJ9) GlowRoot Eclipse MAT AppDynamics* New Relic* distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler flame graphs VisualVM Mission Control GC analysis GCMV heap analysis APM IBM Health Center (for OpenJ9) GlowRoot Eclipse MAT AppDynamics* New Relic* Dynatrace* distributed tracing * not free #IBM this is an incomplete list, because there are a lot of tools out there, and many cost money @holly_cummins
method profiler flame graphs VisualVM Mission Control GC analysis GCMV heap analysis APM IBM Health Center (for OpenJ9) GlowRoot distributed tracing Eclipse MAT AppDynamics* New Relic* Dynatrace* Zipkin
method profiler flame graphs VisualVM IBM Health Center (for OpenJ9) Mission Control GC analysis GCMV heap analysis APM GlowRoot distributed tracing Eclipse MAT AppDynamics* New Relic* Zipkin Dynatrace* Jaeger
optimising a micro-service: is that micro-optimising? Netflix microservice architecture #IBM @holly_cummins
you may need to know the whole system context to know what to optimise #IBMGarage @holly_cummins
“Nines don’t matter if your users aren’t happy.” – Charity Majors #IBM @holly_cummins
don’t forget the edges queueing theory helps us understand where the disasters happen #IBM @holly_cummins
“When it comes to IT performance, amateurs look at averages. Professionals look at distributions.” – Avishai Ish-Shalom #IBM @holly_cummins
slow performance can turn into big cloud bills make cloud costs visible to engineers #IBM @holly_cummins
ok, but you promised bears #IBM @holly_cummins
if you leave the TV on when you’re not using it, you’re a polar bear murderer #IBM @holly_cummins
there is a moral imperative to avoid waste #IBM @holly_cummins
there is a moral imperative to avoid waste electricity hardware #IBM @holly_cummins
data centres use 1-2% of the world’s electricity #IBM @holly_cummins
fewer devices longer lifetime #IBM @holly_cummins
higher ef ciency fewer devices longer lifetime @holly_cummins fi #IBM
higher ef ciency fewer devices lower footprint longer lifetime @holly_cummins fi #IBM
higher ef ciency fewer devices lower footprint more multitenancy longer lifetime @holly_cummins fi #IBM
higher ef ciency fewer devices lower footprint more multitenancy longer lifetime @holly_cummins fi #IBM optimise for longevity
higher ef ciency fewer devices lower footprint more multitenancy longer lifetime the end of planned obsolescence? @holly_cummins fi #IBM optimise for longevity
sooo … you can optimise, and it can be fun measure, don’t guess only optimise what matters now for questions! #IBM @holly_cummins
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.
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