Performance tests - it's a trap

Performance tests - it’s a trap
Andrzej Ludwikowski

About me
➔
➔ aludwikowski.blogspot.com
➔ github.com/aludwiko
➔ @aludwikowski

Disclaimers
● Performance tests with Gatling
● no micro-benchmarking

Performance tests?
It’s a trap!

Simulate production as close as possible
● hardware
○ CPU, RAM, storage, ...
● software
○ OS, Virtualization, DBs, …
● load
● isolation

Data, data, data
● monitoring

Monitoring
● hosts
● DBs
● message buses
● applications
● everything!
Prometheus

Monitoring
AWS
DC/OS
host
Docker
application
JVM

Data, data, data
● monitoring
● logging

Logging
● DBs queries response time
● HTTP endpoints response time
● correlation id
● dynamic configuration
● etc.

Data, data, data
● monitoring
● logging
● profiling

Profiling - JMC + Flight Recorder

Your performance intuition is wrong!
1. collect the data (monitoring, logging, profiling)
2. find the bottleneck (based on data)
3. fix the bottleneck
4. collect the data and check the assumptions
5. go to 1.

Math
Lies, damned lies, and statistics:
● arithmetic mean = 2.9
● median = 1
● standard deviation = 6 (only for normal distribution)

Anscombe’s quartet
http://bravenewgeek.com/tag/coordinated-omission/
Property Value
Mean of x 9
Sample variance of x 11
Mean of y 7.50
Sample variance of y 4.125
Correlation between x and y 0.816
Linear regression line y = 3 + 0.5x
Coefficient of determination of
the linear regression
0.67

Math
● median = 1
Use:
● percentiles
○ 50th = 1
○ 70th = 1
○ 90th = 2.9
○ 95th = 11.45
○ 99th = 18.29

Math
● median = 1
Use:
● percentiles
○ 50th = 1
○ 70th = 1
○ 90th = 2.9
○ 95th = 11.45
○ 99th = 18.29
Check percentiles implementation!

Percentiles
val recordedTimes =
//normal behavior
(1 to 100000).map(_ => random.nextInt(100)) ++
//standard peacks
(1 to 100).map(_ => 1000) ++
//very high peacks
(1 to 10).map(_ => 10000)
//print histogram for the same data set
(1 to 3).foreach{_ =>
printHistogram(recordedTimes)
}
-----------1
75: 75.000000
95: 95.000000
99: 98.000000
99.9: 1000.000000
max: 1000
-----------2
75: 75.000000
95: 94.000000
99: 98.000000
99.9: 99.000000
max: 99
-----------3
75: 77.000000
95: 95.000000
99: 99.000000
99.9: 1000.000000
max: 10000

Percentiles
Reservoir implementation:
● ExponentiallyDecayingReservoir
● UniformReservoir
● SlidingTimeWindowReservoir
● SlidingWindowReservoir

Percentiles - sliding window
10, 15, 20, 10, 20, 55, 15, 20, 15, 10
95th ~ 50

HDRHistogram
● dynamic range of values
● small memory footprint
● reasonable precision
Value Percentile TotalCount 1/(1-Percentile)
0 0.000000000000 970 1.00
63 0.500000000000 64196 2.00
127 0.750000000000 100000 4.00
127 0.875000000000 100000 8.00
127 0.937500000000 100000 16.00
127 0.968750000000 100000 32.00
127 0.984375000000 100000 64.00
127 0.992187500000 100000 128.00
127 0.996093750000 100000 256.00
127 0.998046875000 100000 512.00
1023 0.999023437500 100100 1024.00
1023 0.999511718750 100100 2048.00
1023 0.999755859375 100100 4096.00
1023 0.999877929688 100100 8192.00
16383 0.999938964844 100110 16384.00
16383 1.000000000000 100110
#[Mean = 57, StdDeviation = 129]
#[Max = 16383, Total count = 100110]
#[Buckets = 20, SubBuckets = 2]

Which percentile?
● 70 th
● 80 th
● 90 th
● 95 th
● 99 th
● 99.9 th
● 99.99 th

Coordinated omission problem
● Coordinated omission problem by Gil Tene

system.exit(0)

system.exit(0)
samples 31
50 th 1
70 th 1
90 th 1
95 th 1
99 th 21.30
99.9 th 29.13
99.99 th 29.91
avg 1.93

system.exit(0)
samples 1001
50 th 1
70 th 1
90 th 1
95 th 1
99 th 1
99.9 th 1
99.99 th 27.1
avg 1.02

system.exit(0)
samples 60
50 th 1
70 th 12
90 th 24
95 th 27
99 th 29.4
99.9 th 29.9
99.99 th 29.99
avg 8.25

samples 1001
50 th 1
70 th 1
90 th 1
95 th 1
99 th 1
99.9 th 1
99.99 th 27.1
avg 1.02
samples 31 60
50 th 1 15.5
70 th 1 30
90 th 1 30
95 th 1 30
99 th 21.30 30
99.9 th 29.13 30
99.99 th 29.91 30
avg 1.93 15.5

total time 60 s
max 30 s
99th 1 s

total time 60 s
max 30 s
99th 1 s
time in % for max 50%

total time 60 s
max 30 s
99th 1 s
expected time in % for 99th 50% - 1% = 49%

total time 60 s
max 30 s
99th 1 s
expected time in % for 99th 50% - 1% = 49%
real time for 99th 60 s * 49% = 29.4 s

WRK2
● constant throughput load
● accurate latency details
● LUA api
● no Coordinated Omission problem

Performance tests - why so hard?
● Tests…

Why Gatling?
+ non-blocking, asynchronous stack (scala, akka, netty)
+ scala !!!111oneoneone (maven, sbt support)
+ DSL
+ recorder
+ math is good
+ reports
+ integration & performance tests
- not resistant to Coordinated Omission problem

Readable DSL
class FirstScenario extends MySimulation {
val firstScenario = scenario("First Scenario Name")
.exec(http("Go to root page").get("/"))
.pause(7 seconds)
setUp(firstScenario.inject(atOnceUsers(1)).protocols(httpConf))
}

Modular DSL
class ComplexScenario extends MySimulation {
val complexScenario = scenario("Complex demo scenario")
.exec(http("root page").get("/")).pause(7)
.exec(http("search for").get("/computers?f=macbook")).pause(2)
.exec(http("position at").get("/computers/6")).pause(3)
.exec(http("root page").get("/")).pause(2)
.exec(http("go to page").get("/computers?p=1")).pause(670 milliseconds)
.exec(http("open new computer form").get("/computers/new")).pause(1)
.exec(http("add new computer")
.post("/computers")
.formParam("name", "MyComputer").formParam("introduced", "2012-05-30").formParam("company",
"37"))
setUp(complexScenario.inject(atOnceUsers(1)).protocols(httpConf))

Modular DSL
class ComplexScenarioV2 extends MySimulation {
val complexScenario = scenario("Complex demo scenario")
.exec(goToRootPage).pause(7)
.exec(searchFor("macbook")).pause(2)
.exec(positionAt(6)).pause(3)
.exec(goToRootPage).pause(2)
.exec(goToPage(1)).pause(670 milliseconds)
.exec(openNewComputerForm).pause(1)
.exec(addNewComputer)
}

Modular DSL
class ComplexScenarioV3 extends MySimulation {
val search = exec(goToRootPage).pause(7)
.exec(searchFor("macbook")).pause(2)
.exec(positionAt(6)).pause(3)
val addComputer = exec(goToRootPage).pause(2)
.exec(goToPage(1)).pause(670 milliseconds)
.exec(openNewComputerForm).pause(1)
.exec(addNewComputer)
val complexScenario = scenario("Complex demo scenario").exec(search, addComputer)
}

DSL - checks
scenario("DSL demo")
.exec(http("go to page")
.get("/computers")
.check(status.is(200))
.check(status.in(200 to 210))

DSL - checks
.exec(http("go to page")
.get("/computers")
.check(regex("computers")
.find(1)
.exists)
https://www.pinterest.com/pin/491666484294006138/

DSL - checks
scenario("First Scenario Name")
.exec(http("Request name")
.get("/computers")
.check(jsonPath("$..foo.bar[2].baz").ofType[String].notExists)
.check(xpath("//input[@id='text1']/@value").is("test"))
.check(css("...").transform(_.split('|').toSeq).is(Seq("1", "2")))

DSL - virtual user session
.exec(http("Authorize")
.get("/auth")
.check(regex("token").find(1).exists
.saveAs("authorizationToken")))
.exec(http("Authorized resource")
.get("/authorized_resource?token=${authorizationToken}"))

DSL - looping
repeat(5, "i") {
exec(goToPage("${i}".toInt))
.pause(1)
}
● repeat
● foreach
● during
● asLongAs
● forever
https://blog.hubspot.com/blog/tabid/6307/bid/32019/Why-Every-Marketer-Needs-Closed-Loop-Reporting.aspx#sm.0005lrqj811waf3ntmn1cul3881gr

DSL - polling
exec(
polling
.every(10 seconds)
.exec(searchFor("thinkpad"))
)
http://www.firmus-solutions.com/terms-conditions.html

DSL - conditions
doIf(session =>
s session("user").startsWith("admin")) {
exec(goToAdminPage)
}
● doIf
● doIfEquals
● doIfOrElse
● doSwitch
● doSwitchOrElse
● randomSwitch
https://en.wikipedia.org/wiki/Decision_tree

DSL - error management
exec(sendMoney)
.tryMax(10){
exec(checkIfMoneyReceived)
}
● tryMax
● exitBlockOnFail
● exitHereIfFailed
Alice Bob
Kafka

DSL - setup
setUp(myScenario
.inject(
nothingFor(4 seconds),
atOnceUsers(10),
rampUsers(10) over (5 seconds))
.protocols(httpConf))
.maxDuration(10 minutes)
● constantUsersPerSec
● rampUsersPerSec
● splitUsers
● heavisideUsers

DSL - setup
setUp(myScenario
.inject(atOnceUsers(10))
.assertions(
global.responseTime.max.lt(50),
global.failedRequests.percent.is(0)
)

DSL - setup
setUp(myScenario
.inject(atOnceUsers(10))
.throttle(
reachRps(100) in (30 second),
holdFor(1 minute),
jumpToRps(50),
holdFor(2 hours)
)

DSL - feeders
val companies = List("apple", "lenovo", "hp")
val randomCompanyName = Iterator.continually(
Map("company" -> companies(Random.nextInt(companies.size))))
val searching = scenario("Searching")
.feed(randomCompanyName)
.exec(searchFor("${company}"))
● RecordSeqFeederBuilder
● CSV
● JSON
● JDBC
● Sitemap
● Redis
● … http://favim.com/orig/201104/23/Favim.com-22725.jpg

Reporting
================================================================================
---- Global Information --------------------------------------------------------
> request count 10 (OK=10 KO=0 )
> min response time 40 (OK=40 KO=- )
> max response time 177 (OK=177 KO=- )
> mean response time 55 (OK=55 KO=- )
> std deviation 41 (OK=41 KO=- )
> response time 50th percentile 42 (OK=42 KO=- )
> mean requests/sec 0.909 (OK=0.909 KO=- )
---- Response Time Distribution ------------------------------------------------
> t < 800 ms 10 (100%)
> 800 ms < t < 1200 ms 0 ( 0%)
> t > 1200 ms 0 ( 0%)
> failed 0 ( 0%)
================================================================================

DSL - other goodies
● Custom validators
● HTTP
○ SSL
○ SSE (Server Sent Event)
○ basic cookies support
● WebSocket
● JMS
● Pluginable architecture:
○ cassandra plugin
○ kafka plugin
○ rabbitMQ
○ AMQP

Distributed tests
● manually
● Gatling FrontLine
● Flood.io
IT system

Are performance tests useless?
It depends

Are performance tests useless?
It dependson context!

Performance tests - it's a trap

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