microxchg analyzing response time distributions for microservices

Analyzing Response Time Distributions for Microservices

Adrian Cockcroft @adriancoTechnology Fellow - Battery Ventures

February 2016

What does @adrianco do?

@adrianco

Technology Due Diligence on Deals

Presentations at Conferences

Presentations at Companies

Technical Advice for Portfolio

Companies

Program Committee for Conferences

Networking with Interesting PeopleTinkering with

Technologies

Maintain Relationship with Cloud Vendors

Challenges for Microservice

Platforms

Managing Scale

A Possible Hierarchy Continents

Regions Zones

Services Versions

Containers Instances

How Many? 3 to 5

2-4 per Continent 1-5 per Region 100’s per Zone

Many per Service 1000’s per Version

10,000’s

It’s much more challenging than just a large number of

machines

Some tools can show the request flow

across a few services

Interesting architectures have a lot of microservices! Flow visualization is

a big challenge.

See http://www.slideshare.net/LappleApple/gilt-from-monolith-ruby-app-to-micro-service-scala-service-architecture

Simulated Microservices

Model and visualize microservices Simulate interesting architectures Generate large scale configurations Eventually stress test real tools

See github.com/adrianco/spigo Simulate Protocol Interactions in Go Visualize with D3

ELB Load Balancer

Zuul API Proxy

KaryonBusinessLogic

StaashDataAccessLayerPriam CassandraDatastore

ThreeAvailabilityZones

http://github.com/adrianco/spigo

Spigo Nanoservice Structurefunc Start(listener chan gotocol.Message) { ... for { select { case msg := <-listener:

flow.Instrument(msg, name, hist) switch msg.Imposition { case gotocol.Hello: // get named by parent ... case gotocol.NameDrop: // someone new to talk to ... case gotocol.Put: // upstream request handler ... outmsg := gotocol.Message{gotocol.Replicate, listener, time.Now(), msg.Ctx.NewParent(), msg.Intention} flow.AnnotateSend(outmsg, name) outmsg.GoSend(replicas) } case <-eurekaTicker.C: // poll the service registry ... } } }

Nanoservice simulation total about 200 lines of Go

Flow Trace Recording

riak2us-east-1

zoneC

riak9us-west-2

zoneA

Put s896

Replicate

riak3us-east-1

zoneA

riak8us-west-2

zoneC

riak4us-east-1

zoneB

riak10us-west-2

zoneB

us-east-1.zoneC.riak2 t98p895s896 Put us-east-1.zoneA.riak3 t98p896s908 Replicate us-east-1.zoneB.riak4 t98p896s909 Replicate us-west-2.zoneA.riak9 t98p896s910 Replicate us-west-2.zoneB.riak10 t98p910s912 Replicate us-west-2.zoneC.riak8 t98p910s913 Replicate

staashus-east-1

zoneC

s910 s908s913s909s912

Open Zipkin

A common format for trace annotations A Java tool for visualizing traces Standardization effort to fold in other formats Driven by Adrian Cole (currently at Pivotal) Extended to load Spigo generated trace files

Zipkin Trace Dependencies

Trace for one Spigo Flow

Definition of an architecture

{ "arch": "lamp", "description":"Simple LAMP stack", "version": "arch-0.0", "victim": "webserver", "services": [ { "name": "rds-mysql", "package": "store", "count": 2, "regions": 1, "dependencies": [] }, { "name": "memcache", "package": "store", "count": 1, "regions": 1, "dependencies": [] }, { "name": "webserver", "package": "monolith", "count": 18, "regions": 1, "dependencies": ["memcache", "rds-mysql"] }, { "name": "webserver-elb", "package": "elb", "count": 0, "regions": 1, "dependencies": ["webserver"] }, { "name": "www", "package": "denominator", "count": 0, "regions": 0, "dependencies": ["webserver-elb"] } ] }

Header includeschaos monkey victim

New tier name

Tier package

0 = non Regional

Node count

List of tier dependencies

Running Spigo$ ./spigo -a lamp -j -d 2 2016/01/26 23:04:05 Loading architecture from json_arch/lamp_arch.json 2016/01/26 23:04:05 lamp.edda: starting 2016/01/26 23:04:05 Architecture: lamp Simple LAMP stack 2016/01/26 23:04:05 architecture: scaling to 100% 2016/01/26 23:04:05 lamp.us-east-1.zoneB.eureka01....eureka.eureka: starting 2016/01/26 23:04:05 lamp.us-east-1.zoneA.eureka00....eureka.eureka: starting 2016/01/26 23:04:05 lamp.us-east-1.zoneC.eureka02....eureka.eureka: starting 2016/01/26 23:04:05 Starting: {rds-mysql store 1 2 []} 2016/01/26 23:04:05 Starting: {memcache store 1 1 []} 2016/01/26 23:04:05 Starting: {webserver monolith 1 18 [memcache rds-mysql]} 2016/01/26 23:04:05 Starting: {webserver-elb elb 1 0 [webserver]} 2016/01/26 23:04:05 Starting: {www denominator 0 0 [webserver-elb]} 2016/01/26 23:04:05 lamp.*.*.www00....www.denominator activity rate 10ms 2016/01/26 23:04:06 chaosmonkey delete: lamp.us-east-1.zoneC.webserver02....webserver.monolith 2016/01/26 23:04:07 asgard: Shutdown 2016/01/26 23:04:07 lamp.us-east-1.zoneB.eureka01....eureka.eureka: closing 2016/01/26 23:04:07 lamp.us-east-1.zoneA.eureka00....eureka.eureka: closing 2016/01/26 23:04:07 lamp.us-east-1.zoneC.eureka02....eureka.eureka: closing 2016/01/26 23:04:07 spigo: complete 2016/01/26 23:04:07 lamp.edda: closing

-a architecture lamp-j graph json/lamp.json-d run for 2 seconds

Riak IoT Architecture{ "arch": "riak", "description":"Riak IoT ingestion example for the RICON 2015 presentation", "version": "arch-0.0", "victim": "", "services": [ { "name": "riakTS", "package": "riak", "count": 6, "regions": 1, "dependencies": ["riakTS", "eureka"]}, { "name": "ingester", "package": "staash", "count": 6, "regions": 1, "dependencies": ["riakTS"]}, { "name": "ingestMQ", "package": "karyon", "count": 3, "regions": 1, "dependencies": ["ingester"]}, { "name": "riakKV", "package": "riak", "count": 3, "regions": 1, "dependencies": ["riakKV"]}, { "name": "enricher", "package": "staash", "count": 6, "regions": 1, "dependencies": ["riakKV", "ingestMQ"]}, { "name": "enrichMQ", "package": "karyon", "count": 3, "regions": 1, "dependencies": ["enricher"]}, { "name": "analytics", "package": "karyon", "count": 6, "regions": 1, "dependencies": ["ingester"]}, { "name": "analytics-elb", "package": "elb", "count": 0, "regions": 1, "dependencies": ["analytics"]}, { "name": "analytics-api", "package": "denominator", "count": 0, "regions": 0, "dependencies": ["analytics-elb"]}, { "name": "normalization", "package": "karyon", "count": 6, "regions": 1, "dependencies": ["enrichMQ"]}, { "name": "iot-elb", "package": "elb", "count": 0, "regions": 1, "dependencies": ["normalization"]}, { "name": "iot-api", "package": "denominator", "count": 0, "regions": 0, "dependencies": ["iot-elb"]}, { "name": "stream", "package": "karyon", "count": 6, "regions": 1, "dependencies": ["ingestMQ"]}, { "name": "stream-elb", "package": "elb", "count": 0, "regions": 1, "dependencies": ["stream"]}, { "name": "stream-api", "package": "denominator", "count": 0, "regions": 0, "dependencies": ["stream-elb"]} ] }

New tier name

Tier package

Node count

List of tier dependencies

0 = non Regional

Single Region Riak IoT


IoT Ingestion Endpoint

Stream Endpoint

Analytics Endpoint



Stream Endpoint

Analytics Endpoint

Load Balancer

Load Balancer

Load Balancer



Stream Endpoint

Analytics Endpoint

Load Balancer

Normalization Services

Load Balancer

Load Balancer

Stream Service

Analytics Service



Stream Endpoint

Analytics Endpoint

Load Balancer


Enrich Message Queue Riak KV

Enricher Services

Load Balancer

Load Balancer

Stream Service

Analytics Service



Stream Endpoint

Analytics Endpoint

Load Balancer



Enricher Services

Ingest Message Queue

Load Balancer

Load Balancer

Stream Service

Analytics Service



Stream Endpoint

Analytics Endpoint

Load Balancer



Enricher Services

Ingest Message Queue

Load Balancer

Load Balancer

Stream Service Riak TS

Analytics Service

Ingester Service

Two Region Riak IoT


Stream Endpoint

Analytics Endpoint

East Region Ingestion

West Region Ingestion

Multi Region TS Analytics

Two Region Riak IoT


Stream Endpoint

Analytics Endpoint

East Region Ingestion

West Region Ingestion

Multi Region TS Analytics

What’s the response time of the stream

endpoint?

Response Times

What’s the response time of a simple service?

memcached

rds-msql

rds-msqlwebservers

elb

www

What’s the response time of an even simpler storage backed web service?

memcached

mysql

disk volumeweb service

load generator

See http://www.getguesstimate.com/models/1307 https://github.com/getguesstimate/guesstimate-app by Ozzie Gooen

http://www.getguesstimate.com/models/1307

https://github.com/getguesstimate/guesstimate-app

Hit rates: memcached 40% mysql 70%

memcached hit %

memcached response mysql response

service cpu time

memcached hit mode

mysql cache hit mode

mysql disk access mode


memcached hit %


service cpu time

memcached hit mode




Measuring Response Time With

Histograms

Changes made to codahale/hdrhistogram

Changes made to go-kit/kit/metrics (today!)

Implementation in adrianco/spigo/collect

What to measure?

Client ServerGetRequest

GetResponse

Client Time

Client Send CS

Server Receive SR

Server Send SS

Client Receive CR

Server Time

What to measure?

Client ServerGetRequest

GetResponse

Client Time

Client Send CS

Server Receive SR

Server Send SS

Client Receive CR

Response CR-CS

Service SS-SR

Network SR-CS

Network CR-SS

Net Round Trip (SR-CS) + (CR-SS) (CR-CS) - (SS-SR)

Server Time

Spigo Histogram Collectionfunc Start(listener chan gotocol.Message) { ... for { select { case msg := <-listener: flow.Instrument(msg, name, nethist) switch msg.Imposition { ... case gotocol.GetResponse: // return path from a request, terminate and log response time in histograms flow.End(msg, resphist, servhist, rthist) case gotocol.Goodbye: collect.SaveHist(nethist, name, "_net") collect.SaveHist(resphist, name, "_resp") collect.SaveHist(servhist, name, "_serv") collect.SaveHist(rthist, name, “_rt") collect.SaveAllGuesses(name) gotocol.Message{gotocol.Goodbye, nil, time.Now(), gotocol.NilContext, name}.GoSend(parent) return } case <-chatTicker.C: ... sm = gotocol.Message{gotocol.GetRequest, listener, now, ctx, "Why"} flow.AnnotateSend(sm, name) sm.GoSend(microindex[m]) // send to a randomly chosen dependency } } }

Go-Kit Histogram Collectionconst ( maxHistObservable = 1000000 sampleCount = 500 )

func NewHist(name string) metrics.Histogram { var h metrics.Histogram if name != "" && archaius.Conf.Collect { h = expvar.NewHistogram(name, 1000, maxHistObservable, 1, []int{50, 99}...) if sampleMap == nil { sampleMap = make(map[metrics.Histogram][]int64) } sampleMap[h] = make([]int64, 0, sampleCount) return h } return nil }

func Measure(h metrics.Histogram, d time.Duration) { if h != nil && archaius.Conf.Collect { if d > maxHistObservable { h.Observe(int64(maxHistObservable)) } else { h.Observe(int64(d)) } s := sampleMap[h] if s != nil && len(s) < sampleCount { sampleMap[h] = append(s, int64(d)) } } }

Nanoseconds!

Median and 99%ile

Slice for first 500 values as samples for export to Guesstimate

Spigo Histogram Resultsname: storage.*.*.load00....load.denominator_resp count: 1978 gauges: map[50:126975 99:278527] From, To, Count, Prob, Bar 28672, 29695, 1, 0.0005, : 31744, 32767, 1, 0.0005, : 34816, 36863, 2, 0.0010, :# 36864, 38911, 8, 0.0040, |###### 38912, 40959, 13, 0.0066, |########## 40960, 43007, 18, 0.0091, |############## 43008, 45055, 12, 0.0061, |######### 45056, 47103, 26, 0.0131, |#################### 47104, 49151, 24, 0.0121, |################## 49152, 51199, 33, 0.0167, |######################### 51200, 53247, 29, 0.0147, |###################### 53248, 55295, 35, 0.0177, |########################### 55296, 57343, 39, 0.0197, |############################## 57344, 59391, 35, 0.0177, |########################### 59392, 61439, 43, 0.0217, |################################# 61440, 63487, 31, 0.0157, |######################## 63488, 65535, 39, 0.0197, |############################## 65536, 69631, 74, 0.0374, |######################################################### 69632, 73727, 65, 0.0329, |################################################## 73728, 77823, 57, 0.0288, |############################################ 77824, 81919, 37, 0.0187, |############################ 81920, 86015, 37, 0.0187, |############################ 86016, 90111, 30, 0.0152, |####################### 90112, 94207, 39, 0.0197, |############################## 94208, 98303, 28, 0.0142, |##################### 98304, 102399, 30, 0.0152, |####################### 102400, 106495, 31, 0.0157, |######################## 106496, 110591, 20, 0.0101, |############### 110592, 114687, 26, 0.0131, |#################### 114688, 118783, 44, 0.0222, |################################## 118784, 122879, 41, 0.0207, |############################### 122880, 126975, 54, 0.0273, |########################################## 126976, 131071, 51, 0.0258, |####################################### 131072, 139263, 114, 0.0576, |######################################################################################## 139264, 147455, 123, 0.0622, |############################################################################################### 147456, 155647, 127, 0.0642, |################################################################################################### 155648, 163839, 102, 0.0516, |############################################################################### 163840, 172031, 90, 0.0455, |###################################################################### 172032, 180223, 65, 0.0329, |################################################## 180224, 188415, 43, 0.0217, |################################# 188416, 196607, 60, 0.0303, |############################################## 196608, 204799, 54, 0.0273, |########################################## 204800, 212991, 29, 0.0147, |###################### 212992, 221183, 21, 0.0106, |################ 221184, 229375, 25, 0.0126, |################### 229376, 237567, 18, 0.0091, |############## 237568, 245759, 15, 0.0076, |########### 245760, 253951, 9, 0.0046, |####### 253952, 262143, 8, 0.0040, |###### 262144, 278527, 10, 0.0051, |####### 278528, 294911, 6, 0.0030, |#### 294912, 311295, 2, 0.0010, |# 327680, 344063, 2, 0.0010, :# 344064, 360447, 1, 0.0005, | 376832, 393215, 1, 0.0005, :

name: storage.*.*.load00....load.denominator_resp count: 1978 gauges: map[50:126975 99:278527] From, To, Count, Prob, Bar 28672, 29695, 1, 0.0005, : 31744, 32767, 1, 0.0005, : 34816, 36863, 2, 0.0010, :# 36864, 38911, 8, 0.0040, |###### 38912, 40959, 13, 0.0066, |##########

Normalized probability

Response time distribution measured in nanoseconds using High Dynamic Range Histogram

:# Zero counts skipped|# Contiguous buckets

Total count, median and 99th percentile values

Go Guesstimate Exporthttps://github.com/adrianco/goguesstimate

{ "space": { "name": "gotest", "description": "Testing", "is_private": "true", "graph": { "metrics": [ {"id": "AB", "readableId": "AB", "name": "memcached", "location": {"row": 2, "column":4}}, {"id": "AC", "readableId": "AC", "name": "memcached percent", "location": {"row": 2, "column":3}}, {"id": "AD", "readableId": "AD", "name": "staash cpu", "location": {"row": 3, "column":3}}, {"id": "AE", "readableId": "AE", "name": "staash", "location": {"row": 3, "column":2}} ], "guesstimates": [ {"metric": "AB", "input": null, "guesstimateType": "DATA", "data": [119958,6066,13914,9595,6773,5867,2347,1333,9900,9404,13518,9021,7915,3733,10244,5461,12243,7931,9044,11706,5706,22861,9022,48661,15158,28995,16885,9564,17915,6610,7080,7065,12992,35431,11910,11465,14455,25790,8339,9991]}, {"metric": "AC", "input": "40", "guesstimateType": "POINT"}, {"metric": "AD", "input": "[1000,4000]", "guesstimateType": "NORMAL"}, {"metric": "AE", "input": "=100+((randomInt(0,100)>AC)?AB:AD)", "guesstimateType": "FUNCTION"} ] } } }

https://github.com/adrianco/goguesstimate

See http://www.getguesstimate.com

http://www.getguesstimate.com

See http://www.getguesstimate.com

Response time distributions exported directly from Spigo as 500 samples to json_metrics/storage.guess then posted to guesstimate.

Conference driven development not quite complete, go-kit PR in place to provide full names of histograms

Relationship between services will also be exported soon.

http://www.getguesstimate.com

What’s Next?

Trends to watch for 2016:

Serverless Architectures - AWS Lambda

Teraservices - using terabytes of memory

Teraservices

Terabyte Memory Directions

Engulf dataset in memory for analytics

Balanced config for memory intensive workloads

Replace high end systems at commodity cost point

Explore non-volatile memory implications

Terabyte Memory Options

Now: Diablo DDR4 DIMM containing flash 64/128/256GB Migrates pages to/from companion DRAM DIMM Shipping now as volatile memory, future non-volatile

Announced but not shipped for 2016 AWS X1 Instance Type - over 2TB RAM Easy availability should drive innovation

Diablo Memory1: Flash DIMM

NO CHANGES to CPU or Server

NO CHANGES to Operating System

NO CHANGES to Applications✓ UP TO 256GB DDR4 MEMORY PER MODULE

✓ UP TO 4TB MEMORY IN 2 SOCKET SYSTEM

TM

Q&AAdrian Cockcroft @adrianco

http://slideshare.com/adriancockcroftTechnology Fellow - Battery Ventures

See www.battery.com for a list of portfolio investments

http://slideshare.com/adriancockcroft



Security

Visit http://www.battery.com/our-companies/ for a full list of all portfolio companies in which all Battery Funds have invested.

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Networking

Storage

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