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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
Flow
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
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
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
Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
Load Balancer
Load Balancer
Load Balancer
Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
Load Balancer
Normalization Services
Load Balancer
Load Balancer
Stream Service
Analytics Service
Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
Load Balancer
Normalization Services
Enrich Message Queue Riak KV
Enricher Services
Load Balancer
Load Balancer
Stream Service
Analytics Service
Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
Load Balancer
Normalization Services
Enrich Message Queue Riak KV
Enricher Services
Ingest Message Queue
Load Balancer
Load Balancer
Stream Service
Analytics Service
Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
Load Balancer
Normalization Services
Enrich Message Queue Riak KV
Enricher Services
Ingest Message Queue
Load Balancer
Load Balancer
Stream Service Riak TS
Analytics Service
Ingester Service
Two Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
East Region Ingestion
West Region Ingestion
Multi Region TS Analytics
Two Region Riak IoT
IoT Ingestion Endpoint
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
See http://www.getguesstimate.com/models/1307 https://github.com/getguesstimate/guesstimate-app by Ozzie Gooen
See http://www.getguesstimate.com/models/1307 https://github.com/getguesstimate/guesstimate-app by Ozzie Gooen
See http://www.getguesstimate.com/models/1307 https://github.com/getguesstimate/guesstimate-app by Ozzie Gooen
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
Hit rates: memcached 40% mysql 70%
Hit rates: memcached 60% mysql 70%
memcached hit %
memcached response mysql response
service cpu time
memcached hit mode
mysql cache hit mode
mysql disk access mode
Hit rates: memcached 60% mysql 70%
Hit rates: memcached 20% mysql 90%
memcached hit %
memcached response mysql response
service cpu time
memcached hit mode
mysql cache hit mode
mysql disk access mode
Hit rates: memcached 20% mysql 90%
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"} ] } } }
See 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.
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
Security
Visit http://www.battery.com/our-companies/ for a full list of all portfolio companies in which all Battery Funds have invested.
Palo Alto Networks
Enterprise IT
Operations & Management
Big DataCompute
Networking
Storage