The document summarizes Adrian Cockcroft's work analyzing response time distributions for microservices. It discusses challenges in managing scale for microservice platforms and visualizing request flows across many services. It also introduces Spigo, a tool developed by Cockcroft that can simulate microservice architectures and interactions, generate traces, and collect response time histograms for analysis.

1. Analyzing Response Time
Distributions for Microservices
Adrian Cockcroft @adrianco
Technology Fellow - Battery Ventures
February 2016

2. 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

3. Challenges for
Microservice
Platforms

4. Managing Scale

5. 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

6. Flow

7. Some tools can show
the request flow
across a few services

8. 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

9. 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
Karyon
Business
Logic
Staash
Data
Access
Layer
Priam Cassandra
Datastore
Three
Availability
Zones

10. Spigo Nanoservice Structure
func 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

11. Flow Trace Recording
riak2
us-east-1
zoneC
riak9
us-west-2
zoneA
Put s896
Replicate
riak3
us-east-1
zoneA
riak8
us-west-2
zoneC
riak4
us-east-1
zoneB
riak10
us-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
staash
us-east-1
zoneC
s910 s908s913
s909s912

12. 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

13. Zipkin Trace Dependencies

14. Zipkin Trace Dependencies

15. Trace for one Spigo Flow

16. 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 includes
chaos monkey victim
New tier
name
Tier
package
0 = non
Regional
Node
count
List of tier
dependencies

17. 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

18. 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

19. Single Region Riak IoT

20. Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint

21. Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
Load Balancer
Load Balancer
Load Balancer

22. Single Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
Load Balancer
Normalization Services
Load Balancer
Load Balancer
Stream Service
Analytics Service

23. 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

24. 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

25. 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

26. Two Region Riak IoT
IoT Ingestion Endpoint
Stream Endpoint
Analytics Endpoint
East Region Ingestion
West Region Ingestion
Multi Region TS Analytics

27. 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?

28. Response Times

29. What’s the response time of a simple service?
memcached
rds-msql
rds-msqlwebservers
elb
www

30. What’s the response time of an even simpler storage
backed web service?
memcached
mysql
disk volume
web
service
load
generator

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

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

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

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

35. Hit rates: memcached 40% mysql 70%

36. 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%

37. Hit rates: memcached 60% mysql 70%

38. 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%

39. Hit rates: memcached 20% mysql 90%

40. 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%

41. Measuring
Response Time With
Histograms

42. Changes made to codahale/hdrhistogram
Changes made to go-kit/kit/metrics (today!)
Implementation in adrianco/spigo/collect

43. What to measure?
Client Server
GetRequest
GetResponse
Client
Time
Client Send CS
Server Receive SR
Server Send SS
Client Receive CR
Server
Time

44. What to measure?
Client Server
GetRequest
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

45. Spigo Histogram Collection
func 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
}
}
}

46. Go-Kit Histogram Collection
const (
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

47. Spigo Histogram Results
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, |##########
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

48. Go Guesstimate Export
https://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,
5706,22861,9022,48661,15158,28995,16885,9564,17915,6610,7080,7065,12992,35431,11910,11465,14455,25790,8339,9
991]},
{"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"}
]
}
}
}

49. See http://www.getguesstimate.com

50. 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.

51. What’s Next?

52. Trends to watch for 2016:
Serverless Architectures - AWS Lambda
Teraservices - using terabytes of memory

53. Teraservices

54. 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

55. 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

56. 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

57. Q&A
Adrian Cockcroft @adrianco
http://slideshare.com/adriancockcroft
Technology Fellow - Battery Ventures
See www.battery.com for a list of portfolio investments

58. 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