What's Missing? Microservices Meetup at Cisco

Microservices: What’s Missing…
Adrian Cockcroft @adrianco
Technology Fellow - Battery Ventures
March 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
http://www.slideshare.net/adriancockcroft

What’s Missing?
Trying out new content today
Discussion/feedback
O’Reilly Software Architecture Conference
New York April 13th - for the real thing…

@adrianco
Discussion Points
Failure injection testing
Versioning, Routing, Protocols
Timeouts and retries
Denormalized data models
Monitoring, Tracing
Simplicity through symmetry
See www.battery.com for a list of portfolio investments

@adrianco
Failure Injection Testing
Netflix Chaos Monkey and Simian Army
http://techblog.netflix.com/2011/07/netflix-simian-army.html
http://techblog.netflix.com/2014/10/fit-failure-injection-testing.html
http://techblog.netflix.com/2016/01/automated-failure-testing.html

! Chaos Monkey - enforcing stateless business logic
! Chaos Gorilla - enforcing zone isolation/replication
! Chaos Kong - enforcing region isolation/replication
! Security Monkey - watching for insecure configuration settings
! Latency Monkey & FIT - inject errors to enforce robust dependencies
! See over 100 NetflixOSS projects at netflix.github.com
! Get “Technical Indigestion” reading techblog.netflix.com
Trust with Verification

@adrianco
Benefits of version aware routing
Immediately and safely introduce a new version
Canary test in production
Pin clients to a version so they can’t get disrupted
Change client or dependencies but not both at once
Eventually remove old versions
Incremental or infrequent “break the build” garbage collection

@adrianco
Versioning, Routing
Version numbering: Interface.Feature.Bugfix
V1.2.3 to V1.2.4 - Canary test then remove old version
V1.2.x to V1.3.x - Canary test then remove or keep both
Route V1.3.x clients to new version to get new feature
Remove V1.2.x only after V1.3.x is found to work for V1.2.x clients
V1.x.x to V2.x.x - Route clients to specific versions
Remove old server version when all old clients are gone

@adrianco
Timeouts and Retries
Connection timeout vs. request timeout confusion
Usually setup incorrectly, global defaults
Systems collapse with “retry storms”
Timeouts too long, too many retries
Services doing work that can never be used

@adrianco
Connections and Requests
TCP makes a connection, HTTP makes a request
HTTP hopefully reuses connections for several requests
Both have different timeout and retry needs!
TCP timeout is purely a property of one network latency hop
HTTP timeout depends on the service and its dependencies

@adrianco
Edge
Service
Good
Service
Good
Service
Bad config: Every service defaults to 5 second timeout, two retries
Edge
Service not
responding
Overloaded
service not
responding
Failed
Service
If anything breaks, everything upstream stops responding
Retries add unproductive work

@adrianco
Bad config: Every service defaults to 5 second timeout, two retries
Edge
service
responds
slowly
Overloaded
service
Partially
failed
service
First request from Edge timed out so it ignores the successful
response and keeps retrying. Middle service load increases as
it’s doing work that isn’t being consumed

@adrianco
Timeout and Retry Fixes
Cascading timeout budget
Static settings that decrease from the edge
or dynamic budget passed with request
How often do retries actually succeed?
Don’t ask the same instance the same thing
Only retry on a different connection

@adrianco
Edge
Service
Good
Service
Budgeted timeout, one retry
Failed
Service
5s
1s
1s
Fast fail
response
after 2s
Upstream timeout must always be longer than
total downstream timeout * retries delay
No unproductive work while fast failing

@adrianco
Edge
Service
Good
Service
Budgeted timeout, failover retry
Failed
Service
5s
1s
1s
For replicated services with multiple instances
never retry against a failed instance
No extra retries or unproductive work
Good
Service
Success
response
delayed 1s

@adrianco
Denormalized Data Models
“The Network is Reliable” http://dl.acm.org/citation.cfm?id=2655736
Distributed systems are inconsistent by nature
Clients are inconsistent with servers
Most caches are inconsistent
Versions are inconsistent
Get over it and
Deal with it

@adrianco
Any non-trivial organization has many databases
Cross references exist, inconsistencies exist
Microservices work best with individual simple stores
Scale, operate, mutate, fail them independently
NoSQL allows flexible schema/object versions

@adrianco
Build custom cross-datasource check/repair processes
Ensure all cross references are up to date
Immutability Changes Everything
http://highscalability.com/blog/2015/1/26/paper-immutability-changes-everything-by-pat-helland.html
Memories, Guesses and Apologies
https://blogs.msdn.microsoft.com/pathelland/2007/05/15/memories-guesses-and-apologies/

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

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
...
}
}
}
Skeleton code for replicating a Put message
Instrument incoming requests
Instrument outgoing requests
update trace context

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
Replicate Put

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

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

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
Riak KV
Enricher Services
Ingest Message Queue
Load Balancer
Load Balancer
Stream Service
Analytics Service

Stream Endpoint
Analytics Endpoint
Load Balancer
Riak KV
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?

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 volume
web
service
load
generator

Measuring
Response Time With
Histograms

Changes made to codahale/hdrhistogram
Changes made to go-kit/kit/metrics
Implementation in adrianco/spigo/collect

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

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

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

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

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

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%

Golang Guesstimate Interface
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": "LOGNORMAL"},
{"metric": "AE", "input": "=100+((randomInt(0,100)>AC)?AB:AD)", "guesstimateType": "FUNCTION"}
]
}
}
}

See http://www.getguesstimate.com

@adrianco
Simplicity through symmetry
Symmetry
Invariants
Stable assertions
No special cases

@adrianco
“We see the world as increasingly more complex and chaotic
because we use inadequate concepts to explain it. When we
understand something, we no longer see it as chaotic or complex.”
Jamshid Gharajedaghi - 2011
Systems Thinking: Managing Chaos and Complexity: A Platform for Designing Business Architecture

Q&A
Adrian Cockcroft @adrianco
http://slideshare.com/adriancockcroft
Technology Fellow - Battery Ventures

What's Missing? Microservices Meetup at Cisco

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