"Netflix is actually a log generating application that just happens to stream movies" Building a service/Microservice is itself easy. Scaling it on the cloud is not that hard either but operating, maintaining and iterating a production large scale service is not just about linearisation. As Cockcroft points out, telemetry and monitoring is the most important aspect of building Microservices We discuss 5 patterns that any serious Microservice should have: - Canary (an endpoint reporting health of underlying dependencies) - IO monitor (measuring all calls from Microservice to external dependencies) - A circuit breaker - An ActivityId-Propagator - An exception and short timeout retry policy

1. >>> 5 must-have
Patterns for your web-scale
Microservices
@aliostad
Ali Kheyrollahi, ASOS

2. @aliostad
> stackoverflow
> £1.5 bln
global fashion
destination
> 35% every year

3. @aliostad
/// ASOS in numbers
2 0 1 6 T u r n O v e r → £15 bln
A c t i v e C u s t o m e r s → 12 M
N e w P r o d u c t s / w k → 4 k
U n i q u e V i s i t s / m o → 123 M
P a g e V i e w s / d a y → 95 M
P l a t f o r m T e a m s → 40

4. @aliostad
/// Microservices
Architecture

5. @aliostad
/// why microservices
> Scaling people not the solution
> Decentralising decision centres => Agility
> Frequent deployment => Agility
> Reduced complexity of each ms (Divide/Conquere) => Agility
> Overall solution complex but ...

6. @aliostad
/// anecdote
Often you can measure your success in
implementing Microservice Architecture not
be the number of services you build, but by
the number you decommission.

7. @aliostad
/// microservices vs soa
SOA Microservices
Main Goal Architectual Decoupling Agility
Audience Mainly Architecture Business (Everyone)
Set out to solve Architectural Coupling
Scaling People,
Frequent Deployment
Organisational
Structure Impact
Minimal Huge
Service Cardinality Usually up to a dozen >40 (Commonly >100)
When to do Always teams > ~5**
** Debateable. There are articles and discussions on this very topic

8. @aliostad
/// microservice challenges
> Very difficult to build a complete mental picture of solution
> When things go wrong, need to know where before why
> Potentially increased latency
> Performance outliers intractable to solve
> A complete mind-shift requiring a new operating model

9. @aliostad
/// probability distribution
Response Time
Probabilty

10. @aliostad
/// performance outliers
Microservice
A
Microservie
B
99th Percentile = 500ms 99th Percentile = 500ms
A B Total
<1s 99% 99% 98.01%
>500m 1% 99% 0.99%
>500m 99% 1% 0.99%
>1s 1% 1% 0.01%

11. @aliostad
/// ActivityId
Propagator

12. @aliostad
/// ActivityId
> Every customer request matters
> Every request is unique
> Every request creates a chain (or tree) of calls/events
> Activities are correlated
> You need an ActivityId (or CorrelationId) to link calls/events

13. @aliostad
/// ActivityId
Microservice
Id
IdId Thread Local Storage
Id
To Other APIs
Id
Event

14. @aliostad
/// ActivityId - HTTP
Request
GET /api/v2/foo HTTP/1.1
host: foo.com
activity-id: 96c5a1f106ce468ebcca8303ed7464bd
Response
200 OK
activity-id: 96c5a1f106ce468ebcca8303ed7464bd

15. @aliostad
/// Retry and
Timeout Policy

16. @aliostad
/// Failure
Microservice
A
1% chance of failure
X
Wait (back-off)
X
Wait (back-off longer)
Microservice
B
1% chance of failure

17. @aliostad
/// Preemptive Timeout
Microservice
A
X
retry
X
retry
Short timeout
Short timeout
Microservice
B

18. @aliostad
/// Timeout
C
B
A
A > B > C
A > B + C

19. @aliostad
/// Choosing a timeout?
Static => Based on Server SLO
Dynamic => 95th percentile

20. @aliostad
/// IO
Monitor

21. @aliostad
/// Blame Game
“If there is a single place where
you can play blame game,
instead of collective responsibility,
it is in
Microservices troubleshooting”

22. @aliostad
/// Did you say IO??
Microservice
DB
API
Cache
Measure...
every time your code
goes out of your process

23. @aliostad
/// Recording Methods
> Explicitly by calling record()
> Asking the library to record a closure
> Aspect-oriented
Java (spf4j)
private static final MeasurementRecorder recorder
= RecorderFactory.createScalableCountingRecorder(forWhat, unitOfMeasurement,
sampleTimeMillis);
…
recorder.record(measurement);
.NET (PerfIt)
var ins = new SimpleInstrumentor(new InstrumentationInfo()
{
Counters = CounterTypes.StandardCounters,
Description = "test",
InstanceName = "Test instance",
CategoryName = TestCategory
});
ins.Instrument(() => Thread.Sleep(100), "test...");
Java and .NET
@PerformanceMonitor(warnThresholdMillis=1, errorThresholdMillis=100, recorderSource =
RecorderSourceInstance.Rs5m.class)
[PerfItFilter(“PerfItTests", InstanceName = "Test")]
public string Get()
{
return Guid.NewGuid().ToString();
}

24. @aliostad
/// Publishing Methods
> Local file (various to logstash)
> TCP and HTTP (many, to zipkin, influxdb)
> UDP (statsd, collectd to graphite, logstash)
> Raising Kernel-level event (Windows ETW)
> Local communication (statsd)

25. @aliostad
/// Circuit-
Breaker

26. @aliostad
/// tri-state
> Closed traffic can flow normally
> Open traffic does not flow
> Half-open circuit breaker tests the waters again
Closed
Open
Half-open
Test
Failure
Wait timeout

27. @aliostad
/// Netflix Hysterix
RequestVolumeThreshold
ErrorThresholdPercentage
SleepWindowInMilliseconds
TimeInMilliseconds
NumBuckets

28. @aliostad
/// Fallback
> Custom: e.g. serve content from a local cache (status 206)
> Silent: return null/no-data/empty (status 200/204)
> Fail-fast: Customer experience is important (status 5xx)

29. @aliostad
/// Canary and
Health Endpoint

30. @aliostad
/// Health Endpoints
Ping returns a success code when invoked
Canary returns a connectivity status and
latency on the service and dependencies
“… none of them invoke any application code”

31. @aliostad
/// Ping
Request
GET /api/health HTTP/1.1
host: foo.com
Response
200 OK
Response
500 Server Error

32. @aliostad
/// Canary
Request
GET /api/canary HTTP/1.1
host: foo.com
Response
200 OK
{
[Nested Structure]
}

33. @aliostad
/// ChirpResult
{
"serviceName": "foo",
"latency": "00:00:00.0542172",
"statusCode": 200,
"isCritical": true
}

34. @aliostad
/// ChirpResult

35. @aliostad
/// ChirpResult - critical failure
API
NC
NC
C
200
200
500
500

36. @aliostad
/// ChirpResult - non-critical failure
API
NC
NC
C
500
200
200
200

37. @aliostad
/// AOP / Declarative (c#)
[AzureStorageCanary("Foo-AzureStorage-BarDatabaseServer", “config-key-for-cn“)]
[SqlCanary("SQL-BazActiveDatabase", null, typeof(SqlConnectionFactory))]
[CanaryEndpointCanary("Dependency-Api", “config-key-for-endpoint“)]
public class CanaryController : CanaryBaseController
{
… // some boilerplate code
}

38. @aliostad
/// Deep vs Shallow
API
API
“Deep”“Shallow”
/api/canary?deep=false

39. @aliostad
/// Wrap-up
> If you have more than ~5 teams, consider Microservices
> Logging/Monitoring/Alerting: single most important asset
> Use ActivityId Propagator to correlate (consider zipkin)
> Cloud is a jungleTM
. Without retry/timeout you won’t survive
> Monitor and measure all calls to external services (blame game)
> Protect your systems with circuit-breakers (and isolation)
> Canary helps you detect connectivity from customer view

40. @aliostad
Thomas Wood: Daisy Picture
Thomas Au: Thermometer Picture
Torbakhopper: Cables Picture
Dam Picture - Japan
Hsiung: Lights Picture
Health Endpoint in API Design