5 must have patterns for your microservice - techorama

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

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

/// ASOS in numbers
2 0 1 6 T u r n O v e r → £1.5 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
A z u r e D a t a C e n t r e s → 5

@aliostad
/// Microservices
Architecture

@aliostad
/// why microservices
> Scaling people not the solution
> Decentralising decision centres => Agility
> Frequent deployment => Agility
> Reduced complexity of each ms (Divide/Conquere) => Agility
> Cost of mistakes and bad decisions smaller ...

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

@aliostad
/// microservices vs soa
SOA Microservices
Main Goal Architectual Decoupling Business Agility
Set out to solve Architectural Coupling
Scaling People,
Frequent Deployment
Audience Mainly Architecture Business (Everyone)
Law Conway’s Reverse Conway’s
Impact on Structure of
Organisation
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

@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

@aliostad
/// probability distribution
Response Time
Probabilty

@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%

@aliostad
/// probability dist
Service A Prob.
10ms 33%
15ms 33%
25ms 33%
Service B Prob.
15ms 33%
20ms 33%
30ms 33%
A + B Prob.
25ms 11%
30ms 11%
40ms 11%
30ms 11%
35ms 11%
45ms 11%
40ms 11%
45ms 11%
55ms 11%
X
=
66th=15ms 66th=20ms
44th=35ms
66th=40ms

@aliostad
/// Retry and
Timeout Policy

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

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

@aliostad
/// Backup requests with cross server
cancelation
https://static.googleusercontent.com/media/research.google.com/en//people/jeﬀ/Berkeley-
Latency-Mar2012.pdf
BRCSC Paper
Google, 2012

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

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

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

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

@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();
}

@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)

@aliostad
/// Dapper
Dapper Paper
Google, 2010
Scalable
Transparent Low-overhead
-> Span Id
-> Parent Id
-> Trace Id

@aliostad
/// zipkin
zipkin
by Twitter
zipkin
Collector
Storage
Query
WebWebWebWebWebWebWebWebWeb

@aliostad
/// zipkin
Collector
Kafka
Scribe
EventHub*
Storage
Cassandra
MySQL
Elasticsearch

@aliostad
/// Sampling
“The ﬁrst production version of Dapper used a uniform sampling
probability for all processes at Google, averaging one sampled trace for
every 1024 candidates… [however] we are in the process of deploying
an adaptive sampling scheme that is parameterized not by a uniform
sampling probability, but by a desired rate of sampled traces per unit
time.”
Dapper Paper
Zipkin samples in the collector using a strategy pattern: an
implementation of CollectorSampler abstract class.

@aliostad
/// Circuit-
Breaker

@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

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

@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)

@aliostad
/// ActivityId
Propagator

@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

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

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

@aliostad
/// ActivityId - .NET
// .NET 4.5.2 and below
CallContext.LogicalSetData(“activityId”, activityId)
...
var activityId = (string) CallContext.LogicalGetData(“activityId”);
// .NET 4.6 and above
private static AsyncLocal<string> _activityId = new AsyncLocal<string>();
...
_activityId.Value = “a sensible value”;
...
var activityId = _activityId.Value;

@aliostad
/// Quick Quiz
class Program
{
private static AsyncLocal<string> _activityId = new AsyncLocal<string>();
static void Main(string[] args)
{
_activityId.Value = "initial";
Console.WriteLine(_activityId.Value); // output
new Thread(() =>
{
Console.WriteLine(_activityId.Value);
_activityId.Value = "Inside Thread”; // output
}).Start();
Thread.Sleep(100);
Console.WriteLine(_activityId.Value); // output
}
}
A)
“initial”
“”
“initial”
B)
“initial”
“initial”
“initial”
C)
“initial”
“”
“Inside Thread”
D)
“initial”
“initial”
“Inside Thread”

@aliostad
/// Canary and
Health Endpoint

@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”

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

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

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

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

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

@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
}

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

@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

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

5 must have patterns for your microservice - techorama

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