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Presentation from Gluecon 2013 on building and running the Netflix API.
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Gluecon 2013 netflix api crash course
1.
Netflix API Crash
Course Building & Running the API in 30 minutes Ben Schmaus, Netflix May 2013, Gluecon bschmaus@netflix.com @schmaus
2.
Streaming TV Shows
& Movies Globally
3.
> 1000 Devices
4.
1/3 of Internet at peak
5.
Programmer not Distributor
6.
More than 36
million subscribers in over 40 countries
7.
How does the
API fit into the picture?
8.
Personalization Engine User Info Movie Metadata Ratings Similar Movies Instant Queue A/B Test Engine API
9.
Personalization Engine User Info Movie Metadata Ratings Similar Movies Instant Queue A/B Test Engine API Enable
UX Innovation Insulate from Failure
10.
> 2 Billion
Requests per Day
11.
Growth Over Time
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
Automation Visibility Operational awareness Balance speed &
quality
24.
How's the API put
together?
25.
ELB Routing Cluster Mid-tier Services Backend App Cluster Backend App Cluster + API Layer
26.
ELB Routing Cluster Mid-tier Services Backend App Cluster Backend App Cluster + API Layer
27.
Inside an API App Server RxJava Hystrix Service
Client 1 Service Client 2 Service Client N
28.
HystrixRx+Java Service Layer Service Client (provided JAR) Application Service /device/endpoint (provided
script) Service UI Teams Mid-tier Service Teams API Team
29.
Continually changing UI
scripts and mid-tier services Functionality, resiliency and performance drifts over time
30.
Deployment & Ops
31.
REMOVE MANUAL WORK
pushing code to multiple AWS regions/clusters ENABLE RAPID DEPLOYMENT of code despite limited visibility into how it's changed KEEP TEAM INFORMED about what's happening in prod MITIGATE RISK of systemic failure
32.
Tools
33.
End-to-end Traceability Using Python/Java
Glue
34.
Code Flow
35.
Run 1% of
your traffic on the new code and see how it does
36.
API ami-123 API
ami-456 2xx 4xx 5xx latency busy threads load ...
37.
Manually looking at
graphs and SSH- ing into servers and grep-ing logs doesn't scale (although we used to do that)
38.
Confidence score for
each AMI based on comparison of 1000+ metrics
39.
Scannable visualization of
metric space More important Less important
40.
Cross-reference Jira, Link
to code diffs
41.
Track lib changes
42.
Easy to access
report artifacts for each AMI
43.
Your basic red/black
push
44.
45.
46.
47.
48.
Doing red/black by
hand for multiple clusters across multiple regions is not fun
49.
Automate multi-cluster/region pushes
50.
Automate multi-cluster/region pushes Don't forget
to automate rollbacks, too!
51.
$Who, $What, $Where,
$When e.g., "bschmaus, ami-123, Sandbox Canary, 2013-05-06 19:05" Latest prod change in chat topic
52.
Quickly see status
of all clusters in a region
53.
What the #%*!
just happened!?
54.
Historical & realtime
metrics, sort realtime by error/request rate
55.
Distributed grep +
tail 2013-05-09.20:38:54 MX 200 us-east-1c i-1824cb73 i-1c61b77f prod NFPS3-001-8G50FJCX... 288404769389848058 90ms api-global. netflix.com GET /tvui/release/470/plus/pathEvaluator - amazon.ami-id: ami-502eb039 amazon.availability-zone: us-east-1c amazon.instance-id: i-1824cb73 amazon.instance-type: m2.2xlarge amazon.local-ipv4: 10.6.213.112 amazon.public-hostname: ec2-54-243-4-69.compute-1.amazonaws. com amazon.public-ipv4: 54.243.4.69 cookie_esn: NFPS3-001-8G50FJCX... country: MX currentTime: 1368131934468 duration-millis: 90 esn: NFPS3-001-8G50FJCX... geo.city: CIUDADOBREGON ... $ ./simple_stream.py -f -q 'e["country"]=="MX" && e["esn"] ==~/NFPS3.*/' -r us
56.
Go for haystack
handing you the needle
57.
Or at least
be able to make smaller haystacks
58.
Continuously experiment to
make hard things easier
59.
Even with the
best tools, building software is hard work. Great engineers build great software.
60.
Want to help
us build the API? bschmaus@netflix.com @schmaus
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