Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1ncT8iO. From its simple roots as a PHP program, Uber has grown into a complex distributed system deployed across multiple datacenters using multiple databases and programming languages. Matt Ranney covers the evolution of Uber's architecture and some of the systems they built to handle the current scaling challenges. Filmed at qconsf.com. Matt Ranney is the Chief Systems Architect at Uber. He has a computer science degree which has come in handy over a career of mostly network engineering, operations, and analytics.

1. SCALINGUBERMATT RANNEY

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http://www.infoq.com/presentations
/uber-scalability-arch

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4. As of November 2015:
Uber Cities Worldwide: 356
Countries: 65
Employees: 4,800
Engineers: 1,500
US Driver Payments in 2015: $3.5B

7. UBER ENGINEERING
HISTORY

8. 2009-2010 Outsourced PHP + MySQL
Jan 2011 "dispatch" - Node.JS/MongoDB
Jan 2011 “API” - Python/SQLAlchemy/MySQL
Feb 2012 Dispatch swaps MongoDB for Redis
May 2012 Dispatch adds ON fallback
Jan 2013 First non-API Python services
Feb 2013 API switched to Postgres
Mar 2014 New Python services use MySQL
Mar 2014 Schemaless begins, must finish before pg collapse
Sep 2014 First Schemaless - trips out of Postgres
Aug 2015 Dispatch X.0 / Ringpop / Riak

9. TECHNICAL DEBT

10. MICROSERVICES

12. MICROSERVICES
Immutable?
Append Only?

13. Node.JS
Python
Go
Java

14. SCALING NODE

36. Getting out of the HTTP+JSON business
HTTP is slow, complex, and inconsistent
JSON is hard to validate, awkward in non-node
Thrift is OK, but generated code is bad

39. SERVICE DISCOVERY

41. Lots of services, lots of instances
Mostly Node.JS and Python
Call graph unknowable
Self-inflicted DoS
Cascading failures

49. load balancerservice A
service B
service B

50. load balancer
service A service B
service B

51. horizontal scalability
zipkin tracing
circuit breaking
rate limiting
failure testable
almost no configuration
as available as possible

52. overall latency ≥ latency of slowest component
1ms avg, 1000ms p99
use 1: 1% at least 1000ms
use 100: 63% at least 1000ms
1.0 - 0.99^100 = 0.634 = 63.4%
LATENCY

53. requeststhatareslow
0%
25%
50%
75%
100%
Processes Used
1 2 4 8 16 32 64 128 256 512 1024
p95 p99 p99.9

57. CULTURAL CHANGES

58. FAILURE TESTING

59. RETRIES

60. partner app dispatch DC1
Location Updates
State Digest
dispatch DC2
Location Updates
State Request

63. http://www.principlesofchaos.org/

64. EMBRACE THE CHAOS

65. THANKS

66. Watch the video with slide
synchronization on InfoQ.com!
http://www.infoq.com/presentations/
uber-scalability-arch