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1
Scaling Stack Overflow
David Fullerton, VP Engineering • @df07
QCon NYC • 2015-06-12
InfoQ.com: News & Community Site
• 750,000 unique visitors/month
• Published in 4 languages (English, Chinese, Japanese and Brazilian
Portuguese)
• Post content from our QCon conferences
• News 15-20 / week
• Articles 3-4 / week
• Presentations (videos) 12-15 / week
• Interviews 2-3 / week
• Books 1 / month
Watch the video with slide
synchronization on InfoQ.com!
http://www.infoq.com/presentations
/stack-exchange
Presented at QCon New York
www.qconnewyork.com
Purpose of QCon
- to empower software development by facilitating the spread of
knowledge and innovation
Strategy
- practitioner-driven conference designed for YOU: influencers of
change and innovation in your teams
- speakers and topics driving the evolution and innovation
- connecting and catalyzing the influencers and innovators
Highlights
- attended by more than 12,000 delegates since 2007
- held in 9 cities worldwide
2
**SPOILERS**
3
Conclusions
1. Our architecture is boring
4
Conclusions
1. Our architecture is boring
1. How we keep it boring is interesting
5
What’s Stack Overflow?
6
Q&A for Programmers
• 9.4M questions
• 16M answers
• 45M uniques / month
• 8,000 new questions every day
(quantcast.com/stackoverflow.com)
7
Developer Jobs
• Best place on the internet to get a programming
job or hire a developer
8
Part of Stack Exchange Network
• Stack Overflow-style Q&A in 143 other topics &
languages
9
A Distributed Team
• 34 developers, 6 sysadmins, 6 designers
• 75% remote
10
A Distributed Team
• 34 developers, 6 sysadmins, 6 designers
• 75% remote
11
How do we work?
• Remote work culture
• Hire smart people and get out of their way
• Full-stack developers / sysadmins with a
specialty
12
Our Architecture
(I warned you, it’s boring)
13
(stackexchange.com/performance)
14
“Monolith Plus” architecture
• Almost everything happens in
the web tier + DB
• A few services pulled out and
optimized
15
Scales pretty well (for us)
• 4 billion requests per month, 3000 req/s peak
• 800M SQL queries per day, 8500/s peak
16
(opserver – https://github.com/opserver/opserver)
17
(opserver – https://github.com/opserver/opserver)
18
New York
(primary)
Oregon
(secondary)
Availability (also boring)
19
Deploys
• All day every day
• Rolling deploys through the web tier
(TeamCity)
Fast!
20
Testing
• Test on our users
• Feature flag
– Turn it on for a subset of sites to see how it
performs
21
* Works for us!
• Read-heavy load centered on one page
• Not as much customized content as some sites
• A forgiving community
22
23
How did we get here?
24
Our Process
1. Start with what we know
2. Measure it live
3. Fix the slow
25
Step 1: Start with what we know
• Original developers knew C# and MSSQL
• Started with a bunch of off-the-shelf tools:
– ASP.NET MVC
– LINQ to SQL
– MSSQL + SQL fulltext search
– Built-in caching (no Redis)
26
Step 2: Measure it live
• Performance is a feature!
• Test under real load
• Measure, don’t guess
27
(miniprofiler – https://github.com/MiniProfiler/dotnet)
28
(miniprofiler – https://github.com/MiniProfiler/dotnet)
29
(opserver – https://github.com/opserver/opserver)
30
(opserver – https://github.com/opserver/opserver)
31
Step 3: Fix the slow
• Slow performance is a bug, fix it now!
• Over time, replace major parts of our stack:
– Caching and Redis
– SQL access
– Tag Engine
– Elasticsearch
32
• Already hand-rolling queries for performance
• LINQ to SQL provides basic ORM:
Dapper
33
• Problem:
Dapper
34
• Solution: replace the object mapper
• Idea: emit raw IL, then cache mapper
Dapper
35
• Results (500 iterations):
Dapper
(dapper– https://code.google.com/p/dapper-dot-net/)
36
Tag Engine
37
Tag Engine
• Early hack: use SQL fulltext search to index tags
38
Tag Engine
• Problem:
39
Tag Engine
• Problem:
• Performance!
40
Tag Engine
• Highly custom in-memory tag index cache
• Carefully memory-managed to avoid GC stalls
– Learned the hard way: see “Assault by GC” by
Marc Gravell
• Serialize / deserialize from disk on build
41
Results
42
Results
1. Start with what we know
2. Measure it live
3. Fix the slow
Optimize for performance,
get scale thrown in
43
Results
• “Monolith Plus” architecture
• Extract services that solve real problems, not
imagined ones
• Avoid SOA “tax”
44
So my primary guideline would be don’t even
consider microservices unless you have a
system that’s too complex to manage as a
monolith
- Martin Fowler, “MicroservicePremium”
45
Conclusions
46
Conclusions
1. Our architecture is boring
2. How we keep it boring is interesting:
1. Start with what we know
2. Measure it live
3. Fix the slow
47
Application
• You can optimize for performance and get scale
thrown in (almost for free)
• Your monolith can scale further than you think
• SOA is not the only way
– Know your own problem space
– Fix actual problems
48
Questions?
(We’re all about questions)
Obligatory:
• We’re hiring! stackexchange.com/work-here
• Open source! stackexchange.github.io
• Follow me! twitter.com/df07
49
50
Here Be Dragons
(rejected slides)
51
• Started with basic OutputCache (cache rendered
HTML for a page)
• ~4% cache hit rate
Caching
52
• Add in-memory & Redis caching
Caching
53
StackExchange.Redis
• Wrote our own library for talking to Redis
• Multiplexing operations over a single connection
• Aware of primary / secondary instances
– Can target reads at secondary slave
54
StackExchange.Redis
(opserver – https://github.com/opserver/opserver)
55
Moonspeak (Localization)
Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/stack-
exchange

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Scaling Stack Overflow: Keeping it Vertical by Obsessing Over Performance