Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
MongoDB for Analytics
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
John Nunemaker
PRO
May 04, 2012
Programming
21
2.3k
MongoDB for Analytics
Presented at MongoSF on May 4th, 2012.
John Nunemaker
PRO
May 04, 2012
Tweet
Share
More Decks by John Nunemaker
See All by John Nunemaker
AI: The stuff that nobody shows you
jnunemaker
PRO
2
260
Atom
jnunemaker
PRO
10
4.5k
MongoDB for Analytics
jnunemaker
PRO
11
1k
Addicted to Stable
jnunemaker
PRO
32
2.8k
MongoDB for Analytics
jnunemaker
PRO
16
30k
Why You Should Never Use an ORM
jnunemaker
PRO
61
9.7k
Why NoSQL?
jnunemaker
PRO
10
980
Don't Repeat Yourself, Repeat Others
jnunemaker
PRO
7
3.5k
I Have No Talent
jnunemaker
PRO
14
1k
Other Decks in Programming
See All in Programming
Basic Architectures
denyspoltorak
0
680
Vibe Coding - AI 驅動的軟體開發
mickyp100
0
180
CSC307 Lecture 07
javiergs
PRO
0
550
Amazon Bedrockを活用したRAGの品質管理パイプライン構築
tosuri13
5
720
Honoを使ったリモートMCPサーバでAIツールとの連携を加速させる!
tosuri13
1
180
Raku Raku Notion 20260128
hareyakayuruyaka
0
270
AI Agent の開発と運用を支える Durable Execution #AgentsInProd
izumin5210
7
2.3k
AIによる高速開発をどう制御するか? ガードレール設置で開発速度と品質を両立させたチームの事例
tonkotsuboy_com
7
2.3k
CSC307 Lecture 01
javiergs
PRO
0
690
FOSDEM 2026: STUNMESH-go: Building P2P WireGuard Mesh Without Self-Hosted Infrastructure
tjjh89017
0
170
16年目のピクシブ百科事典を支える最新の技術基盤 / The Modern Tech Stack Powering Pixiv Encyclopedia in its 16th Year
ahuglajbclajep
5
1k
SourceGeneratorのススメ
htkym
0
200
Featured
See All Featured
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
21
1.4k
Tell your own story through comics
letsgokoyo
1
810
GraphQLとの向き合い方2022年版
quramy
50
14k
Un-Boring Meetings
codingconduct
0
200
SEOcharity - Dark patterns in SEO and UX: How to avoid them and build a more ethical web
sarafernandez
0
120
How Fast Is Fast Enough? [PerfNow 2025]
tammyeverts
3
450
YesSQL, Process and Tooling at Scale
rocio
174
15k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
254
22k
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
31
3.1k
Designing Powerful Visuals for Engaging Learning
tmiket
0
230
Unlocking the hidden potential of vector embeddings in international SEO
frankvandijk
0
170
Writing Fast Ruby
sferik
630
62k
Transcript
GitHub John Nunemaker MongoSF 2012 May 4, 2012 MongoDB for
Analytics A loving conversation with @jnunemaker
None
Background How hernias can be good for you
None
None
1 month Of evenings and weekends
1 year Since public launch
13 tiny servers 2 web, 6 app, 3 db, 2
queue
7-8 Million Page views per day
None
None
None
None
Implementation Imma show you how we do what we do
baby
Doing It (mostly) Live No aggregate querying
None
None
get('/track.gif') do track_service.record(...) TrackGif end
class TrackService def record(attrs) message = MessagePack.pack(attrs) @client.set(@queue, message) end
end
class TrackProcessor def run loop { process } end def
process record @client.get(@queue) end def record(message) attrs = MessagePack.unpack(message) Hit.record(attrs) end end
http://bit.ly/rt-kestrel
class Hit def record site.atomic_update(site_updates) Resolution.record(self) Technology.record(self) Location.record(self) Referrer.record(self) Content.record(self)
Search.record(self) Notification.record(self) View.record(self) end end
class Resolution def record(hit) query = {'_id' => "..."} update
= {'$inc' => {}} update['$inc']["sx.#{hit.screenx}"] = 1 update['$inc']["bx.#{hit.browserx}"] = 1 update['$inc']["by.#{hit.browsery}"] = 1 collection(hit.created_on) .update(query, update, :upsert => true) end end end
Pros
Pros Space
Pros Space RAM
Pros Space RAM Reads
Pros Space RAM Reads Live
Cons
Cons Writes
Cons Writes Constraints
Cons Writes Constraints More Forethought
Cons Writes Constraints More Forethought No raw data
http://bit.ly/rt-counters http://bit.ly/rt-counters2
Time Frame Minute, hour, month, day, year, forever?
# of Variations One document vs many
Single Document Per Time Frame
None
{ "t" => 336381, "u" => 158951, "2011" => {
"02" => { "18" => { "t" => 9, "u" => 6 } } } }
{ '$inc' => { 't' => 1, 'u' => 1,
'2011.02.18.t' => 1, '2011.02.18.u' => 1, } }
Single Document For all ranges in time frame
None
{ "_id" =>"...:10", "bx" => { "320" => 85, "480"
=> 318, "800" => 1938, "1024" => 5033, "1280" => 6288, "1440" => 2323, "1600" => 3817, "2000" => 137 }, "by" => { "480" => 2205, "600" => 7359,
"600" => 7359, "768" => 4515, "900" => 3833, "1024"
=> 2026 }, "sx" => { "320" => 191, "480" => 179, "800" => 195, "1024" => 1059, "1280" => 5861, "1440" => 3533, "1600" => 7675, "2000" => 1279 } }
{ '$inc' => { 'sx.1440' => 1, 'bx.1280' => 1,
'by.768' => 1, } }
Many Documents Search terms, content, referrers...
None
[ { "_id" => "<oid>:<hash>", "t" => "ruby class variables",
"sid" => BSON::ObjectId('<oid>'), "v" => 352 }, { "_id" => "<oid>:<hash>", "t" => "ruby unless", "sid" => BSON::ObjectId('<oid>'), "v" => 347 }, ]
Writes {'_id' => "#{sid}:#{hash}"}
Reads [['sid', 1], ['v', -1]]
Growth Don’t say shard, don’t say shard...
Partition Hot Data Currently using collections for time frames
Bigger, Faster Server More CPU, RAM, Disk Space
Users Sites Content Referrers Terms Engines Resolutions Locations Users Sites
Content Referrers Terms Engines Resolutions Locations
Partition by Function Spread writes across a few servers
Users Sites Content Referrers Terms Engines Resolutions Locations
Partition by Server Spread writes across a ton of servers,
way down the road, not worried yet
GitHub Thank you!
[email protected]
John Nunemaker MongoSF 2012 May 4,
2012 @jnunemaker