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
John Nunemaker
PRO
November 13, 2012
Programming
1.1k
11
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
MongoDB for Analytics
Presented at MongoChicago on November 13, 2012.
John Nunemaker
PRO
November 13, 2012
More Decks by John Nunemaker
See All by John Nunemaker
Remote First: Building Distributed Teams that Win
jnunemaker
PRO
1
170
AI: The stuff that nobody shows you
jnunemaker
PRO
9
840
Atom
jnunemaker
PRO
10
5.2k
Addicted to Stable
jnunemaker
PRO
32
2.9k
MongoDB for Analytics
jnunemaker
PRO
21
2.3k
MongoDB for Analytics
jnunemaker
PRO
16
30k
Why You Should Never Use an ORM
jnunemaker
PRO
61
9.9k
Why NoSQL?
jnunemaker
PRO
10
1k
Don't Repeat Yourself, Repeat Others
jnunemaker
PRO
7
3.6k
Other Decks in Programming
See All in Programming
AWS CDK を「作」ってみた 〜フルスクラッチで見えた CDK の裏側〜 / aws-cdk-from-scratch
gotok365
3
470
1年で人数1.5倍、PR数5.5倍増。 品質とアウトカムはどうなったか、 何が効いたか
ike002jp
0
140
Performance Engineering for Everyone
elenatanasoiu
0
270
JAWS-UG横浜 #102 AWSサ終供養LT会 成仏できない AWS サービスたち 〜本日、三体供養します〜
maroon1st
0
220
共通化で考えるべきは、実装より公開する型だった
codeegg
0
260
symfony/aiとlaravel/boost
77web
0
140
AI時代、エンジニアはどう育つのか -未経験エンジニアの成長を間近で見て考えたこと-
thasu0123
0
140
The Bowling Game- From Imperative to Functional Programming - Part 1
philipschwarz
PRO
0
340
Claude Opus 4.6以後の受託開発エンジニアの変化(Claude Code開発ノウハウ大公開スペシャルbyクラスメソッド)
iidatakuma
1
820
【やさしく解説 設計編 #0】DDDのコード、読めるのに分からない人へ
panda728
PRO
2
270
鹿野さんに聞く!『TypeScriptコードレシピ集』で磨く実践力
tonkotsuboy_com
4
1.1k
20260623_Loop Engineeringで自分の分身の問い合わせBotを作る
ryugen04
0
230
Featured
See All Featured
Testing 201, or: Great Expectations
jmmastey
46
8.2k
The Anti-SEO Checklist Checklist. Pubcon Cyber Week
ryanjones
0
190
Taking LLMs out of the black box: A practical guide to human-in-the-loop distillation
inesmontani
PRO
3
2.3k
Measuring & Analyzing Core Web Vitals
bluesmoon
9
890
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
128
56k
Context Engineering - Making Every Token Count
addyosmani
9
1k
Visual Storytelling: How to be a Superhuman Communicator
reverentgeek
2
590
Imperfection Machines: The Place of Print at Facebook
scottboms
270
14k
The Organizational Zoo: Understanding Human Behavior Agility Through Metaphoric Constructive Conversations (based on the works of Arthur Shelley, Ph.D)
kimpetersen
PRO
0
390
Design of three-dimensional binary manipulators for pick-and-place task avoiding obstacles (IECON2024)
konakalab
0
490
Automating Front-end Workflow
addyosmani
1370
210k
HDC tutorial
michielstock
2
750
Transcript
GitHub John Nunemaker MongoChicago 2012 November 12, 2012 MongoDB for
Analytics A loving conversation with @jnunemaker
Background How hernias can be good for you
None
None
1 month Of evenings and weekends
18 months Since public launch
10-15 Million Page views per day
2.7 Billion Page views to date
13 tiny servers 2 web, 6 app, 3 db, 2
queue
requests/sec
ops/sec
cpu %
lock %
Implementation How we do what we do
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
[ "content.2011.7", "content.2011.8", "content.2011.9", "content.2011.10", "content.2011.11", "content.2011.12", "content.2012.1", "content.2012.2", "content.2012.3",
"content.2012.4", ]
[ "resolutions.2011", "resolutions.2012", ]
Move
Move BigintMove
Move BigintMove MakeYouWannaMove
Move BigintMove MakeYouWannaMove DaMove
Move BigintMove MakeYouWannaMove DaMove SmoothMove
Move BigintMove MakeYouWannaMove DaMove SmoothMove NightMove
Move BigintMove MakeYouWannaMove DaMove SmoothMove NightMove DanceMove
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 MongoChicago 2012 November 12,
2012 @jnunemaker