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 Diagnostics and Performance Tuning
Search
dcrosta
January 23, 2012
Technology
1.7k
3
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
MongoDB Diagnostics and Performance Tuning
From MongoDB LA, January 19, 2012.
dcrosta
January 23, 2012
More Decks by dcrosta
See All by dcrosta
Let the computer write the tests
dcrosta
0
110
Good Test, Bad Test
dcrosta
1
790
Exploring Python Code Objects (PyOhio)
dcrosta
4
360
Python Packaging for Humans
dcrosta
13
520
Exploring Python Code Objects
dcrosta
5
310
Keystone: Python Web Development, Simplified
dcrosta
4
360
MongoDB In the Cloud with Amazon EC2
dcrosta
6
470
Evolution without Migration
dcrosta
2
460
Other Decks in Technology
See All in Technology
オートロックマンションなのに、各部屋は施錠なし!? 攻撃者が組織内ネットワークで大暴れする理由 / The Front Door Is Locked, but the Rooms Are Wide Open: Why Attackers Move Freely Inside Enterprise Networks
nttcom
1
1.6k
20260807_第6回_関東kaggler会LT_claw系bot xangiと始める、"寂しくない" kaggle
sugupoko
0
220
【CEDEC2026】『Relink』を拡張せよ - 『GRANBLUE FANTASY: Relink - Endless Ragnarok』の開発速度と品質を守るCI運用
cygames
PRO
0
140
ガバメントクラウドでのランサムウェア対策
techniczna
2
1k
システム思考で問題に対処する
yussak
0
210
【CEDEC2026】ゲームシナリオライターを支援するAIツール開発の実践 ― 設計とプロンプトの工夫 ―
cygames
PRO
1
770
メルカリのグローバルアプリで挑んだ AlloyDB 運用と課題解決の実践記
hatappi
0
240
SO-101×VLAによる3色キューブのピック&プレース
abeja
0
160
【CEDEC2026】次世代デジタルカードゲームのサーバー設計と運用 〜『Shadowverse: Worlds Beyond』の舞台裏~
cygames
PRO
1
980
グローバル基準のSREは、運用現場でどう機能したか:成熟度アセスメントの実践 / SRE NEXT 2026
sorawatanabe
0
140
【Google Cloud Next Tokyo'26】Gemini Enterprise と Oracle AI Database で実現する、業務データ活用を実現する AI エージェント実装
shisyu_gaku
0
230
Webアクセシビリティ入門 2026
recruitengineers
PRO
3
460
Featured
See All Featured
The State of eCommerce SEO: How to Win in Today's Products SERPs - #SEOweek
aleyda
2
11k
How Fast Is Fast Enough? [PerfNow 2025]
tammyeverts
3
740
Taking LLMs out of the black box: A practical guide to human-in-the-loop distillation
inesmontani
PRO
3
2.3k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
49
10k
Utilizing Notion as your number one productivity tool
mfonobong
4
520
Noah Learner - AI + Me: how we built a GSC Bulk Export data pipeline
techseoconnect
PRO
0
350
Heart Work Chapter 1 - Part 1
lfama
PRO
8
36k
My Coaching Mixtape
mlcsv
0
210
Leo the Paperboy
mayatellez
8
2.1k
The Language of Interfaces
destraynor
162
27k
Leading Effective Engineering Teams in the AI Era
addyosmani
9
2.2k
Speed Design
sergeychernyshev
33
2k
Transcript
Diagnostics and Performance Tuning Dan Crosta, 10gen
[email protected]
@lazlofruvous
Agenda •Tools •Performance Indicators
Speed MongoDB is a high-performance database, but how do I
know that I’m getting the best performance
TOOLS
1. mongostat
2.serverStatus > db.serverStatus(); { ! ! "host" : “MacBook.local", "version"
: "2.0.1", "process" : "mongod", "uptime" : 619052, // Lots more stats... }
3.Profiler > db.setProfilingLevel(2); { "was" : 0, "slowms" : 100,
"ok" : 1 }
3.Profiler > db.system.profile.find() { "ts" : ISODate("2011-09-30T02:07:11.370Z"), "op" : "query",
"ns" : "docs.spreadsheets", "query" : { "username": "dcrosta" }, "nscanned" : 20001, "nreturned" : 1, "responseLength" : 241, "millis" : 1407, "client" : "127.0.0.1", "user" : "" }
4.Monitoring Service • MMS: 10gen.com/try-mms • Nagios • Munin
INDICATORS
1.Slow Operations Sun May 22 19:01:47 [conn10] query docs.spreadsheets ntoreturn:100
reslen:510436 nscanned:19976 { username: “dcrosta”} nreturned:100 147ms
2.Replication Lag PRIMARY> rs.status() { "set" : "replSet", "date" :
ISODate("2011-09-30T02:28:21Z"), "myState" : 1, "members" : [ { "_id" : 0, "name" : "MacBook.local:30001", "health" : 1, "state" : 1, "stateStr" : "PRIMARY", "optime" : { "t" : 1317349400000, "i" : 1 }, "optimeDate" : ISODate("2011-09-30T02:23:20Z"), "self" : true }, { "_id" : 1, "name" : "MacBook.local:30002", "health" : 1, "state" : 2, "stateStr" : "SECONDARY", "uptime" : 302, "optime" : { "t" : 1317349400000, "i" : 1 }, "optimeDate" : ISODate("2011-09-28T10:17:47Z"), "lastHeartbeat" : ISODate("2011-09-30T02:28:19Z"),
3.Resident Memory > db.serverStatus().mem { "bits" : 64, // Need
64, not 32 "resident" : 7151, // Physical memory "virtual" : 14248, // Files + heap "mapped" : 6942 // Data files
3.Resident Memory > db.stats() { "db" : "docs", "collections" :
3, "objects" : 805543, "avgObjSize" : 5107.312096312674, "dataSize" : 4114159508, // ~4GB "storageSize" : 4282908160, // ~4GB "numExtents" : 33, "indexes" : 3, "indexSize" : 126984192, // ~126MB "fileSize" : 8519680000, // ~8.5GB "ok" : 1 }
3.Resident Memory ! ! indexSize + dataSize <= RAM
4.Page Faults > db.serverStatus().extra_info { ! "note" : "fields vary
by platform", ! “heap_usage_bytes” : 210656, ! “page_faults” : 2381 }
5.Write Lock Percentage > db.serverStatus().globalLock { "totalTime" : 2809217799, "lockTime"
: 13416655, "ratio" : 0.004775939766854653, }
Concurrency • One writer or many readers • Global RW
Lock • Yields on long-running ops and if we’re likely to go to disk.
High Lock Percentage? You’re Probably Paging!
6.Reader and Writer Queues > db.serverStatus().globalLock { "totalTime" : 2809217799,
"lockTime" : 13416655, "ratio" : 0.004775939766854653, "currentQueue" : { "total" : 1, "readers" : 1, "writers" : 0 }, "activeClients" : { "total" : 2, "readers" : 1, "writers" : 1 }
6.Reader and Writer Queues > db.currentOp() { "inprog" : [
{ "opid" : 6996, "active" : true, "lockType" : "read", "waitingForLock" : true, "secs_running" : 1, "op" : "query", "ns" : "docs.spreadsheets", "query" : { “username” : “Hackett, Bernie” }, "client" : "10.71.194.111:51015", "desc" : "conn", "threadId" : "0x152693000", "numYields" : 0 },
7.Background Flushing > db.serverStatus().backgroundFlushing { "flushes" : 5634, "total_ms" :
83556, "average_ms" : 14.830670926517572, "last_ms" : 4, "last_finished" : ISODate("2011-09-30T03:30:59.052Z") }
Disk Considerations • Raid • SSD • SAN?
8.Connections > db.serverStatus().connections { "current" : 7, "available" : 19993
}
9.Network Speed > db.serverStatus().network { "bytesIn" : 877291, "bytesOut" :
846300, "numRequests" : 9186 }
10.Fragmentation db.spreadsheets.stats() { "ns" : "docs.spreadhseets", "size" : 8200046932, //
~8GB "storageSize" : 11807223808, // ~11GB "paddingFactor" : 1.4302, "totalIndexSize" : 345964544, // ~345MB "indexSizes" : { "_id_" : 66772992, “username_1_filename_1” : 146079744, “username_1_updated_at_1” : 133111808 }, "ok" : 1 }
10.Fragmentation 2 is the Magic Number
storageSize / size > 2 • Might not be reclaiming
free space fast enough • Padding factor might not be correctly calibrated db.spreadsheets.runCommand(“compact”)
paddingFactor > 2 • You might have the wrong data
model • You might be growing documents too much • Should review Schema Design
download at mongoDB.org
We’re Hiring Engineers, Sales, Evangelist, Marketing, Support, Developers @mongodb_jobs http://linkd.in/joinmongo
We’re Always Around For Conferences, Appearances and Meetups 10gen.com/events @mongodb
h2p://bit.ly/mongo8