MongoDB Performance Debugging

Jul 7, 20149 likes3,797 views

The document discusses debugging performance problems in MongoDB. It describes solutions tried such as denormalizing data, adding indexes, sharding, scaling hardware, and tagging shards. While performance improved with these solutions at times, slowness still occasionally occurred. The document advocates using monitoring tools like New Relic and Skylight to further analyze issues and find additional solutions.

MongoDB
Debugging Performance Problems
@ConradIrwin

20M crashes/day
2TB data / 50GB index
11 nodes / 3* availability zones

I don't care if MongoDB is slow
I care if my app is slow

How to make
app fast agai
it's slow bec
of the way I'm

$db.errors. find({project_id: x}). sort({ _id: -1}). limit(30)$

$db.errors. find({project_id: x}). sort({ _id: -1}). limit(30). explain()$

${ "cursor":"BtreeCursor_id_reverse", "isMultiKey":false, "n":0, "nscannedObjects":227756, "nscanned":227756, "nscannedObjectsAllPlans":227756, "nscannedAllPlans":227756, "scanAndOrder":false, "indexOnly":false, "nYields":1779, "nChunkSkips":0, "millis":461, "indexBounds":{ "_id":[ [ { "$maxElement":1 }, { "$minElement":1 } ] ] }, "server":"Jaroussky.local:27017", "filterSet":false }$

${ "cursor" : "BtreeCursor _id_ reverse", "nscanned" : 227756, "indexBounds" : {"_id" : [ [{"$maxElement" : 1}, {"$minElement" : 1}] ] } }$

$db.errors.ensureIndex( {project_id: 1, id: -1} )$

${"cursor" : "BtreeCursor project_id_1__id_1", "nscanned" : 6, "indexBounds" : { "project_id" : [[ ObjectId(x),ObjectId(x) ]], "_id" : [[ {"$minElement" : 1}, {"$maxElement" : 1} ]] }}$

insert queryupdatedeletegetmorecommandflushesmapped
5 97 57 *0 95 127|0 0 320g
5 98 61 *0 113 146|0 0 320g
8 94 61 *0 95 137|0 0 320g
vsize resfaults lockeddbidxmiss% qr|qw ar|aw
641g 8.65g 7bugsnag:47.7% 0 0|0 0|0
641g 8.66g 2bugsnag:21.0% 0 0|0 0|0
641g 8.64g 3bugsnag:23.4% 0 0|0 0|0
netInnetOut conn setrepl time
47k 73k 145bugsnag1 PRI 06:34:05
63k 99k 146bugsnag1 PRI 06:34:06
98k 124k 146bugsnag1 PRI 06:34:07

command locked db time
127|0 bugsnag:47.7% 06:34:05
146|0 bugsnag:21.0% 06:34:06
137|0 bugsnag:23.4% 06:34:07

$sh.shardCollection("errors" { project_id: 1, _id: -1 })$

command locked db time
83|0 bugsnag:3.1% 06:46:12
69|0 bugsnag:4.1% 06:46:13
73|0 bugsnag:2.4% 06:46:14

Only 1 query...
Indexed properly...
Lock % ok...

Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/savgrq-sz
xvdi 0.00 2.00 15.00 743.00 436.00 7905.50 22.01
xvdi 0.00 2.00 0.00 663.00 0.00 6706.00 20.23
xvdi 0.00 1.00 0.00 726.00 0.00 5593.50 15.41
avgqu-sz awaitr_awaitw_await svctm %util
11.02 14.54 5.33 14.73 0.60 45.60
8.04 12.12 0.00 12.12 0.51 33.60
13.01 17.92 0.00 17.92 0.50 36.40

rkB/s wkB/s await %util
7660 138 14.73 83.60
2248 2043 12.12 65.20
7905 436 17.92 45.60

db.stats()['indexSize']
= 3852071824
= 3932614656
free -b(m3.medium)

db.stats()['indexSize']
= 3852071824
= 15775363072
free -b(r3.large)

Main query seems fine...
"Quick" queries sometimes slow...

ns total read write 2
014-06-22T19:11:35
bugsnag.events 35ms 0ms 35ms
bugsnag.errors 30ms 6ms 24ms
bugsnag.system.namespaces 26ms 26ms 0ms
bugsnag.projects 12ms 5ms 7ms
bugsnag.users 15ms 2ms 13ms
bugsnag.error_aggregates 4ms 0ms 4ms
bugsnag.deploys 4ms 3ms 1ms
bugsnag.event_tallies 3ms 0ms 3ms

ns tot r w
bugsnag.events 35 0 35
bugsnag.errors 30 6 24
bugsnag.projects 12 5 7
bugsnag.users 15 7 8

NewRelic / Skylight
explain()
mongostat
iostat
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MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB

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MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB

The document provides a comprehensive guide on using MongoDB services within Kubernetes for both development and production environments. It covers various topics including the setup of Kubernetes clusters, the use of MongoDB operators, Helm for package management, and considerations for database management within Kubernetes. Key components such as custom resource definitions, service catalogs, and operational tools like MongoDB Ops Manager and Cloud Manager are highlighted for efficient database deployment and management.

MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB

The document outlines a comprehensive methodology for data modeling in MongoDB, contrasting document-based with tabular modeling approaches. It details various modeling patterns, such as the use of fields, arrays, and sub-documents, through specific use cases like a coffee shop franchise and social networks. The document also emphasizes the importance of schema design patterns and practical considerations for schema evolution and workload management.

MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB

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MongoDB SoCal 2020: MongoDB Atlas Jump StartMongoDB

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