indexing and query optimization webinar

Indexing  and  Query  OptimizationKevin  Matulef

September  6,  2012

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What’s in store

• What are indexes?

• Picking the right indexes.

• Creating indexes in MongoDB

• Troubleshooting

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Indexes are the single biggesttunable performance factor

in MongoDB.

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Absent or suboptimal indexes are the most common avoidable

MongoDB performance problem.

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So what problem do indexes solve?

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How do you find a chicken recipe?

• An unindexed cookbook might be quite a page turner.

• Probably not what you want, though.

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I know, I’ll use an index!

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Let’s imagine a simple index

ingredient page

aardvark 790

... ...

beef 190,  191,  205,  ...

... ...

chicken 182,  199,  200,  ...  

chorizo 497,  ...

... ...

zucchini 673,  986,  ...

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How do you find a quick chicken recipe?

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Let’s imagine a compound index

ingredient cooking  time page

... ... ...

chicken 15  min 182,  200

chicken 25  min 199

chicken 30  min 289,316,320

chicken 45  min 290,  291,  354

... ... ...

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Consider the ordering of index keys

Chicken,  15  min

Chicken,  45  min

Chicken,  25  min

Chicken,  30  min

Aardvark,  20  min Zuchinni,  45  min

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How about a low-calorie chicken recipe?

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Let’s imagine a 2nd compound index

ingredient calories page

... ... ...

chicken 250 199,  316

chicken 300 289,291

chicken 425 320

... ... ...

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How about a quick, low-calorie recipe?

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Let’s imagine a last compound index

calories cooking  time page

... ... ...

250 25  min 199

250 30  min 316

300 25  min 289

300 45  min 291

425 30  min 320

... ... ...

How do you find dishes from 250 to 300 calories that cook from 30 to 40 minutes?

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Consider the ordering of index keys

250  cal,25  min

250  cal,30  min

300  cal,25  min

300  cal,45  min

How do you find dishes from 250 to 300 calories that cook from 30 to 40 minutes?

4 index entries will be scanned, but only 1 will match!

425  cal,30  min

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Range queries using an index on A, B• A is a range J

• A is constant, B is a range J

• A is constant, order by B J

• A is range, B is constant/range K

• B is constant/range, A unspecified L

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It’s really that straightforward.

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B-Trees (Bayer & McCreight ’72)

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B-Trees (Bayer & McCreight ’72)

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B-Trees (Bayer & McCreight ’72)

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Queries,  Inserts,  Deletes:  O(log  n)

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All this is relevant to MongoDB.

• MongoDB’s indexes are B-Trees, which are designed for range queries.

• Generally, the best index for your queries is going to be a compound index.

• Every additional index slows down inserts & removes, and may slow updates.

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On to MongoDB!

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Declaring Indexes

• db.foo.ensureIndex( { username : 1 } )

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Declaring Indexes

• db.foo.ensureIndex( { username : 1 } )

• db.foo.ensureIndex( { username : 1, created_at : -1 } )

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And managing them....

> db.system.indexes.find() //db.foo.getIndexes()

{ "v" : 1, "key" : { "_id" : 1 }, "ns" : "test.foo", "name" : "_id_" } { "v" : 1, "key" : { "username" : 1 }, "ns" : "test.foo", "name" : "username_1" }

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And managing them....

> db.system.indexes.find() //db.foo.getIndexes()

{ "v" : 1, "key" : { "_id" : 1 }, "ns" : "test.foo", "name" : "_id_" } { "v" : 1, "key" : { "username" : 1 }, "ns" : "test.foo", "name" : "username_1" }

> db.foo.dropIndex( { username : 1} )

{ "nIndexesWas" : 2 , "ok" : 1 }

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Key info about MongoDB’s indexes• A collection may have at most 64 indexes.

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Key info about MongoDB’s indexes• A collection may have at most 64 indexes.

• “_id” index is automatic (except capped collections before 2.2)

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Key info about MongoDB’s indexes• A collection may have at most 64 indexes.

• “_id” index is automatic (except capped collections before 2.2)

• All queries can use just 1 index (except $or queries).

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Key info about MongoDB’s indexes• A collection may have at most 64 indexes.

• “_id” index is automatic (except capped collections before 2.2)

• All queries can use just 1 index (except $or queries).

• The maximum index key size is 1024 bytes.

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Indexes get used where you’d expect

• db.foo.find({x : 42}) • db.foo.find({x : {$in : [42,52]}}) • db.foo.find({x : {$lt : 42})• update, findAndModify that select on x,• count, distinct,• $match in aggregation• left-anchored regexp, e.g. /^Kev/

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But indexes aren’t always helpful

• Most negations: $not, $nin, $ne

• Some corner cases: $mod, $where

• Matching most regular expressions, e.g. /a/ or /foo/i

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Advanced Options

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Arrays: the powerful “multiKey” index

{ title : “Chicken Noodle Soup”, ingredients : [“chicken”, “noodles”] }

ingredients page

chicken 42

... ...

noodles 42

... ...

>  db.foo.ensureIndex(  {  ingredients  :  1  }  )

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Unique Indexes

• db.foo.ensureIndex( { email : 1 } , {unique : true} )

> db.foo.insert({email : “matulef@10gen.com”})> db.foo.insert({email : “matulef@10gen.com”}) E11000 duplicate key error ...

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Sparse Indexes

• db.foo.ensureIndex( { email : 1 } , {sparse : true} )

No index entries for docs without “email” field

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Geospatial Indexes

{ name: "10gen Office", lat_long: [ 52.5184, 13.387 ] }

> db.foo.ensureIndex( { lat_long : “2d” } )

> db.locations.find( { lat_long: {$near: [52.53, 13.4] } } )

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Troubleshooting

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The Query Optimizer

• For each “type” of query, mongoDB periodically tries all useful indexes.

• Aborts as soon as one plan wins.

• Winning plan is temporarily cached.

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Which plan wins? Explain! > db.foo.find( { t: { $lt : 40 } } ).explain( ){ "cursor" : "BtreeCursor t_1" , "n" : 42, “nscannedObjects: 42 "nscanned" : 42, ... "millis" : 0, ...}

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Which plan wins? Explain! > db.foo.find( { t: { $lt : 40 } } ).explain( ){ "cursor" : "BtreeCursor t_1" , "n" : 42, “nscannedObjects: 42 "nscanned" : 42, ... "millis" : 0, ...}

Pay attention to the ratio  n/nscanned!

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Think you know better? Give us a hint> db.foo.find( { t: { $lt : 40 } } ).hint( { _id : 1} )

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Recording slow queries> db.setProfilingLevel( n , slowms=100ms )

n=0 profiler offn=1 record queries longer than slowms n=2 record all queries

> db.system.profile.find()

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Operational Tips

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Background index builds

db.foo.ensureIndex( { user : 1 } , { background : true } )

Caveats:• still resource-intensive• will build in foreground on secondaries

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Minimizing impact on Replica Sets

for (s in secondaries) s.restartAsStandalone() s.buildIndex() s.restartAsReplSetMember() s.waitForCatchup()

p.stepDown()p.restartAsStandalone()p.buildIndex()p.restartAsReplSetMember()

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Absent or suboptimal indexes are the most common avoidable

MongoDB performance problem...

...so take some time and get your indexes right!

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Thanks!

(and thanks to Richard Kreuter for the slides)

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