A17 indexing and query optimization by paul pederson

Indexing and Query Optimization
Paul Pedersen

Monday, October 15, 12

What’s in store

• What are indexes?

• Picking the right indexes.

• Creating indexes in MongoDB

• Troubleshooting


Indexes are the single biggest
tunable performance factor
in MongoDB.


Absent or suboptimal indexes are
the most common avoidable
MongoDB performance problem.


So what problem do indexes solve?


How do you find a chicken recipe?

• An unindexed cookbook might be quite a
page turner.

• Probably not what you want, though.


I know, I’ll use an index!


Let’s imagine a simple index
ingredient page

aardvark 790
... ...

beef 190, 191, 205, ...

... ...

chicken 182, 199, 200, ...

chorizo 497, ...

... ...

zucchini 673, 986, ...


How do you find a quick chicken recipe?


Let’s imagine a compound index
ingredient cooking page
time
... ... ...

chicken 15 min 182, 200

chicken 25 min 199

chicken 30 min 289,316,320

chicken 45 min 290, 291,
354
... ... ...


Consider the ordering of index keys

Aardvark, 20 min Chicken, 15 min Zuchinni, 45 min
Chicken, 25 min
Chicken, 30 min
Chicken, 45 min


How about a low-calorie chicken recipe?


Let’s imagine a 2nd compound index

ingredient calories page

... ... ...

chicken 250 199, 316

chicken 300 289,291

chicken 425 320

... ... ...


How about a quick, low-calorie recipe?


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?


Consider the ordering of index keys

250 cal, 250 cal, 300 cal, 300 cal, 425 cal,
25 min 30 min 25 min 45 min 30 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!


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

• A is constant, B is a range 

• A is constant, order by B 

• A is range, B is constant/range 

• B is constant/range, A unspecified 


It’s really that straightforward.


B-Trees (Bayer & McCreight ’72)



13



13

Queries, Inserts, Deletes: O(log n)


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.


On to MongoDB!


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


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

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


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" }


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 }


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).




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

• The maximum index key size is 1024 bytes.


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/


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


Advanced Options


Arrays: the powerful “multiKey” index
{ title : “Chicken Noodle Soup”,
ingredients : [“chicken”, “noodles”] }

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

ingredients page
chicken 42
... ...
noodles 42
... ...


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 ...


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

No index entries for docs without “email” field


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] } } )


Troubleshooting


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.


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


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


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


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

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

> db.system.profile.find()


Operational Tips


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

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


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()


Absent or suboptimal indexes are
the most common avoidable
MongoDB performance problem...

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


Thanks!


A17 indexing and query optimization by paul pederson

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A17 indexing and query optimization by paul pederson