Indexing and Query Optimisation

#MongoMelbourne

Indexing and Query
Optimisation
Stephen Steneker
Support Engineer, 10gen Australia

Agenda
• What are indexes?
• Why do I need them?
• Working with indexes in MongoDB
• Optimise your queries
• Avoiding common mistakes

What are indexes?
Imagine you’re looking for a recipe in a cookbook
ordered by recipe name. Looking up a recipe by
name is quick and easy.

What are indexes?
• How would you find a recipe using chicken?
• How about a 250-350 calorie recipe using
chicken?

KRISTINE TO INSERT IMAGE OF COOKBOOK

Consult the index!

1 2 3 4 5 6 7

Linked List

4

2 6

1 3 5 7

Finding 7 in Tree

Indexes in MongoDB are B-trees

Queries, inserts and deletes:
O(log(n)) time

Indexes are the single
biggest tuneable
performance factor in
MongoDB

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

Why do I need indexes?
A brief story

Working with Indexes in
MongoDB

How do I create indexes?
// Create an index if one does not exist
db.recipes.createIndex({ main_ingredient: 1 })

// The client remembers the index and raises no errors
db.recipes.ensureIndex({ main_ingredient: 1 })

* 1 means ascending, -1 descending

What can be indexed?
// Multiple fields (compound key indexes)
db.recipes.ensureIndex({
main_ingredient: 1,
calories: -1
})

// Arrays of values (multikey indexes)
{
name: 'Chicken Noodle Soup’,
ingredients : ['chicken', 'noodles']
}

db.recipes.ensureIndex({ ingredients: 1 })

What can be indexed?
// Subdocuments
{
name : 'Pavlova',
contributor: {
name: 'Ima Aussie',
id: 'ima123'
}
}

db.recipes.ensureIndex({ 'contributor.id': 1 })

db.recipes.ensureIndex({ 'contributor': 1 })

How do I manage indexes?
// List a collection's indexes
db.recipes.getIndexes()
db.recipes.getIndexKeys()

// Drop a specific index
db.recipes.dropIndex({ ingredients: 1 })

// Drop all indexes and recreate them
db.recipes.reIndex()

// Default (unique) index on _id

Background Index Builds
// Index creation is a blocking operation that can take a long time
// Background creation yields to other operations
db.recipes.ensureIndex(
{ ingredients: 1 },
{ background: true }
)

Options
• Uniqueness constraints (unique, dropDups)
• Sparse Indexes
• Geospatial (2d) Indexes
• TTL Collections (expireAfterSeconds)

Uniqueness Constraints
// Only one recipe can have a given value for name
db.recipes.ensureIndex( { name: 1 }, { unique: true } )

// Force index on collection with duplicate recipe names – drop the
duplicates
{ name: 1 },
{ unique: true, dropDups: true }
)

* dropDups is probably never what you want

Sparse Indexes
// Only documents with field calories will be indexed
{ calories: -1 },
{ sparse: true }
)
// Allow multiple documents to not have calories field
{ name: 1 , calories: -1 },
{ unique: true, sparse: true }
)
* Missing fields are stored as null(s) in the index

Geospatial Indexes
// Add latitude, longitude coordinates
{
name: '10gen Sydney’,
loc: [ 151.21037, -33.88456 ]
}
// Index the coordinates
db.locations.ensureIndex( { loc : '2d' } )

// Query for locations 'near' a particular coordinate
db.locations.find({
loc: { $near: [ 151.21, -33.88 ] }
})

TTL Collections
// Documents must have a BSON UTC Date field
{ 'status' : ISODate('2012-11-09T11:44:07.211Z'), … }

// Documents are removed after 'expireAfterSeconds' seconds
{ submitted_date: 1 },
{ expireAfterSeconds: 3600 }
)

Limitations
• Collections can not have > 64 indexes.

• Index keys can not be > 1024 bytes (1K).

• The name of an index, including the namespace, must be <
128 characters.
• Queries can only use 1 index*

• Indexes have storage requirements, and impact the
performance of writes.
• In memory sort (no-index) limited to 32mb of return data.

Profiling Slow Ops
db.setProfilingLevel( n , slowms=100ms )

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

db.system.profile.find()

* The profile collection is a capped collection, and fixed in size

The Explain Plan (Pre Index)
db.recipes.find( { calories:
{ $lt : 40 } }
).explain( )
{
"cursor" : "BasicCursor" ,
"n" : 42,
"nscannedObjects” : 12345
"nscanned" : 12345,
...
"millis" : 356,
...
}
* Doesn’t use cached plans, re-evals and resets cache

The Explain Plan (Post Index)
db.recipes.find( { calories:
{ $lt : 40 } }
).explain( )
{
"cursor" : "BtreeCursor calories_-1" ,
"n" : 42,
"nscannedObjects": 42
"nscanned" : 42,
...
"millis" : 0,
...
}
* Doesn’t use cached plans, re-evals and resets cache

The Query Optimiser
• For each "type" of query, MongoDB
periodically tries all useful indexes
• Aborts the rest as soon as one plan wins
• The winning plan is temporarily cached for
each “type” of query

Manually Select Index to Use
// Tell the database what index to use
db.recipes.find({
calories: { $lt: 1000 } }
).hint({ _id: 1 })

// Tell the database to NOT use an index
db.recipes.find(
{ calories: { $lt: 1000 } }
).hint({ $natural: 1 })

Use Indexes to Sort Query
Results
// Given the following index
db.collection.ensureIndex({ a:1, b:1 , c:1, d:1 })

// The following query and sort operations can use the index
db.collection.find( ).sort({ a:1 })
db.collection.find( ).sort({ a:1, b:1 })

db.collection.find({ a:4 }).sort({ a:1, b:1 })
db.collection.find({ b:5 }).sort({ a:1, b:1 })

Indexes that won’t work for
sorting query results
// Given the following index
db.collection.ensureIndex({ a:1, b:1, c:1, d:1 })

// These can not sort using the index
db.collection.find( ).sort({ b: 1 })
db.collection.find({ b: 5 }).sort({ b: 1 })

Index Covered Queries
// MongoDB can return data from just the index
db.recipes.ensureIndex({ main_ingredient: 1, name: 1 })

// Return only the ingredients field
db.recipes.find(
{ main_ingredient: 'chicken’ },
{ _id: 0, name: 1 }
)

// indexOnly will be true in the explain plan
db.recipes.find(
{ main_ingredient: 'chicken' },
{ _id: 0, name: 1 }
).explain()
{
"indexOnly": true,
}

Trying to Use Multiple
Indexes
// MongoDB can only use one index for a query
db.collection.ensureIndex({ a: 1 })
db.collection.ensureIndex({ b: 1 })

// Only one of the above indexes is used
db.collection.find({ a: 3, b: 4 })

Compound Key Mistakes
// Compound key indexes are very effective
db.collection.ensureIndex({ a: 1, b: 1, c: 1 })

// But only if the query is a prefix of the index

// This query can't effectively use the index
db.collection.find({ c: 2 })

// …but this query can
db.collection.find({ a: 3, b: 5 })

Low Selectivity Indexes
db.collection.distinct('status’)
[ 'new', 'processed' ]

db.collection.ensureIndex({ status: 1 })

// Low selectivity indexes provide little benefit
db.collection.find({ status: 'new' })

// Better
db.collection.ensureIndex({ status: 1, created_at: -1 })
db.collection.find(
{ status: 'new' }
).sort({ created_at: -1 })

Regular Expressions
db.users.ensureIndex({ username: 1 })

// Left anchored regex queries can use the index
db.users.find({ username: /^joe smith/ })

// But not generic regexes
db.users.find({username: /smith/ })

// Or case insensitive queries
db.users.find({ username: /Joe/i })

Negation
// Indexes aren't helpful with negations
db.things.ensureIndex({ x: 1 })

// e.g. "not equal" queries
db.things.find({ x: { $ne: 3 } })

// …or "not in" queries
db.things.find({ x: { $nin: [2, 3, 4 ] } })

// …or the $not operator
db.people.find({ name: { $not: 'John Doe' } })

Choosing the right
indexes is one of the
most important things
you can do as a
MongoDB developer so
take the time to get your
indexes right!

#MongoMelbourne

Thank you
Stephen Steneker
Support Engineer, 10gen

Indexing and Query Optimisation

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