2011 Mongo FR - Indexing in MongoDB

MongoDB
Indexing and Query Optimizer Details

Antoine GirbalMongo FRMarch 23, 2011

What will we cover?

Many details of how indexing and the query optimizer work

A full understanding of these details is not required to use mongo, but this knowledge can be helpful when making optimizations.

Well discuss functionality of Mongo 1.8 (for our purposes pretty similar to 1.6 and almost identical to 1.7 edge).

Much of the material will be presented through examples.

Diagrams are to aid understanding some details will be left out.

Btree (conceptual diagram)

123456789

{_id:4,x:6}

Find One Document

db.c.find( {x:6} ).limit( 1 )

Index {x:1}

Find One Document

1234567896?

{_id:4,x:6}

Find One Document

> db.c.find( {x:6} ).limit( 1 ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 1,"nscannedObjects" : 1,"n" : 1,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,"indexBounds" : {"x" : [[6,6]]}}Uses a btree cursor to find the object. Index ranges are around a single value.

Find One Document

123456789

6?

{_id:4,x:6}

Find One Document

123456789

6?

{_id:4,x:6}

Find One Document

1234566696?

{_id:4,x:6}Now we have duplicate x values

Find One Document

123456669

6?

{_id:4,x:6}

Equality Match

db.c.find( {x:6} )

Index {x:1}

Several documents to be returned

9Equality Match

123456666?

{_id:4,x:6}{_id:5,x:6}{_id:1,x:6}

Equality Match

> db.c.find( {x:6} ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 3,"nscannedObjects" : 3,"n" : 3,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,"indexBounds" : {"x" : [[6,6]]}}

Equality Match

123456669

6?

Full Document Matcher

db.c.find( {x:6,y:1} )

Index {x:1}

Object content needs to be checked

9Full Document Matcher

123456666?

{y:4,x:6}{y:5,x:6}{y:1,x:6}

Full Document Matcher

> db.c.find( {x:6,y:1} ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 3,"nscannedObjects" : 3,"n" : 1,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,"indexBounds" : {"x" : [[6,6]]}}Documents for all matching index keys are scanned, but only one document matched on non index keys.

Range Match

db.c.find( {x:{$gte:4,$lte:7}} )

Index {x:1}

8Range Match

123456794

7{_id:4,x:[8,9]}

8

Multikeys

> db.c.find( {x:{$gt:7}} ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 2,"nscannedObjects" : 2,"n" : 1,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : true,"indexOnly" : false,"indexBounds" : {"x" : [[7,1.7976931348623157e+308]]}}All keys in valid range are scanned, but the matcher rejects duplicate documents making n == 1.

Multikeys

123456789

Range Types

Explicit inequality

db.c.find( {x:{$gt:4,$lt:7}} )

db.c.find( {x:{$gt:4}} )

db.c.find( {x:{$ne:4}} )

Regular expression prefix

db.c.find( {x:/â/} )

Data typedb.c.find( {x:/a/} )

Range Types

db.c.find( {x:/â/} )"indexBounds" : {"x" : [["a","b"],[/â/,/â/]]}

2 ranges scanned of 2 different types: string and regex

Range Types

db.c.find( {x:/a/} )"indexBounds" : {"x" : [["",{}],[/a/,/a/]]}

Here the index only helps to restrict type, not efficient in practice

Set Match

db.c.find( {x:{$in:[3,6]}} )

Index {x:1}

8Set Match

123456793,

6

Set Match

> db.c.find( {x:{$in:[3,6]}} ).explain(){"cursor" : "BtreeCursor x_1 multi","nscanned" : 3,"nscannedObjects" : 2,"n" : 2,"millis" : 8,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,"indexBounds" : {"x" : [[3,3],[6,6]]}}Why is nscanned 3? This is an algorithmic detail, when there are disjoint ranges for a key nscanned may be higher than the number of matching keys.

Set Match

123456789

All Match

db.c.find( {x:{$all:[3,6]}} )

Index {x:1}

8All Match

123456793?

{_id:4,x:[3,6]}

All Match

> db.c.find( {x:{$all:[3,6]}} ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 1,"nscannedObjects" : 1,"n" : 1,"millis" : 0,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : true,"indexOnly" : false,"indexBounds" : {"x" : [[3,3]]}}The first entry in the $all match array is always used for index bounds. Note this may not be the least numerous indexed value in the $all array.

All Match

123456789

Limit

db.c.find( {x:{$lt:6},y:3} ).limit( 3 )

Index {x:1}

8Limit

123456796? db.c.find( {x:{$lt:6},y:3} ).limit( 3 ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 4,"nscannedObjects" : 4,"n" : 3,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : true,"indexOnly" : false,"indexBounds" : {"x" : [[-1.7976931348623157e+308,6]]}}Scan until three matches are found, then stop.

Skip

db.c.find( {x:{$lt:6},y:3} ).skip( 3 )

Index {x:1}

8Skip

123456796? db.c.find( {x:{$lt:6},y:3} ).skip( 3 ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 5,"nscannedObjects" : 5,"n" : 1,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : true,"indexOnly" : false,"indexBounds" : {"x" : [[-1.7976931348623157e+308,6]]}}All skipped documents are scanned.

Sort

db.c.find( {x:{$lt:6}} ).sort( {x:1} )

Index {x:1}

Sorting along index key uses index btree ordering

8Sort

123456796? db.c.find( {x:{$lt:6},y:3} ).sort( {x:1} ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 5,"nscannedObjects" : 5,"n" : 4,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : true,"indexOnly" : false,"indexBounds" : {"x" : [[-1.7976931348623157e+308,6]]}}Find uses the btree cursor to easily sort data

Sort

db.c.find( {x:{$lt:6}} ).sort( {y:1} )

Index {x:1}

Using non-indexed key to sort data will need to scan & order

8Sort

123456796? db.c.find( {x:{$lt:6},y:3} ).sort( {y:1} ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 5,"nscannedObjects" : 5,"n" : 4,"scanAndOrder" : true,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : true,"indexOnly" : false,"indexBounds" : {"x" : [[-1.7976931348623157e+308,6]]}}

Sort and scanAndOrder

With scanAndOrder sort, all documents must be touched even if there is a limit spec.

With scanAndOrder, sorting is performed in memory and the memory footprint is constrained by the limit spec if present.

Count

Count uses the same indexed but only scans in the index, not the object data in storage

With some operators the full document must be checked. Some of these cases:

$all

$size

array match

Negation - $ne, $nin, $not, etc.

With current semantics, all multikey elements must match negation constraints

Multikey de duplication works without loading full document

Covered Indexes

db.c.find( {x:6}, {x:1,_id:0} )

Index {x:1}

Id would be returned by default, but isnt in the index so we need to exclude to return only indexed fields.

Covered Indexes

> db.c.find( {x:6}, {x:1,_id:0} ).explain(){"cursor" : "BtreeCursor x_1","nscanned" : 1,"nscannedObjects" : 1,"n" : 1,"millis" : 0,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : true,"indexBounds" : {"x" : [[6,6]]}}IndexOnly is true, and isMultiKey must be false. Currently we set isMultiKey to true the first time we save a doc where the field is a multikey array.

Two Equality Bounds

db.c.find( {x:5,y:c} )

Index {x:1,y:1}

Two Equality Bounds

?

5

c

1

b

3

d

4

g

5

d

5

f

6

c

7

a

9

b

5

c

Two Equality Bounds

> db.c.find( {x:5,y:'c'} ).explain(){"cursor" : "BtreeCursor x_1_y_1","nscanned" : 1,"nscannedObjects" : 1,"n" : 1,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,"indexBounds" : {"x" : [[5,5]],"y" : [["c","c"]]}}2 Ranges applied to narrow down the data to scan.

Two Equality Bounds

?

1

b

3

d

4

g

5

c

5

d

5

f

5

c

6

c

7

a

9

b

Two Set Bounds

db.c.find( {x:{$in:[5,9]},y:{$in:[c,f]}} )

Index {x:1,y:1}

Two Set Bounds

,

5

c

1

b

3

d

4

g

5

d

5

f

6

c

7

a

9

f

5

c

5

f,

9

c

9

f,

Two Set Bounds

> db.c.find( {x:{$in:[5,9]},y:{$in:['c','f']}} ).explain(){"cursor" : "BtreeCursor x_1_y_1 multi","nscanned" : 5,"nscannedObjects" : 3,"n" : 3,"millis" : 0,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,...

"indexBounds" : {"x" : [[5,5],[9,9]],"y" : [["c","c"],["f","f"]]}}

Disjoint $or Criteria

db.c.find( {$or:[{x:5},{y:d}]} )

Indexes {x:1}, {y:1}

Does 2 sequential find for each clause

Must not return same document twice, so it checks whether it satisfies previous clause


?

1

b

3

d

4

g

5

d

6

a

7

e

9

f

5

c

d

7

g

5

?

1

b

3

d

4

g

5

d

6

a

7

e

9

f

5

c

7

g


> db.c.find( {$or:[{x:5},{y:'d'}]} ).explain(){"clauses" : [{"cursor" : "BtreeCursor x_1","nscanned" : 2,"nscannedObjects" : 2,"n" : 2,"millis" : 0,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,"indexBounds" : {"x" : [[5,5]]}},

{"cursor" : "BtreeCursor y_1","nscanned" : 2,"nscannedObjects" : 2,"n" : 1,"millis" : 1,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,"indexBounds" : {"y" : [["d","d"]]}}],"nscanned" : 4,"nscannedObjects" : 4,"n" : 3,"millis" : 1}

Unindexed $or Clause

db.c.find( {$or:[{x:5},{y:d}]} )

Index {x:1} (no index on y)

Unindexed $or Clause

Since y is not indexed, we must do a full collection scan to match y:d. Since a full scan is required, we dont use the index on x to match x:5.

> db.c.find( {$or:[{x:5},{y:'d'}]} ).explain(){"cursor" : "BasicCursor","nscanned" : 9,"nscannedObjects" : 9,"n" : 3,"millis" : 0,"nYields" : 0,"nChunkSkips" : 0,"isMultiKey" : false,"indexOnly" : false,"indexBounds" : {}}

Automatic Index Selection
(Query Optimizer)

Optimal Index

find( {x:5} )

Index {x:1}

Index {x:1,y:1}

find( {x:5} ).sort( {y:1 } )Index {x:1,y:1}

find( {} ).sort( {x:1} )Index {x:1}

find( {x:{$gt:1,$lt:7}} ).sort( {x:1} )Index {x:1}

Optimal Index

Rule of Thumb

No scanAndOrder

All fields with index useful constraints are indexed

If there is a range or sort it is the last field of the index used to resolve the query

If multiple optimal indexes exist, one chosen arbitrarily.

Multiple Candidate Indexes

find( {x:4,y:a} )

Index {x:1} or {y:1}?

find( {x:4} ).sort( {y:1} )Index {x:1} or {y:1}?

Note: {x:1,y:1} is optimal

find( {x:{$gt:2,$lt:7},y:{$gt:a,$lt:f}} )Index {x:1,y:1} or {y:1,x:1}?


The only index selection criterion is nscanned

find( {x:4,y:a} )

Index {x:1} or {y:1} ?

If fewer documents match {y:a} than {x:4} then nscanned for {y:1} will be less so we pick {y:1}

find( {x:{$gt:2,$lt:7},y:{$gt:b,$lt:f}} )Index {x:1,y:1} or {y:1,x:1} ?

If fewer distinct values of 2 < x < 7 than distinct values of b < y < f then {x:1,y:1} chosen (rule of thumb)


The only index selection criterion is nscanned

Pretty good, but doesnt cover every case, eg

Overhead of using an index versus doing a collection scan

Cost of scanAndOrder vs ordered index

Cost of loading full document vs just index key

Cost of scanning adjacent btree keys vs non adjacent keys/documents

Competing Indexes

At most one query plan per index

Run in interleaved fashion

Plans kept in a priority queue ordered by nscanned. We always continue progress on plan with lowest nscanned.

Competing Indexes

Run until one plan returns all results or enough results to satisfy the initial query request (based on soft limit spec / data size requirement for initial query).

We only allow plans to compete in initial query. In getMore, we continue reading from the index cursor established by the initial query.

Learning a Query Plan

When an index is chosen for a query the querys pattern and nscanned are recorded

find( {x:3,y:c} )

{Pattern: {x:equality, y:equality}, Index: {x:1}, nscanned: 50}

find( {x:{$gt:5},y:{$lt:z}} )

{Pattern: {x:gt bound, y:lt bound}, Index: {y:1}, nscanned: 500}

Learning a Query Plan

When a new query matches the same pattern, the same query plan is used

find( {x:5,y:z} )

Use index {x:1}

find( {x:{$gt:20},y:{$lt:b}} )

Use index {y:1}

Un-Learning a Query Plan

100 writes to the collection

Indexes added / removed

Bad Plan Insurance

If nscanned for a new query using a recorded plan is much worse than the recorded nscanned for an earlier query with the same pattern, we start interleaving other plans with the current plan.

Currently much worse means 10x

Thanks!

Feature Requestsjira.mongodb.org

Supportgroups.google.com/group/mongodb-user

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