Mongoseattle indexing-2010-07-27
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The document discusses indexing and query optimization in MongoDB. It covers indexing basics, when indexes can and cannot be used, creating and maintaining indexes, and using explain() to understand query plans. The query optimizer is empirical and tries different plans to select the fastest, and hint() can be used to force a specific plan.
![When can indexes be used?
In short, if you can envision how the index might get used, it
probably is. These will all use an index on x:
db.collection.find( { x: 1 } )
db.collection.find( { x :{ $in : [1,2,3] } } )
db.collection.find( { x : { $gt : 1 } } )
db.collection.find( { x : /^a/ } )
db.collection.count( { x : 2 } )
db.collection.distinct( { x : 2 } )
db.collection.find().sort( { x : 1 } )
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![Some array examples
The following queries will use an index on x, and will match
documents whose x attribute is the arraay [2,10]
db.collection.find({ x : 2 })
db.collection.find({ x : 10 })
db.collection.find({ x : { $gt : 5 } })
db.collection.find({ x : [2,10] })
db.collection.find({ x : { $in : [2,5] }})
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![Geospatial indexes
Geospatial indexes are a sort of special case; the operators that can
take advantage of them can only be used if the relevant indexes
have been created. Some examples:
db.collection.find({ a : [50, 50]}) finds a
document with this point for a.
db.collection.find({a : {$near : [50, 50]}})
sorts results by distance.
db.collection.find({
a:{$within:{$box:[[40,40],[60,60]]}}}})
db.collection.find({
a:{$within:{$center:[[50,50],10]}}}})
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![Schema/index relationships
Sometimes, question isn’t “given the shape of these documents,
how do I index them?”, but “how might I shape the data so I can
take advantage of indexing?”
// Consider a schema that uses a list of
// attribute/value pairs:
db.c.insert({ product : "SuperDooHickey",
attribs : [ { stock : 50,
price : 29.95,
... } ] });
db.c.ensureIndex({ attribs : 1 });
// All attribute queries can use one index.
db.c.find( { attribs : { stock : { $gt : 0 } } } )
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Mongoseattle indexing-2010-07-27
- 1. Indexing,Query Optimization, the Query Optimizer Richard M Kreuter 10gen Inc. [email protected] July 27, 2010 MongoDB – Indexing and Query Optimiz(ation—er)
- 2. Indexing Basics Indexes are tree-structured sets of references to your documents. The query planner can employ indexes to efficiently enumerate and sort matching documents. MongoDB – Indexing and Query Optimiz(ation—er)
- 3. However, indexing strikes people as a gray art As is the case with relational systems, schema design and indexing go hand in hand... ... but you also need to know about your actual (not just predicted) query patterns. MongoDB – Indexing and Query Optimiz(ation—er)
- 4. Some indexing generalities A collection may have at most 64 indexes. A query may only use 1 index (at present). Indexes entail additional work on inserts, updates, deletes. MongoDB – Indexing and Query Optimiz(ation—er)
- 5. Creating Indexes The id attribute is always indexed. Additional indexes can be created with ensureIndex(): // Create an index on the user attribute db.collection.ensureIndex({ user : 1 }) // Create a compound index on // the user and email attributes db.collection.ensureIndex({ user : 1, email : 1 }) // Create an index on the favorites // attribute, will index all values in list db.collection.ensureIndex({ favorites : 1 }) // Create a unique index on the user attribte db.collection.ensureIndex({user:1}, {unique:true}) // Create an index in the background. db.collection.ensureIndex({user:1}, {background:true}) MongoDB – Indexing and Query Optimiz(ation—er)
- 6. Index maintenance // Drops an index on x db.collection.dropIndex({x:1}) // drops all indexes db.collection.dropIndexes() // Rebuild indexes (need for this will go away in 1.6) db.collection.reIndex() MongoDB – Indexing and Query Optimiz(ation—er)
- 7. Indexes are smart about data types and structures Indexes on attributes whose values are of different types in different documents can speed up queries by skipping documents where the relevant attribute isn’t of the appropriate type. Indexes on attributes whose values are lists will index each element, speeding up queries that look into these attributes. (You really want to do this for querying on tags.) MongoDB – Indexing and Query Optimiz(ation—er)
- 8. When can indexes be used? In short, if you can envision how the index might get used, it probably is. These will all use an index on x: db.collection.find( { x: 1 } ) db.collection.find( { x :{ $in : [1,2,3] } } ) db.collection.find( { x : { $gt : 1 } } ) db.collection.find( { x : /^a/ } ) db.collection.count( { x : 2 } ) db.collection.distinct( { x : 2 } ) db.collection.find().sort( { x : 1 } ) MongoDB – Indexing and Query Optimiz(ation—er)
- 9. Trickier cases where indexes can be used db.collection.find({ x : 1 }).sort({ y : 1 }) will use an index on y for sorting, if there’s no index on x. (For this sort of case, use a compound index on both x and y in that order.) db.collection.update( { x : 2 } , { x : 3 } ) will use an index on x (but older mongodb versions didn’t permit $inc and other modifiers on indexed fields.) MongoDB – Indexing and Query Optimiz(ation—er)
- 10. Some array examples The following queries will use an index on x, and will match documents whose x attribute is the arraay [2,10] db.collection.find({ x : 2 }) db.collection.find({ x : 10 }) db.collection.find({ x : { $gt : 5 } }) db.collection.find({ x : [2,10] }) db.collection.find({ x : { $in : [2,5] }}) MongoDB – Indexing and Query Optimiz(ation—er)
- 11. Geospatial indexes Geospatial indexes are a sort of special case; the operators that can take advantage of them can only be used if the relevant indexes have been created. Some examples: db.collection.find({ a : [50, 50]}) finds a document with this point for a. db.collection.find({a : {$near : [50, 50]}}) sorts results by distance. db.collection.find({ a:{$within:{$box:[[40,40],[60,60]]}}}}) db.collection.find({ a:{$within:{$center:[[50,50],10]}}}}) MongoDB – Indexing and Query Optimiz(ation—er)
- 12. When indexes cannot be used Many sorts of negations, e.g., $ne, $not. Tricky arithmetic, e.g., $mod. Most regular expressions (e.g., /a/). Expressions in $where clauses don’t take advantage of indexes. map/reduce can’t take advantage of indexes (mapping function is opaque to the query optimizer). As a rule, if you can’t imagine how an index might be used, it probably can’t! MongoDB – Indexing and Query Optimiz(ation—er)
- 13. Schema/index relationships Sometimes, question isn’t “given the shape of these documents, how do I index them?”, but “how might I shape the data so I can take advantage of indexing?” // Consider a schema that uses a list of // attribute/value pairs: db.c.insert({ product : "SuperDooHickey", attribs : [ { stock : 50, price : 29.95, ... } ] }); db.c.ensureIndex({ attribs : 1 }); // All attribute queries can use one index. db.c.find( { attribs : { stock : { $gt : 0 } } } ) MongoDB – Indexing and Query Optimiz(ation—er)
- 14. Index sizes Of course, indexes take up space. For many interesting databases, real query performance will depend on index sizes; so it’s useful to see these numbers. db.collection.stats() shows indexSizes, the size of each index in the collection. db.collection.totalIndexSize() displays the size of all indexes in the collection. MongoDB – Indexing and Query Optimiz(ation—er)
- 15. explain() It’s useful to be able to ensure that your query is doing what you want it to do. For this, we have explain(). Query plans that use an index have cursor type BtreeCursor. db.collection.find({x:{$gt:5}}).explain() { "cursor" : "BtreeCursor x_1", ... "nscanned" : 12345, ... "n" : 100, "millis" : 4, ... } MongoDB – Indexing and Query Optimiz(ation—er)
- 16. explain(), continued If the query plan doesn’t use the index, the cursor type will be BasicCursor. db.collection.find({x:{$gt:5}}).explain() { "cursor" : "BasicCursor", ... "nscanned" : 12345, ... "n" : 42, "millis" : 4, ... } MongoDB – Indexing and Query Optimiz(ation—er)
- 17. Query Optimizer MongoDB’s query optimizer is empirical, not cost-based. To test query plans, it tries several in parallel, and records the plan that finishes fastest. If a plan’s performance changes over time (e.g., as data changes), the database will reoptimize (i.e., retry all possible plans). MongoDB – Indexing and Query Optimiz(ation—er)
- 18. Hinting the query plan Sometimes, you might want to force the query plan. For this, we have hint(). // Force the use of an index on attribute x: db.collection.find({x: 1, ...}).hint({x:1}) // Force indexes to be avoided! db.collection.find({x: 1, ...}).hint({$natural:1}) MongoDB – Indexing and Query Optimiz(ation—er)
- 19. Going forward www.mongodb.org — downloads, docs, community [email protected] — mailing list #mongodb on irc.freenode.net try.mongodb.org — web-based shell 10gen is hiring. Email [email protected]. 10gen offers support, training, and advising services for mongodb MongoDB – Indexing and Query Optimiz(ation—er)