Schema design with MongoDB (Dwight Merriman)

Schema Design with MongoDB
Dwight Merriman
CEO
10gen

What is document-oriented?
JSON objects
Not relational
Not OODB
Database schema != program “schema”

Terms
Row -> JSON document
Tables -> collections
Indexes -> index
Join -> embedding and linking

Choose a schema that
makes queries easy
makes queries fast
facilitates atomicity
facilitates sharding

Key question: embed vs. link
“Contains relationship” : embed
Embed = “pre-joined”
Links: client/server turnarounds
On a close call, embed. Use rich documents.
Note the 4MB object size limit
Arbitrary limit but pushes one towards good designs

Map-Reduce

{ _id : … }
Should be:
Unique
Invariant
Ideally, not reused (delete; insert)
ObjectID type often best for a sharded collection

Trees
mongodb.org/display/DOCS/Trees+in+MongoDB

Single “Table” Inheritance Works Well
> t.find()
{ type:’irregular-shape’, area:99 }
{ type:’circle’, area:3.14, radius:1 }
{ type:’square’, area:4, d:2 }
{ type:’rect’, area:8, x:2, y:4 }
> t.find( { radius : { $gt : 2.0 } } )
> t.ensureIndex( { radius : 1 } ) // fine

(1) Full tree in one document
{
comments: [
{by: "mathias", text: "...",
replies: []}
{by: "eliot", text: "...",
replies: [
{by: "mike", text: "...", replies: []}
]}
]
}
Pros:
Single document to fetch per page
One location on disk for whole tree
You can see full structure easily
Cons:
Hard to search
Hard to get back partial results
4MB limit

(2) Parent Links
> t = db.tree1;
> t.find()
{ "_id" : 1 }
{ "_id" : 2, "parent" : 1 }
{ "_id" : 3, "parent" : 1 }
{ "_id" : 4, "parent" : 2 }
{ "_id" : 5, "parent" : 4 }
{ "_id" : 6, "parent" : 4 }
> // find children of node 4
> t.ensureIndex({parent:1})
> t.find( {parent : 4 } )
{ "_id" : 5, "parent" : 4 }
{ "_id" : 6, "parent" : 4 }
// hard to get all descendants

(3) Array of Ancestors
> t = db.mytree;
> t.find()
{ "_id" : "a" }
{ "_id" : "b", "ancestors" : [ "a" ], "parent" : "a" }
{ "_id" : "c", "ancestors" : [ "a", "b" ], "parent" : "b" }
{ "_id" : "d", "ancestors" : [ "a", "b" ], "parent" : "b" }
{ "_id" : "e", "ancestors" : [ "a" ], "parent" : "a" }
{ "_id" : "f", "ancestors" : [ "a", "e" ], "parent" : "e" }
{ "_id" : "g", "ancestors" : [ "a", "b", "d" ], "parent" : "d" }
> t.ensureIndex( { ancestors : 1 } )
> // find all descendents of b:
> t.find( { ancestors : 'b' })
{ "_id" : "c", "ancestors" : [ "a", "b" ], "parent" : "b" }
{ "_id" : "d", "ancestors" : [ "a", "b" ], "parent" : "b" }
{ "_id" : "g", "ancestors" : [ "a", "b", "d" ], "parent" : "d" }
> // get all ancestors of f:
> anc = db.mytree.findOne({_id:'f'}).ancestors
[ "a", "e" ]
> db.mytree.find( { _id : { $in : anc } } )
{ "_id" : "a" }
{ "_id" : "e", "ancestors" : [ "a" ], "parent" : "a" }

Atomicity
Atomicity at the document level
$operators
Compare and swap

Compare and Swap
> t=db.inventory
> s = t.findOne( {sku:'abc'} )
> --s.qty;
> t.update({_id:s._id, qty:qty_old}, s);
> db.getLastError()
{"err" : , "updatedExisting" : true , "n" : 1 , "ok" : 1} // it worked

Compare and Swap
> t=db.inventory
> --s.qty;
> db.getLastError()
Oops?

Compare and Swap - Better
> t=db.inventory
> obj_old = Object.extend({}, s);
> --s.qty;
> // t.update({_id:s._id, qty:qty_old}, s);
> t.update( obj_old , s);
> print( db.getLastError().ok ? “worked” : “try again” );

Compare and Swap – versions
update( { _id : myid, ver : last_ver },
{ $set : { x : “abc”, y : 99 },
$inc : { ver : 1 }
} )

Of course, don’t both doing CASwhen you don’t have to
> t.update( { sku : “abc”,
qty : {$gt:0} },
{ $inc : { qty : -1 } }
)
db.getLastError()
{ "updatedExisting" : true , "n" : 1 , "ok" : 1 }
{ "updatedExisting" : false , "n" : 0 , "ok" : 1 }

Compare and Swap
> t=db.inventory
> --s.qty;
> db.getLastError()

Sharding and Schemas
Shard key selection
Restrictions on unique indexes
Consider using may collections when that is natural
10 SN

Other Considerations
Cappedcollections

Questions?
Get involved with the MongoDB project!
Coding, drivers, frameworks, documentation, translation, consulting, evangelism, suggestions, vote on jira…spread the word.

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Schema design with MongoDB (Dwight Merriman)

by MongoSF on May 07, 2010

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