Three Things You Need to Know About Document Data Modeling in NoSQL

Three things you need
to know about data modeling
Matthew Revell
matthew@couchbase.com, @matthewrevell
Lead Developer Advocate, Couchbase
1

Data modeling books
©2014 Couchbase, Inc. 3

Three query methods
Application layer computation Off-load computation
Predictable queries Key-value: pre-computed answers Views
Ad-hoc queries N1QL and key-value with manual
indexes
N1QL and views

Key-value
Pre-compute answers asynchronously
Store object state
Choose when to embed data and when to refer to it
Design your keys well

We’re used to asking questions

Key-value look-ups give us a library of answers

Answer-oriented databases
http://martinfowler.com/bliki/AggregateOrientedDatabase.html

Airline bookings

Embed or refer?
How much should we denormalize?

An e-commerce order

The same order in a document

Embedded vs. referred data

You should embed data when:
 Speed trumps all else
 Slow moving data
 No duplication
 Application layer can keep multiple copies of same data in sync
When to embed data

You should refer to data when:
 Consistency is a priority
 The data has large growth potential
When to refer to data

Key design is as important as document design.
There are three broad types of keys:
 Human readable/deterministic: e.g., an email address
 Computer generated/random: e.g., a UUID
 Compound: e.g., a UUID with a deterministic portion
Three ways to build a key

Human readable/deterministic key
public class user {
private String name;
private String email;
private String streetAddress;
private String city;
private String country;
private String postCode;
private String telephone;
private Array orders;
private Array productsViewed;
}
{
"name": "Matthew Revell",
"address": "11-21 Paul Street",
"city": "London",
"postCode": "EC2A 4JU",
"telephone": "44-20-3837-9130",
"orders": [ 1, 9, 698, 32 ],
“productsViewed”: [8, 33, 99, 100]
}
Key: matthew@couchbase.com

Random/computer-generated key
{
"email": "matthew@couchbase.com",
"city": "London",
"telephone": "44-20-3837-9130",
"orders": [ 1, 9, 698, 32 ],
}
Key: 1001

Using counters to generate keys
Application
user_id = incr(“counter_key")
add(user_id, data)
Creating a new user
add(email_address, user_id)
Application
key = get("matthew@couchbase.com")
get(key)
Finding a user by email address

Multiple look-up documents
u::count
1001
u::1001
{ "name": “Matthew Revell",
"facebook_id": 16172910,
"email": “matthew@couchbase.com”,
“password”: ab02d#Jf02K
"created_at": "5/1/2012 2:30am",
“facebook_access_token”: xox0v2dje20,
“twitter_access_token”: 20jffieieaaixixj }
fb::16172910
1001
nflx::2939202
1001
twtr::2920283830
1001
em::matthew@couchbase.com
1001
em::matthew@understated.co.uk
1001
uname::mrevell
1001

Compound keys
Compound keys are look-up documents with predictable names.
They continue the discussion of embedded versus referred data.

Compound keys: an example
u::1001
{
"city": "London",
"telephone": "44-20-3837-9130",
"orders": [ 1, 9, 698, 32 ],
}

u::1001
{
"city": "London",
"telephone": "44-20-3837-9130",
"orders": [ 1, 9, 698, 32 ]
}
u::1001::productsviewed
{
"productsList": [
8, 33, 99, 100
]
}

u::1001
{
"city": "London",
"telephone": "44-20-3837-9130",
"orders": [ 1, 9, 698, 32 ]
}
{
"productsList": [
8, 33, 99, 100
]
}
p::8
{
id": 1,
"name": "T-shirt",
"description": "Red Couchbase shirt",
"quantityInStock": 99,
"image": "tshirt.jpg”
}

u::1001
{
"city": "London",
"telephone": "44-20-3837-9130",
"orders": [ 1, 9, 698, 32 ]
}
{
"productsList": [
8, 33, 99, 100
]
}
p::8
{
id": 1,
"name": "T-shirt",
"description": "Red Couchbase shirt",
"quantityInStock": 99
}
p::8::img
“http://someurl.com/tshirt.jpg”

N1QL and Views!
The world beyond key-value

We’re not in Kansas any more

N1QL and views
N1QL Views
Ad-hoc querying Predictable queries
Nested JSON in and unnested JSON
out
Number crunching
Large growth clusters Multidimensional and
geospatial queries

N1QL
Indexes
Schema types and document types
Keyspaces

Indexes
• You always need at least one index
• PRIMARY index gives you all of the keys in the bucket
• Secondary indexes enable ad-hoc querying at speed

Indexes

Indexes
NEW Global Secondary Indexes Views
N1QL query to create a new index
JavaScript map/reduce
Option to run dedicated indexing and
query nodes
Queries always run on database nodes
ForestDB-backed Couchstore-backed
Supports multidimensional and geospatial
queries

Maintaining the schema and working with keyspaces
JOINs work on primary keys and secondary keys.
They JOIN across and within keyspaces (for now, that means buckets).
Airlines:
airline_24 ← Key (“primary key”)
{
"id": "24",
"type": "airline",
"name": "American Airlines",
"iata": "AA",
"icao": "AAL",
"callsign": "AMERICAN",
"country": "United States",
"active": "Y"
}
Routes:
route_5966 ← Key
{
"id": "5966",
"type": "route",
"airline": "AA",
"airlineid": "airline_24", ← This is the foreign key
"sourceairport": "MCO",
"destinationairport": "SEA",
"stops": "0",
"equipment": "737",
"schedule": [...
]
}

Offloading computation to N1QL
N1QL allows you to choose which elements are returned at the top level.
This is incredibly useful because it reduces the need to write complex
parsing logic:
• Within the application
• In client-side, front-end frameworks (Angular/Backbone etc.)
SELECT a.name, s.flight, s.utc, r.sourceairport, r.destinationairport,
r.equipment FROM `travel-sample` r UNNEST r.schedule s JOIN `travel-
sample` a ON KEYS r.airlineid WHERE r.sourceairport='LHR' AND
r.destinationairport='LAX' AND s.day=2 ORDER BY a.name

Thanks!
Matthew Revell
matthew@couchbase.com
@matthewrevell

Three Things You Need to Know About Document Data Modeling in NoSQL

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