Practical Ruby Projects with MongoDB - Ruby Midwest

PRACTICAL
PROJECTS WITH

Saturday, July 17, 2010

WHO AM I?

Alex Sharp
Lead Developer at OptimisDev

@ajsharp (on the twitters)
alexjsharp.com (on the ‘tubes)
github.com/ajsharp (on the ‘hubs)


MAJOR POINTS
• Brief intro to MongoDB

• Practical Projects

• Making the case for Mongo (time permitting)


BRIEF INTRO


WHAT IS MONGODB?
mongodb is a high-performance, schema-less,
document-oriented database


STRENGTHS


SCHEMA-LESS


SCHEMA-LESS
• Great for rapid, iterative, agile development


SCHEMA-LESS
• Great for rapid, iterative, agile development

• Makes things possible that in an rdbms are either

a. impossibly hard

b. virtually impossible

c. way harder than they should be


DOCUMENT-ORIENTED


DOCUMENT-ORIENTED
• Mongo stores documents, not rows

• documents are stored as binary json


DOCUMENT-ORIENTED


• Allows for a rich query syntax


DOCUMENT-ORIENTED


• Allows for a rich query syntax

• Embedded documents eliminate many modeling headaches


BIG DATA


BIG DATA
• Built to scale horizontally into the cloudz


BIG DATA
• Built to scale horizontally into the cloudz

• Auto-sharding

• set a shard-key, a cluster of nodes, go

• still in alpha, but production-ready soon


FAST WRITES


FAST WRITES
• Mongo writes are “ﬁre-and-forget”

• you can get a response with getLastError()


FAST WRITES


• In-place updates


FAST WRITES


• In-place updates

• Lot’s of db commands for fast updates


AGGREGATION
Map/reduce for aggregation


READS FAST TOO
Optimize reads with indexes, just like an rdbms


WEAKNESSES


JOINS
nope.


MULTI-DOCUMENT
TRANSACTIONS
no-sir-ee.


RELATIONAL
INTEGRITY
not a chance.


PRACTICAL
PROJECTS


1. Accounting Application
2. Capped Collection Logging
3. Blogging app
4. Reporting app


GENERAL LEDGER
ACCOUNTING
APPLICATION


THE OBJECT MODEL
ledger

*
transactions

*
entries


THE OBJECT MODEL
# Credits Debits

Line item { 1
{ :account => “Cash”,
:amount => 100.00 }
{ :account => “Notes Pay.”,
:amount => 100.00 }

Ledger Entries
Line item { 2
{ :account => “A/R”,
:amount => 25.00 }
{ :account => “Gross Revenue”,
:amount => 25.00 }


THE OBJECT MODEL


THE OBJECT MODEL
• Each ledger line item belongs to a ledger


THE OBJECT MODEL

• Each ledger line item has two ledger entries

• must have at least one credit and one debit

• credits and debits must balance


THE OBJECT MODEL

• Each ledger line item has two ledger entries

• must have at least one credit and one debit

• credits and debits must balance

• These objects must be transactional


SQL-BASED OBJECT MODEL
@debit = Entry.new(:account => 'Cash',
:amount => 100.00, :type => 'credit')

@credit = Entry.new(:account => 'Notes Pay.',
:amount => 100.00, :type => 'debit')

@line_item = LineItem.new :ledger_id => 1, :entries => [@debit, @credit]





In a SQL schema, we need a database
transaction to ensure multi-row atomicity





Remember: we have to create 3 new records in
the database here


MONGO-FIED MODELING
@line_item = LineItem.new(:ledger_id => 1,
:entries => [
{:account => 'Cash', :type => 'debit', :amount => 100},
{:account => 'Notes pay.', :type => 'debit', :amount => 100}
]
)


MONGO-FIED MODELING
:entries => [
]
)

This is the perfect case for embedded documents.


MONGO-FIED MODELING
:entries => [
]
)

We will never have more than a few entries (usually two)


MONGO-FIED MODELING
:entries => [
]
)
Embedded docs are perfect for “contains”
many relationships


MONGO-FIED MODELING
:entries => [
]
)
DB-level transactions no-longer needed.
Mongo has single document atomicity.


LESSONS


LESSONS
• Simpliﬁed, de-normalized data model
• Objects modeled differently in Mongo than SQL


LESSONS
• Simpliﬁed, de-normalized data model
• Objects modeled differently in Mongo than SQL
• Simpler data model + no transactions = WIN


LOGGING WITH
CAPPED
COLLECTIONS


BASELESS STATISTIC
95% of the time logs are NEVER used.


MOST LOGS ARE ONLY USED
WHEN SOMETHING GOES
WRONG


CAPPED COLLECTIONS
• Fixed-sized, limitedoperation, auto age-out collections
(kinda like memcached, except persistent)

• Fixed insertion order

• Super fast (faster than normal writes)

• Ideal for logging and caching


GREAT. WHAT’S THAT MEAN?
We can log additional pieces of arbitrary data
effortlessly due to Mongo’s schemaless nature.


THIS IS AWESOME


QUERY. ANALYZE. PROFIT.


ALSO A REALLY HANDY
TROUBLESHOOTING TOOL


User-reported errors are difﬁcult to
diagnose


Wouldn’t it be useful to see the complete click-
path?


BUNYAN
http://github.com/ajsharp/bunyan

Thin ruby layer around a
MongoDB capped collection


BUNYAN
http://github.com/ajsharp/bunyan

Still needs a middleware api.
Want to contribute?


require 'bunyan'

Bunyan::Logger.configure do |c|
c.database 'my_bunyan_db'
c.collection "#{Rails.env}_bunyan_log"
c.size 1073741824 # 1.gigabyte
end


PAPERMILL
http://github.com/ajsharp/papermill

SINATRA FRONT-END TO BUNYAN


BLOGGING
APPLICATION


BLOGGING APPLICATION

Much easier to model with Mongo than a relational database



A post has an author




A post has many tags





A post has many comments




A post has many comments
Instead of JOINing separate tables,
we can use embedded documents.


MONGOMAPPER


MONGOMAPPER
•MongoDB “ORM” developed by John Nunemaker


MONGOMAPPER
•Very similar syntax to DataMapper


MONGOMAPPER
•Declarative rather than inheritance-based


MONGOMAPPER
•Declarative rather than inheritance-based
•Very easy to drop into rails


MONGOMAPPER
require 'mongo'
connection = Mongo::Connection.new.db("#{Rails.env}_app")

class Post
include MongoMapper::Document

belongs_to :author, :class_name => "User"

key :title, String, :reuqired => true
key :body, String, :required => true
key :author_id, Integer, :required => true
key :published_at, Time
key :published, Boolean, :default => false
key :tags, Array, :default => []
timestamps!
end


REPORTING APP


REPORTING IS HARD
• Synchronization

• Data, application API, schema

• Schema synchronization is the hard one


WHY MONGO?
• Aggregation support with Mongo’s map/reduce support

• Mongo is good (and getting better) at handling big data sets

• Schemaless is perfect for a ﬂattened data set


Amazon EC2 Rackspace

Transform
MongoDB Server

Reporting App
WAN Main App

MySQL


CLIENT REQUESTS Amazon EC2

MongoDB Server

map/reduce

Reporting App

User requests
Client request GET /reports/some_report


TRANFORMATION
Data needs to extracted from the the main app,
transormed (i.e. ﬂattened) into some other form,
and loaded into the reporting app for
aggregation


TRANSFORMATION
Rackspace Amazon EC2

1. GET /object_dependencies
Transform MongoDB Server
{ :visit => [ :insurances, :patients ] }
2. read from
active record

MySQL
2. write to mongo Reporting App
Main App


MAKING THE CASE


THE PROBLEMS OF SQL


A BRIEF HISTORY OF SQL

• Developed at IBM in early 1970’s.

• Designed to manipulate and retrieve data stored in relational
databases


A BRIEF HISTORY OF SQL

• Developed at IBM in early 1970’s.

• Designed to manipulate and retrieve data stored in relational
databases

this is a problem


WHY??


WE DON’T WORK WITH
DATA...


WE WORK WITH OBJECTS


WE DON’T CARE
ABOUT
STORING DATA


WE CARE ABOUT
PERSISTING STATE


DATA != OBJECTS


THEREFORE...


RELATIONAL DBS ARE AN
ANTIQUATED TOOL FOR OUR
NEEDS


OK, SO WHAT?
SQL schemas are designed for storing and
querying data, not persisting objects.


To reconcile this
mismatch, we have
ORM’s


Object Relational
Mappers


OK, SO WHAT?
We need ORMs to bridge the gap
(map) between our native Ruby
object model and a SQL schema


We’re forced to create relationships for data
when what we really want is properties for our
objects.


THIS IS NOT EFFICIENT


@alex = Person.new(
:name => "alex",
:stalkings => [
Friend.new("Jim"),
Friend.new("Bob")]
)


Native ruby object

<Person:0x10017d030 @name="alex",
@stalkings=
[#<Friend:0x10017d0a8 @name="Jim">,
#<Friend:0x10017d058 @name="Bob">
]>


JSON/Mongo Representation

@alex.to_json
{ name: "alex",
stalkings: [{ name: "Jim" }, { name: "Bob" }]
}


SQL Schema Representation
people:
- name

stalkings:
- name
- stalker_id


people:
- name

stalkings: What!?
- name
- stalker_id


people:
- name

stalkings:
what’s a stalker_id?
- name
- stalker_id


people:
- name

stalkings:
this is our sql mapping
- name
- stalker_id


people:
- name

stalkings: this is the
- name “pointless” join
- stalker_id


Ruby -> JSON -> SQL
@stalkings=
Ruby [#<Friend:0x10017d0a8 @name="Jim">,
]>

@alex.to_json
JSON { name: "alex",
}

people:
- name
SQL stalkings:
- name
- stalker_id


Ruby -> JSON -> SQL
@stalkings=
Ruby [#<Friend:0x10017d0a8 @name="Jim">,
]>

@alex.to_json
JSON { name: "alex",
}

people:
- name Feels like we’re having
SQL stalkings: to work too hard here
- name
- stalker_id


You’re probably thinking...
“Listen GUY, SQL isn’t that bad”


Maybe.


But we can do much, much better.


NEEDS FOR A PERSISTENCE
LAYER:


LAYER:
1. To persist the native state of our objects


LAYER:

2. Should NOT interfere w/ application development


LAYER:

2. Should NOT interfere w/ application development

3. Provides features necessary to build modern web apps


NEXT STEPS


USING MONGO W/ RUBY
• Ruby mongo driver

• MongoMapper (github.com/jnunemaker/mongomapper)

• MongoID (github.com/durran/mongoid)

• Many other ORM/ODM’s

• mongo-sequel (github.com/jeremyevans/sequel-mongo)

• moneta (github.com/wycats/moneta)


MONGOMAPPER
• DataMapper-like API

• good for moving from a sql schema to mongo

• easy to drop into rails

• works with rails 3


MONGOID
• DataMapper-like API

• uses ActiveModel, so familiar validation syntax

• more opinionated embedded docs

• easy to drop into rails

• rails 2 - mongoid 1.x

• rails 3 - mongoid 2.x


QUESTIONS?


THANKS!


Practical Ruby Projects with MongoDB - Ruby Midwest

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