This document discusses MongoDB as a fast alternative to relational database management systems (RDBMS). It covers some of the limitations of RDBMS and object databases that MongoDB aims to address, such as speed, scalability, and flexibility in schema. Some key features of MongoDB discussed include using JSON and BSON documents, avoiding normalization, and ensuring business logic is handled separately from data storage. Challenges with MongoDB are also mentioned, such as lack of transactions and immature tooling.

1. MongoDB
The fast (don’t think about this like an
RDBMS) database alternative.
Wednesday, August 24, 2011

2. THE ROAD TO MONGODB
PROJECT
RDBMS + ORM
BUSINESS LOGIC DOMAIN MODEL
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3. Object Database
Attempted to accommodate the OO languages
they were meant to support
Inheritance, associations, etc.
THEY FAILED
Too slow, too complicated, doomed!
Wednesday, August 24, 2011

4. The Google Problem
Relational databases are slow and don’t scale well
Distribution
Replication
ACID compliance
Vendor lock in
Atypical domain model didn’t require ACID
transactions
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5. The Google Solution
Big Table
http://labs.google.com/papers/bigtable.html
email the authors, they’re great!
Petabytes of data across thousands of
commodity servers
Data separate from logic
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6. JSON & BSON
Clear, concise and very AJAX friendly
Flexible and nestable (you can nest documents)
Ubiquitous language support
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7. JSON & BSON cont.
BSON <> JSON translation can be awkward when
dealing with language specific data types
Dates, _id fields, etc.
Well suited to text/numeric data, not much else
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8. Common pitfalls
How to create a view or join
Enforce data integrity
Define data structure
Document vs. Table(s)
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9. Normalization
RDBMS Normalization is intended to...
Reduce complexity
Discourage duplication of code/data
Provide logical mappings for domain elements
In mongodb, the document tells the whole story,
including a definition of the data
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10. Maturity Issues
No log file rolling mechanism
Awkward tools and querying
Accustomed to tabular data (try .pretty())
Very little (if any) feedback regarding outcome of
commands
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11. Queues
Many steps, each in it’s
own collection
Keep high volume
collections small & fast
Maintain large amounts
of statistical data in
low volume collections
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12. Ready for Business?
Business logic must
exist in code (including
querying data)
Transaction boundaries
and concurrency must
exist in code
Speed and reliability
are a go (so far :)
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13. Questions?
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