1. Christian Stark and Odbayar Badamjav
NoSQL: Dynamic DB
Christian Stark and Odbayar Badamjav

2. History of NoSQL
Eric Evans of Rackspace, a committer on the Cassandra project, introduced the term NoSQL in 2009.
Amazon released research paper on Amazon Dynamo in 2007.
MongoDB started in 2007 as a part of an open source cloud
Facebook's open source Cassandra project (now maintained by Apache) in 2008.
The advent of distributed and parallel computing gave rise to alternatives to relational databases.
As cloud computing became more affordable and mainstream, home grown NoSQL DBs went open source.

3. What is NoSQL?

4. What is NoSQL?
Not to be confused with the NoSQL database system (a RDBMS).
"NoSQL" or "not only SQL" is a class of databases that are more broad and encompassing than SQL-based databases.
Most relational databases are subsets of functionality that "NoSQL" databases can offer.

5. More about NoSQL
It doesn't have a fixed data model, or predefined schema.
Is not necessarily a "one-fits-all" solution.
Leaves room for more tailored solutions, on a per- application basis.
Many different types of implementations
SQL
NoSQL
tables
Collections
Rows
Documents
Columns
Fields

6. Usually favorable in distributed, large data settings
Why use NoSQL?
Usually favorable in distributed, large data settings
High availability
High fault tolerance
Open source
Easy to implement
Wide variety of solutions both as a service, and as different implementations
The appeal of NoSQL is that it handles mass quantities of data, quickly, across a cluster of servers that share resources, making it both fast and reliable. The fact that it's open source keeps costs down, and it's easier to use than conventional databases

7. Brewer's CAP Theorem Consistency Availability Partition Tolerance
The theorem states that it is impossible to have all three of these aspects present in a distributed database system. It states that, at most, two may be present if compromises are made in the other. For NoSQL, the trade off is consistency for partition tolerance and availability. Most solutions lie somewhere in a continuum between ACID and BASE.

8. Brewer CAP Theorem

9. ACID or BASE? BASE (NoSQL)
Basic Availability -- Be able to expect a timely, or quick response
Soft-state -- What the database replies is good enough for now.
Eventual Consistency -- Code is written to handle each type of inconsistency as they are discovered.
ACID (RDBMS)
Atomicity -- All or nothing, per transaction
Consistency -- Transactions leave in consistent state
Isolation -- One transaction does not interfere with another.
Durability -- Transactions persist restarts, other interruptions in database engine

10. Amazon Stock Quantity Example
If two users were to place the same item in their carts, and purchase the same product within a short period of time when there was only one of the item left, what should happen?
RDBMS would ensure consistency, a process that would take more time than is tolerable for a customer. If it returned an answer in time, the answer it would return would be a success to one, and failure to another, and possibly offer to backorder the item for the customer.

11. Continued Amazon Example
A NoSQL/Eventual Consistency approach would most likely accept both purchases. When the system discovers the inconsistency in the data, it alert the customer that ordered the item last that it has been placed on back order.
Companies have found that there can be severe penalties for future traffic when there are delays in making these types of transactions.

12. Real world usage What companies or organizations use NoSQL?
Google (BigTable)
eBay (Hadoop)
Amazon (Dynamo)
Twitter (FlockDB, a graph-type db, and Cassandra)
Yahoo (Hadoop)
Facebook (Hadoop)
Craigslist (MongoDB)
Netflix (Apache's Cassandra)
Many companies use NoSQL and RDBMS together for different parts of applications.

13. How it works: A look at MongoDB
Features:
Dynamic schemas, JSON-style documents
Full indexing for all fields/attributes
Scales horizontally
Fast, in-place updates to data
GridFS, store files of any size, distributed
Ad-hoc queries allow for dynamic queries that are similar to those of RDBMS
"Sharding" -- Auto scaling for balancing and fault tolerance
Official Drivers exist for Java, Ruby, PHP, Perl, C, C++, Erlang, Haskell, Javascript, Python, and Scala. Many community supported drivers available.

14. Database Name
CouchDB
MongoDB
MySQL
Data Model
Document-Oriented (JSON)
Document-Oriented (BSON)
Relational
Data Types
string,number,boolean,array,object
string, int, double, boolean, date, bytearray, object, array, others
link
Large Objects (Files)
Yes (attachments)
Yes (GridFS)
Blobs
Horizontal partitioning scheme
CouchDB Lounge
Auto-sharding
Partitioning
Replication
Master-master (with developer supplied conflict resolution)
Master-slave and replica sets
Master-slave, multi-master, and circular replication
Object(row) Storage
One large repository
Collection-based
Table-based
Query Method
Map/reduce of javascript functions to lazily build an index per query
Dynamic; object-based query language
Dynamic; SQL
Secondary Indexes
Yes

15. Database Name
CouchDB
MongoDB
MySQL
Interface
REST
Native drivers ; REST add-on
Native drivers
Server-side batch data manipulation ?
Map/Reduce, server-side javascript
Yes (SQL)
Written in
Erlang
C++
Concurrency Control
MVCC
Update in Place
Geospatial Indexes
GeoCouch
Yes
Spatial extensions
Distributed Consistency Model
Eventually consistent (master-master replication with versioning and
version reconciliation)
Strong consistency. Eventually consistent reads from secondaries are
available.
Atomicity Single document Single document Yes - advanced

16. Available Hosted Services
Amazon's DynamoDB - pay for what you use
Amazon's SimpleDB - pay for what you use
MongoLab (MongoDB) - free plan
IrisCouch (CouchDB) - free for modest use
Cloudant (CouchDB) - free plan
Many more are available with free starter plans as well.
Amazon and most others allow you to pay for only what you use, and save costs.

17. NoSQL Projects
Apache's CouchDB -- Document-store type, incremental replication with "bi-directional conflict detection and resolution"
Apache's Cassandra -- linear scalability and high availability
mongoDB -- "scalable, high performance open source, NoSQL database"
Apache's HBase -- sits on Hadoop/HDFS
Redis -- In memory, distributed key/value store, with optional persistence
Google's BigTable -- Available as Google App Engine Datastore; tabular (3-dimensional mapping)

18. Conclusion
NoSQL can be a valuable tool for large, distributed data sets that need to scale and have high read/write ability.
NoSQL is not a replacement for RDBMS, but a supplement for it.
Most applications use both for different use cases.
NoSQL can be a simple, resilient database that is easy to deploy.
More information available. See Wikipedia.