1. NoSQL Know Your Enemy Shelly Noll SRT Solutions, Ann Arbor, MI
@shellynoll

2. Disclaimer There is lots of disagreement about this topic
Everything I say could be wrong depending on who you ask
Even if it’s right today, it will probably be wrong soon

3. What is nosql? It is a database management system
with the following features:
Queries do not use SQL
Doesn’t guarantee ACID properties
Fault-tolerant, distributed architecture
Coined by Carlo Strozzi in 1998 to describe a database he created that did not expose a SQL interface
Term was co-opted in 2009 when Eric Evans from Rackspace and Johan Oskarsson from Last.fm organized an event to discuss the growing trend of open-source, distributed databases

4. Consistency Availability Partition Tolerance CAP Theorem
All nodes see the same data at the same time
Availability
Every request receives a success/failure response
Partition Tolerance
Operates despite failure of part of the system
A distributed system can satisfy any two of these guarantees at the same time, but not all three
A couple of basic theories we need to talk about to understand the difference between relational and noSQL databases

5. ACID vs BASE Atomicity Consistency Isolation Durability
Basically Available Soft State Eventual Consistency
Instead of ACID properties found in relational database, nosql has something different. What is the opposite of a an acid? Nosql databases exhibit BASE properties
All or nothing (atomicity)
Data must be adhere to schema and rules (consistency)
No transaction interferes with another (isolation)
Permanency (durability)
an application works basically all the time (basically available)
does not have to be
consistent all the time (soft-state)
but will be in some known-state state eventually (eventual consistency,

6. ACID vs BASE ACID BASE Strong consistency Isolation Focus on “commit”
Nested transactions
Conservative (pessimistic)
Difficult to change schema
Weak consistency
Best effort
Approximate answer OK
Aggressive (optimistic)
Simpler
Faster
Easier to change
Consistency – adheres to the rules
Isolation – transactions do not interfere
Dr. Eric A. Brewer (2000)

7. Why Did This Happen??? Data-related reasons
Avoidance of unneeded complexity
Avoidance of object-relational mapping
Avoidance of making schema changes
Performance-related reasons
Higher throughput
Horizontal scalability and running on commodity hardware
Complexity and cost of setting up database clusters
Complexity – consider Twitter – You have users, status updates, relationships between users, direct messages and not much else
Object-relational mapping – object-oriented programmers have to create a layer in their applications that take the data from the database and transforms it into objects the application can use – also creates the overhead in syncing the state of the objects in memory with the entities in the database – expensive, time-consuming, nosql APIs look more like the objects programmers use
NoSQL compromises reliability for better performance

8. Mongodb example

9. Database Types
Key-Value
Graph
Document Store
Column Store

10. Database type disagreement
Stephen Yen
Ken North
Rick Cattel
Jonathan Ellis
Wikipedia
Amazon SimpleDB
Entity-Attribute-Value Data Store
Document Store
Apache Hadoop
Tabular
Cassandra
Wide Columnar Store
Extensible Record Store
Columnfamily
Eventually-Consistent Key-Value Store
Google Bigtable
Key-Value Store
HBase
HyperTable
Redis
Data-Structures Server
Collection
Key-Value Cache

11. Key-Value Data is stored in a schema-less way with a key and a value
Limited querying capability
Values can usually be of any data type, or could be a serialized object
Variations
Eventually consistent
Hierarchical
Ordered
Key-value cache (in RAM or on disk)
Memcached Redis Riak Basho Voldemort

12. Graph Based on graph theory
Data is stored as nodes (entities), properties, and edges (relationship)
Allows for calculations between nodes
Shortest distance between nodes
Analysis of relationships
Bigdata CloudGraph Neo4j OrientDB

13. Document Store Stores document-oriented or semi-structured data
Documents may be encoded as XML, YAML, JSON, BSON, PDF, MS Word, MS Excel, etc.
Documents are not required to adhere to a standard schema
Offers a query language to retrieve documents based on content
Amazon SimpleDB Apache CouchDB Lotus Notes MongoDB

14. Column store Stores data in a tabular format
Different names for the exact same thing
Wide Columnar Store
ColumnFamily
Tabular
Entity-Attribute-Value Data Store
Extensible Record Store
Multivalue
BigTable
Apache Hadoop
Cassandra
Google Bigtable
Hbase
HyperTable

15. Popular databases Type Created By Language Used By Bigtable
Column store
Google
Google File System
Cassandra
Apache
Java
Netflix, Twitter, Constant Contact, Reddit, Digg
CouchDB
Document store
Erlang
Various Facebook applications
Hadoop
Yahoo!
HBase
Facebook's messaging platform
HyperTable
Zvents
C++
Baidu
Memcached
Key-value store
Danga C
LiveJournal, YouTube, Reddit, Zynga, Facebook, Twitter
MongoDB
10gen
MTV Networks, Craigslist, Foursquare
Neo4j
Graph
Neo Technology
Adobe, Cisco
Redis
Vmware
ANSI C
Github, Craigslist, Blizzard, Digg, Twitter, Flickr, Stackoverflow
Riak
Basho
Erlang, C, C++, JavaScript
Comcast, Mozilla, AOL, Ask.com
SimpleDB
Amazon
Voldemort
LinkedIn

16. Comparisons Key-Value Stores High None Column Stores Moderate Low
Performance
Scalability
Flexibility
Complexity
Key-Value Stores
High
None
Column Stores
Moderate
Low
Document Stores
Variable (High)
Graph Databases
Variable
Relational Databases
Ben Scofield (2010)

17. Where wouldn’t you use nosql?
Data is critical to the function of the business/application
Data has strong and/or slowly changing schema
Need true transactional capabilities
Need data mining capabilities
Set-based updates
Banking apps Healthcare apps Enterprise apps

18. Where would you use nosql?
Heavy read/write
Single-user
Simple, non- structured data
Lack of interconnected data
Doesn’t matter if it takes a while to get the data consistent
Data is not critical
Social networking apps Mobile apps

19. Future of nosql UnSQL A query language for NoSQL databases
Does not have data definition language
Acquisition of NoSQL databases by larger companies
Similar to what happened in the BI space where IBM, Microsoft, and HP acquired smaller players

20. Shelly Noll
SRT Solutions, Ann Arbor, MI Twitter