As the {no,new,not only} SQL market cements itself as a backbone for today's Internet Enterprise applications, it behoves us both as technologists and enterprise leaders, to understand "whys" of design choices of NoSQL systems. This talk will reflect upon the rationale of design choices within traditional RDBMS systems (e.g. 3NF, joins, transactions) and contrast them with the similar foundational concepts within NoSQL systems such as Cassandra.

1. The Whys of NoSQL

2. 1 Jargon Galore
2 Schema
3 Modeling and Internals
4 Deployment
5 Conclusion
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3
SQL Jargon

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4
NoSQL Noise?

5. Schema
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Rigid
Schema
Schema
Free
Schema
on read
Schema
Easy to change
In flexible Writes are schema free,
reads are freaking slow
Reads/Writes are schema aware
Schema changes are O(1) operations
BLOBs
Too Slow
Optimized for Agility of change when needed, not theoretical extremes

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Normalization, Joins, Referential Integrity
Database normalization is the process of
organizing the columns (attributes) and
tables (relations) of a relational database
to minimize data redundancy.
Referential integrity is a property of data
which, when satisfied, requires every
value of one column of a table to exist
as a value of another column in a
different table.A JOIN is a means for combining
fields from two tables (or more) by
using values common to each.
Source - https://en.wikipedia.org/

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Not all Data Access is equal
1:168K random vs.
sequential
1:10 random vs.
sequential
Source - https://queue.acm.org/detail.cfm?id=1563874

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Disk Density
Source http://silvertonconsulting.com/blog/2010/04/22/save-the-planet-buy-fatter-disks-and-flash/#sthash.sh2nwqtX.dpbs

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$0.01
$0.10
$1.00
$10.00
$100.00
$1,000.00
$10,000.00
$100,000.00
$1,000,000.00
201420132010200520001995199019851980
HDD Price / GB
Minimize Data
Redundancy?
Disk Price / GB

10. OS Cache
C* Read and Write paths
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Memtable 1 Memtable 2 Memtable N
SSTable 1 SSTable 2 SSTable N
Commit Log
Persistent
Storage
Off Heap
In Process Memory
Reads (memtable + N SSTables where N >= 1)
Mandatory Flush
Writes
Max # of SSTables = N
(based on compaction)
Creation of new memtable during flush operation (cleanup
tombstones, cleanup token ranges, etc.)
Time (memtable_flush_in_ms controls the frequency)
Accounting
SSTable
Compacted
RANDOM ACCESS
SEQUENTIAL ACCESS

11. Execution Engine
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12. Key takeaways
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Optimal utilization of physical resources (random
access, sequential IO and CPU)
No Read before Write (well mostly!)
Plan for Compaction (like commercial paper, you need
a regular pay back)
De-Normalize for optimal application response (use
2NF instead of 3NF)

13. Deployment Semantics
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R/W R
Single BoxDR
GR
ScaleUpby.
Sharding
Replication
GR + DR
San
Francisco
New York
Stockholm
DC1 DC2

14. Linear Scaling
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http://www.datastax.com/apache-cassandra-leads-nosql-benchmark
End Point Report Excerpt: Balanced Read/Write YCSB Test

15. So what's the catch?
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Conclusion
Best in class performance, backed by physics
Enables pragmatic business agility,
Delivering delightful customer experience,
Always on, Linear Scale architecture delivering optimal ROI

17. Thank you