In this slidecast, Dave Rosenthal from FoundatoinDB, the first commercially available NoSQL database that supports global ACID transactions (found only in relational systems) and multi-data models. Database technologies are undergoing rapid evolution, with new approaches being actively explored after decades of relative stability. Dave Rosenthal believes that the next generation of NoSQL will continue to employ shared-nothing, distributed architectures with fault tolerance and scalability. However, they will aggressively explore the strong-consistency permitted by the CAP theorem and will support ACID, allowing for multiple data models. Rosenthal speaks to the history of NoSQL, problems that have arisen due to a lack of data consistency in NoSQL, his views on the CAP Theorem and where it's all headed. Learn more: https://foundationdb.com Watch the presentation video: http://youtu.be/1KsZKRcgmuU

1. NoSQL and ACID
david.rosenthal@foundationdb.com
Twitter: @foundationdb

2. NoSQL‟s Motivation
Make it easy to build and deploy
applications.
 Ease of scaling and operation
 Fault tolerance
 Many data models
 Good price/performance
X ACID transactions

3. The case for ACID in NoSQL

4. Bugs don‟t appear under concurrency
• ACID means isolation.
• Reason locally rather than globally.
– If every transaction maintains an
invariant, then multiple clients running
any combination of concurrent
transactions also maintain that invariant.
• The impact of each client is isolated.

5. Isolation means strong abstractions
• Example interface:
– storeUser(name, SSN)
– getName(SSN)
– getSSN(name)
• Invariant: N == getName(getSSN(N))
– Always works with single client.
– Without ACID: Fails with concurrent clients.
– With ACID: Works with concurrent clients.

6. Abstractions
Abstractions built on a scalable, fault
tolerant, transactional foundation
inherit those properties.
And are easy to build…

7. Remove/decouple data models
• A NoSQL database with ACID can
provide polyglot data models and APIs.
– Key-value, graph, column-oriented,
document, relational, publish-subscribe,
spatial, blobs, ORMs, analytics, etc…
• Without requiring separate physical
databases. This is a huge ops win.

8. So why no ACID NoSQL?

9. Historical Perspective: 2008
In 2008, NoSQL doesn’t really exist yet.
2008

10. Databases in 2008
NoSQL emerges to replace scalable
sharding/caching solutions that had already
thrown out consistency.
• BigTable
• Dynamo
• Voldemort
• Cassandra

11. The CAP2008 theorem
“Pick 2 out of 3”
- Eric Brewer

12. The CAP2008 theorem
“Data inconsistency in large-scale
reliable distributed systems has to be
tolerated … [for performance and to
handle faults]”
- Werner Vogles (CTO Amazon.com)

13. The CAP2008 theorem
“The availability property means that
the system is „online‟ and the client of
the system can expect to receive a
response for its request.”
- Wrong descriptions all over the
web

14. CAP2008 Conclusions?
• Scaling requires distributed design
• Distributed requires high availability
• Availability requires no C
So, if we want scalability we have to
give up C, a cornerstone of ACID,
right?

15. Thinking about CAP2008
CAP availability != High availability

16. Fast forward to CAP2013
“Why ’2 out of 3’ is misleading”
“CAP prohibits… perfect
availability”
- Eric Brewer

17. Fast forward to CAP2013
“Achieving strict consistency can come
at a cost in update or read latency,
and may result in lower throughput…”
- Werner Vogles (Amazon CTO)

18. Fast forward to CAP2013
“…it is better to have application
programmers deal with performance
problems due to overuse of transactions
as bottlenecks arise, rather than always
coding around the lack of transactions.“
- Google (Spanner)

19. ACID + NoSQL!

20. The ACID NoSQL plan
• Maintain both scalability and fault tolerance
• Leverage CAP2013 and deliver a CP system
with true global ACID transactions
• Enable abstractions and many data models
• Deliver high per-node performance

21. Decomposition approach
Decompose the processing pipeline of a
traditional ACID DB into individual
stages.

22. Decomposition approach
Decompose the processing pipeline of a
traditional ACID DB into individual stages.
• Stages:
– Accept client transactions
– Apply concurrency control
– Write to transaction logs
– Update persistent data representation
• Upside: Performance
• Downside: “Ugly” and complex architecture
needs to solve tough problems for each stage

23. The performance surprise
ACID:
10% of work
NoSQL:
90% of work

24. FoundationDB
Database software:
• Scalable
• Fault tolerant
• Transactional
• Ordered key-value API
• Layers
ACID Key-value API
Graph SQL Doc Event

25. A vision for NoSQL
• The next generation should maintain
– Scalability and fault tolerance
– High performance
• While adding
– ACID transactions
– Data model flexibility

26. Thank you
david.rosenthal@foundationdb.com
Twitter: @foundationdb