A stable data model provides numerous advantages in developing Big Data and NoSQL systems, especially when sharing data over the cloud. Adopting an immutable model eases much of the pain of achieving consistency, especially at great scale. There can be trade-offs however that you need to be aware of. This webinar will present details and examples of immutable data models as applied to various NoSQL systems, including MongoDB, Cloudant, Riak and Cassandra. The emphasis will be on the impact to application designers and architects, as well as the technical trade-offs and advantages. The discussion will be well-grounded in real world examples from within and beyond the enterprise.

1. Consistency in
Distributed Systems
Mike Miller
Co-Founder, Chief Scientist
@mlmilleratmit

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Want to learn more?
P. Bailis: “Coordination and the Art of Scaling”

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{Introductions: ‘Me’}
Background -- Big Systems

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MobileBig Data
=> Stress models for consistency, transactional reasoning

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This is your problem when…
!
… data doesn’t fit on one server.
… data replicated between servers (e.g. read slaves).
… data spread between data centers.
… state spread across more than one device (mobile!)
… mixed workloads with concurrency.
… state spread across more than one process.
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This is now everyone’s problem
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Good news — market response:
NewSQL, NoSQL, Cloud, …
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Let’s view this from the
developer’s perspective
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ships with a mobile strategy

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{Install: ‘Cloudant’}
You do this: We give you:
https://<username>.cloudant.com
Done!
Sign Up
Step 1 Step 2 Step 3
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{Cloudant: ‘API’}
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JSON
Documents
Primary
Index
Secondary
Indexes
Search &
Geospatial

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{Write: ‘Local’, Sync: ‘Later’}
Embedded,
Edge, Satellites
Desktop,
Browser
Cloud
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{Grow: ‘More’}
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Multitenant or Dedicated
30+ Locations: Softlayer, Rackspace, Azure, AWS, …

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So…
How do you code for that?
How does that compare to <X>?
What about transactions?
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You do need to understand your
datastore.
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http://www.wired.com/wiredenterprise/2012/08/google-as-xerox-parc/

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Google File System (2003)
http://research.google.com/archive/gfs.html
!
Google MapReduce (2004)
http://research.google.com/archive/mapreduce.html
!
Google BigTable (2006)
http://research.google.com/archive/bigtable.html
!
Amazon’s Dynamo (2007)
http://s3.amazonaws.com/AllThingsDistributed/sosp/amazon-dynamo-sosp2007.pdf

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{Sacrificed: ‘SPOFs’}
Replaced with self healing systems

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{Sacrificed: ‘Manual Sharding’}
http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/spanner-osdi2012.pdf

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{Sacrificed: ‘Locks’}
Block (optionally) on reads, not writes

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{Sacrificed: ‘Schemas’}
“Schema on read”

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{Sacrificed: ‘Tables’}
SQL and JSON poorly matched

24. MySQL, MongoDB,
CouchDB, SOLR, …
Dynamo, Cloudant,
Cassandra, Riak, …

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…
…
http://www.bailis.org/papers/ramp-sigmod2014.pdf
{Sacrificed: ‘Transactions’}
Fundamental reason: CAP Theorem

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{Consistency: ‘Eventual’}
https://amplab.cs.berkeley.edu/wp-content/uploads/2013/04/p20-bailis.pdf
Excellent high level overview

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{Consistency: ‘Eventual’}
https://amplab.cs.berkeley.edu/wp-content/uploads/2012/06/p776_peterbailis_vldb2012.pdf

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{Consistency: ‘Eventual’}
“AP” “C”

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{Consistency: ‘Eventual’}
https://amplab.cs.berkeley.edu/wp-content/uploads/2013/04/p20-bailis.pdf
FoM = Benefit - Cost*Rate

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{Consistency: ‘Eventual’}
3 minutes, 100 points (Dow Jones)

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{Consistency: ‘Eventual’}
What is the penalty? Hedge strategy?

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{Strategy: ‘Immutability’}
Write-only state machine

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http://www.infoq.com/presentations/Value-Values
{Spokesperson: ‘Rich Hickey’}

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Immutability isn’t new
!
‣ “Accountants don’t use erasers”
‣ Functional, concurrent, distributed languages (e.g. Erlang)
‣ File systems (e.g. ZFS)
‣ Storage engines (e.g. LevelDB) & Databases (CouchDB, Datomic, …)
‣ Data model
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‣ Don’t update in place
‣ Keep old versions
‣ Query for newest version
‣ Even works for deletions (write a “tombstone”)
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{Strategy 1: ‘Write Only’}

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{Strategy 2: ‘Minimize Contention’}
‣ Break out one-to-many, many-to-many relationships using foreign keys and links.
‣ Normalize!
Learn your indexing options!

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{Strategy 3: ‘Think Commutative’}
‣ Store “deltas”, just like your checkbook
Account Value via Materialized View

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{Strategy 3: ‘Think Commutative’}
Commutative Replicated Data Types
(2010)
http://pagesperso-systeme.lip6.fr/Marc.Shapiro/papers/RR-6956.pdf

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Future Work
‣ Additional explicit data modeling examples
‣ Advanced reasoning for “AP” systems
‣ CRDTs
‣ Secondary index consistency, maintenance (RAMP)
‣ “New” transactional systems (HAT, Google Spanner)
‣ “Call me maybe”:
• (http://aphyr.com/posts/281-call-me-maybe-carly-rae-jepsen-and-the-perils-of-network-
partitions)
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Keep Learning

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AMP on Consistency
https://amplab.cs.berkeley.edu/tag/consistency/

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cloudant.com
mike@cloudant.com
@mlmilleratmit
#Cloudant
Thanks!
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IRC

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