NoSQL Matters@adriancolyer

1. when choosing a data store / processing platform

2. when it comes to getting the most out of that platform

3. when we take things to the next level

What really matters...

The 13 horsemen of the apocalypse...

Your application(s)

Anomaly (Prevented By) Tolerable? Mitigation (M,G,A…)

Dirty Writes Read Uncommitted

Dirty Reads Read Committed

Fuzzy Reads (non-repeat-able)

Item-Cut Isolation

Phantoms Predicate-Cut Isolation

...

Your application(s)Anomaly (Prevented By) Tolerable? Mitigation

Read Skew MAV Isolation + item-cut

Lost Update Repeatable Read

Cursor Lost Update

Cursor Stability

Write Skew Repeatable Read

Stale Reads Partition-intolerance

Your application(s)

Anomaly (Prevented By) Tolerable? Mitigation

Non-monotonic read

Monotonic reads

Non-monotonic write

Monotonic writes

Invisible cause Writes-follow-reads

Disappearing writes

Read-your-writes

(for sessions)

Your Developers

“we believe there is considerable work to be done to improve the programmability of highly-available systems” - Bailis et al. 2014 (HAT)

Your Developers

“...an unacceptable burden to place on developers” - Google 2012 (F1)

Consistency and all that...

If you accept a weaker consistency model make sure it’s a genuine trade-off and you’re getting something (you need) in return.

You can have causal consistency with (C)AC

PACELC (pass-elk)

Operations & all the other use cases…it is important to consider the data accesses that don’t use the API. These include back-ups, bulk import and deletion of data, bulk migrations from one data format to another, replica creation, asynchronous replication, consistency monitoring tools, and operational debugging. An alternate store would also have to provide atomic write transactions, efficient granular writes, and few latency outliers.- Facebook 2013 (TAO)

“

”

it tears you apart with suspense!

“”

Why is it so hard?“We have found that the standard verification techniques in industry are necessary but not sufficient. We use deep design reviews, code reviews, static code analysis, stress testing, fault-injection testing, and many other techniques, but we still find that subtle bugs can hide in complex concurrent fault-tolerant systems.” - Amazon 2014

In the ALPS...

… or a walk in the park?

(Web)Scale

The USL

Source : McSherry et al. 2015

Credit: Neil Gunther

(Web)Scale

Source : McSherry et al. 2015

Big?!

How Big?“Working sets are Zipf-distributed. We can therefore store in memory all but the very largest datasets, which we avoid storing in memory altogether. For example, the distribution of input sizes of MapReduce jobs at Facebook is heavy-tailed. Furthermore, 96% of active jobs can have their entire data simultaneously fit in the corresponding clusters’ memory” - Tachyon, Lie et al. 2014

Musketeer

Performance

40-80% of all MR jobs would perform better on a single machine!

(and cost less, and be easier to operate, and have many fewer failures…)

COST

The Configuration that Outperforms a Single Thread

“You can have a second computer once you’ve shown you know how to use the first one.” - Paul Barham

vs a single thread...

FlashGraph vs Pregel

● Pregel: 1B vertices, 127B edges, 300 machines

● FlashGraph: 3.4B vertices, 129B edges, 1 machine

ApproxHadoop

BlinkDB

Sometimes it pays to wait (a little bit)

What’s the bottleneck?

● Network I/O?● Disk I/O?● CPU?

Measure before optimising… and avoid excessive serialization and deserialization!

X (multi-core)

Distributed X

In-memory XFlash Optimised X

NVMM X

NVMM & RDMA X

X (establish baseline COST)

ALPS, ACID 2.0, CRDTs, CAC, COPS, CRON, CALM, CAP, & CRAP!

Coordination Avoidance

Invariant-Confluence for application level constraints

● NOT NULL ● PRIMARY KEY (read & delete, but not insert)● UNIQUE (read & delete, insert?)● FOREIGN KEY (insert, cascade delete, but delete)

Life Beyond...“In recent years, many ‘NoSQL’ designs have avoided cross-partition transactions entirely, effectively providing Read Uncommitted isolation…” - Bailis et al. 2014

From: “Life Beyond Distributed Transactions”,To: “Read-Atomic Multiple Partition” Transactions (RAMP)

Your application(s)

From anomalies to invariants...Invariant Type Affected Txns I-Confluent?

Some closing thoughts

● Do you need eventual? ● Have you planned for anomalies?● Does it actually work?● Are you distributing for the right reasons? (AL…)● Do you need exact?● Do you need it ASAP?● Can you keep CALM?● Do you understand your application’s invariants?

http://blog.acolyer.org @adriancolyer

References● Highly Available Transactions, Virtues & Limitations - Bailis et al. 2014 http:

//blog.acolyer.org/2014/11/07/highly-available-transactions-virtues-and-limitations/

● Building on Quicksand - Helland 2009 http://blog.acolyer.org/2015/03/23/building-on-quicksand/

● F1: A Distributed SQL Database that Scales - Google 2012 http://blog.acolyer.org/2015/01/06/f1-a-distributed-sql-database-that-scales/

● Scalability! But at what COST? - McSherry et al. 2015 http://blog.acolyer.org/?p=941 (to appear, June 5th 2015)

● Applying the Universal Scalability Law to Organisations - Colyer 2015 http://blog.acolyer.org/2015/04/29/applying-the-universal-scalability-law-to-organisations/

References● Don’t Settle for Eventual: Scalable Causal Consistency for Wide-Area

Storage with COPS - LLoyd et al. 2011 http://blog.acolyer.org/2015/03/17/consistency-availability-and-convergence-cops/

● Consistency, Availability, and Convergence - Mahajan et al. 2014 http://blog.acolyer.org/2015/03/17/consistency-availability-and-convergence-cops/

● Tachyon: Reliable, Memory-Speed Storage for Cluster Computing - Lie et al. 2014 http://blog.acolyer.org/2014/12/04/tachyon-reliable-memory-speed-storage-for-cluster-computing/

References● Musketeer: all for one, one for all in data processing systems - Gog et al.

2015 http://blog.acolyer.org/2015/04/27/musketeer-part-i-whats-the-best-data-processing-system/ and http://blog.acolyer.org/2015/04/28/musketeer-part-ii-one-for-all-and-all-for-one/

● Pregel: A System for Large-Scale Graph Processing - Google 2010 http://blog.acolyer.org/2015/05/26/pregel-a-system-for-large-scale-graph-processing/

● FlashGraph: Processing Billion Node Graphs on an array of commodity SSDs - Zheng et al. 2015 http://blog.acolyer.org/?p=935

References● ApproxHadoop: Bringing Approximations to Hadoop Frameworks - Goiri

2015 http://blog.acolyer.org/2015/04/16/approxhadoop-bringing-approximations-to-mapreduce-frameworks/

● BlinkDB: http://blinkdb.org/ ● Making Sense of Performance in Data Analytics Frameworks - Ousterhout

et al 2015 http://blog.acolyer.org/2015/04/20/making-sense-of-performance-in-data-analytics-frameworks/

● A Comprehensive Study of Convergent and Commutative Replicated Data Types - Shapiro et al. 2011 http://blog.acolyer.org/2015/03/18/a-comprehensive-study-of-convergent-and-commutative-replicated-data-types/

References● The Declarative Imperative: Experiences and Conjectures in Distributed

Logic - Hellerstein 2010 http://blog.acolyer.org/2014/11/13/the-declarative-imperative-experiences-and-conjectures-in-distributed-logic/

● Fast Remote Memory - Dragojevic et al. 2014 http://blog.acolyer.org/2015/05/20/farm-fast-remote-memory/

● Mojim: A Reliable and Highly-Available Non-Volatile Memory System - Zhang et al. 2015 http://blog.acolyer.org/2015/04/14/mojim-a-reliable-and-highly-available-non-volatile-memory-system/

References● Consistency Analysis in Bloom: A Calm and Collected Approach - Alvaro et

al. 2011 http://blog.acolyer.org/2015/03/16/consistency-analysis-in-bloom-a-calm-and-collected-approach/

● Edelweiss: Automatic Storage Reclamation for Distributed Programming - Conway et al. 2014 http://blog.acolyer.org/2015/02/20/edelweiss-automatic-storage-reclamation-for-distributed-programming/

● Scalable Atomic Visibility with RAMP Transactions - Bailis et al. 2014 http://blog.acolyer.org/2015/03/27/scalable-atomic-visibility-with-ramp-transactions/

References● Coordination Avoidance in Database Systems - Bailis et al. 2014 http:

//blog.acolyer.org/2015/03/19/coordination-avoidance-in-database-systems/

● Putting Consistency Back into Eventual Consistency - Balegas et al. 2015 http://blog.acolyer.org/2015/05/04/putting-consistency-back-into-eventual-consistency/

● Use of Formal Methods at Amazon Web Services - Newcombe et al. 2014 http://blog.acolyer.org/2014/11/24/use-of-formal-methods-at-amazon-web-services/

● Consistency Trade-offs in Modern Distributed Database Systems Design - Abadi 2012 http://cs-www.cs.yale.edu/homes/dna/papers/abadi-pacelc.pdf

References● Life Beyond Distributed Transactions - Helland 2007 http://blog.acolyer.

org/2014/11/20/life-beyond-distributed-transactions/

Image Credits● ALPS + Dublin Park: Wikimedia Commons● Movies: IMDB● Monotone Commuters: http://www.yenko.net/ubbthreads/ubbthreads.

php/topics/312207/re-old-street-scenes ● Elk picture by Jim Richmond: http://commons.wikimedia.org/wiki/File:Rm-

elk-locking-antlers.jpg