PayPal's presentation from MySQL Connect conference, including their analysis of big data solutions and selection of MySQL Cluter

1. Big Data is a Big Scam (Most of the Time)
Daniel Austin, PayPal Technical Staff
MySQL Connect Conference
September 30, 2012 v1.2

2. Today’s Agenda
Big Myths about Big Data
YESQL: A Counterexample
Q&A
Global In-memory MySQL Confidential and Proprietary 2

3. THE FUNDAMENTAL PROBLEM IN
DISTRIBUTED DATA SYSTEMS
“How Do We Manage Reliable
Distribution of Data Across Geographical
Distances?”
Confidential and Proprietary

4. The NoSQL Solution
• NoSQL Systems provide a solution that relaxes
many of the common constraints of typical
RDBMS systems
– Slow - RDBMS has not scaled with CPUs
– Often require complex data management (SOX,
SOR)
– Costly to build and maintain, slow to change and
adapt
– Intolerant of CAP models (more on this later)
• Non-relational models, usually key-value
• May be batched or streaming
• Not necessarily distributed geographically
Confidential and Proprietary

5. Big Data Myth #1: Big Data = NoSQL
• ‘Big Data’ Refers to a Common Set of Problems
– Large Volumes
– High Rates of Change
• Of Data
• Of Data Models
• Of Data Presentation and Output
– Often Require ‘Fast Data’ as well as ‘Big’
• Near-real Time Analytics
• Mapping Complex Structures
Takeaway: Big Data is the problem, NoSQL is one
(proposed) solution
Confidential and Proprietary

6. 3 Kinds of Big Data Systems
1. Columnar K-V Systems
– Hadoop, Hbase, Cassandra, PNUTs
2. Document-Based
– MongoDB, TerraCotta
3. Graph-Based
– FlockDB, Voldemort
Takeaway: These were originally designed as
solutions to specific problems because no
commercial solution would work.
Confidential and Proprietary

7. Big Data Hype Cycle: Where Are We Now?
There are currently more than 120+ NoSQL
databases listed at nosql-databases.com!
You Are Here ?
As the pace of new technology solutions has slowed, some clear winners have emerged.
Confidential and Proprietary

8. Big Data Myth #2: The CAP Theorem Doesn’t
Say What You Think It Does
• Consistency, Availability, (Network) Partition
• The Real Story: These are not Independent
Variables
• AP =CP (Um, what? But…A != C )
• Variations:
– PACELC (adds latency tolerance)
Takeaway: the real story here is about the tradeoffs
made by designers of different systems, and the
main tradeoff is between consistency and
availability, usually in favor of the latter.
Confidential and Proprietary

9. Big Data Myth: You Need A Big Data System
Well, Maybe….But Before You Go There…
There are essentially two ‘Big Data Problems’:
“I have too much data and it’s coming in too fast to
handle with any RDBMS.”
“I have a lot of data distributed geographically and
need to be able to read and write from anywhere in
near real-time.”
Takeaway: if you have one of these Big Data
problems, a NoSQL solution might work for you.
But there are also other alternatives…
Confidential and Proprietary

10. BIG DATA MYTH #3: BIG DATA AND NOSQL
ARE NEW IDEAS
• The first and most successful
such system is DNS, created in
1983.
• Began with flat files
• Currently serves the entire
Internet (!)
• DNS is an AP system,
availability is #1
• Many extensions complicate a
simple design
• Suggests a new term for CAP-
like ideas: variability
• DNS variability is very high,
often 2-3x the mean
Confidential and Proprietary

11. Today’s Agenda
Big Myths About Big Data
YESQL: A Counterexample
Q&A
Global In-memory MySQL Confidential and Proprietary 11

12. Mission YESQL
“Develop a globally distributed DB For
user-related data.”
• Must Not Fail (99.999%)
• Must Not Lose Data. Period.
• Must Support Transactions
• Must Support (some) SQL
• Must WriteRead 32-bit integer globally in
1000ms
• Maximum Data Volume: 100 TB
• Must Scale Linearly with Costs
Confidential and Proprietary

13. What about “High Performance”?
• Maximum lightspeed distance on Earth’s
Surface: ~67 ms
• Target: data available worldwide in < 1000 ms
Sound Easy?
Think Again!
Confidential and Proprietary

14. WHY MYSQL CLUSTER?
Pro Con
• True HA by design • Some semantic
– Fast recovery limitations on fields
• Supports (some) X- • Size constraints (2
actions TB?)
• Relational Model – Hardware limits
• In-memory also
architecture = high • Higher cost/byte
performance • Requires reasonable
• Disk storage for data partitioning
non-indexed data • Higher complexity
• APIs, APIs, APIs
Confidential and Proprietary

15. How MySQL Cluster Works in 1 Slide
Graphics courtesy dev.mysql.com
Confidential and Proprietary

16. CIRCULAR REPLICATION/FAILOVER
Graphics courtesy O’Reilly OnLamp.com
Confidential and Proprietary

17. AVAILABILITY DEFINED
• Availability of the entire
system:
n m
Asys = 1 – Π(1-Πri)j V
i=1 j=1 I
P
• Number of Parallel
Components Needed to
Achieve Availability Amin:
Parallel
Serial
Nmin = [ln(1-Amin)/ln(1-r)]
Confidential and Proprietary

18. AWS Meets MySQL Cluster
• Why AWS?
– Cheap and easy infrastructure-in-a-box
(Or so I thought! Ha!)
• Services Used:
– EC2 (Centos 5.3, small instances for mgm
& query nodes, XL for data
– Elastic IPs/ELB
– EBS Volumes
– S3
– Cloudwatch
Confidential and Proprietary

19. ARCHITECTURAL TILES
AWS Availability Zones
Tiling Rules
• Never separate NDB & SQL
A B
• Ndb:2-SQL:1-MGM:1
• Scale by adding more tiles
• Failover 1st to nearest AZ
• Then to nearest DC
• At least 1 replica/AZ
C ELB
• Don’t share nodes
• Mgmt nodes are redundant
Limitations Unused (not present in all locations)
• AWS is network-bound @
250 MBPS – ouch!
• Need specific ACL across AZ Data Mgmt SQL
boundaries Node Node Node
• AZs not uniform!
• No GSLB
• Dynamic IPs
• ELB sticky sessions !reliable
Confidential and Proprietary

20. Architecture Stack
Scale by Tiling
A B A B A B
A B
A B
A B A B
5 AWS Data Centers: US-E, US-W, TK, EU, AS
Confidential and Proprietary

21. Other Technologies Considered
• Paxos
– Elegant-but-complex consensus-based
messaging protocol
– Used in Google Megastore, Bing metadata
• Java Query Caching
– Queries as serialized objects
– Not yet working
• Multiple Ring Architectures
– Even more complicated = no way
Confidential and Proprietary

22. SYSTEM READ/WRITE PERFORMANCE (!)
What we tested:
• 32 & 256 byte char fields
In-region replication tests
• Reads, writes, query speed vs. volume
• Data replication speeds
Results:
• Global replication < 350 ms
• 256 byte read < 10ms worldwide
06/19/2011 06/20/2011 06/21/2011 06/22/2011 06/23/2011
Confidential and Proprietary

23. Data Models and Query Optimization
• Network Latency is an obvious issue
• Data model requires all segments present
in each geo-region
• Parameterized (Linked) Joins
– Adaptive Query Localization (SIP)
technique from Clustra (see Clement
Frazer’s blog for details)
Confidential and Proprietary

24. Conservation of Timestamps or The
Commit Ordering Problem
• Why does commit ordering matter?
• Write operators are non-commutative
[W(d,t1),W(d,t2)] != 0 unless t1=t2
– Can lead to inconsistency
– Can lead to timestamp corruption
– Forcing sequential writes defeats Amdahl’s
rule
• Can show up in GSLB scenarios
Confidential and Proprietary

25. Hard Lessons, Shared
• Be Careful…
– With “Eventual Consistency”-related concepts
– ACID, CAP are not really as well-defined as we’d like
considering how often we invoke them
• MySQL Cluster is a good solution
– Real HA, real SQL
– Notable limitations around fields, datatypes
– Successfully competes with NoSQL systems for most use
cases – better in many cases
• NoSQL Systems
– All have relatively low levels of maturity
– More suitable for simpler key-value models
– Victim of Tech Fashion
Confidential and Proprietary

26. Future Directions
• Alternate solution using Pacemaker,
Heartbeat
– From Yves Trudeau @ Percona
– Uses InnoDB, not NDB
• Implement Memcached plugin
– To test NoSQL functionality, APIs
• Add simple connection-based persistence
to preserve connections during failover
• Better data node distribution
• Better testing & monitoring
Confidential and Proprietary

27. Summing Up On YESQL v0.85
• It works! Far better than expected.
• Very fast, very reliable
• Reduced complexity since v0.7
• AWS poses challenges that private data
centers may not experience
• You can achieve high performance and
availability without giving up relational
models and read consistency!
Confidential and Proprietary

28. The Big Picture on Big Data
• Only use Big Data solutions when you have a real
Big Data problem.
– Don’t be a Dedicated Follower of Tech Fashion!
• Not all Big Data solutions are created equal
– What tradeoffs are most important to you?
– Consistency, Fault Tolerance, Availability,
Performance, Variability
• Is your data model a fit for NoSQL?
– You don’t have to give up the relational model in
most cases, so don’t!
• You can achieve high performance and availability
without giving up relational models and read
consistency! Just say YESQL!
Confidential and Proprietary

29. “In the long run, we are all dead
eventually consistent.”
Maynard Keynes on NoSQL Databases
Twitter: @daniel_b_austin
Email: daaustin@paypal.com
With apologies and thanks to the real DB experts, Andrew Goodman, Yves
Trudeau, Frazer Clement, Daniel Abadi, Kent Beck, and everyone else who
contributed. It really works!