NoSQL Rollercoaster

A NoSQL Rollercoaster
Saturday, November 6, 2010

IMS
The Hierarchical
Database
(1966)
Vern Watts

“A Relational
Model for Large
Shared
Databanks”
(1970)
Ted Codd

“"SEQUEL: A
Structured
English Query
Language"
Don Chamberlin & Ray
Boyce
(1974)
Don Chamberlin

ACID
(late 1970’s)
Jim Gray

“NoSQL”
coined
(1998)
Carlo Strozzi

“NoSQL” Reintroduced
(2008)
Eric Evans

select fun, proﬁt from
real_world where
relational=false?

“In striving to make every
user happy, a technology an
actually leave the majority
unhappy.”
“Every good idea is
generalized to its level of
inapplicability.”
(Peter Principle)

Why?

Internet Scale
• Massive data
collections
• Huge number of
requests
• Coming from
geographic areas
across the globe
• 24/7

Total Cost of Ownership
• The price of a license
• The price of support
• The price of hardware

Availability

Data Models

Column Oriented
…
key
named
column
named
column
named
column
named
column
named
column
Column Family ≈ Table
Empty cells are
cheap (sparse
table)
Can grow “indeﬁnitely”
Schemaless
No
secundary
indexes

Column Oriented + Super Columns
…
key
named
column
named
column
named
column
named
column
named
column
…
named
column
named
column
named
column
…
Super Columns

Key Value Store
•Schemaless
•Versioning
1011
0110

Graph Database
SPARQL?

Document Store
Improved
Indexing
<persons>
<person>
<name>Wilfred</name>
<surname>Springer
</person>
…
</persons>
[{ "Name" :
"Wilfred",
"Surname" :
"Springer"},
…
]
JSON
XML
Serverside
Processing

Challenges

CAP
Theorem
Eric Brewer

Availability Consistency
Partition
Tolerance
Pick two

Consistency
Consistency
Strong
Consistency
Weak
Consistency
Eventual
Consistency
Other
Causal
Consistency
Read-your-
writes
Consistency
Session
Consistency
Monotonic Read
Consistency
Monotonic Write
Consistency

Strong Consistency
A
B
C
1
2
2
2
0 value = "foo"
value = "bar"
value = "bar"
value = "bar"
value = "bar"
After the update, any subsequent access will return the
updated value.

Weak Consistency
A
B
C
1 value = "bar"
value = "bar" /
"foo"
value = "bar" / "foo"
value = "bar" / "foo"
0 value = "foo"
>1
>1
>1
The system does not guarantee that at any given point in
the future subsequent access will return the updated
value

Eventual Consistency
If no updates are made to the object, eventually all
accesses will return the last updated value.
A
B
C
1 value = "bar"
value = "bar"
value = "bar"
value = "bar"
0 value = "foo"
t
t
t
t ≥ 1

Session Consistency
Within the “session”, the system guarantees read-your-
writes consistency
2 value = "foo"
Session 1
Session 2
A
B
C
1 value = "bar"
0 value = "foo"
2 value = "bar"

Partition Tolerance
A
writes new value
replicates
new value
reads new
value

Partition Tolerance
A
writes new value
replicates
new value
reads new
value
!

Partition Tolerance
A
writes new value
fails to
replicate
new value
reads old
value

Partition Tolerance
A
failing attempt
to write a new
value
fails to
replicate
new value

Byzantine Failures

Faults that might
occur in a
distributed
system
•Lying
•Fabrication of messages
•Collisions
•Selective non-participation

Byzantine
Generals
Problem

Solution
Strategies

Strong
Consistency
1

Παξοί

Leader Proposer
Acceptor Learner
Four Roles

Paxos in a Nutshell
Request
Propose
Promise
Accept
Accepted
Response

Processors
combine
various
roles
Consensus
based on
majority vote
Paxos guarantees
progress for 2F + 1
processors in face of
failure of F processors

“Either Paxos,
Paxos with
cruft, or
broken”
Mike Burrows
(Google Chubby)

Partitioning
2

Consistent Hashing
Key K
A
B
C
D
E
F
G
H

High
Availability
3

Replication
A
B
C
D
E
F
G
H

W = 3
N = 4
R = 2
A
Replication Factor

W = 3
N = 4
R = 2
A
Reconciliation during reads
Reconciliation

Handling
Temporary
Failures
4

Problem
Reconcilation
?
Alice Ben CathyDave
Wednesday?
Thursday
Tuesday
What's it gonna be?
Tuesday ThursdayX
? ? ?

Vector Clocks
date = Wednesday
vclock = Alice:1
date = Tuesday
vclock = Alice:1, Ben:1
date = Tuesday
vclock = Alice:1, Ben:1, Dave:1
date = Tuesday
vclock = Alice:1, Cathy:1
date = Thursday
vclock = Alice:1, Ben:1, Cathy:1, Dave:2

Conﬂict Resolution
date = Tuesday
vclock = Alice:1, Ben:1, Dave:1
date = Thursday
vclock = Alice:1, Ben:1, Cathy:1, Dave:2
6

Recovering from
permanent
failures
5

What if nodes die permantly?
A
B
C
D
E
F
G
H

Merkle Trees
#
#
#
#
#
#
#
#
#
#
#
#
#
#

Enough is enough!!!

Conclusions

Conclusions
Scale!!!

Conclusions
Scale!!!
No free ride

Conclusions
Scale!!!
No free ride
You gain some

Conclusions
Scale!!!
No free ride
You gain some
You lose some

Conclusions
Scale!!!
No free ride
You gain some
You lose some
Choice

Conclusions
Scale!!!
No free ride
You gain some
You lose some
Choice
Control

Conclusions
Scale!!!
No free ride
You gain some
You lose some
Choice
Control
Take control

Conclusions
Scale!!!
No free ride
You gain some
You lose some
Choice
Control
Take control
Understand your options

Conclusions
Scale!!!
No free ride
You gain some
You lose some
Choice
Control
Take control
Fun!!!

Conclusions
Scale!!!
No free ride
You gain some
You lose some
Choice
Control
Take control
Fun!!!
Changes your way of thinking

No SQL!!!
wilfredspringer@gmail.com

BASE
• Basically Available
• Soft State
• Eventually Consistent

NoSQL Rollercoaster

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NoSQL Rollercoaster