nosql rollercoaster

A NoSQL Rollercoaster

Saturday, November 6, 2010

IMSThe Hierarchical

Database

(1966)

Vern WattsSaturday, November 6, 2010

“A Relational Model for Large

Shared Databanks”

(1970)

Ted CoddSaturday, November 6, 2010

“"SEQUEL: A Structured

English Query Language"

Don Chamberlin & Ray Boyce

(1974)

Don ChamberlinSaturday, November 6, 2010

ACID

(late 1970’s)

Jim GraySaturday, November 6, 2010

“NoSQL” coined

(1998)

Carlo StrozziSaturday, November 6, 2010

“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 namedcolumn

namedcolumn

namedcolumn

namedcolumn

namedcolumn

Column Family ≈ Table

Empty cells are cheap (sparse

table)

Can grow “indefinitely”

Schemaless No secundary

indexes


Column Oriented + Super Columns

…

key namedcolumn

namedcolumn

namedcolumn

namedcolumn

namedcolumn

…

namedcolumn

namedcolumn

namedcolumn

…

Super Columns


Key Value Store

•Schemaless•Versioning

10110110


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

PartitionTolerance

Pick two


ConsistencyConsistency

Strong Consistency

Weak Consistency

Eventual Consistency Other

Causal Consistency

Read-your-writes

ConsistencySession

ConsistencyMonotonic Read

ConsistencyMonotonic Write

Consistency


Strong Consistency

AB

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

AB

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.

AB

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

AB

C

1 value = "bar"

0 value = "foo"

2 value = "bar"


Partition Tolerance

Awrites new value

replicates new value

reads new value


Partition Tolerance

Awrites new value

replicates new value

reads new value

!


Partition Tolerance

Awrites 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

RequestPropose

Promise

AcceptAcceptedResponse


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)


Partitioning2


Consistent HashingKey KA

B

C

DE

F

G

H


High Availability

3


ReplicationA

B

C

DE

F

G

H


W = 3

N = 4R = 2

A

Replication Factor


W = 3

N = 4R = 2

A

Reconciliation during reads

Reconciliation


Handling Temporary

Failures

4


ProblemReconcilation

?

Alice Ben CathyDave

Wednesday?

Thursday

Tuesday

What's it gonna be?

Tuesday ThursdayX? ? ?


Vector Clocksdate = Wednesdayvclock = Alice:1

date = Tuesdayvclock = Alice:1, Ben:1

date = Tuesdayvclock = Alice:1, Ben:1, Dave:1

date = Tuesdayvclock = Alice:1, Cathy:1

date = Thursdayvclock = Alice:1, Ben:1, Cathy:1, Dave:2


Conflict Resolution

date = Tuesdayvclock = Alice:1, Ben:1, Dave:1

date = Thursdayvclock = Alice:1, Ben:1, Cathy:1, Dave:2

6


Recovering from permanent

failures

5


What if nodes die permantly?A

B

C

DE

F

G

H


Merkle Trees

##

#

#

##

##

#

#

#

##

#


Enough is enough!!!


Conclusions


Conclusions

Scale!!!


Conclusions

Scale!!!No free ride


Conclusions


You gain some


Conclusions


You gain some

You lose some


Conclusions


You gain some

You lose someChoice


Conclusions


You gain some

You lose someChoice

Control


Conclusions


You gain some

You lose someChoice

ControlTake control


Conclusions


You gain some

You lose someChoice

ControlTake control

Understand your options


Conclusions


You gain some

You lose someChoice

ControlTake control


Fun!!!


Conclusions


You gain some

You lose someChoice

ControlTake control


Fun!!!

Changes your way of thinking


No SQL!!!

[email protected]


mailto:[email protected]

mailto:[email protected]

BASE

• Basically Available

• Soft State

• Eventually Consistent


nosql rollercoaster

Technology

bar value

bar foo value

updated value

old value

causal consistency

column column family

super columns key named

column super columns