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CC5212-1
PROCESAMIENTO MASIVO DE DATOS
OTOÑO 2014
Aidan Hogan
[email protected]
Lecture III: 2014/03/23
Lab 1.1: Mensaje
• New deadline: Tuesday 10am 
• −1 (out of 10) for every day late after that
TYPES OF
DISTRIBUTED SYSTEMS …
Client–Server Model
• Client makes request to server
• Server acts and responds
(For example: Email, WWW, Printing, etc.)
Client–Server: Three-Tier Server
Server
Data
Logic
Presentation
Add all the
salaries
Create
HTML page
SQL: Query
salary of all
employees
HTTP GET:
Total salary
of all
employees
Peer-to-Peer: Unstructured
Pixie’s new
album?
(For example: Kazaa, Gnutella)
Peer-to-Peer: Structured (DHT)
• Circular DHT:
– Only aware of
neighbours
– O(n) lookups
000
111
001
110
010
• Implement shortcuts
– Skips ahead
– Enables binary-searchlike behaviour
– O(log(n)) lookups
101
011
100
Pixie’s new
album? 111
Desirable Criteria for Distributed Systems
• Transparency:
– Appears as one machine
• Flexibility:
– Supports more machines, more applications
• Reliability:
– System doesn’t fail when a machine does
• Performance:
– Quick runtimes, quick processing
• Scalability:
– Handles more machines/data efficiently
LIMITATIONS OF DISTRIBUTED
SYSTEMS: EIGHT FALLACIES
Eight Fallacies
• By L. Peter Deutsch (1994)
– James Gosling (1997)
“Essentially everyone, when they first build a
distributed application, makes the following eight
assumptions. All prove to be false in the long run
and all cause big trouble and painful learning
experiences.” — L. Peter Deutsch
• Each fallacy is a false statement!
What might these fallacies of distributed computing be
based on our experience?
1. The network is reliable
Machines fail,
connections fail, firewall
eats messages
• flexible routing
• retry messages
• acknowledgements!
2. Latency is zero
There are significant
communication delays
• avoid “races”
• local order ≠ remote
order
• acknowledgements
• minimise remote calls
M2:
Copy X
from M1
M1:
Store X
M2
– batch data!
• avoid waiting
– multiple-threads
M1
3. Bandwidth is infinite
M1:
Copy X
(10GB)
Limited in amount of
data that can be
transferred
• avoid resending data
• direct connections
• caching!!
M1:
Copy X
(10GB)
M2
M1
4. The network is secure
Network is vulnerable to
hackers, eavesdropping,
viruses, etc.
M1:
Send Medical
History
• send sensitive data
directly
• isolate hacked nodes
– hack one node ≠ hack all
nodes
• authenticate messages
• secure connections
M1
5. Topology doesn’t change
How machines are
physically connected
may change (“churn”)!
• avoid fixed routing
– next-hop routing?
• abstract physical
addresses
• flexible content
structure
M3
M2
Message M5 thru
M2, M3, M4
M4
M1
M5
6. There is one administrator
Different machines
have different policies!
• Beware of firewalls!
• Don’t assume most
recent version
– Backwards compat.
7. Transport cost is zero
It costs time/money to
transport data: not just
bandwidth
(Again)
• minimise redundant
data transfer
– avoid shuffling data
– caching
• direct connection
• compression?
8. The network is homogeneous
Devices and connections
are not uniform
• interoperability!
– Java vs. .NET?
• route for speed
– not hops
• load-balancing
Eight Fallacies (to avoid)
1.
2.
3.
4.
5.
6.
7.
8.
Severity of fallacies vary
in different scenarios!
Which fallacies apply/do
not apply for:
The network is reliable
Latency is zero
Bandwidth is infinite
•
The network is secure
•
Topology doesn’t change •
There is one administrator •
Transport cost is zero
The network is homogeneous
Gigabit ethernet LAN?
BitTorrent
The Web
Laboratorio II
LIMITATIONS OF DISTRIBUTED
COMPUTING: CAP THEOREM
But first … ACID
Have you heard of ACID guarantees in a database class?
For traditional (non-distributed) databases …
1. Atomicity:
–
Transactions all or nothing: fail cleanly
2. Consistency:
–
Doesn’t break constraints/rules
3. Isolation:
–
Parallel transactions act as if sequential
4. Durability
–
System remembers changes
What is CAP?
Three guarantees a distributed sys. could make
1. Consistency:
– All nodes have a consistent view of the system
2. Availability:
– Every read/write is acted upon
3. Partition-tolerance:
– The system works even if messages are lost
A Distributed System (Replication)
–
–
–
–
Consistency
–
–
–
–
There’s 891
users in ‘M’
There’s 891
users in ‘M’
Availability
891
How many users
start with ‘M’
–
–
–
–
Partition-Tolerance
How many users
start with ‘M’
–
–
891
–
–
The CAP Question
Can a distributed system guarantee consistency
(all nodes have the same up-to-date view), availability (every
read/write is acted upon) and partition-tolerance (the system
works even if messages are lost) at the same time?
What do you think?
Can a distributed system guarantee consistency
and availability and partition-tolerance at the same
time, or not?
The CAP Answer
The CAP “Proof”
How many users
start with ‘M’
–
891
There’s 891
users in ‘M’
–
–
–
892
There’s 891
users in ‘M’
The Cap “Proof” (in boring words)
• Consider machines m1 and m2 on either side
of a partition:
– If an update is allowed on m2 (Availability), then
m1 cannot see the change: (loses Consistency)
– To make sure that m1 and m2 have the same, upto-date view (Consistency), neither m1 nor m2 can
accept any requests/updates (lose Availability)
– Thus, only when m1 and m2 can communicate
(lose Partition tolerance) can Availability and
Consistency be guaranteed
The CAP Theorem
A distributed system cannot guarantee
consistency (all nodes have the same up-to-date view),
availability (every read/write is acted upon) and partitiontolerance (the system works even if messages are lost) at the
same time.
(“Proof” as shown on previous slide )
The CAP Triangle
C
Choose
Two
A
P
CAP Systems
CA: Guarantees to give a
CP: Guarantees responses
correct response but only
while network works fine
(Centralised / Traditional)
are correct even if there are
network failures, but response
may fail (Weak availability)
C
A
P
AP: Always provides a
“best-effort” response even
in presence of network
failures (Eventual consistency)
(No intersection)
892
CA System
How many users
start with ‘M’
–
–
–
891
There’s 892
users in ‘M’
–
892
There’s 891
users in ‘M’
891
CP System
How many users
start with ‘M’
–
There’s 891
users in ‘M’
–
–
–
There’s 891
users in ‘M’
891
AP System
How many users
start with ‘M’
–
There’s 891
users in ‘M’
–
–
–
892
There’s 891
users in ‘M’
BASE (AP)
• Basically Available
– Pretty much always “up”
• Soft State
– Replicated, cached data
• Eventual Consistency
– Stale data tolerated, for a while
• Amazon, eBay, Google, DNS …
The CAP Theorem
• C,A in CAP ≠ C,A in ACID
• Simplified model
– Partitions are rare
– Systems may be a mix of CA/CP/AP
– C/A/P often continuous in reality!
• But concept useful/frequently discussed:
– How to handle Partitions?
• Availability? or
• Consistency?
LABS PREP:
AIDAN LEARNS SPANISH 
Help me learn Spanish!
What are the top
500 most common
words in Spanish
Word
Count
Help me learn Spanish!
How should we design the distributed system?
(for now it will be in-memory)
• How can we distribute the word count?
• How can we call the machines / send the data?
• How can we merge the word counts?
• How to implement in the lab?
RECAP
Distributed Systems have limitations
• Eight fallacies and what they mean
1.
2.
3.
4.
5.
6.
7.
8.
The network is reliable
Latency is zero
Bandwidth is infinite
The network is secure
Topology doesn’t change
There is one administrator
Transport cost is zero
The network is homogeneous
Distributed Systems have limitations
CAP Theorem
A distributed system cannot guarantee consistency (all
nodes have the same up-to-date view and will give a correct answer),
availability (every request is acted upon) and partition-tolerance
(the system works even if messages are lost) at the same time.
CAP Systems
CA: Guarantees to give a
CP: Guarantees responses
correct response but only
while network works fine
(Centralised / Traditional)
are correct even if there are
network failures, but response
may fail (Weak availability)
C
A
P
AP: Always provides a
“best-effort” response even
in presence of network
failures (Eventual consistency)
(No intersection)
Design of a Distributed Algorithm
• How to distribute/split data for processing
• Embarrassingly parallel execution
• How to merge data (naively for now)
• How to help me learn Spanish
Questions?