Big Data Technology
CS 236620, Technion, Spring 2014
Edward Bortnikov & Ronny Lempel
Yahoo Labs, Haifa
Data = Systems
How to Get the Big Systems Right?
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A multidisciplinary science on its own right
n Distributed Computing, Networking
n Hardware and Software Architecture
n Operations Research, Measurement, Performance Evaluation
n Power Management
n … and even Civil Engineering
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In this course - aspects related to Computer Science
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We’ll start with some principles …
An Ideal System Should …
Architect’s Dream -‐ Throughput
How many requests can be served in a unit of time?
Architect’s Dream -‐ Latency
How long does a single request take?
Scaling Up? Scaling Out?
Scale up
Example: Network Filesystems
NFS server
“server:/a/b/z.txt”
“/users/bob/courses/CS101.txt” “<server_123, block 20>”
R/W request
Metadata service (namenode)
Data service (datanode)
Monolithic
(e.g., historical NFS)
Distributed
Scale-‐Out Philosophy
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Scalability through Decoupling
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Whatever is split can be scaled independently
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HDFS: Metadata and Data accesses decoupled
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Minimize centralized processing
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Metadata accesses coordinated but lean
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Maximize I/O parallelism
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Clients access the data nodes concurrently
The Peer-‐to-‐Peer Approach
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Completely server-less
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All nodes and functions are
fully symmetric
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E.g., in a distributed data store every node has a
serving function and a management function
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Less favored in managed DC environments
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Very hard to maintain consistency guarantees
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Very hard to optimize globally
An Ideal System Should …
The Tail at Scale
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Problems are aggravated in large systems
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Component-level variability amplified by scale
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Failures and slow components are part of normal life,
not an exception
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Two ways of addressing service variability
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Prevent
bad things from happening by detecting and
isolating the slow/flawed components
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Contain
bad things through redundancy
An Ideal System Should …
Expected Workload Matters
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Latency-oriented
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Interactive, user-facing systems
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Example: Web search serving
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Throughput-oriented
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Back-end heavyweights
Data Accessibility Matters
Stream
Warehouse
Access Patterns Matter
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Data Analytics
n Throughput-oriented applications
n Write-once (typically, append)
n Read-many (typically, large sequential reads)
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Online Transaction Processing (OLTP)
n Latency-oriented applications
n Write-intensive
n Typically, many small direct accesses
Hardware Constraints Matter
Compute- or Data-Intensive?
Storage
Compute
Locality Matters
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Can computation and storage be aligned?
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Optimization?
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How repetitive is the workload?
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Optimization?
α
−
>
X
X
x
)
~
Pr(
Dominant Items Long tail Power-law distributionConsistency MaZers
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Stricter properties = stronger consistency
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Are you prepared to handle weird stuff?
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Fancy stock alerts
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Is it okay to lose an event once in a while?
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Fancy a social network
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Bob deletes photos with his ex-date Alice
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Bob befriends Carol
A Dialogue in the Wild
Engineer: we afraid of any kind of synchronization
Scientist: what kind of guarantee do you want to get?
Engineer: let’s build something simple Relax your consistency models
We want the systems to be eventually consistent
Scientist: this is an interesting problem
Example: Amazon’s Outage
Weak consistency models can lead
Elasticity Matters
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Resource demands often unknown in advance
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Driven by application popularity
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Goal: enablement of organic growth
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Add- (and pay-) as-you-grow
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Economies of scale
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Pool multiple datasets and services in huge DC’s
nBetter use of shared resources (personnel, real
Cloud Computing
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Computing resources delivered
over a network
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Infrastructure issues
abstracted away
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***-as-a-Service
Designing the Air Flows
Power Efficiency - Surprising Facts
n “At Facebook's Prineville, OR, facility, ambient air flows into the
building, passing first through a series of filters to remove bugs, dust, and other contaminants.”
n “Previous estimates suggested that electricity consumption in
massive server farms would double between 2005 and 2010. Instead, the number rose by 56% worldwide, and merely 36% in the US.”
n “The most efficient data centers now hover at temperatures
closer to 80 degrees Fahrenheit, and instead of sweaters, the technicians walk around in shorts.”
Summary
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Design for scale
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Design for fault-tolerance
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Know what you design for
Further Reading
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