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Evolution of Big Data
Architecture
Gang Tao
Computing Trend
Non Functional Requirements
• Latency

• Throughput

• Fault Tolerant

• Scalable

• Exactly Once Semantic
Hadoop
Hadoop Eco System
Map Reduce
Move computation to Data
Map Reduce
Hadoop the Limitation
• Map/Reduce is hard to use

• Latency is high

• inevitable data movement
Lambda
Lambda Architecture
query = function(all data)
Design Principle
human fault-tolerance 

– the system is unsusceptible to data loss or data corruption because
at scale it could be irreparable. 

data immutability 

– store data in it’s rawest form immutable and for perpetuity. (INSERT/
SELECT/DELETE but no UPDATE !)

recomputation 

– with the two principles above it is always possible to (re)-compute
results by running a function on the raw data.
Lambda Architecture
Batch Processing
Batch Processing
Using only batch processing, leaves you always with a portion
of non- processed data.
Realtime Stream Processing
query = function(all data) +

function(new data)
Lambda Architecture
A Reference Case
Lambda the Bad?
What is Good?
What is Bad?
Inmutable Data , Reprocessing
Keeping code written in two
different systems perfectly in sync
Over complicated Lambda
Batch VS. Stream
Stream & Realtime
Storm Logic View
Bolts
Spouts
Move data to Computation
Storm Deployment View
Code Compare
DAG
• DAG : Directed Acyclic Graph

• Used in Spark, Storm, Flink etc.
Out of Control
Build Complexity with Simplicity
Stream Processing Model
One at a time Micro batch
Stream Processing Model
One at a time Micro Batch
Low Latency Y N
High Throughput N Y
at least once Y Y
excatly once Sometimes Y
simple programing model Y N
Stream Computing the Limitation
• Queries must be written before data

• There should be another way to query past data

• Queries cannot be run twice

• All results will be lost when any error occurs All data have gone
when bugs found

• Disorders of events break results

• Recorded time based queries? Or arrival time based queries?
Batch/Stream Unification
Stream with Spark
Apache Flink
Flink Distributed Snapshot
Fault Tolerance in Stream
• At Least Once : ensure all operators see all events 

• Stream -> Replay on failure

• Exactly Once :

• Flink : distributed Snapshot

• Spark : Micro Batch
Jay Kreps
Kappa Architecture

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Big Data Computing Architecture