Bigdata and Hadoop with Docker

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This document provides an introduction to big data and Hadoop. It discusses how distributed systems can scale to handle large data volumes and discusses Hadoop's architecture. It also provides instructions on setting up a Hadoop cluster on a laptop and summarizes Hadoop's MapReduce programming model and YARN framework. Finally, it announces an upcoming workshop on Spark and Pyspark.

Data & Analytics

Bigdata and Hadoop
Haridas Narayanaswamy
https://haridas.in
06/Mar/2019

Quick reminder
1. HYD and BLR: Online Attendance registration link
https://docs.google.com/spreadsheets/d/1DAuclSqS6xdv4QtixUQIs6
OjpxXRLVEEmnCeAkgD9z8/edit#gid=0
2.
docker pull haridasn/hadoop-2.8.5
docker pull haridasn/hadoop-cli

Agenda
• Introduction to the big-data problems
• How we can scale systems.
• Hadoop introduction
• Setup a Hadoop cluster on your laptop.

IO bound vs CPU bound problems
• Downloading a file
• Bitcoin mining ( proof of work )
• Watching a movie
• ETL jobs
• ML training

Latency Numbers
• nano second - unit
speed

Application Architectures
Online
Standalone
Distributed

Monolithic
• All on single machine
• Every application starts here

N-tier systems
• Multiple services in your application
• Separation of concerns
• Master-slave database systems
• Load balancer
• Caching layer
• Pretty good for standard application
workloads.

SOA and Microservices
• Service Oriented Architecture
• Each service will do only one task – fine
grained separation, hence named as
micro-services
• API Gateways
• Messaging Systems
• Scalable Databases accessible to micro
services
• Caching services
• Etc.

CAP Theorem
• Consistency - Reads from all
machines on the cluster would give
same data.
• Availability - All operation would
produce some result, even though
they aren’t consistent.
• Partition tolerance - System
perform normally even after some
nodes got disconnected from
network.
• Eventual Consistent systems
• Split brain problem

Bigdata platforms
Where they fit in ?
Offline vs Online systems

Count number of unique hits from India
• Consider this scenario for Facebook
• Daily total log file comes is in Petabytes
• One machine can’t save it
• Other scenarios
• User click tracking
• Tracking effectiveness of an Ad
• All flavours of ETL jobs

Hadoop
• Every node can store the data - HDFS
• Every node can do processing on data it
holds locally.
• Easily scalable
• Redundant and fault tolerant
• Main Components
• Name Node
• Data Node
• Resource Manager
• Node Manager

Map-Reduce compute model
• Main stages of map-reduce
• Copy code not data.
• Data locality
• How we can use map-reduce to count
unique hits from a region ?

Yarn and HDFS
• Main stages of map-reduce
• Dynamic programming ?
• Bring code to data location
• Data locality
• How we can use mapreduce to count
unique hits from a region ?

Yarn Framework
• Client Submits jobs into cluster
• AM handles application orchestration,
once it has been started by RM
• Containers are actual resources (
CPU/Ram/IO) allocated to AM.
• Job progress tracking

Hybrid environments
• Storage on cloud storages s3/azure storage
• Execution engine on our cloud or Kubernetes.

Next: Spark on Hadoop Yarn and Pyspark
ON 13-March-2019

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Bigdata and Hadoop with Docker

1. Bigdata and Hadoop Haridas Narayanaswamy https://haridas.in 06/Mar/2019

2. Quick reminder 1. HYD and BLR: Online Attendance registration link https://docs.google.com/spreadsheets/d/1DAuclSqS6xdv4QtixUQIs6 OjpxXRLVEEmnCeAkgD9z8/edit#gid=0 2. docker pull haridasn/hadoop-2.8.5 docker pull haridasn/hadoop-cli

3. Agenda • Introduction to the big-data problems • How we can scale systems. • Hadoop introduction • Setup a Hadoop cluster on your laptop.

4. IO bound vs CPU bound problems • Downloading a file • Bitcoin mining ( proof of work ) • Watching a movie • ETL jobs • ML training

5. Latency Numbers • nano second - unit speed

6. Application Architectures Online Standalone Distributed

7. Monolithic • All on single machine • Every application starts here

8. N-tier systems • Multiple services in your application • Separation of concerns • Master-slave database systems • Load balancer • Caching layer • Pretty good for standard application workloads.

9. SOA and Microservices • Service Oriented Architecture • Each service will do only one task – fine grained separation, hence named as micro-services • API Gateways • Messaging Systems • Scalable Databases accessible to micro services • Caching services • Etc.

10. CAP Theorem • Consistency - Reads from all machines on the cluster would give same data. • Availability - All operation would produce some result, even though they aren’t consistent. • Partition tolerance - System perform normally even after some nodes got disconnected from network. • Eventual Consistent systems • Split brain problem

11. Bigdata platforms Where they fit in ? Offline vs Online systems

12. Count number of unique hits from India • Consider this scenario for Facebook • Daily total log file comes is in Petabytes • One machine can’t save it • Other scenarios • User click tracking • Tracking effectiveness of an Ad • All flavours of ETL jobs

13. Hadoop • Every node can store the data - HDFS • Every node can do processing on data it holds locally. • Easily scalable • Redundant and fault tolerant • Main Components • Name Node • Data Node • Resource Manager • Node Manager

14. Map-Reduce compute model • Main stages of map-reduce • Copy code not data. • Data locality • How we can use map-reduce to count unique hits from a region ?

15. Yarn and HDFS • Main stages of map-reduce • Dynamic programming ? • Bring code to data location • Data locality • How we can use mapreduce to count unique hits from a region ?

16. Yarn Framework • Client Submits jobs into cluster • AM handles application orchestration, once it has been started by RM • Containers are actual resources ( CPU/Ram/IO) allocated to AM. • Job progress tracking

17. Hybrid environments • Storage on cloud storages s3/azure storage • Execution engine on our cloud or Kubernetes.

18. Workshop

19. Next: Spark on Hadoop Yarn and Pyspark ON 13-March-2019

20. Thank you Haridas N <hn@haridas.in>

Bigdata and Hadoop with Docker

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