scalable tools - part i introduction to scalable...

Scalable Tools - Part I

Introduction to Scalable Tools

Adisak Sukul, Ph.D.,

Lecturer,

Department of Computer Science,

[email protected]

http://web.cs.iastate.edu/~adisak/MBDS2018/

mailto:[email protected]

Scalable Tools session

• Before we begin:

• Do you have a VirtualBox and Ubuntu vm

created?

• You can copy it from a usb disk

• Options 2: Run on cloud (if you can't run it

locally):

• Setup Google cloud or Amazon EC2 with

Python and Spark

• We will be using Spark, Python and PySpark.2

What is Big Data?

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Reference: Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Hadoop Training | Edureka

https://www.youtube.com/watch?v=mafw2-CVYnA&t=1995s

Why scale?

• In early 2000s, every company have to paying

more and more to DBMS company.

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Scalable tools for Big Data

• MapReduce is a programming model and an

associated implementation for processing and

generating big data sets with

a parallel, distributed algorithm on a cluster.

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https://en.wikipedia.org/wiki/Programming_model

https://en.wikipedia.org/wiki/Big_data

https://en.wikipedia.org/wiki/Parallel_computing

https://en.wikipedia.org/wiki/Distributed_computing

https://en.wikipedia.org/wiki/Cluster_(computing)

What is the problems with Big Data

in Traditional System

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Traditional scenario

• Manageable workload

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When data increased, traditional

systems would fail• Data come in to fast (high velocity)

• Data come in unstructured (high verity)

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How to solve this problem?• Issue 1: Too many order per hours?

• Answer??

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Hire more Cook! (distributed workers)



• Same thing happened with the servers and

stroage

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• Issue 2: Food shelf becomes Bottleneck

• Now, how to solve it???Distributed and Parallel Approach

Data locality concept in Hadoop: data is locally available for each processing unit



• Sounds good?

• How do we solve Big Data problems (storing

and processing Big Data) by using Distributed

and Parallel Approach like that?

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• Yes, we can use Hadoop!

• Hadoop is a framework that allow us to store

and process large data sets in parallel and

distributed fashion

• Hadoop is a framework that allow us to store and

process large data sets in parallel and distributed

fashion

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Who came up with MapReduce

concept?

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Hadoop Master/Slave Architecture

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cont.1

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cont.2

got backup worker for all projects

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How it translate to actual architecture

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Let’s play a game• Spit to four group,

• Assign 1 manager, 1 assistant

• Assistant collect result, time the process

• Group A: everybody read the whole paper (5 pages), manager combine (average) the

result

• Group B: each person read one page, manager combine the result

• Group A: everybody read the whole paper (5 pages), manager combine (average) the

result

• Missing Page 2 result

• Group B: each person read one page, manager combine the result

• Missing Page 2 result

• Task for team member:

• Read the paper

• Count the word (not case-sensitive):

• Year

• Dream

• Will

• Describe

• Soul

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HDFS Data Block

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Fault tolerance

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Fault tolerance: Replication Factor

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Example: MapReduce for word count

process

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Apache Spark

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Apache Spark• is a lightning fast real-time processing framework.

• It does in-memory computations to analyze data in real-time.

• It came into picture as Apache Hadoop MapReduce was

performing batch processing only and lacked a real-time

processing feature.

• Hence, Apache Spark was introduced as it can perform stream

processing in real-time and can also take care of batch

processing.

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Apache Spark

• It leverages Apache Hadoop for both storage

and processing.

• It uses HDFS (Hadoop Distributed File system)

for storage.

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Spark is fast!

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But it could cast more, depend on the memory cost

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pyspark

• PySpark, you can work with RDDs in Python

programming language also. It is because of a

library called Py4j that they are able to achieve

this.

• PySpark offers PySpark Shell which links the

Python API to the spark core and initializes the

Spark context. Majority of data scientists and

analytics experts today use Python because of

its rich library set. Integrating Python with Spark

is a boon to them.

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Spark benchmark (PySpark and

Pandas)• https://databricks.com/blog/2018/05/03/benchmarking-apache-spark-on-a-single-node-machine.html

• Benchmarking Apache Spark on a Single Node Machine The benchmark

involves running the SQL queries over the table “store_sales” (scale 10 to 260) in Parquet

file format.

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https://databricks.com/blog/2018/05/03/benchmarking-apache-spark-on-a-single-node-machine.html

• What we learn from this?

def NewDataProject():

if dataset is large:

use Spark or Hadoop

else:

use Python Pandas

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