benchmarking interactive social networking actions

Benchmarking Interactive Social Benchmarking Interactive Social Networking ActionsNetworking Actions

Shahram GhandeharizadehShahram GhandeharizadehDirector of Database LabDirector of Database LabComputer Science DepartmentComputer Science DepartmentUniversity of Southern CaliforniaUniversity of Southern California

Outline

Motivation Research questions

Survey use cases BG Benchmark FORSEE Future research

Motivation Data Stores

Cloud Services

Person-to-person cloud services

Research Questions

What is the tradeoff between alternative data models? E.g., Is JSON superior to the relational

data model?

How do alternative architectures compare with one another? E.g., Is cache augmented SQL as good as

a document/extensible store?

Do NewSQL data stores scale as well as NoSQL data stores?

Survey Use Case

S. Barahmand and S. Ghandeharizadeh. BG: A Benchmark to Evaluate Interactive Social Networking Actions. CIDR ‘13, Asilomar, CA.

Data Model

Accounts

Friend

MembersPages

Follow

Resources Own

Share

Share

News Feed Displays Ownd

BG Architecture

Scalable

Emulates User Behavior

Service Level AgreementQuick and Efficient Rating

Visualization Tool

S. Barahmand and S. Ghandeharizadeh. Expedited Benchmarking of Social Networking Actions with Agile Data Loading Techniques. CIKM ‘13, SF, CA.

http://bgbenchmark.org

Good Benchmark = FORSEE

Focus on an important debate & provide relevant metrics to facilitate progress.

One number to describe alternative designs/solution.

Runs in a reasonable amount of time.

Scalable.

Effective abstraction with meaningful requests.

Extendible.

Good Benchmark = FORSEE

F

One number to describe alternative designs/solution.

Runs in a reasonable amount of time.

Scalable.

Effective abstraction with meaningful requests.

Extendible.

+ Unpredictable data

Good Benchmark = FORSEE

F

O

Runs in a reasonable amount of time.

Scalable.

Effective abstraction with meaningful requests.

Extendible.

+ Unpredictable data

SoAR

Good Benchmark = FORSEE

F

O

R

Scalable.

Effective abstraction with meaningful requests.

Extendible.

+ Unpredictable data

SoAR

4 months to rate =1 Week to rate =

Good Benchmark = FORSEE

F

O

R

S

Effective abstraction with meaningful requests.

Extendible.

+ Unpredictable data

SoAR

4 months to rate =1 Week to rate =

Good Benchmark = FORSEE

F

O

R

S

E

Extendible.

+ Unpredictable data

SoAR

4 months to rate =1 Week to rate =

Only when two members are NOT friends!

Good Benchmark = FORSEE

F

O

R

S

E

E

+ Unpredictable data

SoAR

4 months to rate =1 Week to rate =

Only when two members are NOT friends!

FORSEE = PREDICT

Good Benchmark = FORSEE

F

O

R

S

E

E

+ Unpredictable data

SoAR

4 months to rate =1 Week to rate =

Only when two members are NOT friends!

A good benchmark helps settle debates

quickly to enable its discipline to make rapid progress.

Future Research: Data Sciences

Challenge: Wide variety of science applications with diverse debates.

Hypothesis: A benchmark generator.

BenchmarkGenerator

ER diagram

Actions & their dependencies

Key Metrics

Application (data science)

SpecificBenchmark

Future Reseach Evaluate the hypothesis using BG.

Extend to other data science applications.

BenchmarkGenerator

Unpredictable data

Big Data: Operations

Simple Complex

Off-line

Interactive

Ad-hoc

Pre-specified

Big Data: Google Analytics

Simple Complex

Off-line

Interactive

Ad-hoc

Pre-specified

1. Gather click stream data: Optimized for writes, 2. Compute aggregated data: MapReduce/Hadoop

Objective:1. Advertising ROI2. Frequency of access to pages

Big Data: Google Analytics

Simple Complex

Off-line

Interactive

Ad-hoc

Pre-specified

1. Gather click stream data: Optimized for writes, 2. Compute aggregated data: MapReduce/Hadoop3. Enable users to view aggregated data.

Objective:1. Advertising ROI2. Frequency of access to pages

Big Data: Facebook

Show profile page of Farah FawcettFollow Barak ObamaFriend Lady Gaga

Simple Complex

Off-line

Interactive

Ad-hoc

Pre-specified

3 Vs: Facebook High Volume:

1.2 billion user profiles, 150 billion friend connections, 1.13 trillion likes, 17 billion tagged locations, 240 billion photos, ….

High Velocity: 700 million active users daily, 4.5 billion likes daily, 350

million photos uploaded daily, …

High Variety: Mix of data types: Structured records, multimedia content,

text.

Source: http://expandedramblings.com/index.php/by-the-numbers-17-amazing-facebook-stats/ posted on Oct 6, 2013.

Expertise/Contributions

BG Benchmark to evaluate performance of alternative data stores: SQL, NoSQL, NewSQL.

http://bgbenchmark.org

A high performance CASQL solution that minimizes software development life cycle.

KOSAR, a prototype of a CASQL solution.

Simple ComplexOff-line

Interactive

Ad-hoc

Pre-specified

BG, http://bgbenchmark.org

Joint work with Sumita Barahmand Benchmark for interactive social

networking actions. Consists of 11 actions:

CASQL

Joint work with Jason Yap. Key insight: Query result look up is

faster than query processing. Contribution is physical data

independence in CASQL systems: Transparent caching Serial schedules Detection of race conditions and

prevention of inconsistent states.

KOSAR

Joint work with Reihane Boghrati, Lakshmy Mohanan and Neeraj Narang.

A software prototype of CASQL Scalable Highly available Elastic

Boosts performance of a leading industrial strength RDBMS vendor from 2 actions per second to more than 300,000 actions per second.

BG Coordinator

Delta Analyzer

BGClient 2 BGClient NBGClient 1

Experiment

Load

Agile Data Loading Techniques

Experiment

…

Data Store Server

…