Big Data Beyond Hadoop*: Research Directions for the Future

ACAS002

Jason Dai, Engineering Director and Principal Engineer, Software and Solutions Group Michael Wrinn, PhD, Research Program Director, University Research Office, Intel Labs

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Agenda

• Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production

The PDF for this Session presentation is available from our Technical Session Catalog at the end of the day at: intel.com/go/idfsessionsBJ

URL is on top of Session Agenda Pages in Pocket Guide

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Agenda

• Big data and the Hadoop* ecosystem • Intel university collaborations on bit data research • Efficient in-memory implementations of mapreduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production

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What is Big Data? Big Data is data that is too big, too fast or too hard for existing systems and algorithms to handle • Too Big

– Terabytes going on petabytes – Smart (not brute force) massive parallelism required

• Too Fast – Sensor tagging everything creates a firehose – Ingest problem

• Too Hard – Complex analytics are required (e.g., to find patterns, trends

and relationships) – Need to combine diverse data types (No Schema, Uncurated,

Inconsistent Syntax and Semantics)

Samuel Madden ISTC Director and Professor EECS, MIT

Data should be a resource, not a load Existing data processing tools are not a good fit

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Example: Web Analytics

Large web enterprises: thousands of servers, millions of users, and terabytes per day of “click data” Not just simple reporting: e.g., in real time, determine what users are likely to do next, or what ad to serve them, or which user they are most similar to Existing analytics systems either: do not scale to required volumes, or do not provide required sophistication

Samuel Madden ISTC Director and Professor EECS, MIT

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Example: Sensor Analytics Smartphone providers tolling agencies municipalities insurance companies doctors businesses Capturing massive streams of video, position, acceleration, and other data from phones and other devices This data needs to be stored, processed, and mined, e.g., to measure traffic, driving risk, or medical prognosis

Samuel Madden ISTC Director and Professor EECS, MIT

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Era of Data Exchange

Traditional Business Solutions Connecting to New Analytics Models for Real-time Value Opportunities

New Analytics Models

Cost-effective Vertical

Solutions

Compute Platform Topology Fabric

MIC

EP

EX

EXALYTICS

Traditional Business Solutions

Business Processing Innovation In-Memory DB – Integrated

Analytics –Systems & Appliances

Healthcare Energy - Scientific Manufacturing FSI eCommerce

Big Data

Hadoop* in the Big Data Ecosystem

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Agenda

• Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production

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Data Delivery Computing and Storage Platform

Data Management and Processing

Analytics

Data Usage Visualization End user tools

Apps Services

Intel Activity Landscape on Big Data

Distributed Machine Learning

(university collaborators)

Internet of Things / M2M (Intel Labs &

university collaborators)

Intel Software

Intel Architecture

Intel Labs

Intel IT

HiTune* and other tools for Hadoop

Business Intelligence and Hadoop*

Compression & Decompression IPs

Microservers

Hadoop Distribution & Service

Others

Trust Broker (McAfee*)

Location Based Service (Telmap)

End-to-End Data Security

Federated Device Architecture

Video Analytics

Distributed Video Analytics

Distributed Architecture (Guavus)

Healthcare, Telco, …

Large Object Storage

Corporate Data Solution Programs for Big Data and Analytics

Big Data Market

Sizing and Segmentation

(with Bain)

Hadoop performance & Architecture

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Agenda

• Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production

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Algorithms, Machines, People (AMPLab)

Adaptive/Active Machine Learning

and Analytics

Cloud Computing CrowdSourcing/

Human Computation

Massive and

Diverse Data

All software released as BSD Open Source

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Berkeley Data Analysis System • Mesos*: resource management platform • SCADS: scale-independent storage systems • PIQL, Spark: processing frameworks

Higher Query Languages / Processing Frameworks

Resource Management

Storage

Mesos

AMPLab 3rd party

HDFS SCADS

Hadoop*

Hive* Pig* … MPI PIQL

Shark

Spark … …

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Data Center Programming: Spark

• In-memory cluster computing framework for applications that reuse working sets of data – Iterative algorithms: machine learning, graph

processing, optimization – Interactive data mining: order of magnitude faster

than disk-based tools

• Key idea: RDDs “resilient distributed datasets” that can automatically be rebuilt on failure – Keep large working sets in memory – Fault tolerance mechanism based on “lineage”

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Spark: Motivation

Complex jobs, interactive queries and online processing all need one thing that Hadoop* MR lacks: • Efficient primitives for data sharing

Sta

ge 1

Sta

ge 2

Sta

ge 3

Iterative job

Query 1

Query 2

Query 3

Interactive mining

Job

1

Job

2

…

Stream processing

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Xfer and Sharing in Hadoop*

Iter. 1 Iter. 2 . . .

Input

HDFS read

HDFS write

HDFS read

HDFS write

Input

Query 1

Query 2

Query 3

Result 1

Result 2

Result 3

. . .

HDFS read

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Iter. 1 Iter. 2 . . .

Input

Spark: In-Memory Data Sharing

Distributed memory

Input

Query 1

Query 2

Query 3

. . .

One-time processing

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Introducing Shark

• Spark + Hive* (the SQL in NoSQL)

• Utilizes Spark’s in-memory RDD caching

and flexible language capabilities: result

reuse, and low latency

• Scalable, fault-tolerant, fast

• Query Compatible with Hive

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Benchmarks: Query 1

SELECT * FROM grep WHERE field LIKE ‘%XYZ%’;

30GB input table

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Benchmark: Query 2

5 GB input table

SELECT pagerank, pageURL FROM rankings WHERE pagerank > 10;

*

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Agenda

• Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production

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CPU 1 CPU 2 CPU 3 CPU 4

Data Parallelism (MapReduce)

1 2 . 9

4 2 . 3

2 1 . 3

2 5 . 8

2 4 . 1

8 4 . 3

1 8 . 4

8 4 . 4

1 7 . 5

6 7 . 5

1 4 . 9

3 4 . 3

Solve a huge number of independent subproblems

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MapReduce for Data-Parallel ML

• Excellent for large data-parallel tasks!

Data-Parallel Graph-Parallel

Cross Validation

Feature Extraction

MapReduce

Computing Sufficient Statistics

Is there more to Machine Learning

?

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Data

Machine Learning Pipeline

images

docs

movie ratings

Extract Features

faces

important words

side info

Graph Formation

similar faces

shared words

rated movies

Structured Machine Learning Algorithm

belief propagation

LDA

collaborative filtering

Value from Data

face labels

doc topics

movie recommend

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Data

Parallelizing Machine Learning

Extract Features

Graph Formation Structured

Machine Learning Algorithm

Value from Data

Graph Ingress mostly data-parallel

Graph-Structured Computation

graph-parallel

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Addressing Graph-Parallel ML

Data-Parallel Graph-Parallel

Cross Validation

Feature Extraction

Map Reduce

Computing Sufficient Statistics

Graphical Models

Gibbs Sampling Belief Propagation Variational Opt.

Semi-Supervised Learning

Label Propagation CoEM

Data-Mining PageRank

Triangle Counting

Collaborative Filtering

Tensor Factorization

Map Reduce? Graph-Parallel Abstraction

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0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14 16

Sp

eed

up

Number of CPUs

Bett

er

Optimal

GraphLab CoEM

Example: Never Ending Learner Project (CoEM)

GraphLab 16 Cores 30 min

15x Faster! 6x fewer CPUs!

Hadoop* 95 Cores 7.5 hrs

Distributed GraphLab

32 EC2 machines

80 secs

0.3% of Hadoop time

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Example: PageRank

40M Webpages, 1.4 Billion Links

GraphLab

Twister

Hadoop5.5 hrs.

1 hr.

8 min.

*

*

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Agenda

• Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production

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Intel’s Efforts on Hadoop*

• Intel® Distribution for Apache Hadoop*

– Performance, security and management – Downloadable from http://hadoop.intel.com/

• Intel’s open source initiatives for Hadoop – HiBench: comprehensive Hadoop benchmark suite

https://github.com/intel-hadoop/hibench – Project Panthera: efficient support of standard SQL features

on Hadoop https://github.com/intel-hadoop/project-panthera

– Project Rhino: enhanced data protection for the Apache Hadoop ecosystem https://github.com/intel-hadoop/project-rhino

– Graph Builder: scalable graph construction using Hadoop http://graphlab.org/intel-graphbuilder/

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Using Spark/Shark for In-memory, Real-time Data Analysis

• Use case 1: ad-hoc & interactive queries – Interactive queries (exploratory ad-hoc queries, BI charting &

mining) – Similar projects: Google* Dremel, Facebook* Peregrine,

Cloudera* Impala, Apache* Drill, etc. (several seconds latency) – Use Shark/Spark to achieve close to sub-second latency for

interactive queries • Use case 2: in-memory, real-time analysis

– Iterative data mining, online analysis (e.g., loading table into memory for online analysis, caching intermediate results for iterative machine learning)

– Similar projects: Google PowerDrill – Use Shark/Spark to reliably load data in distributed memory for

online analysis

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Using Spark/Shark for In-memory, Real-time Data Analysis

• Use case 3: stream processing – Streaming analysis, CEP (e.g., intrusion detection, real-time

statistics, etc.) – Similar projects: Twitter* Storm, Apache* S4, Facebook* Puma – Use Spark streaming for stream processing

Better reliability Unified framework and application for both offline, online &

streaming analysis • Use case 4: graph-parallel analysis & machine learning

– Use case: graph algorithms, machine learning (e.g., social network analysis, recommendation engine)

– Similar projects: Google* Pregel, CMU GraphLab*

– Use Bagel (Pregel on Spark) for graph parallel analysis & machine learning on Spark

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Summary

• MapReduce as implemented in Hadoop* is extremely useful, but:

– In-memory implementations show serious advantages

– Graph algorithms may be more suitable for problem at hand

• Intel continues to work with university researchers

• Intel works to implement research results into production environments

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Call to Action

• Use Intel Research results in your own big data efforts!

• Work with us on the next-gen, in-memory, real-time analysis using Spark/Shark

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