introduction to bigdata analysis stack - sjtu · introduction to bigdata analysis stack jeremy wei...

Lecture 11Introduction to BigData Analysis Stack

Jeremy WeiCenter for HPC, SJTU

May 8th, 2017

1896 1920 1987 2006CS075

Outline

• What is BigData?

• Hadoop Ecosystem

• Spark’s Improvement over Hadoop

What is Big Data?

Volume- exceeds limits of traditional

column and row relational DB

- constantly growing

Velocity- arrives rapidly, often in real

Variety- does not have a standard

structure, e.g. text, images

“Big data are high volume, high velocity, and high variety

information assets that require new forms of processing to

enable enhanced decision making, insight discovery and

process optimization” (Gartner 2012)

Requires

Vertical scalability- ability to grow storage to

accommodate new ‘records’

Requires

Data streaming- real time processing,

analysis and transformation

Requires

Horizontal scalability

- ability to add additional data

structures

How is big data generated?

Social media: posts, pictures and

videos

Purchase transaction records

Mobile phone GPS signals

High volume administrative

& transactional records

Sensors gathering information: e.g.

Climate, traffic etc.

Digital satellite images

Pilot 1: Smart meter project

aily e

icity c

Irish smart meter pilot study:

Single meter, total daily electricity consumption

July 2

er 2009

ary 20

t 2010

Christmas 2009 Christmas 2010

Consecutive days w ith low

consumption, possibly a w eek aw ay?

Pilot 1: Smart meter projectResearch Question: Investigate the potential of

smart meter electricity data (high frequency – 30

mins) to identify household occupancy levels,

potentially household structure

• England and Ireland both conducted

pilots of rollout in 2009-2010 – data now

available for research

• Southampton University commissioned

by Beyond 2011 to conduct preliminary

research (due mid Feb 2014)

Pilot 2: Mobile Phone Project

• 4 pilot projects:

– Smartmeter

– Mobile Phones

– Prices

– Twitter

• RD&I Research Innovation Labs

Pilot 2: Mobile Phone ProjectResearch Question: To investigate the

possibility of using mobile phone data to model

population flows, eg travel to work statistics

• Location data:

– Telefonica proposal to provide aggregate

data on origin-destination flows

• Requirement to engage with GDS

before proceeding further

Pilot 3: Prices ProjectResearch Question: To investigate how we

can scrape prices data from the internet and

how this data could be used within price

statistics

• 2-day workshop held with big data

experts from Statistics Netherlands

• Focus on groceries

• Early prototype code in place

• Engagement with Billion Prices Project

Pilot 3: Prices by webscraping

Rendered webpage:

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Pilot 3:

The Billion Prices Project @ MIT

Daily Online

Price Index

(United

States)

Lehman Brothers files

for bankruptcy (15 Sept

Pilot 4: Twitter ProjectResearch Question: To investigate how to capture

geo-located tweets from Twitter and how this data

might provide insights on commuting patterns and

internal migration

• Opportunity to start experimenting early on with big

data technologies

• Pilot work has successfully harvested geo-located

tweets from the live Twitter feed using Python and

Twitter API

• Need to determine whether planned application will

exceed rate-limits

Pilot 4: Twitter ProjectTemporal Patterns of International Mobility by selected country

Pilot 4: Mobility patterns from

Twitter

Calais

Implication of BigData

• Analyzing the whole collection, not sampled ones.

• Long-tailed low-density heterogeneous data.

• Correlation, not causation.

ArchitectureofBigDataAnalytics

Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications

Mining

Reports

QueriesHadoop

MapReduce

Zookeeper

Cassandra

Mahout

Others

Middleware

Extract

Transform

Warehouse

Traditional

Format

Tables

* Internal

* External

* Multiple

formats

* Multiple

locations

* Multiple

applications

BigData

SourcesBigData

Transformation

BigData

Platforms&Tools

BigData

Analytics

Applications

BigData

Analytics

Transformed

DataRaw

MapReduce Processing

Storage

Source: http://hadoop.apache.org/

Why Hadoop?

MapReduce Workflow

MapReduce

+ HBase:HadoopDatabase

Source: Shiva Achari (2015), Hadoop Essentials - Tackling the Challenges of Big Data with Hadoop, Packt Publishing

+Pig:High-levelscriptsforHadoop

+ Sqoop:SQLimporterforHadoop

+ Hive:SQLonMapReduce

+Impala:High-performanceSQLonMapReduce

+ HadoopEcosystem

Limitations of Hadoop

• Needs synchronization before shuffle, in which the slowest task may become the bottleneck.

• Needs serialization between iteration, which is unfit for iterative jobs.

• Batch jobs is NOT optimized for streaming interactive queries.

• Do NOT utilize memory efficiently.

Next-gen Big Data Processing Goals

• Low latency (interactive) queries on

historical data: enable faster decisions

– E.g., identify why a site is slow and fix it

• Low latency queries on live data (streaming):

enable decisions on real-time data

– E.g., detect & block worms in real-time (a

worm may infect 1mil hosts in 1.3sec)

• Sophisticated data processing: enable

‚better‛ decisions

– E.g., anomaly detection, trend analysis

Today’s Open Analytics Stack…

• ..mostly focused on large on-disk datasets: great for

batch but slow

Application

Storage

Data Processing

Infrastructure

Interactive Streaming

One stack to

rule them all!

§ Easy to combine batch, streaming, and interactive computations

§ Easy to develop sophisticated algorithms

§ Compatible with existing open source ecosystem (Hadoop/HDFS)

Support Interactive and Streaming Comp.

• Aggressive use of memory

• Why?

1. Memory transfer rates >> disk or SSDs

2. Many datasets already fit into memory

• Inputs of over 90% of jobs in

Facebook, Yahoo!, and Bing clusters

fit into memory

• e.g., 1TB = 1 billion records @ 1KB

3. Memory density (still) grows with Moore’s law

• RAM/SSD hybrid memories at

horizon High end datacenter node

16 cores

10-30TB

128-512GB

10Gbps

0.2-1GB/s

(x10 disks)

1-4GB/s(x4 disks)

40-60GB/s

• Increase parallelism

• Why?

– Reduce work per node à

improve latency

• Techniques:

– Low latency parallel scheduler

that achieve high locality

– Optimized parallel communication

patterns (e.g., shuffle, broadcast)

– Efficient recovery from failures

and straggler mitigation

result

Tnew (< T)

• Trade between result accuracy and

response times

• Why?

– In-memory processing does not

guarantee interactive query

processing

• e.g., ~10’s sec just to scan 512

GB RAM!

• Gap between memory capacity

and transfer rate increasing

• Challenges:

– accurately estimate error and

running time for…

– … arbitrary computations

128-512GB

16 cores

40-60GB/s

doubles every 18 months

doubles every 36 months

In-Memory Cluster Computing for

Iterative and Interactive Applications

UC Berkeley

Background

• Commodity clusters have become an important computing

platform for a variety of applications

– In industry: search, machine translation, ad targeting, …

– In research: bioinformatics, NLP, climate simulation, …

• High-level cluster programming models like MapReduce

power many of these apps

• Theme of this work: provide similarly powerful abstractions

for a broader class of applications

Motivation

Current popular programming models for clusters transform data flowing from stable storage to stable storage

e.g., MapReduce:

Reduce

Input Output

Motivation• Acyclic data flow is a powerful abstraction, but is

not efficient for applications that repeatedly reuse a

working set of data:

– Iterative algorithms (many in machine learning)

– Interactive data mining tools (R, Excel, Python)

• Spark makes working sets a first-class concept to

efficiently support these apps

Spark Goal

• Provide distributed memory abstractions for clusters to

support apps with working sets

• Retain the attractive properties of MapReduce:

– Fault tolerance (for crashes & stragglers)

– Data locality

– Scalability

Solution: augment data flow model with

“resilient distributed datasets” (RDDs)

Programming Model

• Resilient distributed datasets (RDDs)

– Immutable collections partitioned across cluster that can be rebuilt if a partition is lost

– Created by transforming data in stable storage using data flow operators (map, filter, group-by, …)

– Can be cached across parallel operations

• Parallel operations on RDDs

– Reduce, collect, count, save, …

• Restricted shared variables

– Accumulators, broadcast variables

Example: Log Mining

•Load error messages from a log into memory,

then interactively search for various patterns

lines = spark.textFile(“hdfs://...”)

errors = lines.filter(_.startsWith(“ERROR”))

messages = errors.map(_.split(‘\t’)(2))

cachedMsgs = messages.cache()

Block 1

Block 2

Block 3

Driver

cachedMsgs.filter(_.contains(“foo”)).count

cachedMsgs.filter(_.contains(“bar”)).count

results

Cache 1

Cache 2

Cache 3

RDDTransformed

Cached RDD Parallel

operation

Result: full-text search of

Wikipedia in <1 sec (vs 20 sec

for on-disk data)

RDDs in More Detail• An RDD is an immutable, partitioned, logical collection of records

– Need not be materialized, but rather contains information to rebuild a dataset from stable storage

• Partitioning can be based on a key in each record (using hash or range partitioning)

• Built using bulk transformations on other RDDs

• Can be cached for future reuse

RDD Operations

Transformations

(define a new RDD)

filter

sample

groupByKey

reduceByKey

Parallel operations (Actions)

(return a result to driver)

reduce

collect

lookupKey

RDD Fault Tolerance

• RDDs maintain lineage information that can be used to

reconstruct lost partitions

• e.g.:cachedMsgs = textFile(...).filter(_.contains(“error”))

.map(_.split(‘\t’)(2))

.cache()

HdfsRDDpath: hdfs://…

FilteredRDDfunc:

contains(...)

MappedRDDfunc: split(…)

CachedRDD

Example 1: Logistic Regression

• Goal: find best line separating two sets of points

target

random initial line

Logistic Regression Code

• val data = spark.textFile(...).map(readPoint).cache()

• var w = Vector.random(D)

• for (i <- 1 to ITERATIONS) {

• val gradient = data.map(p =>

• (1 / (1 + exp(-p.y*(w dot p.x))) - 1) * p.y * p.x

• ).reduce(_ + _)

• w -= gradient

• println("Final w: " + w)

Logistic Regression Performance

127 s / iteration

first iteration 174 s

further iterations 6 s

Example 2: MapReduce

• MapReduce data flow can be expressed using RDD

transformations

res = data.flatMap(rec => myMapFunc(rec)).groupByKey().map((key, vals) => myReduceFunc(key, vals))

Or with combiners:

res = data.flatMap(rec => myMapFunc(rec)).reduceByKey(myCombiner).map((key, val) => myReduceFunc(key, val))

Example 3

Other Spark Applications

• Twitter spam classification (Justin Ma)

• EM alg. for traffic prediction (Mobile Millennium)

• K-means clustering

• Alternating Least Squares matrix factorization

• In-memory OLAP aggregation on Hive data

• SQL on Spark (future work)

Berkeley Data Analytics Stack

(BDAS)

Infrastructure

Storage

Data Processing

Application

Resource Management

Data Management

Share infrastructure across

frameworks

(multi-programming for datacenters)

Efficient data sharing across

frameworks

Data Processing

• in-memory processing

• trade between time, quality, and

Application

New apps: AMP-Genomics, Carat, …

Berkeley AMPLab

§ ‚Launched‛ January 2011: 6 Year Plan

– 8 CS Faculty

– ~40 students

– 3 software engineers

• Organized for collaboration:

Berkeley AMPLab

• Funding:

– XData, CISE Expedition Grant

– Industrial, founding sponsors

– 18 other sponsors, including

Goal: Next Generation of Analytics Data Stack for Industry &

Research:

• Berkeley Data Analytics Stack (BDAS)

• Release as Open Source

Berkeley Data Analytics Stack

(BDAS)• Existing stack components….

MPI…

Resource

Mgmnt.

Processing

Hadoop

HIVE Pig

HBase Storm

Data Management

Data Processing

Resource Management

Mesos• Management platform that allows multiple framework to share

cluster

• Compatible with existing open analytics stack

• Deployed in production at Twitter on 3,500+ servers

MPI…

Resource

Mgmnt.

Processing

Hadoop

HIVE Pig

HBase Storm

Spark• In-memory framework for interactive and iterative

computations

– Resilient Distributed Dataset (RDD): fault-tolerance, in-

memory storage abstraction

• Scala interface, Java and Python APIs

Resource

Mgmnt.

ProcessingStorm…

Spark Hadoop

HIVE Pig

Spark Streaming [Alpha Release]

• Large scale streaming computation

• Ensure exactly one semantics

• Integrated with Spark à unifies batch, interactive, and streaming

computations!

Resource

Mgmnt.

Processing

Hadoop

HIVE PigStor

Streamin

Shark à Spark SQL

• HIVE over Spark: SQL-like interface (supports Hive 0.9)

– up to 100x faster for in-memory data, and 5-10x for disk

• In tests on hundreds node cluster at

Resource

Mgmnt.

ProcessingStor

Streamin

g Shark

Hadoop

HIVE Pig

Tachyon

• High-throughput, fault-tolerant in-memory storage

• Interface compatible to HDFS

• Support for Spark and Hadoop

Resource

Mgmnt.

Processing

Hadoop

HIVE PigStor

Streamin

g Shark

Tachyon

BlinkDB

• Large scale approximate query engine

• Allow users to specify error or time bounds

• Preliminary prototype starting being tested at Facebook

Resource

Mgmnt.

ProcessingStor

Streamin

g Shark

BlinkDB

HDFSData

Mgmnt.Tachyon

Hadoop

SparkGraph

• GraphLab API and Toolkits on top of Spark

• Fault tolerance by leveraging Spark

Resource

Mgmnt.

ProcessingStor

Streamin

g Shark

BlinkDB

HDFSData

Mgmnt.Tachyon

Hadoop

PigSpark

• Declarative approach to ML

• Develop scalable ML algorithms

• Make ML accessible to non-experts

Resource

Mgmnt.

ProcessingStor

Streamin

g Shark

BlinkDB

HDFSData

Mgmnt.Tachyon

Hadoop

PigSpark

Compatible with Open Source Ecosystem

• Support existing interfaces whenever possible

Resource

Mgmnt.

ProcessingStor

Streamin

g Shark

BlinkDB

HDFSData

Mgmnt.Tachyon

Hadoop

PigSpark

GraphLab API

Hive Interface

and Shell

HDFS API

Compatibility layer for

Hadoop, Storm, MPI,

etc to run over Mesos

Compatible with Open Source Ecosystem

• Use existing interfaces whenever possible

Resource

Mgmnt.

ProcessingStor

Streamin

g Shark

BlinkDB

HDFSData

Mgmnt.Tachyon

Hadoop

PigSpark

Support HDFS

API, S3 API, and

Hive metadata

Support Hive API

Accept inputs

from Kafka,

Flume, Twitter,

Sockets, …

Summary• Support interactive and streaming computations

– In-memory, fault-tolerant storage abstraction, low-latency scheduling,...

• Easy to combine batch, streaming, and interactivecomputations

– Spark execution engine supports all comp. models

• Easy to develop sophisticated algorithms

– Scala interface, APIs for Java, Python, Hive QL, …

– New frameworks targeted to graph based and ML algorithms

• Compatible with existing open source ecosystem

• Open source (Apache/BSD) and fully committed to release high quality software

– Three-person software engineering team lead by Matt Massie (creator of Ganglia, 5th Cloudera engineer)

Interactive

Streaming

Conclusion

• By making distributed datasets a first-class primitive, Spark provides a simple, efficient programming model for stateful data analytics

• RDDs provide:

– Lineage info for fault recovery and debugging

– Adjustable in-memory caching

– Locality-aware parallel operations

• We plan to make Spark the basis of a suite of batch and interactive data analysis tools

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