pptx

Report
Introduction to Big Data,
mostly from
www.cs.kent.edu/~jin/BigData
Course in big data, Spring 2014
Ruoming Jin
What’s Big Data?
No single definition; here is from Wikipedia:
• Big data is the term for a collection of data sets so large and
complex that it becomes difficult to process using on-hand
database management tools or traditional data processing
applications.
• The challenges include capture, curation, storage, search,
sharing, transfer, analysis, and visualization.
• The trend to larger data sets is due to the additional
information derivable from analysis of a single large set of
related data, as compared to separate smaller sets with the
same total amount of data, allowing correlations to be found
to "spot business trends, determine quality of research,
prevent diseases, link legal citations, combat crime, and
determine real-time roadway traffic conditions.”
2
Big Data: 3V’s
3
30 billion RFID
12+ TBs
camera
phones
world wide
100s of
millions
of GPS
enabled
data every day
? TBs of
of tweet data
every day
tags today
(1.3B in 2005)
4.6
billion
devices sold
annually
25+ TBs of
2+
billion
log data
every day
76 million smart meters
in 2009…
200M by 2014
people on
the Web
by end
2011
Maximilien Brice, © CERN
CERN’s Large Hydron Collider (LHC) generates 15 PB a year
Variety (Complexity)
•
•
•
•
Relational Data (Tables/Transaction/Legacy Data)
Text Data (Web)
Semi-structured Data (XML)
Graph Data
– Social Network, Semantic Web (RDF), …
•
Streaming Data
– You can only scan the data once
•
A single application can be generating/collecting
many types of data
•
Big Public Data (online, weather, finance, etc)
To extract knowledge all these types of
data need to linked together
6
Velocity (Speed)
• Data is begin generated fast and need to be
processed fast
• Online Data Analytics
• Late decisions  missing opportunities
• Examples
– E-Promotions: Based on your current location, your purchase history,
what you like  send promotions right now for store next to you
– Healthcare monitoring: sensors monitoring your activities and body 
any abnormal measurements require immediate reaction
7
Real-time/Fast Data
Mobile devices
(tracking all objects all the time)
Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Sensor technology and networks
(measuring all kinds of data)
•
•
The progress and innovation is no longer hindered by the ability to collect data
But, by the ability to manage, analyze, summarize, visualize, and discover
knowledge from the collected data in a timely manner and in a scalable fashion
8
Real-Time Analytics/Decision Requirement
Product
Recommendations
that are Relevant
& Compelling
Improving the
Marketing
Effectiveness of a
Promotion while it
is still in Play
Influence
Behavior
Learning why Customers
Switch to competitors
and their offers; in
time to Counter
Customer
Preventing Fraud
as it is Occurring
& preventing more
proactively
Friend Invitations
to join a
Game or Activity
that expands
business
Harnessing Big Data
•
•
•
OLTP: Online Transaction Processing (DBMSs)
OLAP: Online Analytical Processing (Data Warehousing)
RTAP: Real-Time Analytics Processing (Big Data Architecture & technology)
10
The Model Has Changed…
• The Model of Generating/Consuming Data has Changed
Old Model: Few companies are generating data, all others are consuming data
New Model: all of us are generating data, and all of us are consuming data
11
THE EVOLUTION OF BUSINESS INTELLIGENCE
Speed
BI Reporting
OLAP &
Data warehouse
Business Objects, SAS,
Informatica, Cognos other SQL
Reporting Tools
Interactive Business
Intelligence &
In-memory RDBMS
QliqView, Tableau, HANA
Scale
Big Data:
Real Time &
Single View
Graph Databases
Big Data:
Batch Processing &
Distributed Data Store
Scale
Hadoop/Spark; HBase/Cassandra
1990’s
2000’s
2010’s
Speed
Big Data Analytics
• Big data is more real-time in nature
than traditional DW applications
• Traditional DW architectures (e.g.
Exadata, Teradata) are not wellsuited for big data apps
• Shared nothing, massively parallel
processing, scale out architectures
are well-suited for big data apps
13
Big Data Technology
15
Cloud Computing
• IT resources provided as a service
– Compute, storage, databases, queues
• Clouds leverage economies of scale of
commodity hardware
– Cheap storage, high bandwidth networks &
multicore processors
– Geographically distributed data centers
• Offerings from Microsoft, Amazon, Google, …
Topic 2: Hadoop/MapReduce
Programming & Data Processing
• Architecture of Hadoop, HDFS, and Yarn
• Programming on Hadoop
•
•
•
•
Basic Data Processing: Sort and Join
Information Retrieval using Hadoop
Data Mining using Hadoop (Kmeans+Histograms)
Machine Learning on Hadoop (EM)
• Hive/Pig
• HBase and Cassandra
References
• References:
•
•
•
•
Hadoop: The Definitive Guide, Tom White, O’Reilly
Hadoop In Action, Chuck Lam, Manning
Doing Data Science, Rachel Schutt and Cathy O’Neil, O’Reilly
Data-Intensive Text Processing with MapReduce, Jimmy Lin and
Chris Dyer (www.umiacs.umd.edu/~jimmylin/MapReduce-bookfinal.pdf)
• Good tutorial presentation & examples at:
• http://research.google.com/pubs/pub36249.html
• The definitive original paper:
http://research.google.com/archive/mapreduce.html
18
Cloud Resources
• Hadoop on your local machine
• Hadoop in a virtual machine on your local
machine (Pseudo-Distributed on Ubuntu)
• Hadoop in the clouds with Amazon EC2
Introduction to
MapReduce/Hadoop
From Ruoming Jin’s Slides,
themselves adapted from Jimmy Lin’s
slides (at UMD)
Limitations of Existing Data Analytics Architecture
BI Reports + Interactive Apps
RDBMS (aggregated data)
Can’t Explore Original
High Fidelity Raw Data
ETL Compute Grid
Moving Data To
Compute Doesn’t Scale
Storage Only Grid (original raw data)
Mostly Append
Collection
Archiving =
Premature
Data Death
Instrumentation
2
©2011 Cloudera, Inc. All Rights Reserved.
Slides from Dr. Amr Awadallah’s Hadoop talk at Stanford, CTO & VPE from Cloudera
Key Ideas
• Scale “out”, not “up”
– Limits of SMP and large shared-memory machines
• Move processing to the data
– Cluster may have limited bandwidth
• Process data sequentially, avoid random access
– Seeks are expensive, disk throughput is reasonable
• Seamless scalability
– From the mythical man-month to the tradable
machine-hour
The datacenter is the computer!
Image from http://wiki.apache.org/hadoop-data/attachments/HadoopPresentations/attachments/aw-apachecon-eu-2009.pdf
Apache Hadoop
• Scalable fault-tolerant distributed system for Big Data:
–
–
–
–
Data Storage
Data Processing
A virtual Big Data machine
Borrowed concepts/Ideas from Google; Open source
under the Apache license
• Core Hadoop has two main systems:
– Hadoop/MapReduce: distributed big data processing
infrastructure (abstract/paradigm, fault-tolerant, schedule,
execution)
– HDFS (Hadoop Distributed File System): fault-tolerant,
high-bandwidth, high availability distributed storage
MapReduce: Big Data Processing Abstraction
Example: word counts
Millions of documents in
Word counts out:
brown, 2
fox, 2
how, 1
now, 1
the, 3 …
In practice, before MapReduce and related technologies:
The first 10 computers are easy;
The first 100 computers are hard;
The first 1000 computers are impossible;
But now with MapReduce, engineers at Google often use 10000 computers!
What’s wrong with 1000 computers?
Some will crash while you’re working…
If probability of crash = .001
Then probability of all up = (1-.001)1000 = 0.37
MapReduce expects crashes, tracks partial work, keeps going
Typical Large-Data Problem
•
•
•
•
•
Iterate over a large number of records
Extract something of interest from each
Shuffle and sort intermediate results
Aggregate intermediate results
Generate final output
Key idea: provide a functional abstraction for these two
operations
(Dean and Ghemawat, OSDI 2004)
MapReduce
• Programmers specify two functions:
map (k, v) → [(k’, v’)]
reduce (k’, [v’]) → [(k’, v’’)]
– All values with the same key (k’) are sent to the
same reducer, in k’ order for each reducer
– Here [] means a sequence
• The execution framework handles everything
else…
“Hello World”: Word Count
Map(String docid, String text):
for each word w in text:
Emit(w, 1);
Reduce(String term, Iterator<Int> values):
int sum = 0;
for each v in values:
sum += v;
Emit(term, sum);
MapReduce “Runtime”
• Handles scheduling
– Assigns workers to map and reduce tasks
• Handles “data distribution”
– Moves processes to data
• Handles synchronization
– Gathers, sorts, and shuffles intermediate data
• Handles errors and faults
– Detects worker failures and restarts
• Everything happens on top of a distributed FS
(later)
MapReduce
• Programmers specify two functions:
map (k, v) → [(k’, v’)]
reduce (k’, [v’]) → [(k’, v’’)]
– All values with the same key are reduced together
• The execution framework handles everything else…
• Not quite…usually, programmers also specify:
partition (k’, number of partitions) → partition for k’
– Often a simple hash of the key, e.g., hash(k’) mod n
– Divides up key space for parallel reduce operations
• and eventual delivery of results to certain partitions
combine (k’, [v’]) → [(k’, v’’)]
– Mini-reducers that run in memory after the map phase
– Used as an optimization to reduce network traffic
k1 v1
k2 v2
map
a 1
k4 v4
map
b 2
c
combine
a 1
k3 v3
3
c
c
partition
k6 v6
map
6
a 5
combine
b 2
k5 v5
c
map
2
b 7
combine
9
a 5
partition
c
c
combine
2
b 7
partition
c
partition
Shuffle and Sort: aggregate values by keys
a
1 5
b
2 7
8
c
2 3
9 6
8 8
reduce
reduce
reduce
r1 s1
r2 s2
r3 s3
8
Word Count Execution
Input
the quick
brown fox
Map
Map
Shuffle & Sort
Reduce
brown: 1,1
fox: 1,1
how:1
now:1
the:1,1,1
the, 1
brown, 1
fox, 1
Reduce
the, 1
fox, 1
the, 1
the fox ate
the mouse
Map
how, 1
now, 1
brown, 1
how now
brown cow
Map
quick, 1
ate, 1
mouse, 1
cow, 1
ate: 1
cow: 1
mouse: 1
quick: 1
Reduce
Output
brown, 2
fox, 2
how, 1
now, 1
the, 3
ate, 1
cow, 1
mouse, 1
quick, 1
MapReduce Implementations
• Google has a proprietary implementation in C++
– Bindings in Java, Python
• Hadoop is an open-source implementation in Java
– Development led by Yahoo, used in production
– Now an Apache project
– Rapidly expanding software ecosystem
• Lots of custom research implementations
– For GPUs, cell processors, etc.
Hadoop History
– Google GFS paper published
July 2005 – Nutch uses MapReduce
Feb 2006 – Becomes Lucene subproject
Apr 2007 – Yahoo! on 1000-node cluster
Jan 2008 – An Apache Top Level Project
Jul 2008 – A 4000 node test cluster
• Dec 2004
•
•
•
•
•
• Sept 2008 – Hive becomes a Hadoop subproject
• Feb 2009 – The Yahoo! Search Webmap is a Hadoop application
that runs on more than 10,000 core Linux cluster and produces data
that is now used in every Yahoo! Web search query.
• June 2009 – On June 10, 2009, Yahoo! made available the source
code to the version of Hadoop it runs in production.
• In 2010 Facebook claimed that they have the largest Hadoop cluster
in the world with 21 PB of storage. On July 27, 2011 they
announced the data has grown to 30 PB.
Who uses Hadoop?
•
•
•
•
•
•
•
•
•
•
Amazon/A9
Facebook
Google
IBM
Joost
Last.fm
New York Times
PowerSet
Veoh
Yahoo!
Example Word Count (Map)
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word,one);
}
}
}
Example Word Count (Reduce)
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
Example Word Count (Driver)
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf,
args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
Word Count Execution
Input
the quick
brown fox
Map
Map
Shuffle & Sort
Reduce
brown: 1,1
fox: 1,1
how:1
now:1
the:1,1,1
the, 1
brown, 1
fox, 1
Reduce
the, 1
fox, 1
the, 1
the fox ate
the mouse
Map
how, 1
now, 1
brown, 1
how now
brown cow
Map
quick, 1
ate, 1
mouse, 1
cow, 1
ate: 1
cow: 1
mouse: 1
quick: 1
Reduce
Output
brown, 2
fox, 2
how, 1
now, 1
the, 3
ate, 1
cow, 1
mouse, 1
quick, 1
An Optimization: The Combiner
• A combiner is a local aggregation function
for repeated keys produced by same map
• For associative ops. like sum, count, max
• Decreases size of intermediate data
• Example: local counting for Word Count:
def combiner(key, values):
output(key, sum(values))
Word Count with Combiner
Input
the quick
brown fox
Map & Combine
Map
Shuffle & Sort
Reduce
Output
Reduce
brown, 2
fox, 2
how, 1
now, 1
the, 3
Reduce
ate, 1
cow, 1
mouse, 1
quick, 1
the, 1
brown, 1
fox, 1
the, 2
fox, 1
the fox ate
the mouse
Map
quick, 1
how, 1
now, 1
brown, 1
how now
brown cow
Map
ate, 1
mouse, 1
cow, 1
User
Program
(1) submit
Master
(2) schedule map
(2) schedule reduce
worker
split 0
split 1
split 2
split 3
split 4
(5) remote read
(3) read
worker
worker
(6) write
output
file 0
(4) local write
worker
output
file 1
worker
Input
files
Adapted from (Dean and Ghemawat, OSDI 2004)
Map
phase
Intermediate files
(on local disk)
Reduce
phase
Output
files
Distributed File System
• Don’t move data to workers… move workers to
the data!
– Store data on the local disks of nodes in the cluster
– Start up the workers on the node that has the data
local
• Why?
– Not enough RAM to hold all the data in memory
– Disk access is slow, but disk throughput is reasonable
• A distributed file system is the answer
– GFS (Google File System) for Google’s MapReduce
– HDFS (Hadoop Distributed File System) for Hadoop
Another example of MapReduce
• Clickstream-like data: for each ad viewing,
user info and whether they clicked on the ad:
• {userid, ip, zip, adnum, clicked}
• Want unique users who saw, clicked, by zip
First Try
• First try key as zip:
• Map can emit {90210, {0,1}} if saw and failed
to click, {90210, {1,1}} if saw and clicked
• Reduce receives, say:
• {90210, [{1,1}, {1,0}, {1,0}]}
• This shows three visits, one click, but we don’t
know if these visits were by different users, so
we don’t know the number of unique users
Second try
•
•
•
•
•
•
•
We need to preserve user identity longer
Use {zip, userid} as key
Value: again {0,1} or {1,1} if saw and clicked
Map emits {90210,user123}, {0,1}}, etc.
Reducer gets info on one user, one zip:
{{90210,user123}, [{0,1}, {1,1}]}
Reducer can process list, emit {90210,user123},
{1,2}}
• But not done yet…
Second MapReduce pass
• Reducer (pass 1) emits {90210,user123}, {1,2}}
• Second Map reads this, emits its contribution
to zip’s stats (one user saw and clicked):
{90210, {1, 1}}
• Second Reduce counts up unique users and
their clicks: emits {90210, {1056, 2210}} for
2210 unique users viewed ads, 1056 of them
clicked.
Compare to SQL
• Table T of {userid, ip, zip, adnum, clicked}
• Using a trick, we can do this in one select:
select zip, count (distinct userid), count (distinct
clicked*userid) from T
group by zip, clicked
having clicked=1
• Assumes clicked = 0 or 1 in T row
• Note that DB2, Oracle, and mysql can do
count (distinct expr),
though entry SQL92 only requires
count(distinct column)
Compare to SQL
• Table T of {userid, ip, zip, adnum, clicked}
Closer to MapReduce processing
select zip, userid, count (clicked) cc from T
group by zip, userid
• Put results into table T1 (zip, userid, cc)
select zip, count(*), sum(sign(cc)) from T1
group by zip
Scalar function sign(x) = -1, 0, +1 is available on
Oracle, DB2, mysql, but not in Entry SQL92
Do it in SQL92?
• CASE is the conditional value capability in
SQL92, but is not required for Entry SQL92 (it
is supported by all respectable DBs)
• Sign(x) as case:
case
when x < 0 then -1
when x > 0 then 1
else 0
End
Something better?
• We see that using MapReduce means telling
the system in detail how to solve the problem
• SQL just states the problem, lets the QP figure
out how to do it
• Next time: Hive, the SQL-like query language
built on top of MapReduce

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