What is the future of Hadoop? What is the new future of Hadoop? How is that different from the old one? Here is how Ted Dunning answered these questions at the winter Hadoop Conference of Japan 2013.

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The Power of Hadoop to
Transform Business

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My Background
 University, Startups
– Aptex, MusicMatch, ID Analytics, Veoh
– big data since before it was big
 Open source
– even before the internet
– Apache Hadoop, Mahout, Zookeeper, Drill
– bought the beer at first HUG
 MapR
 Founding member of Apache Drill

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MapR Technologies
 Silicon Valley Startup
– Top investors
– Top technical and management team
• Google, Microsoft, EMC, NetApp, Oracle
 Enterprise quality distribution for
Hadoop
 Many extensions to basic Hadoop function
 Strong supporter of Apache Drill

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Philosophy First
What is History?

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The study of the past
(what came before now)

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What is the future?
(it comes after now)

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But the future also
has a past!

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Do you remember the
future?

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Some things
turned out
as expected

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Many things
are
different!

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Hadoop has
a history

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Hadoop also
has a
future

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The Old Future of Hadoop
 Map-reduce and HDFS
– more and more, but not really different
 Eco-system additions
– Simpler programming (Hive and Pig)
– Key-value store
– Ad hoc query
 Stands apart from other computing
– Required by HDFS and other limitations

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The New Future of Hadoop
 Real-time processing
– Combines real-time and long-time
 Integration with traditional IT
– No need to stand apart
 Integration with new technologies
– Solr, Node.js, Twisted all should interface directly
 Fast and flexible computation
– Drill logical plan language

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Example #1
Search Abuse

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History matrix
One row per user
One column per thing

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Recommendation based on
cooccurrence
Cooccurrence gives item-item
mapping
One row and column per thing

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Cooccurrence matrix can also be
implemented as a search index

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SolR
Indexer
SolR
Indexer
Solr
indexing
Cooccurrence
(Mahout)
Item meta-
data
Index
shards
Complete
history

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SolR
Indexer
SolR
Indexer
Solr
search
Web tier
Item meta-
data
Index
shards
User
history

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Objective Results
 At a very large credit card company
 History is all transactions, all web interaction
 Processing time cut from 20 hours per day to 3
 Recommendation engine load time decreased from 8 hours to 3
minutes

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Example #2
Web Technology

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Fast analysis
(Storm)
Analytic
output
Real-time
data
Raw logs

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Large analysis
(map-reduce)
Analytic
output
Raw logs

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Presentation
tier (d3 +
node.js)
Analytic
output
Browser
query
Raw logs

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Objective Results
 Real-time + long-time analysis is seamless
 Web tier can be rooted directly on Hadoop cluster
 No need to move data

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Example #3
Apache Drill

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Big Data Processing – Hadoop
Batch processing
Query runtime Minutes to hours
Data volume TBs to PBs
Programming
model
MapReduce
Users Developers
Google project MapReduce
Open source
project
Hadoop
MapReduce

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Big Data Processing – Hadoop and Storm
Batch processing Stream processing
Query runtime Minutes to hours Never-ending
Data volume TBs to PBs Continuous stream
Programming
model
MapReduce DAG
(pre-programmed)
Users Developers Developers
Google project MapReduce
Open source
project
Hadoop
MapReduce
Storm or Apache S4

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Big Data Processing – The missing part
Batch processing Interactive analysis Stream processing
Query runtime Minutes to hours Never-ending
Data volume TBs to PBs Continuous stream
Programming
model
MapReduce DAG
(pre-programmed)
Users Developers Developers
Google project MapReduce
Open source
project
Hadoop
MapReduce
Storm and S4

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Big Data Processing – The missing part
Batch processing Interactive analysis Stream processing
Query runtime Minutes to hours Milliseconds to
minutes
Never-ending
Data volume TBs to PBs GBs to PBs Continuous stream
Programming
model
MapReduce Queries
(ad hoc)
DAG
(pre-programmed)
Users Developers Analysts and
developers
Developers
Google project MapReduce
Open source
project
Hadoop
MapReduce
Storm and S4

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Big Data Processing
Batch processing Interactive analysis Stream processing
Query runtime Minutes to hours Milliseconds to
minutes
Never-ending
Data volume TBs to PBs GBs to PBs Continuous stream
Programming
model
MapReduce Queries DAG
Users Developers Analysts and
developers
Developers
Google project MapReduce Dremel
Open source
project
Hadoop
MapReduce
Storm and S4

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Big Data Processing
Batch processing Interactive analysis Stream processing
Query runtime Minutes to hours Milliseconds to
minutes
Never-ending
Data volume TBs to PBs GBs to PBs Continuous stream
Programming
model
MapReduce Queries DAG
Users Developers Analysts and
developers
Developers
Google project MapReduce Dremel
Open source
project
Hadoop
MapReduce
Storm and S4
Apache Drill

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Design Principles
Flexible
• Pluggable query languages
• Extensible execution engine
• Pluggable data formats
• Column-based and row-based
• Schema and schema-less
• Pluggable data sources
Easy
• Unzip and run
• Zero configuration
• Reverse DNS not needed
• IP addresses can change
• Clear and concise log messages
Dependable
• No SPOF
• Instant recovery from crashes
Fast
• C/C++ core with Java support
• Google C++ style guide
• Min latency and max throughput
(limited only by hardware)

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Simple Architecture
Interface
Query
language
Transform
Logical
Language
Optimize
Physical
Plan
Execute

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Standard Interfaces
Interface
Query
language
Transform
Logical
Language
Optimize
Physical
Plan
Execute
SQL 2003
Drill logical
syntax
Scanner
API

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query:[
{
op:"sequence", do:[
{ op: "scan",
memo: "initial_scan",
ref: "donuts",
source: "local-logs",
selection: {data: "activity"}
},
{ op: "transform",
transforms: [ { ref: "donuts.quanity", expr: "donuts.sales”} ]
},
{ op: "filter",
expr: "donuts.ppu < 1.00"
},
…
Logical Plan Syntax:

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Logical Streaming Example
{ @id: <refnum>, op: “window-frame”,
input: <input>,
keys: [
<name>,...
],
ref: <name>,
before: 2,
after: here
}
0 1
2 3
4
0
0 1
0 1 2
1 2 3
2 3 4

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scan-json
filter
flatten
aggregate
exp1
exp2
"table-1"
Logical Plan

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Execution Plan
scan-json
filter
flatten
aggregate
exp1
exp2
"table-1" scan-json
filter
flatten
exp1
"table-1" scan-json
filter
flatten
exp1
"table-1"
node1 node2 node3

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Representing a DAG
{ @id: 19, op: "aggregate",
input: 18,
type: <simple|running|repeat>,
keys: [<name>,...],
aggregations: [
{ref: <name>, expr: <aggexpr> },...
]
}
aggregate exp2
18
19

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Non-SQL queries
scan-json
streaming
k-means
ball k-
means
aggregate exp2
"table-1"
k
k-means
join
cluster
features
scan-json "table-1"

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Design Principles
Flexible
• Pluggable query languages
• Extensible execution engine
• Pluggable data formats
• Column-based and row-based
• Schema and schema-less
• Pluggable data sources
Easy
• Unzip and run
• Zero configuration
• Reverse DNS not needed
• IP addresses can change
• Clear and concise log messages
Dependable
• No SPOF
• Instant recovery from crashes
Fast
• C/C++ core with Java support
• Google C++ style guide
• Min latency and max throughput
(limited only by hardware)

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The future is
not what we
thought it
would be

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It is better!

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Get Involved!
Tweet:
#hcj13w
#mapr
@ted_dunning

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Get Involved!
 Download these slides
– http://www.mapr.com/company/events/hcj-01-21-2013
 Join the Drill project
– drill-dev-subscribe@incubator.apache.org
– #apachedrill
 Contact me:
– tdunning@maprtech.com
– tdunning@apache.org
– @ted_dunning
 Join MapR (in Japan!)
– jobs@mapr.com