Agile Data Science Applications with Hadoop

Agile Data Science
January 2014
Agile Analytics Applications with Hadoop

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About Me…Bearding.
• Bearding is my #1 natural talent.
• I’m going to beat this guy.
• Seriously.
• Salty Sea Beard
• Fortified with Pacific Ocean Minerals
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Agile Data Science: The Book
A philosophy.
Not the only way,
but it’s a really good way!
Code: ‘AUTHD’ – 50% off
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We Go Fast, But Don’t Worry!
• Download the slides - click the links - read examples!
• If it’s not on the blog (Hortonworks, Data Syndrome), it’s in
the book!
• Order now: http://shop.oreilly.com/product/0636920025054.do
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Agile Application
Development: Check
• LAMP stack mature
• Post-Rails frameworks to choose from
• Enable rapid feedback and agility
+ NoSQL
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Scientific Computing / HPC
Tubes and Mercury (Old School) Cores and Spindles (New School)
UNIVAC and Deep Blue both fill a warehouse. We’re back!
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‘Smart Kid’ Only: MPI, Globus, etc. Until Hadoop

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Data Science?
Application
Development
Data Warehousing
Scientific Computing / HPC
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Data Center as Computer
“A key challenge for architects of WSCs is to smooth out these discrepancies in a cost efficient
manner.” Click here for a paper on operating a ‘data center as computer.’
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Warehouse Scale Computers and Applications

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Hadoop to the Rescue!
• Easy to use (Pig, Hive, Cascading)
• CHEAP: 1% the cost of SAN/NAS
• A department can afford its own Hadoop cluster!
• Dump all your data in one place: Hadoop DFS
• Silos come CRASHING DOWN!
• JOIN like crazy!
• ETL like whoa!
• An army of mappers and reducers at your command
• OMGWTFBBQ ITS SO GREAT! I FEEL AWESOME!
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Analytics Apps: It takes a Team
• Broad skill-set
• Nobody has them all
• Inherently collaborative
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Data Science Team
• 3-4 team members with broad, diverse skill-sets that overlap
• Transactional overhead dominates at 5+ people
• Expert researchers: lend 25-50% of their time to teams
• Creative workers. Like a studio, not an assembly line
• Total freedom... with goals and deliverables.
• Work environment matters most
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How To Get Insight Into Product
• Back-end has gotten THICKER
• Generating $$$ insight can take 10-100x app dev
• Timeline disjoint: analytics vs agile app-dev/design
• How do you ship insights efficiently?
• Can you collaborate on research vs developer timeline?
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The Wrong Way - Part One
“We made a great design.
Your job is to predict the future for it.”
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The Wrong Way - Part Two
“What is taking you so long
to reliably predict the future?”
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The Wrong Way - Part Three
“The users don’t understand
what 86% true means.”
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The Wrong Way - Part Four
GHJIAEHGIEhjagigehganb!!!!!RJ(@J?!!
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The Wrong Way - Conclusion
Inevitable Conclusion
Plane Mountain
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Reminds me of... the waterfall
model
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Chief Problem
You can’t design insight in analytics applications.
You discover it.
You discover by exploring.
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-> Strategy
So make an app for exploring your data.
Which becomes a palette for what you ship.
Iterate and publish intermediate results.
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Data Design
• Not the 1st query that = insight, it’s the 15th, or 150th
• Capturing “Ah ha!” moments
• Slow to do those in batch…
• Faster, better context in an interactive web application.
• Pre-designed charts wind up terrible. So bad.
• Easy to invest man-years in wrong statistical models
• Semantics of presenting predictions are complex
• Opportunity lies at intersection of data & design
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How Do We Get Back to Agile?
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Statement of Principles
(Then Tricks With Code)
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Setup An Environment Where:
• Insights repeatedly produced
• Iterative work shared with entire team
• Interactive from day Zero
• Data model is consistent end-to-end
• Minimal impedance between layers
• Scope and depth of insights grow
• Insights form the palette for what you ship
• Until the application pays for itself and more
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Value Document > Relation
Most data is dirty. Most data is semi-structured or unstructured. Rejoice!
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Value Document > Relation
Note: Hive/ArrayQL/NewSQL’s support of documents/array types blur this distinction.
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Relational Data = Legacy Format
• Why JOIN? Storage is fundamentally cheap!
• Duplicate that JOIN data in one big record type!
• ETL once to document format on import, NOT every job
• Not zero JOINs, but far fewer JOINs
• Semi-structured documents preserve data’s actual structur
• Column compressed document formats beat JOINs!
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Value Imperative > Declarative
• We don’t know what we want to SELECT.
• Data is dirty - check each step, clean iteratively.
• 85% of data scientist’s time spent munging. ETL.
• Imperative is optimized for our process.
• Process = iterative, snowballing insight
• Efficiency matters, self optimize
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Ex. Dataflow: ETL +
Email Sent Count
(I can’t read this either. Get a big version here.)
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Value Pig > Hive (for app-dev)
• Pigs eat ANYTHING
• Pig is optimized for refining data, as opposed to consuming it
• Pig is imperative, iterative
• Pig is dataflows, and SQLish (but not SQL)
• Code modularization/re-use: Pig Macros
• ILLUSTRATE speeds dev time (even UDFs)
• Easy UDFs in Java, JRuby, Jython, Javascript
• Pig Streaming = use any tool, period.
• Easily prepare our data as it will appear in our app.
• If you prefer Hive, use Hive.
Actually, I wish Pig and Hive were one tool. Pig, then Hive, then Pig, then Hive.
See: HCatalog for Pig/Hive integration.
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Localhost vs Petabyte Scale:
Same Tools
• Simplicity essential to scalability: highest level tools we can
• Prepare a good sample - tricky with joins, easy with documents
• Local mode: pig -l /tmp -x local -v -w
• Frequent use of ILLUSTRATE
• 1st: Iterate, debug & publish locally
• 2nd: Run on cluster, publish to team/customer
• Consider skipping Object-Relational-Mapping (ORM)
• We do not trust ‘databases,’ only HDFS @ n=3
• Everything we serve in our app is re-creatable via Hadoop.
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Data-Value Pyramid
Climb it. Do not skip steps. See here.
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0/1) Display Atomic Records
On The Web
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0.0) Document - Serialize Events
• Protobuf
• Thrift
• JSON
• Avro - I use Avro because the schema is onboard.
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0.1) Documents Via Relation ETL
enron_messages = load '/enron/enron_messages.tsv' as (
message_id:chararray,
sql_date:chararray,
from_address:chararray,
from_name:chararray,
subject:chararray,
body:chararray);
enron_recipients = load '/enron/enron_recipients.tsv' as ( message_id:chararray, reciptype:chararray, address:chararray,
name:chararray);
split enron_recipients into tos IF reciptype=='to', ccs IF reciptype=='cc', bccs IF reciptype=='bcc';
headers = cogroup tos by message_id, ccs by message_id, bccs by message_id parallel 10;
with_headers = join headers by group, enron_messages by message_id parallel 10;
emails = foreach with_headers generate enron_messages::message_id as message_id,
CustomFormatToISO(enron_messages::sql_date, 'yyyy-MM-dd HH:mm:ss') as date,
TOTUPLE(enron_messages::from_address, enron_messages::from_name) as from:tuple(address:chararray,
name:chararray), enron_messages::subject as subject,
enron_messages::body as body,
headers::tos.(address, name) as tos,
headers::ccs.(address, name) as ccs,
headers::bccs.(address, name) as bccs;
store emails into '/enron/emails.avro' using AvroStorage(
Example here.
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0.2) Serialize Events From
Streamsclass GmailSlurper(object):
...
def init_imap(self, username, password):
self.username = username
self.password = password
try:
imap.shutdown()
except:
pass
self.imap = imaplib.IMAP4_SSL('imap.gmail.com', 993)
self.imap.login(username, password)
self.imap.is_readonly = True
...
def write(self, record):
self.avro_writer.append(record)
...
def slurp(self):
if(self.imap and self.imap_folder):
for email_id in self.id_list:
(status, email_hash, charset) = self.fetch_email(email_id)
if(status == 'OK' and charset and 'thread_id' in email_hash and 'froms' in email_hash):
print email_id, charset, email_hash['thread_id']
self.write(email_hash)
Scrape your own gmail in Python and Ruby.
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0.3) ETL Logs
log_data = LOAD 'access_log'
USING org.apache.pig.piggybank.storage.apachelog.CommongLogLoader
AS (remoteAddr,
remoteLogname,
user,
time,
method,
uri,
proto,
bytes);
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1) Plumb Atomic Events->Browser
(Example stack that enables high productivity)
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1.1) Cat Avro Serialized Events
me$ cat_avro ~/Data/enron.avro
{
u'bccs': [],
u'body': u'scamming people, blah blah',
u'ccs': [], u'date': u'2000-08-28T01:50:00.000Z',
u'from': {u'address': u'bob.dobbs@enron.com', u'name': None},
u'message_id': u'<1731.10095812390082.JavaMail.evans@thyme>',
u'subject': u'Re: Enron trade for frop futures',
u'tos': [
{u'address': u'connie@enron.com', u'name': None}
]
}
Get cat_avro in python, ruby
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1.2) Load Events in Pig
me$ pig -l /tmp -x local -v -w
grunt> enron_emails = LOAD '/enron/emails.avro' USING AvroStorage();
grunt> describe enron_emails
emails: {
message_id: chararray,
datetime: chararray,
from:tuple(address:chararray,name:chararray)
subject: chararray,
body: chararray,
tos: {to: (address: chararray,name: chararray)},
ccs: {cc: (address: chararray,name: chararray)},
bccs: {bcc: (address: chararray,name: chararray)}
}

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1.3) ILLUSTRATE Events in Pig
grunt> illustrate enron_emails
---------------------------------------------------------------------------
| emails |
| message_id:chararray |
| datetime:chararray |
| from:tuple(address:chararray,name:chararray) |
| subject:chararray |
| body:chararray |
tos:bag{to:tuple(address:chararray,name:chararray)} |
| ccs:bag{cc:tuple(address:chararray,name:chararray)} |
| bccs:bag{bcc:tuple(address:chararray,name:chararray)} |
---------------------------------------------------------------------------
| |
| <1731.10095812390082.JavaMail.evans@thyme> |
| 2001-01-09T06:38:00.000Z |
| (bob.dobbs@enron.com, J.R. Bob Dobbs) |
| Re: Enron trade for frop futures |
| scamming people, blah blah |
| {(connie@enron.com,)} |
| {} |
| {} |
Upgrade to Pig 0.10+
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1.4) Publish Events to a ‘Database’
pig -l /tmp -x local -v -w -param avros=enron.avro
-param mongourl='mongodb://localhost/enron.emails' avro_to_mongo.pig
/* MongoDB libraries and configuration */
register /me/mongo-hadoop/mongo-2.7.3.jar
register /me/mongo-hadoop/core/target/mongo-hadoop-core-1.1.0-SNAPSHOT.jar
register /me/mongo-hadoop/pig/target/mongo-hadoop-pig-1.1.0-SNAPSHOT.jar
/* Set speculative execution off to avoid chance of duplicate records in Mongo */
set mapred.map.tasks.speculative.execution false
set mapred.reduce.tasks.speculative.execution false
define MongoStorage com.mongodb.hadoop.pig.MongoStorage(); /* Shortcut */
/* By default, lets have 5 reducers */
set default_parallel 5
avros = load '$avros' using AvroStorage();
store avros into '$mongourl' using MongoStorage();
Full instructions here.
Which does this:
From Avro to MongoDB in one command:
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1.5) Check Events in ‘Database’
$ mongo enron
MongoDB shell version: 2.0.2
connecting to: enron
show collections
Emails
system.indexes
>db.emails.findOne({message_id: "<1731.10095812390082.JavaMail.evans@thyme>"})
{
"_id" : ObjectId("502b4ae703643a6a49c8d180"),
"message_id" : "<1731.10095812390082.JavaMail.evans@thyme>",
"date" : "2001-01-09T06:38:00.000Z",
"from" : { "address" : "bob.dobbs@enron.com", "name" : "J.R. Bob Dobbs" },
"subject" : Re: Enron trade for frop futures,
"body" : "Scamming more people...",
"tos" : [ { "address" : "connie@enron", "name" : null } ],
"ccs" : [ ],
"bccs" : [ ]
}
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1.6) Publish Events on the Web
require 'rubygems'
require 'sinatra'
require 'mongo'
require 'json'
connection = Mongo::Connection.new
database = connection['agile_data']
collection = database['emails']
get '/email/:message_id' do |message_id|
data = collection.find_one({:message_id => message_id})
JSON.generate(data)
end
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1.6) Publish events on the web
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One-Liner to Transition Stack
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What’s the Point?
• A designer can work against real data.
• An application developer can work against real data.
• A product manager can think in terms of real data.
• Entire team is grounded in reality!
• You’ll see how ugly your data really is.
• You’ll see how much work you have yet to do.
• Ship early and often!
• Feels agile, don’t it? Keep it up!
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1.7) Wrap Events with Bootstrap
<link href="/static/bootstrap/docs/assets/css/bootstrap.css" rel="stylesheet">
</head>
<body>
<div class="container" style="margin-top: 100px;">
<table class="table table-striped table-bordered table-condensed">
<thead>
{% for key in data['keys'] %}
<th>{{ key }}</th>
{% endfor %}
</thead>
<tbody>
<tr>
{% for value in data['values'] %}
<td>{{ value }}</td>
{% endfor %}
</tr>
</tbody>
</table>
</div>
</body>
Complete example here with code here.
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1.7) Wrap Events with Bootstrap
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Refine. Add Links
Between Documents.
Not the Mona Lisa, but coming along... See: here
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1.8) List Links to Sorted Events
mongo enron
> db.emails.ensureIndex({message_id: 1})
> db.emails.find().sort({date:0}).limit(10).pretty()
{
{
"_id" : ObjectId("4f7a5da2414e4dd0645d1176"),
"message_id" : "<CA+bvURyn-rLcH_JXeuzhyq8T9RNq+YJ_Hkvhnrpk8zfYshL-wA@mail.gmail.com>",
"from" : [
...
pig -l /tmp -x local -v -w
emails_per_user = foreach (group emails by from.address) {
sorted = order emails by date;
last_1000 = limit sorted 1000;
generate group as from_address, emails as emails;
};
store emails_per_user into '$mongourl' using MongoStorage();
Use Pig, serve/cache a bag/array of email documents:
Use your ‘database’, if it can sort.
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1.8) List Links
to Sorted Documents
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1.9) Make It Searchable
If you have list, search is easy with
ElasticSearch and Wonderdog...
/* Load ElasticSearch integration */
register '/me/wonderdog/target/wonderdog-1.0-SNAPSHOT.jar';
register '/me/elasticsearch-0.18.6/lib/*';
define ElasticSearch com.infochimps.elasticsearch.pig.ElasticSearchStorage();
emails = load '/me/tmp/emails' using AvroStorage();
store emails into 'es://email/email?json=false&size=1000' using ElasticSearch('/me/elasticsearch-
0.18.6/config/elasticsearch.yml', '/me/elasticsearch-0.18.6/plugins');
curl -XGET 'http://localhost:9200/email/email/_search?q=hadoop&pretty=true&size=1'
Test it with curl:
ElasticSearch has no security features. Take note. Isolate.
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2) Create Simple Tables and
Charts
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2) Create Simple Charts
• Start with an HTML table on general principle.
• Then use nvd3.js - reusable charts for d3.js
• Aggregate by properties & displaying is first step in entity resolution
• Start extracting entities. Ex: people, places, topics, time series
• Group documents by entities, rank and count.
• Publish top N, time series, etc.
• Fill a page with charts.
• Add a chart to your event page.
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2.1) Top N (of Anything) in Pig
top_things = foreach (group things by key) {
sorted = order things by arbitrary_rank desc;
top_10_things = limit sorted 10;
generate group as key, top_10_things as top_10_things;
};
store top_n into '$mongourl' using MongoStorage();
Remember, this is the same structure the browser gets as json.
This would make a good Pig Macro.
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2.2) Time Series (of Anything) in
Pig
/* Group by our key and date rounded to the month, get a total */
things_by_month = foreach (group things by (key, ISOToMonth(datetime))
generate flatten(group) as (key, month),
COUNT_STAR(things) as total;
/* Sort our totals per key by month to get a time series */
things_timeseries = foreach (group things_by_month by key) {
timeseries = order things by month;
generate group as key, timeseries as timeseries;
};
store things_timeseries into '$mongourl' using MongoStorage();
Yet another good Pig Macro.
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Data Processing in Our Stack
A new feature in our application might begin at any layer…
GREAT!
Any team member can add new features, no problemo!
I’m creative!
I know Pig!
I’m creative too!
I <3 Javascript!
omghi2u!
where r my legs?
send halp
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Data Processing in Our Stack
... but we shift the data-processing towards batch, as we are able.
Ex: Overall total emails calculated in each layer
See real example here.
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3) Exploring with Reports
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3) Exploring with Reports
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3.0) From Charts to Reports
• Extract entities from properties we aggregated by in charts (Step 2)
• Each entity gets its own type of web page
• Each unique entity gets its own web page
• Link to entities as they appear in atomic event documents (Step 1)
• Link most related entities together, same and between types.
• More visualizations!
• Parametize results via forms.
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3.2) Cultivate Common Keyspaces
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3.3) Get People Clicking. Learn.
• Explore this web of generated pages, charts and links!
• Everyone on the team gets to know your data.
• Keep trying out different charts, metrics, entities, links.
• See whats interesting.
• Figure out what data needs cleaning and clean it.
• Start thinking about predictions & recommendations.
‘People’ could be just your team, if data is sensitive.
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4) Predictions and
Recommendations
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4.0) Preparation
• We’ve already extracted entities, their properties and relationships
• Our charts show where our signal is rich
• We’ve cleaned our data to make it presentable
• The entire team has an intuitive understanding of the data
• They got that understanding by exploring the data
• We are all on the same page!
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4.2) Think in Different
Perspectives
• Networks
• Time Series / Distributions
• Natural Language Processing
• Conditional Probabilities / Bayesian Inference
• Check out Chapter 2 of the book
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4.3.1) Weighted Email
Networks in Pig
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4.3.2) Networks Viz with Gephi
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4.3.4) Social Network Analysis
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4.4) Time Series & Distributions
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4.4.1) Smooth Sparse Data
See here. 81

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4.4.2) Regress to Find Trends
JRuby Linear Regression UDF Pig to use the UDF
Trend Line in your Application
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4.5.1) Natural Language
Processing
Example with code here and macro here.
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4.5.2) NLP: Extract Topics!
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4.5.3) NLP for All: Extract Topics!
• TF-IDF in Pig - 2 lines of code with Pig Macros:
• http://hortonworks.com/blog/pig-macro-for-tf-idf-makes-
topic-summarization-2-lines-of-pig/
• LDA with Pig and the Lucene Tokenizer:
• http://thedatachef.blogspot.be/2012/03/topic-discovery-
with-apache-pig-and.html
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4.6) Probability & Bayesian
Inference
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4.6.1) Gmail Suggested Recipients
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4.6.1) Reproducing it with Pig
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4.6.2) Step 1: COUNT (From -> To)
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4.6.2) Step 2: COUNT
(From, To, Cc)/Total
P(cc | to) = Probability of cc’ing someone, given that you’ve to’d someone
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4.6.3) Wait - Stop Here! It Works!
They match…
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4.4) Add Predictions to Reports
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Why Doesn’t Kate Reply
to My Emails?
• What time is best to catch her?
• Are they too long?
• Are they meant to be replied to (original content)?
• Are they nice? (sentiment analysis)
• Do I reply to her emails (reciprocity)?
• Do I cc the wrong people (my mom)?
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Thank You!
•Questions & Answers
97
• Follow: @rjurney
• Read the Blog: datasyndrome.com

Agile Data Science Applications with Hadoop

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