Agile analytics applications on hadoop

Agile Analytics Applications
Russell Jurney
1
Wednesday, May 8, 13

About me…Bearding.
• I’m going to beat this guy
• Seriously
• Bearding is my #1 natural talent
• Salty Sea Beard
• Fortified with Pacific Ocean Minerals
2

Agile Data: The Book (August, 2013)
3
Read @ Safari Rough Cuts
A philosophy,
not the only way
But still, its good! Really!

We go fast... but don’t worry!
• Download the slides - click the links - read examples!
• If its not on the blog, its in the book!
• Order now: http://shop.oreilly.com/product/0636920025054.do
• Read the book on Safari Rough Cuts
4

Agile Application Development: Check
• LAMP stack mature
• Post-Rails frameworks to choose from
• Enable rapid feedback and agility
5
+ NoSQL

Data Warehousing
6

Scientific Computing / HPC
• ‘Smart kid’ only: MPI, Globus, etc. until Hadoop
7
Tubes and Mercury (old school) Cores and Spindles (new school)
UNIVAC and Deep Blue both fill a warehouse. We’re back...

Data Science?
8
33%
33%
33%
Application
Development
Data Warehousing
Scientiﬁc Computing / HPC

Data Center as Computer
• Warehouse Scale Computers and applications
9
“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.’

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 whoah!
• An army of mappers and reducers at your command
• OMGWTFBBQ ITS SO GREAT! I FEEL AWESOME!
10

NOW
WHAT?
11

Analytics Apps: It takes a Team
12
• Broad skill-set
• Nobody has them all
• Inherently collaborative

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. Run like a studio, not an assembly line
• Total freedom... with goals and deliverables.
• Work environment matters most
13

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?
• How do you collaborate on research vs developer timeline?
14

The Wrong Way - Part One
15
“We made a great design.
Your job is to predict the future for it.”

The Wrong Way - Part Two
16
“What is taking you so long
to reliably predict the future?”

The Wrong Way - Part Three
17
“The users don’t understand
what 86% true means.”

The Wrong Way - Part Four
18
GHJIAEHGIEhjagigehganb!!!!!RJ(@J?!!

The Wrong Way - Inevitable Conclusion
19
Plane Mountain

Reminds me of... the waterfall model
20:(Wednesday, May 8, 13

Chief Problem
21
You can’t design insight in analytics applications.
You discover it.
You discover by exploring.

-> Strategy
22
So make an app for exploring your data.
Which becomes a palette for what you ship.
Iterate and publish intermediate results.

Data Design
• Not the 1st query that = insight, it’s the 15th, or the 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 the wrong statistical models
• Semantics of presenting predictions are complex, delicate
• Opportunity lies at intersection of data & design
23

How do we get back to Agile?
24

Statement of Principles
25
(then tricks, with code)

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
26

Snowballing Audience
27

Value document > relation
28
Most data is dirty. Most data is semi-structured or unstructured. Rejoice!

Value document > relation
29
Note: Hive/ArrayQL/NewSQL’s support of documents/array types blur this distinction.

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 structure
• Column compressed document formats beat JOINs!
30

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. See: ETL.
• Imperative is optimized for our process.
• Process = iterative, snowballing insight
• Efficiency matters, self optimize
31

Value dataflow > SELECT
32

Ex. dataflow: ETL + email sent count
33(I can’t read this either. Get a big version here.)

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.
34
But actually, I wish Pig and Hive were one tool. Pig, then Hive, then Pig, then Hive…
See: HCatalog for Pig/Hive integration.

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.
35

Data-Value Pyramid
36
Climb it. Do not skip steps. See here.

0/1) Display atomic records on the web
37

0.0) Document-serialize events
• Protobuf
• Thrift
• JSON
• Avro - I use Avro because the schema is onboard.
38

0.1) Documents via Relation ETL
39
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.

0.2) Serialize events from streams
40
class
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.

0.3) ETL Logs
41
log_data
=
LOAD
'access_log'

USING
org.apache.pig.piggybank.storage.apachelog.CommongLogLoader

AS
(remoteAddr,

remoteLogname,

user,

time,

method,

uri,

proto,

bytes);

1) Plumb atomic events -> browser
42
(Example stack that enables high productivity)

1.1) cat our Avro serialized events
43
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

1.2) Load our events in Pig
44
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)}
}


1.3) ILLUSTRATE our events in Pig
45
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+

1.4) Publish our events to a ‘database’
46
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:

1.5) Check events in our ‘database’
47
$ 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" : [ ]
}

1.6) Publish events on the web
48
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

1.6) Publish events on the web
49

One-Liner to Transition Stack
50

Whats 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!
51

1.7) Wrap events with Bootstrap
52
<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.

1.7) Wrap events with Bootstrap
53

Refine. Add links between documents.
54Not the Mona Lisa, but coming along... See: here

1.8) List links to sorted events
55
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.

1.8) List links to sorted documents
56

1.9) Make it searchable...
57
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.

2) Create Simple Charts
58

2) Create Simple Tables and Charts
59

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.
60

2.1) Top N (of anything) in Pig
61
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.

2.2) Time Series (of anything) in Pig
62
/* 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.

Data processing in our stack
63
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

Data processing in our stack
64
... but we shift the data-processing towards batch, as we are able.
Ex: Overall total emails calculated in each layer
See real example here.

3) Exploring with Reports
65

3) Exploring with Reports
66

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.
67

3.1) Looks like this...
68

3.2) Cultivate common keyspaces
69

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.
70
‘People’ could be just your team, if data is sensitive.

4) Predictions and Recommendations
71

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!
72

4.2) Think in different perspectives
• Networks
• Time Series / Distributions
• Natural Language Processing
• Conditional Probabilities / Bayesian Inference
• Check out Chapter 2 of the book
73

4.3) Networks
74

4.3.1) Weighted Email Networks in Pig
75
DEFINE header_pairs(email, col1, col2) RETURNS pairs {
filtered = FILTER $email BY ($col1 IS NOT NULL) AND ($col2 IS NOT NULL);
flat = FOREACH filtered GENERATE FLATTEN($col1) AS $col1, FLATTEN($col2) AS $col2;
$pairs = FOREACH flat GENERATE LOWER($col1) AS ego1, LOWER($col2) AS ego2;
}
/* Get email address pairs for each type of connection, and union them together */
emails = LOAD '/me/Data/enron.avro' USING AvroStorage();
from_to = header_pairs(emails, from, to);
from_cc = header_pairs(emails, from, cc);
from_bcc = header_pairs(emails, from, bcc);
pairs = UNION from_to, from_cc, from_bcc;
/* Get a count of emails over these edges. */
pair_groups = GROUP pairs BY (ego1, ego2);
sent_counts = FOREACH pair_groups GENERATE FLATTEN(group) AS (ego1, ego2), COUNT_STAR(pairs) AS total;

4.3.2) Networks Viz with Gephi
76

4.3.3) Gephi = Easy
77

4.3.4) Social Network Analysis
78

4.4) Time Series & Distributions
79
/* Count things per day */
things_per_day = foreach (group things by (key, ISOToDay(datetime))
generate flatten(group) as (key, day),
COUNT_STAR(things) as total;
/* Sort our totals per key by day to get a sorted time series */
things_timeseries = foreach (group things_by_day by key) {
timeseries = order things by day;
generate group as key, timeseries as timeseries;
};
store things_timeseries into '$mongourl' using MongoStorage();

4.4.1) Smooth Sparse Data
80See here.

4.4.2) Regress to find Trends
81
JRuby Linear Regression UDF Pig to use the UDF
Trend Line in your Application

4.5.1) Natural Language Processing
82
Example with code here and macro here.
import 'tfidf.macro';
my_tf_idf_scores = tf_idf(id_body, 'message_id', 'body');
/* Get the top 10 Tf*Idf scores per message */
per_message_cassandra = foreach (group tfidf_all by message_id) {
sorted = order tfidf_all by value desc;
top_10_topics = limit sorted 10;
generate group, top_10_topics.(score, value);
}

4.5.2) NLP: Extract Topics!
83

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
84

4.6) Probability & Bayesian Inference
85

4.6.1) Gmail Suggested Recipients
86

4.6.1) Reproducing it with Pig...
87

4.6.2) Step 1: COUNT(From -> To)
88

4.6.2) Step 2: COUNT(From, To, Cc)/Total
89
P(cc | to) = Probability of cc’ing someone, given that you’ve to’d someone

4.6.3) Wait - Stop Here! It works!
90
They match…

4.4) Add predictions to reports
91

5) Enable new actions
92

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 (contain original content)?
• Are they nice? (sentiment analysis)
• Do I reply to her emails (reciprocity)?
• Do I cc the wrong people (my mom)?
93

Example: Packetpig and PacketLoop
94
snort_alerts
=
LOAD
'$pcap'

USING

com.packetloop.packetpig.loaders.pcap.detection.SnortLoader('$snortconfig');
countries
=
FOREACH
snort_alerts

GENERATE

com.packetloop.packetpig.udf.geoip.Country(src)
as
country,

priority;
countries
=
GROUP
countries
BY
country;
countries
=
FOREACH
countries

GENERATE

group,

AVG(countries.priority)
as
average_severity;
STORE
countries
into
'output/choropleth_countries'
using
PigStorage(',');
Code here.

Example: Packetpig and PacketLoop
95

Thank You!
Questions & Answers
Follow: @rjurney
Read the Blog: datasyndrome.com
96

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