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

2. 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
Wednesday, May 8, 13

3. Agile Data: The Book (August, 2013)
3
Read @ Safari Rough Cuts
A philosophy,
not the only way
But still, its good! Really!
Wednesday, May 8, 13

4. 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
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5. Agile Application Development: Check
• LAMP stack mature
• Post-Rails frameworks to choose from
• Enable rapid feedback and agility
5
+ NoSQL
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6. Data Warehousing
6
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7. 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...
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8. Data Science?
8
33%
33%
33%
Application
Development
Data Warehousing
Scientific Computing / HPC
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9. 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.’
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10. 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
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11. NOW
WHAT?
11
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12. Analytics Apps: It takes a Team
12
• Broad skill-set
• Nobody has them all
• Inherently collaborative
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13. 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
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14. 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
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15. The Wrong Way - Part One
15
“We made a great design.
Your job is to predict the future for it.”
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16. The Wrong Way - Part Two
16
“What is taking you so long
to reliably predict the future?”
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17. The Wrong Way - Part Three
17
“The users don’t understand
what 86% true means.”
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18. The Wrong Way - Part Four
18
GHJIAEHGIEhjagigehganb!!!!!RJ(@J?!!
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19. The Wrong Way - Inevitable Conclusion
19
Plane Mountain
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20. Reminds me of... the waterfall model
20:(Wednesday, May 8, 13

21. Chief Problem
21
You can’t design insight in analytics applications.
You discover it.
You discover by exploring.
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22. -> Strategy
22
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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23. 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
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24. How do we get back to Agile?
24
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25. Statement of Principles
25
(then tricks, with code)
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26. 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
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27. Snowballing Audience
27
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28. Value document > relation
28
Most data is dirty. Most data is semi-structured or unstructured. Rejoice!
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29. Value document > relation
29
Note: Hive/ArrayQL/NewSQL’s support of documents/array types blur this distinction.
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30. 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
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31. 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
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32. Value dataflow > SELECT
32
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33. Ex. dataflow: ETL + email sent count
33(I can’t read this either. Get a big version here.)
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34. 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.
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35. 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
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36. Data-Value Pyramid
36
Climb it. Do not skip steps. See here.
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37. 0/1) Display atomic records on the web
37
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38. 0.0) Document-serialize events
• Protobuf
• Thrift
• JSON
• Avro - I use Avro because the schema is onboard.
38
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39. 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.
Wednesday, May 8, 13

40. 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.
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41. 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);
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42. 1) Plumb atomic events -> browser
42
(Example stack that enables high productivity)
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43. 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
Wednesday, May 8, 13

44. 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)}
}
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45. 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+
Wednesday, May 8, 13

46. 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:
Wednesday, May 8, 13

47. 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" : [ ]
}
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48. 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
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49. 1.6) Publish events on the web
49
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50. One-Liner to Transition Stack
50
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51. 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
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52. 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.
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53. 1.7) Wrap events with Bootstrap
53
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54. Refine. Add links between documents.
54Not the Mona Lisa, but coming along... See: here
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55. 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 '$mongourl' using MongoStorage();
Use Pig, serve/cache a bag/array of email documents:
Use your ‘database’, if it can sort.
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56. 1.8) List links to sorted documents
56
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57. 1.9) Make it searchable...
57
If you have list, search is easy with ElasticSearch and Wonderdog...
/* Load ElasticSearch integration */
register '/me/wonderdog/target/wonderdog-1.0-SNAPSHOT.jar';
register '/me/elasticsearch-0.18.6/lib/*';
define ElasticSearch com.infochimps.elasticsearch.pig.ElasticSearchStorage();
emails = load '/me/tmp/emails' using AvroStorage();
store emails into 'es://email/email?json=false&size=1000' using ElasticSearch('/me/
elasticsearch-0.18.6/config/elasticsearch.yml', '/me/elasticsearch-0.18.6/plugins');
curl -XGET 'http://localhost:9200/email/email/_search?q=hadoop&pretty=true&size=1'
Test it with curl:
ElasticSearch has no security features. Take note. Isolate.
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58. 2) Create Simple Charts
58
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59. 2) Create Simple Tables and Charts
59
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60. 2) Create Simple Charts
• Start with an HTML table on general principle.
• Then use nvd3.js - reusable charts for d3.js
• Aggregate by properties & displaying is first step in entity
resolution
• Start extracting entities. Ex: people, places, topics, time series
• Group documents by entities, rank and count.
• Publish top N, time series, etc.
• Fill a page with charts.
• Add a chart to your event page.
60
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61. 2.1) Top N (of anything) in Pig
61
pig -l /tmp -x local -v -w
top_things = foreach (group things by key) {
sorted = order things by arbitrary_rank desc;
top_10_things = limit sorted 10;
generate group as key, top_10_things as top_10_things;
};
store top_n into '$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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62. 2.2) Time Series (of anything) in Pig
62
pig -l /tmp -x local -v -w
/* 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.
Wednesday, May 8, 13

63. 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
Wednesday, May 8, 13

64. 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.
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65. 3) Exploring with Reports
65
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66. 3) Exploring with Reports
66
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67. 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
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68. 3.1) Looks like this...
68
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69. 3.2) Cultivate common keyspaces
69
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70. 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.
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71. 4) Predictions and Recommendations
71
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72. 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
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73. 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
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74. 4.3) Networks
74
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75. 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($col2) AS ego2;
}
/* Get email address pairs for each type of connection, and union them together */
emails = LOAD '/me/Data/enron.avro' USING AvroStorage();
from_to = header_pairs(emails, from, to);
from_cc = header_pairs(emails, from, cc);
from_bcc = header_pairs(emails, from, bcc);
pairs = UNION from_to, from_cc, from_bcc;
/* Get a count of emails over these edges. */
pair_groups = GROUP pairs BY (ego1, ego2);
sent_counts = FOREACH pair_groups GENERATE FLATTEN(group) AS (ego1, ego2), COUNT_STAR(pairs) AS total;
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76. 4.3.2) Networks Viz with Gephi
76
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77. 4.3.3) Gephi = Easy
77
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78. 4.3.4) Social Network Analysis
78
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79. 4.4) Time Series & Distributions
79
pig -l /tmp -x local -v -w
/* Count things per day */
things_per_day = foreach (group things by (key, ISOToDay(datetime))
generate flatten(group) as (key, day),
COUNT_STAR(things) as total;
/* Sort our totals per key by day to get a sorted time series */
things_timeseries = foreach (group things_by_day by key) {
timeseries = order things by day;
generate group as key, timeseries as timeseries;
};
store things_timeseries into '$mongourl' using MongoStorage();
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80. 4.4.1) Smooth Sparse Data
80See here.
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81. 4.4.2) Regress to find Trends
81
JRuby Linear Regression UDF Pig to use the UDF
Trend Line in your Application
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82. 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);
}
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83. 4.5.2) NLP: Extract Topics!
83
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84. 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
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85. 4.6) Probability & Bayesian Inference
85
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86. 4.6.1) Gmail Suggested Recipients
86
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87. 4.6.1) Reproducing it with Pig...
87
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88. 4.6.2) Step 1: COUNT(From -> To)
88
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89. 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
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90. 4.6.3) Wait - Stop Here! It works!
90
They match…
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91. 4.4) Add predictions to reports
91
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92. 5) Enable new actions
92
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93. 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
Wednesday, May 8, 13

94. 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.
Wednesday, May 8, 13

95. Example: Packetpig and PacketLoop
95
Wednesday, May 8, 13

96. Thank You!
Questions & Answers
Follow: @rjurney
Read the Blog: datasyndrome.com
96
Wednesday, May 8, 13