functional programming for optimization problems in big data
DESCRIPTION
Enterprise Data Workflows with Cascading. Silicon Valley Cloud Computing Meetup talk at Cloud Tech IV, 4/20 2013 http://www.meetup.com/cloudcomputing/events/111082032/TRANSCRIPT
Copyright @2013, Concurrent, Inc.
Paco NathanConcurrent, Inc.San Francisco, CA@pacoid
“Functional programming for optimization problems in Big Data”
1
Cascading: Workflow Abstraction
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
RMachine DataCascadingSample CodeA Little Theory…WorkflowsOpen Data Example
2
Q3 1997: inflection point
Four independent teams were working toward horizontal scale-out of workflows based on commodity hardware.
This effort prepared the way for huge Internet successesin the 1997 holiday season… AMZN, EBAY, Inktomi (YHOO Search), then GOOG
MapReduce and the Apache Hadoop open source stack emerged from this.
3
RDBMS
Stakeholder
SQL Queryresult sets
Excel pivot tablesPowerPoint slide decks
Web App
Customers
transactions
Product
strategy
Engineering
requirements
BIAnalysts
optimizedcode
Circa 1996: pre- inflection point
4
RDBMS
Stakeholder
SQL Queryresult sets
Excel pivot tablesPowerPoint slide decks
Web App
Customers
transactions
Product
strategy
Engineering
requirements
BIAnalysts
optimizedcode
Circa 1996: pre- inflection point
“Throw it over the wall”
5
RDBMS
SQL Queryresult sets
recommenders+
classifiersWeb Apps
customertransactions
AlgorithmicModeling
Logs
eventhistory
aggregation
dashboards
Product
EngineeringUX
Stakeholder Customers
DW ETL
Middleware
servletsmodels
Circa 2001: post- big ecommerce successes
6
RDBMS
SQL Queryresult sets
recommenders+
classifiersWeb Apps
customertransactions
AlgorithmicModeling
Logs
eventhistory
aggregation
dashboards
Product
EngineeringUX
Stakeholder Customers
DW ETL
Middleware
servletsmodels
Circa 2001: post- big ecommerce successes
“Data products”
7
Workflow
RDBMS
near timebatch
services
transactions,content
socialinteractions
Web Apps,Mobile, etc.History
Data Products Customers
RDBMS
LogEvents
In-Memory Data Grid
Hadoop, etc.
Cluster Scheduler
Prod
Eng
DW
Use Cases Across Topologies
s/wdev
datascience
discovery+
modeling
Planner
Ops
dashboardmetrics
businessprocess
optimizedcapacitytaps
DataScientist
App Dev
Ops
DomainExpert
introducedcapability
existingSDLC
Circa 2013: clusters everywhere
8
Workflow
RDBMS
near timebatch
services
transactions,content
socialinteractions
Web Apps,Mobile, etc.History
Data Products Customers
RDBMS
LogEvents
In-Memory Data Grid
Hadoop, etc.
Cluster Scheduler
Prod
Eng
DW
Use Cases Across Topologies
s/wdev
datascience
discovery+
modeling
Planner
Ops
dashboardmetrics
businessprocess
optimizedcapacitytaps
DataScientist
App Dev
Ops
DomainExpert
introducedcapability
existingSDLC
Circa 2013: clusters everywhere
“Optimizing topologies”
9
by Leo Breiman
Statistical Modeling: The Two CulturesStatistical Science, 2001
bit.ly/eUTh9L
references…
10
Amazon“Early Amazon: Splitting the website” – Greg Lindenglinden.blogspot.com/2006/02/early-amazon-splitting-website.html
eBay“The eBay Architecture” – Randy Shoup, Dan Pritchettaddsimplicity.com/adding_simplicity_an_engi/2006/11/you_scaled_your.htmladdsimplicity.com.nyud.net:8080/downloads/eBaySDForum2006-11-29.pdf
Inktomi (YHOO Search)“Inktomi’s Wild Ride” – Erik Brewer (0:05:31 ff)youtube.com/watch?v=E91oEn1bnXM
Google“Underneath the Covers at Google” – Jeff Dean (0:06:54 ff)youtube.com/watch?v=qsan-GQaeykperspectives.mvdirona.com/2008/06/11/JeffDeanOnGoogleInfrastructure.aspx
references…
11
Cascading: Workflow Abstraction
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
RMachine DataCascadingSample CodeA Little Theory…WorkflowsOpen Data Example
12
Cascading – origins
API author Chris Wensel worked as a system architect at an Enterprise firm well-known for many popular data products.
Wensel was following the Nutch open source project – where Hadoop started.
Observation: would be difficult to find Java developers to write complex Enterprise apps in MapReduce – a potential blocker for leveraging new open source technology.
13
Cascading – functional programming
Key insight: MapReduce is based on functional programming – back to LISP in 1970s. Apache Hadoop use cases are mostly about data pipelines, which are functional in nature.
To ease staffing problems as “Main Street” Enterprise firms began to embrace Hadoop, Cascading was introduced in late 2007, as a new Java API to implement functional programming for large-scale data workflows:
• leverages JVM and Java-based tools without anyneed to create new languages
• allows programmers who have J2EE expertise to leverage the economics of Hadoop clusters
14
Cascading – functional programming
• Twitter, eBay, LinkedIn, Nokia, YieldBot, uSwitch, etc., have invested in open source projects atop Cascading – used for their large-scale production deployments
• new case studies for Cascading apps are mostly based on domain-specific languages (DSLs) in JVM languages which emphasize functional programming:
Cascalog in Clojure (2010)Scalding in Scala (2012)
github.com/nathanmarz/cascalog/wikigithub.com/twitter/scalding/wiki
Why Adopting the Declarative Programming Practices Will Improve Your Return from TechnologyDan Woods, 2013-04-17 Forbes
forbes.com/sites/danwoods/2013/04/17/why-adopting-the-declarative-programming-practices-will-improve-your-return-from-technology/
15
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Cascading – definitions
• a pattern language for Enterprise Data Workflows
• simple to build, easy to test, robust in production
• design principles ⟹ ensure best practices at scale
16
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Cascading – usage
• Java API, DSLs in Scala, Clojure, Jython, JRuby, Groovy, ANSI SQL
• ASL 2 license, GitHub src, http://conjars.org
• 5+ yrs production use, multiple Enterprise verticals
17
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Cascading – integrations
• partners: Microsoft Azure, Hortonworks, Amazon AWS, MapR, EMC, SpringSource, Cloudera
• taps: Memcached, Cassandra, MongoDB, HBase, JDBC, Parquet, etc.
• serialization: Avro, Thrift, Kryo, JSON, etc.
• topologies: Apache Hadoop, tuple spaces, local mode
18
Cascading – deployments
• case studies: Climate Corp, Twitter, Etsy, Williams-Sonoma, uSwitch, Airbnb, Nokia, YieldBot, Square, Harvard, Factual, etc.
• use cases: ETL, marketing funnel, anti-fraud, social media, retail pricing, search analytics, recommenders, eCRM, utility grids, telecom, genomics, climatology, agronomics, etc.
19
Cascading – deployments
• case studies: Climate Corp, Twitter, Etsy, Williams-Sonoma, uSwitch, Airbnb, Nokia, YieldBot, Square, Harvard, Factual, etc.
• use cases: ETL, marketing funnel, anti-fraud, social media, retail pricing, search analytics, recommenders, eCRM, utility grids, telecom, genomics, climatology, agronomics, etc.
workflow abstraction addresses: • staffing bottleneck; • system integration; • operational complexity; • test-driven development
20
Cascading: Workflow Abstraction
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
RMachine DataCascadingSample CodeA Little Theory…WorkflowsOpen Data Example
21
void map (String doc_id, String text):
for each word w in segment(text):
emit(w, "1");
void reduce (String word, Iterator group):
int count = 0;
for each pc in group:
count += Int(pc);
emit(word, String(count));
The Ubiquitous Word Count
Definition: count how often each word appears in a collection of text documents
This simple program provides an excellent test case for parallel processing, since it illustrates:
• requires a minimal amount of code
• demonstrates use of both symbolic and numeric values
• shows a dependency graph of tuples as an abstraction
• is not many steps away from useful search indexing
• serves as a “Hello World” for Hadoop apps
Any distributed computing framework which can run Word Count efficiently in parallel at scale can handle much larger and more interesting compute problems.
DocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
count how often each word appears in a collection of text documents
22
DocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
1 map 1 reduce18 lines code gist.github.com/3900702
word count – conceptual flow diagram
cascading.org/category/impatient
23
word count – Cascading app in Java
String docPath = args[ 0 ];String wcPath = args[ 1 ];Properties properties = new Properties();AppProps.setApplicationJarClass( properties, Main.class );HadoopFlowConnector flowConnector = new HadoopFlowConnector( properties );
// create source and sink tapsTap docTap = new Hfs( new TextDelimited( true, "\t" ), docPath );Tap wcTap = new Hfs( new TextDelimited( true, "\t" ), wcPath );
// specify a regex to split "document" text lines into token streamFields token = new Fields( "token" );Fields text = new Fields( "text" );RegexSplitGenerator splitter = new RegexSplitGenerator( token, "[ \\[\\]\\(\\),.]" );// only returns "token"Pipe docPipe = new Each( "token", text, splitter, Fields.RESULTS );// determine the word countsPipe wcPipe = new Pipe( "wc", docPipe );wcPipe = new GroupBy( wcPipe, token );wcPipe = new Every( wcPipe, Fields.ALL, new Count(), Fields.ALL );
// connect the taps, pipes, etc., into a flowFlowDef flowDef = FlowDef.flowDef().setName( "wc" ) .addSource( docPipe, docTap ) .addTailSink( wcPipe, wcTap );// write a DOT file and run the flowFlow wcFlow = flowConnector.connect( flowDef );wcFlow.writeDOT( "dot/wc.dot" );wcFlow.complete();
DocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
24
map
reduceEvery('wc')[Count[decl:'count']]
Hfs['TextDelimited[[UNKNOWN]->['token', 'count']]']['output/wc']']
GroupBy('wc')[by:['token']]
Each('token')[RegexSplitGenerator[decl:'token'][args:1]]
Hfs['TextDelimited[['doc_id', 'text']->[ALL]]']['data/rain.txt']']
[head]
[tail]
[{2}:'token', 'count'][{1}:'token']
[{2}:'doc_id', 'text'][{2}:'doc_id', 'text']
wc[{1}:'token'][{1}:'token']
[{2}:'token', 'count'][{2}:'token', 'count']
[{1}:'token'][{1}:'token']
word count – generated flow diagramDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
25
(ns impatient.core (:use [cascalog.api] [cascalog.more-taps :only (hfs-delimited)]) (:require [clojure.string :as s] [cascalog.ops :as c]) (:gen-class))
(defmapcatop split [line] "reads in a line of string and splits it by regex" (s/split line #"[\[\]\\\(\),.)\s]+"))
(defn -main [in out & args] (?<- (hfs-delimited out) [?word ?count] ((hfs-delimited in :skip-header? true) _ ?line) (split ?line :> ?word) (c/count ?count)))
; Paul Lam; github.com/Quantisan/Impatient
word count – Cascalog / ClojureDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
26
github.com/nathanmarz/cascalog/wiki
• implements Datalog in Clojure, with predicates backed by Cascading – for a highly declarative language
• run ad-hoc queries from the Clojure REPL –approx. 10:1 code reduction compared with SQL
• composable subqueries, used for test-driven development (TDD) practices at scale
• Leiningen build: simple, no surprises, in Clojure itself
• more new deployments than other Cascading DSLs – Climate Corp is largest use case: 90% Clojure/Cascalog
• has a learning curve, limited number of Clojure developers
• aggregators are the magic, and those take effort to learn
word count – Cascalog / ClojureDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
27
import com.twitter.scalding._ class WordCount(args : Args) extends Job(args) { Tsv(args("doc"), ('doc_id, 'text), skipHeader = true) .read .flatMap('text -> 'token) { text : String => text.split("[ \\[\\]\\(\\),.]") } .groupBy('token) { _.size('count) } .write(Tsv(args("wc"), writeHeader = true))}
word count – Scalding / ScalaDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
28
github.com/twitter/scalding/wiki
• extends the Scala collections API so that distributed lists become “pipes” backed by Cascading
• code is compact, easy to understand
• nearly 1:1 between elements of conceptual flow diagram and function calls
• extensive libraries are available for linear algebra, abstract algebra, machine learning – e.g., Matrix API, Algebird, etc.
• significant investments by Twitter, Etsy, eBay, etc.
• great for data services at scale
• less learning curve than Cascalog
word count – Scalding / ScalaDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
29
Cascading: Workflow Abstraction
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
RMachine DataCascadingSample CodeA Little Theory…WorkflowsOpen Data Example
30
workflow abstraction – pattern language
Cascading uses a “plumbing” metaphor in the Java API, to define workflows out of familiar elements: Pipes, Taps, Tuple Flows, Filters, Joins, Traps, etc.
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
R
Data is represented as flows of tuples. Operations within the flows bring functional programming aspects into Java
In formal terms, this provides a pattern language
31
references…
pattern language: a structured method for solving large, complex design problems, where the syntax of the language promotes the use of best practices
amazon.com/dp/0195019199
design patterns: the notion originated in consensus negotiation for architecture, later applied in OOP software engineering by “Gang of Four”
amazon.com/dp/0201633612
32
workflow abstraction – literate programming
Cascading workflows generate their own visual documentation: flow diagrams
In formal terms, flow diagrams leverage a methodology called literate programming
Provides intuitive, visual representations for apps –great for cross-team collaboration
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
R
33
references…
by Don Knuth
Literate ProgrammingUniv of Chicago Press, 1992
literateprogramming.com/
“Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do.”
34
workflow abstraction – business process
Following the essence of literate programming, Cascading workflows provide statements of business process
This recalls a sense of business process management for Enterprise apps (think BPM/BPEL for Big Data)
Cascading creates a separation of concerns between business process and implementation details (Hadoop, etc.)
This is especially apparent in large-scale Cascalog apps:
“Specify what you require, not how to achieve it.”
By virtue of the pattern language, the flow planner then determines how to translate business process into efficient, parallel jobs at scale
35
references…
by Edgar Codd
“A relational model of data for large shared data banks”Communications of the ACM, 1970 dl.acm.org/citation.cfm?id=362685
Rather than arguing between SQL vs. NoSQL…structured vs. unstructured data frameworks… this approach focuses on what apps do:
the process of structuring data
36
workflow abstraction – functional relational programming
The combination of functional programming, pattern language, DSLs, literate programming, business process, etc., traces back to the original definition of the relational model (Codd, 1970) prior to SQL.
Cascalog, in particular, implements more of what Codd intended for a “data sublanguage” and is considered to be close to a full implementation of the functional relational programming paradigm defined in:
Moseley & Marks, 2006“Out of the Tar Pit”goo.gl/SKspn
37
workflow abstraction – functional relational programming
The combination of functional programming, pattern language, DSLs, literate programming, business process, etc., traces back to the original definition of the relational model (Codd, 1970) prior to SQL.
Cascalog, in particular, implements more of what Codd intended for a “data sublanguage” and is considered to be close to a full implementation of the functional relational programming paradigm defined in:
Moseley & Marks, 2006“Out of the Tar Pit”goo.gl/SKspn
several theoretical aspects converge into software engineering practices which minimize the complexity of building and maintaining Enterprise data workflows
38
source: National Geographic
“A kind of Cambrian explosion”
algorithmic modeling + machine data + curation, metadata + Open Data ⇒ evolution of feedback loops
internet of things + complex analytics ⇒ accelerated evolution, additional feedback loops
39
A Thought Exercise
Consider that when a company like Catepillar moves into data science, they won’t be building the world’s next search engine or social network
They will be optimizing supply chain, optimizing fuel costs, automating data feedback loops integrated into their equipment…
Operations Research –crunching amazing amounts of data
$50B company, in a $250B market segment
Upcoming: tractors as drones – guided by complex, distributed data apps
40
Two Avenues to the App Layer:
scale ➞co
mpl
exity
➞
Enterprise: must contend with complexity at scale everyday…
incumbents extend current practices and infrastructure investments – using J2EE, ANSI SQL, SAS, etc. – to migrate workflows onto Apache Hadoop while leveraging existing staff
Start-ups: crave complexity and scale to become viable…
new ventures move into Enterprise space to compete using relatively lean staff, while leveraging sophisticated engineering practices, e.g., Cascalog and Scalding
42
Cascading: Workflow Abstraction
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
RMachine DataCascadingSample CodeA Little Theory…WorkflowsOpen Data Example
43
Anatomy of an Enterprise app
Definition a typical Enterprise workflow crosses through multiple departments and frameworks…
ETL dataprep
predictivemodel
datasources
enduses
44
Anatomy of an Enterprise app
Definition a typical Enterprise workflow crosses through multiple departments and frameworks…
ETL dataprep
predictivemodel
datasources
enduses
system integration
45
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Cascading workflows – taps
• taps integrate other data frameworks, as tuple streams
• these are “plumbing” endpoints in the pattern language
• sources (inputs), sinks (outputs), traps (exceptions)
• text delimited, JDBC, Memcached, HBase, Cassandra, MongoDB, etc.
• data serialization: Avro, Thrift, Kryo, JSON, etc.
• extend a new kind of tap in just a few lines of Java
schema and provenance get derived from analysis of the taps
46
Anatomy of an Enterprise app
Definition a typical Enterprise workflow crosses through multiple departments and frameworks…
ETL dataprep
predictivemodel
datasources
enduses
ANSI SQL for ETL
47
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Cascading workflows – ANSI SQL
• collab with Optiq – industry-proven code base
• ANSI SQL parser/optimizer atop Cascading flow planner
• JDBC driver to integrate into existing tools and app servers
• relational catalog over a collection of unstructured data
• SQL shell prompt to run queries
• enable analysts without retraining on Hadoop, etc.
• transparency for Support, Ops, Finance, et al.
a language for queries – not a database,but ANSI SQL as a DSL for workflows
48
Lingual – shell prompt, catalog
cascading.org/lingual
49
Lingual – queries
cascading.org/lingual
50
# load the JDBC packagelibrary(RJDBC) # set up the driverdrv <- JDBC("cascading.lingual.jdbc.Driver", "~/src/concur/lingual/lingual-local/build/libs/lingual-local-1.0.0-wip-dev-jdbc.jar") # set up a database connection to a local repositoryconnection <- dbConnect(drv, "jdbc:lingual:local;catalog=~/src/concur/lingual/lingual-examples/tables;schema=EMPLOYEES") # query the repository: in this case the MySQL sample database (CSV files)df <- dbGetQuery(connection, "SELECT * FROM EMPLOYEES.EMPLOYEES WHERE FIRST_NAME = 'Gina'")head(df) # use R functions to summarize and visualize part of the datadf$hire_age <- as.integer(as.Date(df$HIRE_DATE) - as.Date(df$BIRTH_DATE)) / 365.25summary(df$hire_age)
library(ggplot2)m <- ggplot(df, aes(x=hire_age))m <- m + ggtitle("Age at hire, people named Gina")m + geom_histogram(binwidth=1, aes(y=..density.., fill=..count..)) + geom_density()
Lingual – connecting Hadoop and R
51
> summary(df$hire_age) Min. 1st Qu. Median Mean 3rd Qu. Max. 20.86 27.89 31.70 31.61 35.01 43.92
Lingual – connecting Hadoop and R
cascading.org/lingual
52
Anatomy of an Enterprise app
Definition a typical Enterprise workflow crosses through multiple departments and frameworks…
ETL dataprep
predictivemodel
datasources
endusesJ2EE for business logic
53
Cascading workflows – business logic
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
R
54
Anatomy of an Enterprise app
Definition a typical Enterprise workflow crosses through multiple departments and frameworks…
ETL dataprep
predictivemodel
datasources
enduses
SAS for predictive models
55
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Pattern – model scoring
• migrate workloads: SAS,Teradata, etc., exporting predictive models as PMML
• great open source tools – R, Weka, KNIME, Matlab, RapidMiner, etc.
• integrate with other libraries –Matrix API, etc.
• leverage PMML as another kind of DSL
cascading.org/pattern
56
## train a RandomForest model f <- as.formula("as.factor(label) ~ .")fit <- randomForest(f, data_train, ntree=50) ## test the model on the holdout test set print(fit$importance)print(fit) predicted <- predict(fit, data)data$predicted <- predictedconfuse <- table(pred = predicted, true = data[,1])print(confuse) ## export predicted labels to TSV write.table(data, file=paste(dat_folder, "sample.tsv", sep="/"), quote=FALSE, sep="\t", row.names=FALSE) ## export RF model to PMML saveXML(pmml(fit), file=paste(dat_folder, "sample.rf.xml", sep="/"))
Pattern – create a model in R
57
public class Main { public static void main( String[] args ) { String pmmlPath = args[ 0 ]; String ordersPath = args[ 1 ]; String classifyPath = args[ 2 ]; String trapPath = args[ 3 ];
Properties properties = new Properties(); AppProps.setApplicationJarClass( properties, Main.class ); HadoopFlowConnector flowConnector = new HadoopFlowConnector( properties );
// create source and sink taps Tap ordersTap = new Hfs( new TextDelimited( true, "\t" ), ordersPath ); Tap classifyTap = new Hfs( new TextDelimited( true, "\t" ), classifyPath ); Tap trapTap = new Hfs( new TextDelimited( true, "\t" ), trapPath );
// define a "Classifier" model from PMML to evaluate the orders ClassifierFunction classFunc = new ClassifierFunction( new Fields( "score" ), pmmlPath ); Pipe classifyPipe = new Each( new Pipe( "classify" ), classFunc.getInputFields(), classFunc, Fields.ALL );
// connect the taps, pipes, etc., into a flow FlowDef flowDef = FlowDef.flowDef().setName( "classify" ) .addSource( classifyPipe, ordersTap ) .addTrap( classifyPipe, trapTap ) .addSink( classifyPipe, classifyTap );
// write a DOT file and run the flow Flow classifyFlow = flowConnector.connect( flowDef ); classifyFlow.writeDOT( "dot/classify.dot" ); classifyFlow.complete(); }}
Pattern – score a model, within an app
58
CustomerOrders
Classify ScoredOrders
GroupBytoken
Count
PMMLModel
M R
FailureTraps
Assert
ConfusionMatrix
Pattern – score a model, using pre-defined Cascading app
cascading.org/pattern
59
PMML – vendor coverage
60
ETL dataprep
predictivemodel
datasources
enduses
Lingual:DW → ANSI SQL
Pattern:SAS, R, etc. → PMML
business logic in Java, Clojure, Scala, etc.
sink taps for Memcached, HBase, MongoDB, etc.
source taps for Cassandra, JDBC,Splunk, etc.
Anatomy of an Enterprise app
Cascading allows multiple departments to integrate their workflow components into one app, one JAR file
61
Cascading: Workflow Abstraction
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
RMachine DataCascadingSample CodeA Little Theory…WorkflowsOpen Data Example
62
Palo Alto is quite a pleasant place
• temperate weather
• lots of parks, enormous trees
• great coffeehouses
• walkable downtown
• not particularly crowded
On a nice summer day, who wants to be stuck indoors on a phone call?
Instead, take it outside – go for a walk
And example open source project: github.com/Cascading/CoPA/wiki
63
1. Open Data about municipal infrastructure(GIS data: trees, roads, parks)
✚
2. Big Data about where people like to walk(smartphone GPS logs)
✚
3. some curated metadata(which surfaces the value)
⇒4. personalized recommendations:
“Find a shady spot on a summer day in which to walk near downtown Palo Alto. While on a long conference call. Sipping a latte or enjoying some fro-yo.”
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
R
64
The City of Palo Alto recently began to support Open Data to give the local community greater visibility into how their city government operates
This effort is intended to encourage students, entrepreneurs, local organizations, etc., to build new apps which contribute to the public good
paloalto.opendata.junar.com/dashboards/7576/geographic-information/
discovery
65
GIS about trees in Palo Alto:discovery
66
Geographic_Information,,,
"Tree: 29 site 2 at 203 ADDISON AV, on ADDISON AV 44 from pl"," Private: -1 Tree ID: 29 Street_Name: ADDISON AV Situs Number: 203 Tree Site: 2 Species: Celtis australis Source: davey tree Protected: Designated: Heritage: Appraised Value: Hardscape: None Identifier: 40 Active Numeric: 1 Location Feature ID: 13872 Provisional: Install Date: ","37.4409634615283,-122.15648458861,0.0 ","Point""Wilkie Way from West Meadow Drive to Victoria Place"," Sequence: 20 Street_Name: Wilkie Way From Street PMMS: West Meadow Drive To Street PMMS: Victoria Place Street ID: 598 (Wilkie Wy, Palo Alto) From Street ID PMMS: 689 To Street ID PMMS: 567 Year Constructed: 1950 Traffic Count: 596 Traffic Index: residential local Traffic Class: local residential Traffic Date: 08/24/90 Paving Length: 208 Paving Width: 40 Paving Area: 8320 Surface Type: asphalt concrete Surface Thickness: 2.0 Base Type Pvmt: crusher run base Base Thickness: 6.0 Soil Class: 2 Soil Value: 15 Curb Type: Curb Thickness: Gutter Width: 36.0 Book: 22 Page: 1 District Number: 18 Land Use PMMS: 1 Overlay Year: 1990 Overlay Thickness: 1.5 Base Failure Year: 1990 Base Failure Thickness: 6 Surface Treatment Year: Surface Treatment Type: Alligator Severity: none Alligator Extent: 0 Block Severity: none Block Extent: 0 Longitude and Transverse Severity: none Longitude and Transverse Extent: 0 Ravelling Severity: none Ravelling Extent: 0 Ridability Severity: none Trench Severity: none Trench Extent: 0 Rutting Severity: none Rutting Extent: 0 Road Performance: UL (Urban Local) Bike Lane: 0 Bus Route: 0 Truck Route: 0 Remediation: Deduct Value: 100 Priority: Pavement Condition: excellent Street Cut Fee per SqFt: 10.00 Source Date: 6/10/2009 User Modified By: mnicols Identifier System: 21410 ","-122.1249640794,37.4155803115645,0.0 -122.124661859039,37.4154224594993,0.0 -122.124587720719,37.4153758330704,0.0 -122.12451895942,37.4153242300888,0.0 -122.124456098457,37.4152680432944,0.0 -122.124399616238,37.4152077003122,0.0 -122.124374937753,37.4151774433318,0.0 ","Line"
discovery
(unstructured data…)
67
(defn parse-gis [line] "leverages parse-csv for complex CSV format in GIS export" (first (csv/parse-csv line)) ) (defn etl-gis [gis trap] "subquery to parse data sets from the GIS source tap" (<- [?blurb ?misc ?geo ?kind] (gis ?line) (parse-gis ?line :> ?blurb ?misc ?geo ?kind) (:trap (hfs-textline trap)) ))
discovery
(specify what you require, not how to achieve it…
80/20 rule of data prep cost)
68
discovery
(ad-hoc queries get refined into composable predicates)
Identifier: 474 Tree ID: 412 Tree: 412 site 1 at 115 HAWTHORNE AVTree Site: 1 Street_Name: HAWTHORNE AV Situs Number: 115 Private: -1 Species: Liquidambar styraciflua Source: davey tree Hardscape: None 37.446001565119,-122.167713417554,0.0Point
69
discovery
(curate valuable metadata)
70
(defn get-trees [src trap tree_meta] "subquery to parse/filter the tree data" (<- [?blurb ?tree_id ?situs ?tree_site ?species ?wikipedia ?calflora ?avg_height ?tree_lat ?tree_lng ?tree_alt ?geohash ] (src ?blurb ?misc ?geo ?kind) (re-matches #"^\s+Private.*Tree ID.*" ?misc) (parse-tree ?misc :> _ ?priv ?tree_id ?situs ?tree_site ?raw_species) ((c/comp s/trim s/lower-case) ?raw_species :> ?species) (tree_meta ?species ?wikipedia ?calflora ?min_height ?max_height) (avg ?min_height ?max_height :> ?avg_height) (geo-tree ?geo :> _ ?tree_lat ?tree_lng ?tree_alt) (read-string ?tree_lat :> ?lat) (read-string ?tree_lng :> ?lng) (geohash ?lat ?lng :> ?geohash) (:trap (hfs-textline trap)) ))
discovery
?blurb!! Tree: 412 site 1 at 115 HAWTHORNE AV, on HAWTHORNE AV 22 from pl?tree_id! " 412?situs"" 115?tree_site" 1?species" " liquidambar styraciflua?wikipedia" http://en.wikipedia.org/wiki/Liquidambar_styraciflua?calflora http://calflora.org/cgi-bin/species_query.cgi?where-calrecnum=8598?avg_height" 27.5?tree_lat" 37.446001565119?tree_lng" -122.167713417554?tree_alt" 0.0?geohash" " 9q9jh0
71
// run analysis and visualization in Rlibrary(ggplot2)
dat_folder <- '~/src/concur/CoPA/out/tree'data <- read.table(file=paste(dat_folder, "part-00000", sep="/"), sep="\t", quote="", na.strings="NULL", header=FALSE, encoding="UTF8") summary(data)
t <- head(sort(table(data$V5), decreasing=TRUE)trees <- as.data.frame.table(t, n=20))colnames(trees) <- c("species", "count") m <- ggplot(data, aes(x=V8))m <- m + ggtitle("Estimated Tree Height (meters)")m + geom_histogram(aes(y = ..density.., fill = ..count..)) + geom_density() par(mar = c(7, 4, 4, 2) + 0.1)plot(trees, xaxt="n", xlab="")axis(1, labels=FALSE)text(1:nrow(trees), par("usr")[3] - 0.25, srt=45, adj=1, labels=trees$species, xpd=TRUE)grid(nx=nrow(trees))
discovery
72
discovery
sweetgum
analysis of the tree data:
73
M
tree
GISexport
Regexparse-gis
src
Scrubspecies
Geohash
Regexparse-tree
tree
TreeMetadata
Join
FailureTraps
Estimateheight
M
discovery
(flow diagram, gis ⇒ tree)
74
9q9jh0
geohash with 6-digit resolution
approximates a 5-block square
centered lat: 37.445, lng: -122.162
modeling
75
Each road in the GIS export is listed as a block between two cross roads, and each may have multiple road segments to represent turns:
" -122.161776959558,37.4518836690781,0.0 " -122.161390381489,37.4516410983794,0.0 " -122.160786011735,37.4512589903357,0.0 " -122.160531178368,37.4510977281699,0.0
modeling
( lat0, lng0, alt0 )
( lat1, lng1, alt1 )
( lat2, lng2, alt2 )
( lat3, lng3, alt3 )
NB: segments in the raw GIS have the order of geo coordinates scrambled: (lng, lat, alt)
76
9q9jh0
X X
X
Filter trees which are too far away to provide shade. Calculate a sum of moments for tree height × distance, as an estimator for shade:
modeling
77
(defn get-shade [trees roads] "subquery to join tree and road estimates, maximize for shade" (<- [?road_name ?geohash ?road_lat ?road_lng
?road_alt ?road_metric ?tree_metric] (roads ?road_name _ _ _
?albedo ?road_lat ?road_lng ?road_alt ?geohash ?traffic_count _ ?traffic_class _ _ _ _)
(road-metric ?traffic_class ?traffic_count ?albedo :> ?road_metric)
(trees _ _ _ _ _ _ _ ?avg_height ?tree_lat ?tree_lng ?tree_alt ?geohash)
(read-string ?avg_height :> ?height) ;; limit to trees which are higher than people (> ?height 2.0) (tree-distance
?tree_lat ?tree_lng ?road_lat ?road_lng :> ?distance) ;; limit to trees within a one-block radius (not meters) (<= ?distance 25.0) (/ ?height ?distance :> ?tree_moment) (c/sum ?tree_moment :> ?sum_tree_moment) ;; magic number 200000.0 used to scale tree moment
;; based on median (/ ?sum_tree_moment 200000.0 :> ?tree_metric) ))
modeling
78
M
tree
Join Calculatedistance
shade
Filterheight
Summoment
REstimatetraffic
Rroad
Filterdistance
M M
Filtersum_moment
(flow diagram, shade)
modeling
79
(defn get-gps [gps_logs trap] "subquery to aggregate and rank GPS tracks per user" (<- [?uuid ?geohash ?gps_count ?recent_visit] (gps_logs
?date ?uuid ?gps_lat ?gps_lng ?alt ?speed ?heading ?elapsed ?distance)
(read-string ?gps_lat :> ?lat) (read-string ?gps_lng :> ?lng) (geohash ?lat ?lng :> ?geohash) (c/count :> ?gps_count) (date-num ?date :> ?visit) (c/max ?visit :> ?recent_visit) ))
modeling
?uuid ?geohash ?gps_count ?recent_visitcf660e041e994929b37cc5645209c8ae 9q8yym 7 1972376866448342ac6fd3f5f44c6b97724d618d587cf 9q9htz 4 197237669096932cc09e69bc042f1ad22fc16ee275e21 9q9hv3 3 1972376670935342ac6fd3f5f44c6b97724d618d587cf 9q9hv3 3 1972376691356342ac6fd3f5f44c6b97724d618d587cf 9q9hwn 13 1972376690782342ac6fd3f5f44c6b97724d618d587cf 9q9hwp 58 1972376690965482dc171ef0342b79134d77de0f31c4f 9q9jh0 15 1972376952532b1b4d653f5d9468a8dd18a77edcc5143 9q9jh0 18 1972376945348
80
Recommenders often combine multiple signals, via weighted averages, to rank personalized results:
•GPS of person ∩ road segment
• frequency and recency of visit
• traffic class and rate
• road albedo (sunlight reflection)
• tree shade estimator
Adjusting the mix allows for further personalization at the end use
modeling
(defn get-reco [tracks shades] "subquery to recommend road segments based on GPS tracks" (<- [?uuid ?road ?geohash ?lat ?lng ?alt ?gps_count ?recent_visit ?road_metric ?tree_metric] (tracks ?uuid ?geohash ?gps_count ?recent_visit) (shades ?road ?geohash ?lat ?lng ?alt ?road_metric ?tree_metric) ))
81
‣ addr: 115 HAWTHORNE AVE‣ lat/lng: 37.446, -122.168‣ geohash: 9q9jh0‣ tree: 413 site 2‣ species: Liquidambar styraciflua‣ est. height: 23 m‣ shade metric: 4.363‣ traffic: local residential, light traffic‣ recent visit: 1972376952532‣ a short walk from my train stop ✔
apps
82
Enterprise Data Workflowswith Cascading
O’Reilly, 2013amazon.com/dp/1449358721
references…
83
blog, dev community, code/wiki/gists, maven repo, commercial products, career opportunities:
cascading.org
zest.to/group11
github.com/Cascading
conjars.org
goo.gl/KQtUL
concurrentinc.com
drill-down…
Copyright @2013, Concurrent, Inc.Hiring for Java API developers in SF!
84