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July Clojure Users Group Meeting: "Using Cascalog with Palo Alto Open Data"
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Transcript of July Clojure Users Group Meeting: "Using Cascalog with Palo Alto Open Data"
Paco Nathanliber118.com/pxn/
“Using Cascalog with Palo Alto Open Data”
Licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
LA Clojure User Group
1Friday, 19 July 13
Cascading / Cascalog / Scalding
Enterprise Data Workflows with Cascading
Cluster Computing with Mesos
Using Cascalog with Palo Alto Open Data
2Friday, 19 July 13
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 – potential blocker for leveraging new open source technology.
3Friday, 19 July 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
4Friday, 19 July 13
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/
5Friday, 19 July 13
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
6Friday, 19 July 13
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.
7Friday, 19 July 13
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
8Friday, 19 July 13
DocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
1 map 1 reduce18 lines code gist.github.com/3900702
WordCount – conceptual flow diagram
cascading.org/category/impatient
9Friday, 19 July 13
WordCount – 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
10Friday, 19 July 13
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']
WordCount – generated flow diagramDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
11Friday, 19 July 13
(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
WordCount – Cascalog / ClojureDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
12Friday, 19 July 13
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
WordCount – Cascalog / ClojureDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
13Friday, 19 July 13
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))}
WordCount – Scalding / ScalaDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
14Friday, 19 July 13
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
WordCount – Scalding / ScalaDocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
15Friday, 19 July 13
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
A Pattern LanguageChristopher Alexander, et al.amazon.com/dp/0195019199
16Friday, 19 July 13
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
Literate ProgrammingDon Knuthliterateprogramming.com
17Friday, 19 July 13
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
18Friday, 19 July 13
Cascading / Cascalog / Scalding
Enterprise Data Workflows with Cascading
Cluster Computing with Mesos
Using Cascalog with Palo Alto Open Data
19Friday, 19 July 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
20Friday, 19 July 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
ANSI SQL for ETL
21Friday, 19 July 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
endusesJ2EE for business logic
22Friday, 19 July 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
SAS for predictive models
23Friday, 19 July 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
SAS for predictive modelsANSI SQL for ETL most of the licensing costs…
24Friday, 19 July 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
endusesJ2EE for business logic
most of the project costs…
25Friday, 19 July 13
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 combine their workflow components into an integrated app – one among many, typically – based on 100% open source
a compiler sees it all…
cascading.org
26Friday, 19 July 13
a compiler sees it all…
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 combine their workflow components into an integrated app – one among many, typically – based on 100% open source
FlowDef flowDef = FlowDef.flowDef() .setName( "etl" ) .addSource( "example.employee", emplTap ) .addSource( "example.sales", salesTap ) .addSink( "results", resultsTap ); SQLPlanner sqlPlanner = new SQLPlanner() .setSql( sqlStatement ); flowDef.addAssemblyPlanner( sqlPlanner );
cascading.org
27Friday, 19 July 13
a compiler sees it all…
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 combine their workflow components into an integrated app – one among many, typically – based on 100% open source
FlowDef flowDef = FlowDef.flowDef() .setName( "classifier" ) .addSource( "input", inputTap ) .addSink( "classify", classifyTap ); PMMLPlanner pmmlPlanner = new PMMLPlanner() .setPMMLInput( new File( pmmlModel ) ) .retainOnlyActiveIncomingFields(); flowDef.addAssemblyPlanner( pmmlPlanner );
28Friday, 19 July 13
cascading.orgETL data
preppredictivemodel
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 combine their workflow components into an integrated app – one among many, typically – based on 100% open source
visual collaboration for the business logic is a great way to improve how teams work together
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
29Friday, 19 July 13
Lingual – CSV data in local file system
cascading.org/lingual
30Friday, 19 July 13
Lingual – shell prompt, catalog
cascading.org/lingual
31Friday, 19 July 13
Lingual – queries
cascading.org/lingual
32Friday, 19 July 13
# 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
33Friday, 19 July 13
> 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
34Friday, 19 July 13
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
35Friday, 19 July 13
• established XML standard for predictive model markup
• organized by Data Mining Group (DMG), since 1997 http://dmg.org/
• members: IBM, SAS, Visa, NASA, Equifax, Microstrategy, Microsoft, etc.
• PMML concepts for metadata, ensembles, etc., translate directly into Cascading tuple flows
“PMML is the leading standard for statistical and data mining models and supported by over 20 vendors and organizations. With PMML, it is easy to develop a model on one system using one application and deploy the model on another system using another application.”
PMML – standard
wikipedia.org/wiki/Predictive_Model_Markup_Language
36Friday, 19 July 13
PMML – vendor coverage
37Friday, 19 July 13
• Association Rules: AssociationModel element
• Cluster Models: ClusteringModel element
• Decision Trees: TreeModel element
• Naïve Bayes Classifiers: NaiveBayesModel element
• Neural Networks: NeuralNetwork element
• Regression: RegressionModel and GeneralRegressionModel elements
• Rulesets: RuleSetModel element
• Sequences: SequenceModel element
• Support Vector Machines: SupportVectorMachineModel element
• Text Models: TextModel element
• Time Series: TimeSeriesModel element
PMML – model coverage
ibm.com/developerworks/industry/library/ind-PMML2/
38Friday, 19 July 13
## 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
39Friday, 19 July 13
<?xml version="1.0"?><PMML version="4.0" xmlns="http://www.dmg.org/PMML-4_0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.dmg.org/PMML-4_0 http://www.dmg.org/v4-0/pmml-4-0.xsd"> <Header copyright="Copyright (c)2012 Concurrent, Inc." description="Random Forest Tree Model"> <Extension name="user" value="ceteri" extender="Rattle/PMML"/> <Application name="Rattle/PMML" version="1.2.30"/> <Timestamp>2012-10-22 19:39:28</Timestamp> </Header> <DataDictionary numberOfFields="4"> <DataField name="label" optype="categorical" dataType="string"> <Value value="0"/> <Value value="1"/> </DataField> <DataField name="var0" optype="continuous" dataType="double"/> <DataField name="var1" optype="continuous" dataType="double"/> <DataField name="var2" optype="continuous" dataType="double"/> </DataDictionary> <MiningModel modelName="randomForest_Model" functionName="classification"> <MiningSchema> <MiningField name="label" usageType="predicted"/> <MiningField name="var0" usageType="active"/> <MiningField name="var1" usageType="active"/> <MiningField name="var2" usageType="active"/> </MiningSchema> <Segmentation multipleModelMethod="majorityVote"> <Segment id="1"> <True/> <TreeModel modelName="randomForest_Model" functionName="classification" algorithmName="randomForest" splitCharacteristic="binarySplit"> <MiningSchema> <MiningField name="label" usageType="predicted"/> <MiningField name="var0" usageType="active"/> <MiningField name="var1" usageType="active"/> <MiningField name="var2" usageType="active"/> </MiningSchema>...
Pattern – capture model parameters as PMML
40Friday, 19 July 13
public static void main( String[] args ) throws RuntimeException { String inputPath = args[ 0 ]; String classifyPath = args[ 1 ]; // set up the config properties Properties properties = new Properties(); AppProps.setApplicationJarClass( properties, Main.class ); HadoopFlowConnector flowConnector = new HadoopFlowConnector( properties ); // create source and sink taps Tap inputTap = new Hfs( new TextDelimited( true, "\t" ), inputPath ); Tap classifyTap = new Hfs( new TextDelimited( true, "\t" ), classifyPath ); // handle command line options OptionParser optParser = new OptionParser(); optParser.accepts( "pmml" ).withRequiredArg(); OptionSet options = optParser.parse( args ); // connect the taps, pipes, etc., into a flow FlowDef flowDef = FlowDef.flowDef().setName( "classify" ) .addSource( "input", inputTap ) .addSink( "classify", classifyTap ); if( options.hasArgument( "pmml" ) ) { String pmmlPath = (String) options.valuesOf( "pmml" ).get( 0 ); PMMLPlanner pmmlPlanner = new PMMLPlanner() .setPMMLInput( new File( pmmlPath ) ) .retainOnlyActiveIncomingFields() .setDefaultPredictedField( new Fields( "predict", Double.class ) ); // default value if missing from the model flowDef.addAssemblyPlanner( pmmlPlanner ); } // 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
41Friday, 19 July 13
CustomerOrders
Classify ScoredOrders
GroupBytoken
Count
PMMLModel
M R
FailureTraps
Assert
ConfusionMatrix
Pattern – score a model, using pre-defined Cascading app
cascading.org/pattern
42Friday, 19 July 13
Roadmap – existing algorithms for scoring
• Random Forest
• Decision Trees
• Linear Regression
• GLM
• Logistic Regression
• K-Means Clustering
• Hierarchical Clustering
• Multinomial
• Support Vector Machines (prepared for release)
also, model chaining and general support for ensembles
cascading.org/pattern
43Friday, 19 July 13
Roadmap – next priorities for scoring
• Time Series (ARIMA forecast)
• Association Rules (basket analysis)
• Naïve Bayes
• Neural Networks
algorithms extended based on customer use cases – contact groups.google.com/forum/?fromgroups#!forum/pattern-user
cascading.org/pattern
44Friday, 19 July 13
Cascading / Cascalog / Scalding
Enterprise Data Workflows with Cascading
Cluster Computing with Mesos
Using Cascalog with Palo Alto Open Data
45Friday, 19 July 13
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
46Friday, 19 July 13
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
47Friday, 19 July 13
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”
48Friday, 19 July 13
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
49Friday, 19 July 13
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”
50Friday, 19 July 13
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
51Friday, 19 July 13
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
“optimize topologies”
52Friday, 19 July 13
Operating Systems, redux
meanwhile, GOOG is 3+ generations ahead, with much improved ROI on data centers
John Wilkes, et al.Borg/Omega: data center “secret sauce”youtu.be/0ZFMlO98Jkc
0%
25%
50%
75%
100%
RAILS CPU LOAD
MEMCACHED CPU LOAD
0%
25%
50%
75%
100%
HADOOP CPU LOAD
0%
25%
50%
75%
100%
t t
0%
25%
50%
75%
100%
Rails MemcachedHadoop
COMBINED CPU LOAD (RAILS, MEMCACHED, HADOOP)
Florian Leibert, Chronos/Mesos @ Airbnb
Mesos, open source cloud OS – like Borggoo.gl/jPtTP
53Friday, 19 July 13
Mesos
mesos.apache.org
Return of the Borg: How Twitter Rebuilt Google’s Secret WeaponCade Metzwired.com/wiredenterprise/2013/03/google-borg-twitter-mesos/
54Friday, 19 July 13
Mesos
a common substrate for cluster computing
• scale to 10,000s of nodes using fast, event-driven C++ impl
• improve utilization across workloads
• run long-lived services (e.g., Hypertable and HBase) on the same nodes as batch app and share resources
• build new cluster computing frameworks without reinventing low-level facilities, and have them coexist with existing work
• run multiple instances/versions of Hadoop on the same cluster to isolate production and experimental jobs
• reshape cluster resources based on ML from app history
• reduce latency in transferring data products from one cluster to another
• enable new kinds of apps, which combine frameworks with lower latency
55Friday, 19 July 13
Cascading / Cascalog / Scalding
Enterprise Data Workflows with Cascading
Cluster Computing with Mesos
Using Cascalog with Palo Alto Open Data
56Friday, 19 July 13
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
57Friday, 19 July 13
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
58Friday, 19 July 13
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
59Friday, 19 July 13
GIS about trees in Palo Alto:discovery
60Friday, 19 July 13
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…)
61Friday, 19 July 13
(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)
62Friday, 19 July 13
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
63Friday, 19 July 13
discovery
(curate valuable metadata)
64Friday, 19 July 13
(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
65Friday, 19 July 13
// 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
66Friday, 19 July 13
discovery
sweetgum
analysis of the tree data:
67Friday, 19 July 13
M
tree
GISexport
Regexparse-gis
src
Scrubspecies
Geohash
Regexparse-tree
tree
TreeMetadata
Join
FailureTraps
Estimateheight
M
discovery
(flow diagram, gis ⇒ tree)
68Friday, 19 July 13
9q9jh0
geohash with 6-digit resolution
approximates a 5-block square
centered lat: 37.445, lng: -122.162
modeling
69Friday, 19 July 13
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)
70Friday, 19 July 13
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
71Friday, 19 July 13
(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
72Friday, 19 July 13
M
tree
Join Calculatedistance
shade
Filterheight
Summoment
REstimatetraffic
Rroad
Filterdistance
M M
Filtersum_moment
(flow diagram, shade)
modeling
73Friday, 19 July 13
(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
74Friday, 19 July 13
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) ))
75Friday, 19 July 13
‣ 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
76Friday, 19 July 13
Could combine this with a variety of data APIs:
• Trulia neighborhood data, housing prices
• Factual local business (FB Places, etc.)
• CommonCrawl open source full web crawl
• Wunderground local weather data
• WalkScore neighborhood data, walkability
• Data.gov US federal open data
• Data.NASA.gov NASA open data
• DBpedia datasets derived from Wikipedia
• GeoWordNet semantic knowledge base
• Geolytics demographics, GIS, etc.
• Foursquare, Yelp, CityGrid, Localeze, YP
• various photo sharing
apps
77Friday, 19 July 13
Enterprise Data Workflowswith Cascading
O’Reilly, 2013shop.oreilly.com/product/0636920028536.do
Follow-Up…
also, check out this newsletterfor updates:
liber118.com/pxn/
78Friday, 19 July 13
Follow-Up…
blog, developer community, code/wiki/gists, maven repo, commercial products, etc.:
cascading.org
zest.to/group11
github.com/Cascading
conjars.org
goo.gl/KQtUL
concurrentinc.com
79Friday, 19 July 13