Copyright @2013, Concurrent, Inc. Paco Nathan Concurrent, Inc. San Francisco, CA @pacoid “Cascading: Enterprise Data Workflows based on Functional Programming” 1
Cascading: Enterprise Data Workflows based on Functional Programming
Aug 20, 2015
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Copyright @2013, Concurrent, Inc.
Paco NathanConcurrent, Inc.San Francisco, CA@pacoid
“Cascading: Enterprise Data Workflows based on Functional Programming”
1
Cascading: Workflow Abstraction
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1. Machine Data2. Cascading3. Sample Code4. A Little Theory…5. Workflows6. Lingual7. Pattern8. Open Data
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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…
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Cascading: Workflow Abstraction
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1. Machine Data2. Cascading3. Sample Code4. A Little Theory…5. Workflows6. Lingual7. Pattern8. Open Data
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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.
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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
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functional programming… in production
• 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/wiki
github.com/twitter/scalding/wiki
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
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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
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Cascading: Workflow Abstraction
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1. Machine Data2. Cascading3. Sample Code4. A Little Theory…5. Workflows6. Lingual7. Pattern8. Open Data
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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
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R
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count how often each word appears in a collection of text documents
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DocumentCollection
WordCount
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1 map 1 reduce18 lines code gist.github.com/3900702
word count – conceptual flow diagram
cascading.org/category/impatient
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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();
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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
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(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
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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
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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
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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
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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
Cascalog and Scalding DSLs leverage the functional aspects of MapReduce, helping limit complexity in process
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Cascading: Workflow Abstraction
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1. Machine Data2. Cascading3. Sample Code4. A Little Theory…5. Workflows6. Lingual7. Pattern8. Open Data
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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.
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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
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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
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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
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WordCount
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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
design principles of the pattern language ensure best practices for robust, parallel data workflows at scale
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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
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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.”
36
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
37
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
38
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
39
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
40
Cascading: Workflow Abstraction
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1. Machine Data2. Cascading3. Sample Code4. A Little Theory…5. Workflows6. Lingual7. Pattern8. Open Data
41
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Enterprise Data Workflows
Let’s consider a “strawman” architecture for an example app… at the front end
LOB use cases drive demand for apps
42
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Enterprise Data Workflows
Same example… in the back office
Organizations have substantial investmentsin people, infrastructure, process
43
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Enterprise Data Workflows
Same example… the heavy lifting!
“Main Street” firms are migratingworkflows to Hadoop, for cost savings and scale-out
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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
45
Cascading workflows – taps
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();
source and sink tapsfor TSV data in HDFS
46
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 – topologies
• topologies execute workflows on clusters
• flow planner is like a compiler for queries
- Hadoop (MapReduce jobs)
- local mode (dev/test or special config)
- in-memory data grids (real-time)
• flow planner can be extended to support other topologies
blend flows in different topologies into the same app – for example,batch (Hadoop) + transactions (IMDG)
47
Cascading workflows – topologies
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();
flow planner for Apache Hadoop topology
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example topologies…
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Hadoop Cluster
sourcetap
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taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Cascading workflows – test-driven development
• assert patterns (regex) on the tuple streams
• adjust assert levels, like log4j levels
• trap edge cases as “data exceptions”
• TDD at scale:
1.start from raw inputs in the flow graph
2.define stream assertions for each stage of transforms
3.verify exceptions, code to remove them
4.when impl is complete, app has full test coverage
redirect traps in production to Ops, QA, Support, Audit, etc.
50
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
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Cascading: Workflow Abstraction
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1. Machine Data2. Cascading3. Sample Code4. A Little Theory…5. Workflows6. Lingual7. Pattern8. Open Data
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Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
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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
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Lingual – CSV data in local file system
cascading.org/lingual
54
Lingual – shell prompt, catalog
cascading.org/lingual
55
Lingual – queries
cascading.org/lingual
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abstraction RDBMS JVM Cluster
parser ANSI SQLcompliant parser
ANSI SQLcompliant parser
optimizer logical plan, optimized based on stats
logical plan, optimized based on stats
planner physical plan API “plumbing”
machinedata
query history,table stats
app history, tuple stats
topology b-trees, etc. heterogenous, distributed: Hadoop, in-memory, etc.
visualization ERD flow diagram
schema table schema tuple schema
catalog relational catalog tap usage DB
provenance (manual audit) data setproducers/consumers
abstraction layers in queries…
57
Lingual – JDBC driver
public void run() throws ClassNotFoundException, SQLException { Class.forName( "cascading.lingual.jdbc.Driver" ); Connection connection = DriverManager.getConnection( "jdbc:lingual:local;schemas=src/main/resources/data/example" ); Statement statement = connection.createStatement(); ResultSet resultSet = statement.executeQuery( "select *\n" + "from \"EXAMPLE\".\"SALES_FACT_1997\" as s\n" + "join \"EXAMPLE\".\"EMPLOYEE\" as e\n" + "on e.\"EMPID\" = s.\"CUST_ID\"" ); while( resultSet.next() ) { int n = resultSet.getMetaData().getColumnCount(); StringBuilder builder = new StringBuilder(); for( int i = 1; i <= n; i++ ) { builder.append( ( i > 1 ? "; " : "" ) + resultSet.getMetaData().getColumnLabel( i ) + "=" + resultSet.getObject( i ) ); }
System.out.println( builder ); } resultSet.close(); statement.close(); connection.close(); }
58
Lingual – JDBC result set
$ gradle clean jar$ hadoop jar build/libs/lingual-examples–1.0.0-wip-dev.jar CUST_ID=100; PROD_ID=10; EMPID=100; NAME=BillCUST_ID=150; PROD_ID=20; EMPID=150; NAME=Sebastian
Caveat: if you absolutely positively must have sub-second SQL query response for Pb-scale data on a 1000+ node cluster… Good luck with that! (call the MPP vendors)
This ANSI SQL library is primarily intended for batch workflows – high throughput, not low-latency –for many under-represented use cases in Enterprise IT.
In other words, SQL as a DSL.
cascading.org/lingual
59
# 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
60
> summary(df$hire_age) Min. 1st Qu. Median Mean 3rd Qu. Max. 20.86 27.89 31.70 31.61 35.01 43.92
Lingual – connecting Hadoop and R
cascading.org/lingual
61
Cascading: Workflow Abstraction
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
R
1. Machine Data2. Cascading3. Sample Code4. A Little Theory…5. Workflows6. Lingual7. Pattern8. Open Data
62
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Pattern – model scoring
• migrate workloads: SAS,Teradata, etc., exporting predictive models as PMML
• great open source tools – R, Weka, KNIME, Matlab, RapidMiner, etc.
• integrate with other libraries –Matrix API, etc.
• leverage PMML as another kind of DSL
cascading.org/pattern
63
## train a RandomForest model f <- as.formula("as.factor(label) ~ .")fit <- randomForest(f, data_train, ntree=50) ## test the model on the holdout test set print(fit$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
64
<?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
65
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
66
CustomerOrders
Classify ScoredOrders
GroupBytoken
Count
PMMLModel
M R
FailureTraps
Assert
ConfusionMatrix
Pattern – score a model, using pre-defined Cascading app
cascading.org/pattern
67
## run an RF classifier at scale hadoop jar build/libs/pattern.jar data/sample.tsv out/classify out/trap \ --pmml data/sample.rf.xml
## run an RF classifier at scale, assert regression test, measure confusion matrix hadoop jar build/libs/pattern.jar data/sample.tsv out/classify out/trap \ --pmml data/sample.rf.xml --assert --measure out/measure
## run a predictive model at scale, measure RMSE hadoop jar build/libs/pattern.jar data/iris.lm_p.tsv out/classify out/trap \ --pmml data/iris.lm_p.xml --rmse out/measure
Pattern – score a model, using pre-defined Cascading app
68
• 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/
69
PMML – vendor coverage
70
## train a Random Forest model## example: http://mkseo.pe.kr/stats/?p=220 f <- as.formula("as.factor(label) ~ var0 + var1 + var2")fit <- randomForest(f, data=data, proximity=TRUE, ntree=25)print(fit)saveXML(pmml(fit), file=paste(out_folder, "sample.rf.xml", sep="/"))
experiments – Random Forest model
OOB estimate of error rate: 14%Confusion matrix: 0 1 class.error0 69 16 0.18823531 12 103 0.1043478
71
## train a Logistic Regression model (special case of GLM)## example: http://www.stat.cmu.edu/~cshalizi/490/clustering/clustering01.r f <- as.formula("as.factor(label) ~ var0 + var2")fit <- glm(f, family=binomial, data=data)print(summary(fit))saveXML(pmml(fit), file=paste(out_folder, "sample.lr.xml", sep="/"))
experiments – Logistic Regression model
Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.8524 0.3803 4.871 1.11e-06 ***var0 -1.3755 0.4355 -3.159 0.00159 ** var2 -3.7742 0.5794 -6.514 7.30e-11 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
NB: this model has “var1” intentionally omitted
72
experiments – evaluating results
• use a confusion matrix to compare results for the classifiers
• Logistic Regression has a lower “false negative” rate (5% vs. 11%)however it has a much higher “false positive” rate (52% vs. 14%)
• assign a cost model to select a winner –for example, in an ecommerce anti-fraud classifier:
FN ∼ chargeback risk FP ∼ customer support costs
• can extend this to evaluateN models, M labels in anN × M × M matrix
73
Cascading: Workflow Abstraction
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
RHS
M
R
1. Machine Data2. Cascading3. Sample Code4. A Little Theory…5. Workflows6. Lingual7. Pattern8. Open Data
74
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
75
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
76
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
77
GIS about trees in Palo Alto:discovery
78
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…)
79
(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…
data prep costs are 80/20)
80
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
81
discovery
(curate valuable metadata)
82
(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
83
// 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
84
discovery
sweetgum
analysis of the tree data:
85
M
tree
GISexport
Regexparse-gis
src
Scrubspecies
Geohash
Regexparse-tree
tree
TreeMetadata
Join
FailureTraps
Estimateheight
M
discovery
(flow diagram, gis ⇒ tree)
86
9q9jh0
geohash with 6-digit resolution
approximates a 5-block square
centered lat: 37.445, lng: -122.162
modeling
87
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)
88
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
89
(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
90
M
tree
Join Calculatedistance
shade
Filterheight
Summoment
REstimatetraffic
Rroad
Filterdistance
M M
Filtersum_moment
(flow diagram, shade)
modeling
91
(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
92
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) ))
93
‣ 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
94
Enterprise Data Workflowswith Cascading
O’Reilly, 2013amazon.com/dp/1449358721
references…
95
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.
96
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