Boulder/Denver BigData: Cluster Computing with Apache Mesos and Cascading

1. Boulder/Denver BigData, 2013-09-25: Cluster Computing with Apache Mesos and Cascading Paco Nathan @pacoid Chief Scientist, Mesosphere.io

2. Cluster Computing with Apache Mesos and Cascading: 1. Enterprise Data Workﬂows 2. Lingual and Pattern Examples 3. An Evolution of Cluster Computing Boulder, 2013-09-25

3. Enterprise Data Workﬂows middleware for Big Data applications is evolving, with commercial examples that include: Cascading, Lingual, Pattern, etc. Concurrent ParAccel Big Data Analytics Platform Actian Anaconda supporting IPython Notebook, Pandas,Augustus, etc. Continuum Analytics ETL data prep predictive model data sources end uses

4. Anatomy of an Enterprise app deﬁnition of a typical Enterprise workﬂow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end uses ANSI SQL for ETL

5. Anatomy of an Enterprise app deﬁnition of a typical Enterprise workﬂow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end usesJ2EE for business logic

6. Anatomy of an Enterprise app deﬁnition of a typical Enterprise workﬂow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end uses SAS for predictive models

7. Anatomy of an Enterprise app deﬁnition of a typical Enterprise workﬂow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end uses SAS for predictive modelsANSI SQL for ETL most of the licensing costs…

8. Anatomy of an Enterprise app deﬁnition of a typical Enterprise workﬂow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end usesJ2EE for business logic most of the project costs…

9. ETL data prep predictive model data sources end uses 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… one connected DAG: • optimization • troubleshooting • exception handling • notiﬁcations cascading.org

10. a compiler sees it all… ETL data prep predictive model data sources end uses 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

11. a compiler sees it all… ETL data prep predictive model data sources end uses 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 );

12. 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 any need to create new languages • allows programmers who have J2EE expertise to leverage the economics of Hadoop clusters Edgar Codd alluded to this (DSLs for structuring data) in his original paper about relational model

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/wiki github.com/twitter/scalding/wiki Why Adopting the Declarative Programming PracticesWill ImproveYour Return fromTechnology Dan Woods, 2013-04-17 Forbes forbes.com/sites/danwoods/2013/04/17/why-adopting-the-declarative-programming- practices-will-improve-your-return-from-technology/

14. Functional Programming for Big Data WordCount with token scrubbing… Apache Hive: 52 lines HQL + 8 lines Python (UDF) compared to Scalding: 18 lines Scala/Cascading functional programming languages help reduce software engineering costs at scale, over time

15. 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.

16. Workflow Abstraction – pattern language Cascading uses a “plumbing” metaphor in Java to define workflows out of familiar elements: Pipes, Taps, Tuple Flows, Filters, Joins, Traps, etc. Scrub token Document Collection Tokenize Word Count GroupBy token Count Stop Word List Regex token HashJoin Left RHS M R data is represented as flows of tuples operations in the flows bring functional programming aspects into Java A Pattern Language Christopher Alexander, et al. amazon.com/dp/0195019199

17. 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 Scrub token Document Collection Tokenize Word Count GroupBy token Count Stop Word List Regex token HashJoin Left RHS M R Literate Programming Don Knuth literateprogramming.com

18. 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

19. 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: this simple program provides an excellent test case for parallel processing: • 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 “HelloWorld” for Hadoop apps a distributed computing framework that runsWord Count efficiently in parallel at scale can handle much larger and more interesting compute problems count how often each word appears in a collection of text documents

20. Document Collection Word Count Tokenize GroupBy token Count R M 1 map 1 reduce 18 lines code gist.github.com/3900702 WordCount – conceptual ﬂow diagram cascading.org/category/impatient

21. 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 taps Tap 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 stream Fields 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 counts Pipe 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 flow FlowDef flowDef = FlowDef.flowDef().setName( "wc" ) .addSource( docPipe, docTap ) .addTailSink( wcPipe, wcTap ); // write a DOT file and run the flow Flow wcFlow = flowConnector.connect( flowDef ); wcFlow.writeDOT( "dot/wc.dot" ); wcFlow.complete(); Document Collection Word Count Tokenize GroupBy token Count R M

22. mapreduce Every('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 diagram Document Collection Word Count Tokenize GroupBy token Count R M

23. (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 / Clojure Document Collection Word Count Tokenize GroupBy token Count R M

24. 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 / Clojure Document Collection Word Count Tokenize GroupBy token Count R M

25. 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 / Scala Document Collection Word Count Tokenize GroupBy token Count R M

26. 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 / Scala Document Collection Word Count Tokenize GroupBy token Count R M

27. A Thought Exercise Consider that when a company like Caterpillar 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

28. Alternatively… climate.com

29. Two Avenues to the App Layer… scale ➞ complexity➞ 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

31. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap Modeling PMML Lingual – 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

32. Lingual – CSV data in local ﬁle system cascading.org/lingual

33. Lingual – shell prompt, catalog cascading.org/lingual

34. Lingual – queries cascading.org/lingual

35. # load the JDBC package library(RJDBC) # set up the driver drv <- 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 repository connection <- 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 data df$hire_age <- as.integer(as.Date(df$HIRE_DATE) - as.Date(df$BIRTH_DATE)) / 365.25 summary(df$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

36. > 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

37. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap 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

38. • 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

39. PMML – vendor coverage

40. • 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 • SupportVector Machines: SupportVectorMachineModel element • Text Models: TextModel element • Time Series: TimeSeriesModel element PMML – model coverage ibm.com/developerworks/industry/library/ind-PMML2/

41. ## 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 <- predicted confuse <- 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

42. 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

44. 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 successes in the 1997 holiday season… AMZN, EBAY, Inktomi (YHOO Search), then GOOG MapReduce and the Apache Hadoop open source stack emerged from this period

45. RDBMS Stakeholder SQL Query result sets Excel pivot tables PowerPoint slide decks Web App Customers transactions Product strategy Engineering requirements BI Analysts optimized code Circa 1996: pre- inﬂection point

46. RDBMS Stakeholder SQL Query result sets Excel pivot tables PowerPoint slide decks Web App Customers transactions Product strategy Engineering requirements BI Analysts optimized code Circa 1996: pre- inﬂection point “throw it over the wall”

47. RDBMS SQL Query result sets recommenders + classiﬁers Web Apps customer transactions Algorithmic Modeling Logs event history aggregation dashboards Product Engineering UX Stakeholder Customers DW ETL Middleware servletsmodels Circa 2001: post- big ecommerce successes

48. RDBMS SQL Query result sets recommenders + classiﬁers Web Apps customer transactions Algorithmic Modeling Logs event history aggregation dashboards Product Engineering UX Stakeholder Customers DW ETL Middleware servletsmodels Circa 2001: post- big ecommerce successes “data products”

49. Workﬂow RDBMS near timebatch services transactions, content social interactions Web Apps, Mobile, etc.History Data Products Customers RDBMS Log Events In-Memory Data Grid Hadoop, etc. Cluster Scheduler Prod Eng DW Use Cases Across Topologies s/w dev data science discovery + modeling Planner Ops dashboard metrics business process optimized capacitytaps Data Scientist App Dev Ops Domain Expert introduced capability existing SDLC Circa 2013: clusters everywhere

50. Workﬂow RDBMS near timebatch services transactions, content social interactions Web Apps, Mobile, etc.History Data Products Customers RDBMS Log Events In-Memory Data Grid Hadoop, etc. Cluster Scheduler Prod Eng DW Use Cases Across Topologies s/w dev data science discovery + modeling Planner Ops dashboard metrics business process optimized capacitytaps Data Scientist App Dev Ops Domain Expert introduced capability existing SDLC Circa 2013: clusters everywhere “optimize topologies”

51. Amazon “Early Amazon: Splitting the website” – Greg Linden glinden.blogspot.com/2006/02/early-amazon-splitting-website.html eBay “The eBay Architecture” – Randy Shoup, Dan Pritchett addsimplicity.com/adding_simplicity_an_engi/2006/11/you_scaled_your.html addsimplicity.com.nyud.net:8080/downloads/eBaySDForum2006-11-29.pdf Inktomi (YHOO Search) “Inktomi’s Wild Ride” – Erik Brewer (0:05:31 ff) youtu.be/E91oEn1bnXM Google “Underneath the Covers at Google” – Jeff Dean (0:06:54 ff) youtu.be/qsan-GQaeyk perspectives.mvdirona.com/2008/06/11/JeffDeanOnGoogleInfrastructure.aspx MIT Media Lab “Social Information Filtering for Music Recommendation” – Pattie Maes pubs.media.mit.edu/pubs/papers/32paper.ps ted.com/speakers/pattie_maes.html Primary Sources

52. Cluster Computing’s Dirty Little Secret many of us make a good living by leveraging high ROI apps based on clusters, and so execs agree to build out more data centers… clusters for Hadoop/HBase, for Storm, for MySQL, for Memcached, for Cassandra, for Nginx, etc. this becomes expensive! a single class of workloads on a given cluster is simpler to manage, but terrible for utilization… various notions of “cloud” help… Cloudera, Hortonworks, probably EMC soon: sell a notion of “Hadoop as OS” All your workloads are belong to us Google Data Center, Fox News ~2002

53. Three Laws, or more? meanwhile, architectures evolve toward much, much larger data… pistoncloud.com/ ... Rich Freitas, IBM Research Q: what disruptions in topologies+algorithms could this imply? given there’s no such thing as RAM anymore…

54. Three Laws, or more? meanwhile, architectures evolve toward much, much larger data… pistoncloud.com/ ... Rich Freitas, IBM Research regardless of how architectures change, death and taxes will endure: servers fail, data must move Q: what disruptions in topologies+algorithms could this imply? given there’s no such thing as RAM anymore…

55. The Modern Kernel: Top Linux Contributors…

56. Beyond Hadoop Hadoop – an open source solution for fault-tolerant parallel processing of batch jobs at scale, based on commodity hardware… however, other priorities have emerged for the analytics lifecycle: • apps require integration beyond Hadoop • multiple topologies, mixed workloads, multi-tenancy • higher utilization • lower latency • highly-available, long running services • more than “Just JVM” – e.g., Python growth keep in mind the priority for multi-disciplinary efforts, to break down even more silos – well beyond the de facto “priesthood” of data engineering

57. Beyond Hadoop Google has been doing data center computing for years, to address the complexities of large-scale data workﬂows: • leveraging the modern kernel: isolation in lieu of VMs • “most (>80%) jobs are batch jobs, but the majority of resources (55–80%) are allocated to service jobs” • mixed workloads, multi-tenancy • relatively high utilization rates • JVM? not so much… • reality: scheduling batch is simple; scheduling services is hard/expensive

58. “Return of the Borg” Return of the Borg: HowTwitter Rebuilt Google’s SecretWeapon Cade Metz wired.com/wiredenterprise/ 2013/03/google-borg-twitter-mesos The Datacenter as a Computer: An Introduction to the Design ofWarehouse-Scale Machines Luiz André Barroso, Urs Hölzle research.google.com/pubs/ pub35290.html 2011 GAFS Omega John Wilkes, et al. youtu.be/0ZFMlO98Jkc

59. “Return of the Borg” Omega: ﬂexible, scalable schedulers for large compute clusters Malte Schwarzkopf,Andy Konwinski, Michael Abd-El-Malek, John Wilkes eurosys2013.tudos.org/wp-content/uploads/2013/paper/Schwarzkopf.pdf

60. Mesos – deﬁnitions a common substrate for cluster computing heterogenous assets in your data center or cloud made available as a homogenous set of resources • top-level Apache project • scalability to 10,000s of nodes • obviates the need for virtual machines • isolation (pluggable) for CPU, RAM, I/O, FS, etc. • fault-tolerant replicated master using ZooKeeper • multi-resource scheduling (memory and CPU aware) • APIs in C++, Java, Python • web UI for inspecting cluster state • available for Linux, OpenSolaris, Mac OSX

61. Mesos – architecture R uby Kernel Apps servicesbatch Frameworks Python JVM C ++ Workloads distributed ﬁle system Chronos DFS distributed resources: CPU, RAM, I/O, FS, rack locality, etc. Cluster Storm Kafka JBoss Django RailsSharkImpalaScalding Marathon SparkHadoopMPI MySQL

62. Mesos – architecture given use of Mesos as a Data Center OS kernel… • Chronos provides complex scheduling capabilities, much like a distributed Unix “cron” • Marathon provides highly-available long-running services, much like a distributed Unix “init.d” • next time you need to build a distributed app, consider using these as building blocks a major lesson learned from Spark: • leveraging these kinds of building blocks, one can rebuild Hadoop 100x faster, in much less code

63. Mesos – data center OS stack HADOOP STORM CHRONOS RAILS JBOSS TELEMETRY Kernel OS Apps MESOS CAPACITY PLANNING GUISECURITYSMARTER SCHEDULING

64. Prior Practice: Dedicated Servers DATACENTER • low utilization rates • longer time to ramp up new services

65. Prior Practice: Virtualization DATACENTER PROVISIONED VMS • even more machines to manage • substantial performance decrease due to virtualization • VM licensing costs

66. Prior Practice: Static Partitioning DATACENTER STATIC PARTITIONING • even more machines to manage • substantial performance decrease due to virtualization • VM licensing costs • static partitioning limits elasticity

67. MESOS Mesos: One Large Pool Of Resources DATACENTER “We wanted people to be able to program for the data center just like they program for their laptop." Ben Hindman

68. What are the costs of Virtualization? benchmark type OpenVZ improvement mixed workloads 210%-300% LAMP (related) 38%-200% I/O throughput 200%-500% response time order magnitude more pronounced at higher loads

69. What are the costs of Single Tenancy? 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 Memcached Hadoop COMBINED CPU LOAD (RAILS, MEMCACHED, HADOOP)

70. M Master Docker Registry index.docker.io Local Docker Registry ( optional ) M M S S S S S S marathon docker docker docker Mesos master servers Mesos slave servers Marathon can launch and monitor service containers from one or more Docker registries, using the Docker executor for Mesos S S S S S S … … … … … … … mesosphere.io/2013/09/26/docker-on-mesos/ Example: Docker on Mesos

71. Mesos Master Server init | + mesos-master | + marathon | Mesos Slave Server init | + docker | | | + lxc | | | + (user task, under container init system) | | | + mesos-slave | | | + /var/lib/mesos/executors/docker | | | | | + docker run … | | | The executor, monitored by the Mesos slave, delegates to the local Docker daemon for image discovery and management. The executor communicates with Marathon via the Mesos master and ensures that Docker enforces the speciﬁed resource limitations. mesosphere.io/2013/09/26/docker-on-mesos/ Example: Docker on Mesos

73. Arguments for Data Center Computing rather than running several specialized clusters, each at relatively low utilization rates, instead run many mixed workloads obvious beneﬁts are realized in terms of: • scalability, elasticity, fault tolerance, performance, utilization • reduced equipment capex, Ops overhead, etc. • reduced licensing, eliminating need forVMs or potential vendor lockin subtle beneﬁts – arguably, more important for Enterprise IT: • reduced time for engineers to rampup new services at scale • reduced latency between batch and services, enabling new highROI use cases • enables Dev/Test apps to run safely on a Production cluster

74. Deployments

75. Opposite Ends of the Spectrum, One Substrate Built-in / bare metal Hypervisors Solaris Zones Linux CGroups

76. Opposite Ends of the Spectrum, One Substrate Request / Response Batch

77. Case Study: Twitter (bare metal / on premise) “Mesos is the cornerstone of our elastic compute infrastructure – it’s how we build all our new services and is critical forTwitter’s continued success at scale. It's one of the primary keys to our data center efﬁciency." Chris Fry, SVP Engineering blog.twitter.com/2013/mesos-graduates-from-apache-incubation • key services run in production: analytics, typeahead, ads • Twitter engineers rely on Mesos to build all new services • instead of thinking about static machines, engineers think about resources like CPU, memory and disk • allows services to scale and leverage a shared pool of servers across data centers efﬁciently • reduces the time between prototyping and launching

78. Case Study: Airbnb (fungible cloud infrastructure) “We think we might be pushing data science in the ﬁeld of travel more so than anyone has ever done before… a smaller number of engineers can have higher impact through automation on Mesos." Mike Curtis,VP Engineering gigaom.com/2013/07/29/airbnb-is-engineering-itself-into-a-data-driven... • improves resource management and efﬁciency • helps advance engineering strategy of building small teams that can move fast • key to letting engineers make the most of AWS-based infrastructure beyond just Hadoop • allowed company to migrate off Elastic MapReduce • enables use of Hadoop along with Chronos, Spark, Storm, etc.

79. Media Coverage Play Framework Grid Deployment with Mesos James Ward, Flo Leibert, et al. Typesafe blog (2013-09-19) typesafe.com/blog/play-framework-grid... Mesosphere Launches Marathon Framework Adrian Bridgwater Dr. Dobbs (2013-09-18) drdobbs.com/open-source/mesosphere... New open source tech Marathon wants to make your data center run like Google’s Derrick Harris GigaOM (2013-09-04) gigaom.com/2013/09/04/... Running batch and long-running, highly available service jobs on the same cluster Ben Lorica O’Reilly (2013-09-01) strata.oreilly.com/2013/09/...

80. Resources Apache Mesos Project mesos.apache.org Mesosphere mesosphere.io Tutorial mesosphere.io/2013/08/01/... Documentation mesos.apache.org/documentation 2011 USENIX Research Paper usenix.org/legacy/event/nsdi11/tech/full_papers/Hindman_new.pdf Collected Notes/Archives goo.gl/jPtTP

81. Cluster Computing with Apache Mesos and Cascading: 1. Enterprise Data Workﬂows 2. Lingual and Pattern Examples 3. An Evolution of Cluster Computing SUMMARY… Boulder, 2013-09-25

82. Workﬂow RDBMS near timebatch services transactions, content social interactions Web Apps, Mobile, etc.History Data Products Customers RDBMS Log Events In-Memory Data Grid Hadoop, etc. Cluster Scheduler Prod Eng DW Use Cases Across Topologies s/w dev data science discovery + modeling Planner Ops dashboard metrics business process optimized capacitytaps Data Scientist App Dev Ops Domain Expert introduced capability existing SDLC Circa 2013: clusters everywhere – Four-Part Harmony

83. Workﬂow RDBMS near timebatch services transactions, content social interactions Web Apps, Mobile, etc.History Data Products Customers RDBMS Log Events In-Memory Data Grid Hadoop, etc. Cluster Scheduler Prod Eng DW Use Cases Across Topologies s/w dev data science discovery + modeling Planner Ops dashboard metrics business process optimized capacitytaps Data Scientist App Dev Ops Domain Expert introduced capability existing SDLC Circa 2013: clusters everywhere – Four-Part Harmony 1. End Use Cases, the drivers

84. Workﬂow RDBMS near timebatch services transactions, content social interactions Web Apps, Mobile, etc.History Data Products Customers RDBMS Log Events In-Memory Data Grid Hadoop, etc. Cluster Scheduler Prod Eng DW Use Cases Across Topologies s/w dev data science discovery + modeling Planner Ops dashboard metrics business process optimized capacitytaps Data Scientist App Dev Ops Domain Expert introduced capability existing SDLC Circa 2013: clusters everywhere – Four-Part Harmony 2. A new kind of team process

85. Workﬂow RDBMS near timebatch services transactions, content social interactions Web Apps, Mobile, etc.History Data Products Customers RDBMS Log Events In-Memory Data Grid Hadoop, etc. Cluster Scheduler Prod Eng DW Use Cases Across Topologies s/w dev data science discovery + modeling Planner Ops dashboard metrics business process optimized capacitytaps Data Scientist App Dev Ops Domain Expert introduced capability existing SDLC Circa 2013: clusters everywhere – Four-Part Harmony 3. Abstraction layer as optimizing middleware, e.g., Cascading

86. Workﬂow RDBMS near timebatch services transactions, content social interactions Web Apps, Mobile, etc.History Data Products Customers RDBMS Log Events In-Memory Data Grid Hadoop, etc. Cluster Scheduler Prod Eng DW Use Cases Across Topologies s/w dev data science discovery + modeling Planner Ops dashboard metrics business process optimized capacitytaps Data Scientist App Dev Ops Domain Expert introduced capability existing SDLC Circa 2013: clusters everywhere – Four-Part Harmony 4. Data Center OS, e.g., Mesos

87. Enterprise DataWorkﬂows with Cascading O’Reilly, 2013 shop.oreilly.com/product/ 0636920028536.do monthly newsletter for updates, events, conference summaries, etc.: liber118.com/pxn/