boulder/denver bigdata: cluster computing with apache mesos and cascading
TRANSCRIPT
Boulder/Denver BigData, 2013-09-25:
Cluster Computing with Apache Mesos and Cascading
Paco Nathan @pacoidChief Scientist, Mesosphere.io
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
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 dataprep
predictivemodel
datasources
enduses
Anatomy of an Enterprise app
definition of a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
ANSI SQL for ETL
Anatomy of an Enterprise app
definition of a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
endusesJ2EE for business logic
Anatomy of an Enterprise app
definition of a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
SAS for predictive models
Anatomy of an Enterprise app
definition of 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…
Anatomy of an Enterprise app
definition of 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…
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…one connected DAG:
• optimization
• troubleshooting
• exception handling
• notifications
cascading.org
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
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 );
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
Edgar Codd alluded to this (DSLs for structuring data) in his original paper about relational model
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/
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
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 – pattern language
Cascading uses a “plumbing” metaphor in Java 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 in the flows bring functional programming aspects into Java
A Pattern LanguageChristopher Alexander, et al.amazon.com/dp/0195019199
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
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
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 “Hello World” for Hadoop apps
a distributed computing framework that runs Word 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
DocumentCollection
WordCount
TokenizeGroupBytoken Count
R
M
1 map 1 reduce18 lines code gist.github.com/3900702
WordCount – conceptual flow diagram
cascading.org/category/impatient
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
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
(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
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
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
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
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
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
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
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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
Lingual – CSV data in local file system
cascading.org/lingual
Lingual – shell prompt, catalog
cascading.org/lingual
Lingual – queries
cascading.org/lingual
# 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$hire_age)
library(ggplot2)m <- ggplot(df, aes(x=hire_age))m <- m + ggtitle("Age at hire, people named Gina")m + geom_histogram(binwidth=1, aes(y=..density.., fill=..count..)) + geom_density()
Lingual – connecting Hadoop and R
> summary(df$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
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
• 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
PMML – vendor coverage
• 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/
## 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
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
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
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 period
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
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”
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
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”
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
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”
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)youtu.be/E91oEn1bnXM
Google“Underneath the Covers at Google” – Jeff Dean (0:06:54 ff)youtu.be/qsan-GQaeykperspectives.mvdirona.com/2008/06/11/JeffDeanOnGoogleInfrastructure.aspx
MIT Media Lab“Social Information Filtering for Music Recommendation” – Pattie Maespubs.media.mit.edu/pubs/papers/32paper.psted.com/speakers/pattie_maes.html
Primary Sources
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
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…
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…
The Modern Kernel: Top Linux Contributors…
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
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
“Return of the Borg”
Return of the Borg: How Twitter Rebuilt Google’s Secret WeaponCade Metzwired.com/wiredenterprise/2013/03/google-borg-twitter-mesos
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale MachinesLuiz André Barroso, Urs Hölzleresearch.google.com/pubs/pub35290.html
2011 GAFS OmegaJohn Wilkes, et al.youtu.be/0ZFMlO98Jkc
“Return of the Borg”
Omega: flexible, scalable schedulers for large compute clustersMalte Schwarzkopf, Andy Konwinski, Michael Abd-El-Malek, John Wilkeseurosys2013.tudos.org/wp-content/uploads/2013/paper/Schwarzkopf.pdf
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
Mesos – architecture
Ruby
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
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
Mesos – data center OS stack
HADOOP STORM CHRONOS RAILS JBOSS
TELEMETRY
Kernel
OS
Apps
MESOS
CAPACITY PLANNING GUISECURITYSMARTER SCHEDULING
Prior Practice: Dedicated Servers
DATACENTER
• low utilization rates
• longer time to ramp up new services
Prior Practice: Virtualization
DATACENTER PROVISIONED VMS
• even more machines to manage
• substantial performance decrease due to virtualization
• VM licensing costs
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
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
What are the costs of Virtualization?
benchmarktype
OpenVZimprovement
mixed workloads 210%-300%
LAMP (related) 38%-200%
I/O throughput 200%-500%
response time order magnitude
more pronounced at higher loads
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 MemcachedHadoop
COMBINED CPU LOAD (RAILS, MEMCACHED, HADOOP)
M
MasterDockerRegistry
index.docker.io
LocalDockerRegistry
( optional )
M
M
S
S
S
S
S
S
marathon
docker
docker
docker
Mesosmaster servers
Mesosslave 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
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
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 … | | |
DockerRegistry
When a user requests a container…
Mesos, LXC, and Docker are tied together for launch
21
3
4
5
6
7
8
Example: Docker on Mesos
mesosphere.io/2013/09/26/docker-on-mesos/
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 for VMs 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
Deployments
Opposite Ends of the Spectrum, One Substrate
Built-in /bare metal
Hypervisors
Solaris Zones
Linux CGroups
Opposite Ends of the Spectrum, One Substrate
Request /Response Batch
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 for Twitter’s continued success at scale. It's one of the primary keys to our data center efficiency."
Chris Fry, SVP Engineeringblog.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
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 Engineeringgigaom.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.
Media Coverage
Play Framework Grid Deployment with MesosJames Ward, Flo Leibert, et al.Typesafe blog (2013-09-19)typesafe.com/blog/play-framework-grid...
Mesosphere Launches Marathon FrameworkAdrian BridgwaterDr. Dobbs (2013-09-18)drdobbs.com/open-source/mesosphere...
New open source tech Marathon wants to make your data center run like Google’sDerrick HarrisGigaOM (2013-09-04)gigaom.com/2013/09/04/...
Running batch and long-running, highly available service jobs on the same clusterBen LoricaO’Reilly (2013-09-01)strata.oreilly.com/2013/09/...
Resources
Apache Mesos Projectmesos.apache.org
Mesospheremesosphere.io
Tutorialmesosphere.io/2013/08/01/...
Documentationmesos.apache.org/documentation
2011 USENIX Research Paperusenix.org/legacy/event/nsdi11/tech/full_papers/Hindman_new.pdf
Collected Notes/Archivesgoo.gl/jPtTP
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
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 – Four-Part Harmony
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 – Four-Part Harmony
1. End Use Cases, the drivers
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 – Four-Part Harmony
2. A new kind of team process
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 – Four-Part Harmony
3. Abstraction layer as optimizing middleware, e.g., Cascading
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 – Four-Part Harmony
4. Data Center OS, e.g., Mesos
Enterprise Data Workflows with Cascading
O’Reilly, 2013shop.oreilly.com/product/0636920028536.do
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