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Java on Windows Azure (Cloud Computing Expo 2010)

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Deck delivered at Cloud Computing Expo 2010 Santa Clara - Java on Windows Azure; updated version from JavaOne presentation

Deck delivered at Cloud Computing Expo 2010 Santa Clara - Java on Windows Azure; updated version from JavaOne presentation

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  • Microsoft's Windows Azure platform is a virtualized and abstracted application platform that can be used to build highly scalable and reliable applications, with Java. The environment consists of a set of services such as NoSQL table storage, blob storage, queues, relational database service, internet service bus, access control, and more. Java applications can be built using these services via Web services APIs, and your own Java Virtual Machine, without worrying about the underlying server OS and infrastructure. Highlights of this session will include:
    • An overview of the Windows Azure environment
    • How to develop and deploy Java applications in Windows Azure
    • How to architect horizontally scalable applications in Windows Azure
  • To build for big scale – use more of the same pieces, not bigger pieces; though a different approach may be needed
  • Source: http://danga.com/words/2007_06_usenix/usenix.pdf
  • Source: http://highscalability.com/blog/2007/11/13/flickr-architecture.html
  • Source: http://www.slideshare.net/jboutelle/scalable-web-architectures-w-ruby-and-amazon-s3
  • Source: http://www.slideshare.net/netik/billions-of-hits-scaling-twitter
    Source: http://highscalability.com/blog/2009/6/27/scaling-twitter-making-twitter-10000-percent-faster.html
  • Source: http://highscalability.com/blog/2009/10/12/high-performance-at-massive-scale-lessons-learned-at-faceboo-1.html
  • Picture source: http://pdp.protopak.net/Belltheous90/DeathStarII.gif
  • Transcript

    • 1. Building Highly Scalable Java Applications on Windows Azure David Chou david.chou@microsoft.com blogs.msdn.com/dachou
    • 2. Agenda • Overview of Windows Azure • Java How-to • Architecting for Scale > Introduction
    • 3. What is Windows Azure? • A cloud computing platform (as-a-service) – on-demand application platform capabilities – geo-distributed Microsoft data centers – automated, model-driven services provisioning and management • You manage code, data, content, policies, service models, etc. – not servers (unless you want to) • We manage the platform – application containers and services, distributed storage systems – service lifecycle, data replication and synchronization – server operating system, patching, monitoring, management – physical infrastructure, virtualization networking – security – “fabric controller” (automated, distributed service management system) > Azure Overview
    • 4. Anatomy of a Windows Azure instance Guest VM Guest VM Guest VM Host VM Maintenance OS, Hardware-optimized hypervisor > Azure Overview > Anatomy of a Windows Azure instance The Fabric Controller communicates with every server within the Fabric. It manages Windows Azure, monitors every application, decides where new applications should run – optimizing hardware utilization. Storage – distributed storage systems that are highly consistent, reliable, and scalable. Compute – instance types: Web Role & Worker Role. Windows Azure applications are built with web role instances, worker role instances, or a combination of both. Each instance runs on its own VM (virtual machine) and local transient storage; replicated as needed HTTP/HTTPS
    • 5. Application Platform Services > Azure Overview > Application Platform Services Storage Dynamic Tabular Data Blobs Message Queues Distributed File System Content Distribution Data Transact-SQL Data Synchronization Relational Database ADO.NET, ODBC, PHP Integration Registry RegistryService Bus Security Claims-Based Identity Federated Identities Secure Token Service Declarative Policies Marketplace Application Marketplace Information Marketplace Frameworks Workflow Hosting Distributed Cache Services Hosting Compute C/C++ Win32 VHD On-Premises Bridging Networking
    • 6. Application Platform Services > Azure Overview > Application Platform Services Compute Storage Data Relational Database Integration Security Marketplace Frameworks Table Storage Blob Storage Queue Drive Content Delivery Network VM Role Networking Connect ApplicationsDataMarket Access Control Service Bus Composite App Caching Web Role Worker Role ReportingDataSync Integration Connect (BizTalk)
    • 7. How this may be interesting to you • Not managing and interacting with server OS – less work for you – don’t have to care it is “Windows Server” (you can if you want to) – but have to live with some limits and constraints • Some level of control – process isolation (runs inside your own VM/guest OS) – service and data geo-location – allocated capacity, scale on-demand – full spectrum of application architectures and programming models • You can run Java! – plus PHP, Python, Ruby, MySQL, memcached, etc. – and eventually anything that runs on Windows > Azure Overview
    • 8. Java and Windows Azure • Provide your JVM – any version or flavor that runs on Windows • Provide your code – no programming constraints (e.g., whitelisting libraries, execution time limit, multi-threading, etc.) – use existing frameworks – use your preferred tools (Eclipse, emacs, etc.) • File-based deployment – no OS-level installation (conceptually extracting a tar/zip with run.bat) • Windows Azure “Worker Role” sandbox – standard user (non-admin privileges; “full trust” environment) – native code execution (via launching sub-processes) – service end points (behind VIPs and load balancers) > Java How-To
    • 9. Some boot-strapping in C# • Kick-off process in WorkerRole.run() – get environment info (assigned end point ports, file locations) – set up local storage (if needed; for configuration, temp files, etc.) – configure diagnostics (Windows Server logging subsystem for monitoring) – launch sub-process(es) to run executable (launch the JVM) • Additional hooks (optional) – Manage role lifecycle – Handle dynamic configuration changes • Free tools – Visual Studio Express – Windows Azure Tools for Visual Studio – Windows Azure Tools for Eclipse/Java (CTP 2010H2) – Windows Azure SDK > Java How-To > Boot-strapping
    • 10. Running Tomcat in Windows Azure Service Instance Service Instance Worker Role RoleEntry Point Sub-Process JVM Tomcat server.xmlCatalina Fabric Controller Load Balancer Table Storage Blob Storage Queue Service Bus Access Control SQL Database new Process() bind port(x) http://instance:x http://instance:y listen port(x) http://app:80 get runtime info index.jsp > Java How-To > Tomcat
    • 11. • Boot-strapping code in WorkerRole.run() • Service end point(s) in ServiceDefinition.csdef Running Jetty in Windows Azure > Java How-To > Jetty string response = ""; try { System.IO.StreamReader sr; string port = RoleEnvironment.CurrentRoleInstance.InstanceEndpoints["HttpIn"].IPEndpoint.Port.ToString(); string roleRoot = Environment.GetEnvironmentVariable("RoleRoot"); string jettyHome = roleRoot + @"approotappjetty7"; string jreHome = roleRoot + @"approotappjre6"; Process proc = new Process(); proc.StartInfo.UseShellExecute = false; proc.StartInfo.RedirectStandardOutput = true; proc.StartInfo.FileName = String.Format(""{0}binjava.exe"", jreHome); proc.StartInfo.Arguments = String.Format("-Djetty.port={0} -Djetty.home="{1}" -jar "{1}start.jar"", port, jettyHome); proc.EnableRaisingEvents = false; proc.Start(); sr = proc.StandardOutput; response = sr.ReadToEnd(); } catch (Exception ex) { response = ex.Message; Trace.TraceError(response); } <Endpoints> <InputEndpoint name="HttpIn" port="80" protocol="tcp" /> </Endpoints>
    • 12. Current constraints Platform – Dynamic networking • <your app>.cloudapp.net • no naked domain • CNAME re-direct from custom domain • sending traffic to loopback addresses not allowed and cannot open arbitrary ports – No OS-level access – Non-persistent local file system • allocate local storage directory • read-only: Windows directory, machine configuration files, service configuration files – Available registry resources • read-only: HKEY_CLASSES_ROOT, HKEY_LOCAL_MACHINE, HKEY_USERS, HKEY_CURRENT_CONFIG • full access: HKEY_CURRENT_USER > Java How-To > Limitations Java – Sandboxed networking • NIO (java.nio) not supported • engine and host-level clustering • JNDI, JMS, JMX, RMI, etc. • need to configure networking – Non-persistent local file system • logging, configuration, etc. – REST-based APIs to services • Table Storage – schema-less (noSQL) • Blob Storage – large files (<200GB block blobs; <1TB page blobs) • Queues • Service Bus • Access Control
    • 13. Improvements on the way Platform – Networking modeling • well known ports (or fixed VM ports) • port ranges (for inbound traffic) • load balancer control (on/off) • network filters (inter-role access control) – Improved automation • startup tasks (execute OS-level scripts) • role plugins (remote desktop, virtual network, diagnostics, etc.) – Full IIS • multiple websites in same role • virtual directories • applications, modules – Admin access • full administrative access to role instances • reboot/re-image support > Java How-To > Platform Enhancements (Announced; Not Yet Released) Java – Traditional deployment models • deploy your own Java EE stack • configure internal networking – More frameworks, packages, and extended languages • verify deployment and configuration – Solution accelerators • with bootstrapping and configuration – Java API support • Windows Azure SDK v2.0 (announced at PDC10) • www.windowsazure4j.org – Development tools • Windows Azure Tools for Eclipse/Java (CTP 2010H2)
    • 14. • Execute startup script in ServiceDefinition.csdef • Service end point(s) in ServiceDefinition.csdef Running Jetty with admin access + fixed ports > Java How-To > Platform Enhancements (Announced; Not Yet Released) <Startup> <Task commandLine=“runme.cmd" executionContext=“elevated" TaskType=“background"> </Task> </Startup> <Endpoints> <InputEndpoint name="HttpIn" protocol=“http" port="80" localPort="80" /> </Endpoints>
    • 15. Running Fujitsu Interstage App Server JavaEE 6 – Based on Glassfish 3.1 – Complete JavaEE execution environment – Service Integrator SOA Platform > Java How-To > Platform Enhancements (Announced; Not Yet Released) Windows Azure 1.3 SDK – Loopback adapter is no longer blocked – Enabled Java NIO – Enabled Port Ranges: for “inbound traffic” (as opposed to 5 in the past) Service end point(s) in ServiceDefinition.csdef
    • 16. • Worker Role – fabric sandbox native deployment – automated, need additional code – available now • Admin Access – script-based installation and execution – automated, need scripts – available shortly (not yet released) • VM Role – host your own pre-configured VM image – automated, full control – available later (not yet released) Deployment Options > Java How-To > Platform Enhancements (Announced; Not Yet Released)
    • 17. • Runtime – Multiple Java app servers – Any Windows-based JRE • Supports – Windows Azure Storage – Windows Azure Drive – Windows Azure AppFabric – SQL Azure • One-click cloud deployment • Integrated diagnostics, monitoring, and logging Windows Azure Tools for Eclipse/Java > Java How-To > Platform Enhancements (Announced; Not Yet Released)
    • 18. Accessing SQL Azure from Java > Java How-To > SQL Azure • SQL Azure Database – Full relational Database as a Service • Supports TDS & OData • Direct connectivity to SQL Azure – Connect with JDBC/ODBC using the latest driver – Eclipse tooling support • Interoperability using REST – Easily wrap SQL Azure with WCF Data Services – Restlet extension for OData (Java) • Committed to OSS support and app compatibility
    • 19. Accessing Windows Azure Storage from Java > Java How-To > Windows Azure Storage • Windows Azure SDK for Java – Enables Java developers to develop against Windows Azure Storage & Service Management infrastructure using familiar & consistent programming model • Features – Set of Java classes for Windows Azure Blobs, Tables & Queues (for CRUD operations) & Service Management – Helper Classes for HTTP transport, AuhN/AuthZ, REST & Error Management – Manageability, Instrumentation & Logging(log4j) • Open Source Project site: – Developed by our Partner Soyatec – www.windowsazure4j.org Windows Azure SDK for Java Blobs, Tables, Queues Manageability, Instrumentation, logging Helper for Http, Auth, REST, Error Your Java application REST
    • 20. Accessing Windows Azure AppFabric from Java > Java How-To > Windows Azure AppFabric • Usage Patterns – Extends reach of applications securely through the cloud – Securely integrates partners outside of org boundaries – Extends reach of on-premises web services layer – Enables leveraging cloud quickly without having to rewrite apps • App Fabric SDK for Java Developers – Open source software development kit (SDK) – a set of libraries, tools, Prescriptive guidance – sample applications • Open Source Project site: – Developed by our partner Persistent Systems Limited – www.jdotnetservices.com
    • 21. Additional Cloud Interop Options Application-layer Connectivity & Messaging > Cloud Scenarios
    • 22. Facebook (2009) • +200B pageviews /month • >3.9T feed actions /day • +300M active users • >1B chat mesgs /day • 100M search queries /day • >6B minutes spent /day (ranked #2 on Internet) • +20B photos, +2B/month growth • 600,000 photos served /sec • 25TB log data /day processed thru Scribe • 120M queries /sec on memcache > Architecting for Scale Size matters Twitter (2009) • 600 requests /sec • avg 200-300 connections /sec; peak at 800 • MySQL handles 2,400 requests /sec • 30+ processes for handling odd jobs • process a request in 200 milliseconds in Rails • average time spent in the database is 50-100 milliseconds • +16 GB of memcached Google (2007) • +20 petabytes of data processed /day by +100K MapReduce jobs • 1 petabyte sort took ~6 hours on ~4K servers replicated onto ~48K disks • +200 GFS clusters, each at 1- 5K nodes, handling +5 petabytes of storage • ~40 GB /sec aggregate read/write throughput across the cluster • +500 servers for each search query < 500ms • >1B views / day on Youtube (2009) Myspace (2007) • 115B pageviews /month • 5M concurrent users @ peak • +3B images, mp3, videos • +10M new images/day • 160 Gbit/sec peak bandwidth Flickr (2007) • +4B queries /day • +2B photos served • ~35M photos in squid cache • ~2M photos in squid’s RAM • 38k req/sec to memcached (12M objects) • 2 PB raw storage • +400K photos added /day Source: multiple articles, High Scalability http://highscalability.com/
    • 23. app server • Common characteristics – synchronous processes – sequential units of work – tight coupling – stateful – pessimistic concurrency – clustering for HA – vertical scaling Traditional scale-up architecture > Architecting for Scale > Vertical Scaling app serverweb data store units of work web data store
    • 24. app server app server Traditional scale-up architecture > Architecting for Scale > Vertical Scaling web data storeweb • To scale, get bigger servers – expensive – has scaling limits – inefficient use of resources
    • 25. data storeapp server Traditional scale-up architecture > Architecting for Scale > Vertical Scaling app serverweb web • When problems occur – bigger failure impact
    • 26. Traditional scale-up architecture > Architecting for Scale > Vertical Scaling app serverweb data storeweb • When problems occur – bigger failure impact – more complex recovery
    • 27. Use more pieces, not bigger pieces LEGO 10179 Ultimate Collector's Millennium Falcon • 33 x 22 x 8.3 inches (L/W/H) • 5,195 pieces LEGO 7778 Midi-scale Millennium Falcon • 9.3 x 6.7 x 3.2 inches (L/W/H) • 356 pieces > Architecting for Scale > Horizontal scaling
    • 28. app server Scale-out architecture > Architecting for Scale > Horizontal scaling app serverweb data store • Common characteristics – small logical units of work – loosely-coupled processes – stateless – event-driven design – optimistic concurrency – partitioned data – redundancy fault-tolerance – re-try-based recoverability web data store
    • 29. app server app server app server app server app server Scale-out architecture > Architecting for Scale > Horizontal scaling app serverweb data store web web web data store web web • To scale, add more servers – not bigger servers data store data store data store data store
    • 30. app server app server app server app server app server app server Scale-out architecture > Architecting for Scale > Horizontal scaling web data store web web web data store web web • When problems occur – smaller failure impact – higher perceived availability data store data store data store data store
    • 31. app server app server app server Scale-out architecture > Architecting for Scale > Horizontal scaling app serverweb data store web web app server web data store web web • When problems occur – smaller failure impact – higher perceived availability – simpler recovery data store data store data store data store
    • 32. app server app server • Scalable performance at extreme scale – asynchronous processes – parallelization – smaller footprint – optimized resource usage – reduced response time – improved throughput app server app server Scale-out architecture + distributed computing > Architecting for Scale > Horizontal scaling app serverweb data store web web app server web data store web web data store data store data store data store parallel tasks async tasks perceived response time
    • 33. app server app server • When problems occur – smaller units of work – decoupling shields impact app server app server Scale-out architecture + distributed computing > Architecting for Scale > Horizontal scaling app serverweb data store web web app server web data store web web data store data store data store data store
    • 34. app server • When problems occur – smaller units of work – decoupling shields impact – even simpler recovery app server app server Scale-out architecture + distributed computing > Architecting for Scale > Horizontal scaling app serverweb data store web web app server web data store web web data store data store data store data store
    • 35. Live Journal (from Brad Fitzpatrick, then Founder at Live Journal, 2007) > Architecting for Scale > Cloud Architecture Patterns Partitioned Data Distributed Cache Web Frontend Distributed Storage Apps & Services
    • 36. Flickr (from Cal Henderson, then Director of Engineering at Yahoo, 2007) > Architecting for Scale > Cloud Architecture Patterns Partitioned Data Distributed Cache Web Frontend Distributed Storage Apps & Services
    • 37. SlideShare (from John Boutelle, CTO at Slideshare, 2008) > Architecting for Scale > Cloud Architecture Patterns Partitioned Data Distributed Cache Web Frontend Distributed Storage Apps & Services
    • 38. Twitter (from John Adams, Ops Engineer at Twitter, 2010) > Architecting for Scale > Cloud Architecture Patterns Partitioned Data Distributed Cache Web Frontend Distributed Storage Apps & Services Queues Async Processes
    • 39. 2010 stats (Source: http://www.facebook.com/press/info.php?statistics) – People • +500M active users • 50% of active users log on in any given day • people spend +700B minutes /month – Activity on Facebook • +900M objects that people interact with • +30B pieces of content shared /month – Global Reach • +70 translations available on the site • ~70% of users outside the US • +300K users helped translate the site through the translations application – Platform • +1M developers from +180 countries • +70% of users engage with applications /month • +550K active applications • +1M websites have integrated with Facebook Platform • +150M people engage with Facebook on external websites /month > Architecting for Scale > Cloud Architecture Patterns Facebook (from Jeff Rothschild, VP Technology at Facebook, 2009) Partitioned Data Distributed Cache Web Frontend Distributed Storage Apps & Services Parallel Processes Async Processes
    • 40. Windows Azure platform components Apps & Services Services Web Frontend QueuesDistributed Storage Distributed Cache Partitioned Data > Architecting for Scale > Cloud Architecture Patterns Content Delivery Network Load Balancer IIS Web Server VM Role Worker Role Web Role Caching Queues Access Control Composite App Blobs Relational Database Tables Drives Service Bus Reporting DataSync Virtual Network Connect
    • 41. Fundamental concepts > Architecting for Scale • Vertical scaling still works
    • 42. Fundamental concepts > Architecting for Scale • Horizontal scaling for cloud computing • Small pieces, loosely coupled • Distributed computing best practices – asynchronous processes (event-driven design) – parallelization – idempotent operations (handle duplicity) – de-normalized, partitioned data (sharding) – shared nothing architecture – optimistic concurrency – fault-tolerance by redundancy and replication – etc.
    • 43. Partitioned data > Architecting for Scale > Fundamental Concepts Shared nothing architecture – transaction locality (partition based on an entity that is the “atomic” target of majority of transactional processing) – loosened referential integrity (avoid distributed transactions across shard and entity boundaries) – design for dynamic redistribution and growth of data (elasticity) Cloud computing friendly – divide & conquer – size growth with virtually no limits – smaller failure surface Windows Azure platform services – Table Storage Service – SQL Azure – AppFabric Caching (coming soon) – SQL Azure DB federation (coming soon) Web Role QueuesWeb Role Web Role Worker Role Relational Database Relational Database Relational Database Web Role read write
    • 44. Asynchronous processes & parallelization > Architecting for Scale > Fundamental Concepts Defer work as late as possible – return to user as quickly as possible – event-driven design (instead of request- driven) Cloud computing friendly – distributes work to more servers (divide & conquer) – smaller resource usage/footprint – smaller failure surface – decouples process dependencies Windows Azure platform services – Queue Service – AppFabric Service Bus – inter-node communication Worker Role Web Role Queues Service BusWeb Role Web Role Web Role Worker Role Worker Role Worker Role
    • 45. Idempotent operations > Architecting for Scale > Fundamental Concepts Repeatable processes – allow duplicates (additive) – allow re-tries (overwrite) – reject duplicates (optimistic locking) – stateless design Cloud computing friendly – resiliency Windows Azure platform services – Queue Service – AppFabric Service Bus Worker Role Service Bus Worker Role Worker Role
    • 46. At most two of these properties for any shared-data system C A P Consistency + Availability • High data integrity • Single site, cluster database, LDAP, xFS file system, etc. • 2-phase commit, data replication, etc. C A P Consistency + Partition • Distributed database, distributed locking, etc. • Pessimistic locking, minority partition unavailable, etc. C A P Availability + Partition • High scalability • Distributed cache, DNS, etc. • Optimistic locking, expiration/leases, etc. CAP (Consistency, Availability, Partition) Theorem > Architecting for Scale > Fundamental Concepts Source: “Towards Robust Distributed Systems”, Dr. Eric A. Brewer, UC Berkeley
    • 47. Hybrid architectures > Architecting for Scale > Fundamental Concepts Scale-out (horizontal) – BASE: Basically Available, Soft state, Eventually consistent – focus on “commit” – conservative (pessimistic) – shared nothing – favor extreme size – e.g., user requests, data collection & processing, etc. Scale-up (vertical) – ACID: Atomicity, Consistency, Isolation, Durability – availability first; best effort – aggressive (optimistic) – transactional – favor accuracy/consistency – e.g., BI & analytics, financial processing, etc. Most distributed systems employ both approaches
    • 48. Lastly… Windows Azure is an open & interoperable cloud platform Microsoft is committed to Java, and we are on a journey – please give us your feedback & participate in open source projects Diverse Choice of Development Tools for Java Developers – Eclipse Tools for Windows Azure – Write Modern Cloud Application – Tomcat Solutions Accelerator – Admin Access & VM Role – Windows Azure Platform SDKs for Java Developers • Windows Azure SDK (Storage, Diagnostics & Service Management) • App Fabric SDK (Service Bus & Access Control Services) • Restlet extension for OData (Java) For more information: – http://windowsazure.com/interop – http://www.interoperabilitybridges.com > Wrap-Up
    • 49. © 2010 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. Thank you! David Chou david.chou@microsoft.com blogs.msdn.com/dachou

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