Applications in the Cloud
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Eberhard Wolff is an architecture and technology manager at adesso, a leading IT consultancy in Germany. He discusses how cloud computing differs from traditional enterprise computing by having many inexpensive instances with unreliable networks. To build reliable systems, applications need to be stateless, easily scaled, and handle failures. He uses the example of Spring Biking, an e-commerce site, to illustrate an architecture with stateless frontends, elastic scaling, and database replication to achieve reliability in the cloud.
Applications in the Cloud
- 1. Eberhard Wolff adesso AG Applications in the Cloud
- 2. About me • Eberhard Wolff • Architecture & Technology Manager at adesso • adesso is a leading IT consultancy in Germany • Speaker • Author (e.g. first German Spring book) • Blog: http://ewolff.com • Twitter: @ewolff • http://slideshare.com/ewolff • eberhard.wolff@adesso.de
- 3. How Is Cloud Different?
- 4. How Is Cloud Different?
- 6. • How is Cloud Different? Can easily and cheaply add new resources – Prefer starting new instances over highly available instances – Prefer adding instances over using a more powerful instance – Might end up with lots of instances • Prefer dealing with failure over providing a highly available network • So basically lots of non powerful instances with an unreliable network • How can you end up with a reliable system then?
- 7. Enter Spring Biking! • The revolutionary web site to create customized bikes! • We got a few million € Venture Capital • We need... – Catalog of all Mountain Bike parts and bikes – System to configure custom Mountain Bikes – Order system • Cloud good idea – No CapEx – Rapid elasticity -> easy to grow • Focusing on German market
- 8. Spring Biking: Architecture Application • Standard (Order, Enterprise Configuration, Catalog) Architecture • Relational database Database
- 9. Spring Biking: Architecture Wait, didn’t you Application (Order, • Standard Enterprise say it should run Configuration, Catalog) Architecture • Relational in the Cloud? Database database
- 10. How Spring Biking Deals with Cloud Challenges • No state on the web tier – i.e. no session Application – State stored in database • Easy to automatically start new (Order, instances if load increases Configuration, • Every PaaS should deal with elastic Catalog) scaling • Example: Amazon Elastic Beanstalk – Takes a standard Java WAR – Deploys it – Add elastic scaling • Could build something similar yourself with an IaaS – Automated deployment – Elastic scaling and load balancing available from Amazon IaaS offerings
- 11. How Spring Biking Deals with Cloud Challenges • Relational database fine for now – Example: Amazon RDS (Relational Database Service) – MySQL and Oracle Database – MySQL: Multi data center replication – Can deal with failure of one data center • Add Content Delivery Network (CDN) – Not too many PaaS in Europe or Germany – Latency effects revenue • Every 100ms latency costs Amazon 1% of revenue – So add CDN to lower latency
- 12. Benefits for the Development Process • Trivial to get a new version out • Easy to create a production like environment for test or staging – Take snapshot from production database – Set up new database with snapshot – Create a new environment with a different release of the software – Automated for production – Production-like sizing acceptable: You pay by the hour • This can also be done using Private Clouds! • Can be more important than cost reduction • Business Agility is a major driver for (private) Cloud!
- 13. Next step: Spring Biking Goes Global! • Global demand for bikes is on all time high! • We need to globalize the offering • A central RDBMS for the global system is not acceptable – Latency – Amazon RDS offers one uniform database for a Region (e.g. US-East, EU-West) – Need a different solution for a global system • Just an example • Traditional Enterprise scales to a certain limit • The question is which • We are not all going to build Twitter or Facebook
- 14. CAP Theorem • Consistency – All nodes see the same data • Availability – Node failure do not prevent survivors from operating C • Partition Tolerance – System continues to operate despite arbitrary message loss P A • Can at max have two
- 15. CAP Theorem Consistency RDBMS Quorum 2 Phase Commit Partition Tolerance Replication DNS Availability
- 16. CAP Theorem in the Cloud • Need A – Availability – A system that is not available is usually the worst thing C – Shutting down nodes is no option • Need P – Partition Tolerance – Network is not under your control P A – Lots of nodes -> partitioning even more likely • No chance for C – Consistency – Because we can’t
- 17. BASE • Basically Available Soft state Eventually consistent • I.e. trade consistency for availability • Eventually consistent – If no updates are sent for a while all previous updates will eventually propagate through the system – Then all replicas are consistent – Can deal with network partitioning: Message will be transferred later • All replicas are always available • Pun concerning ACID…
- 18. BASE in Spring Biking Application Application Application Database Database Database EU-West US-East Asia-Pacific Changes to catalog Eventually propagated
- 19. Network Partitioning Application Application Application Database Database Database Eventually Inconsistent data is EU-West US-East Asia-Pacific consistent Network Partitioning
- 20. BASE Using Event Sourcing Event Domain • Do it yourself using a Model messaging system – JMS, RabbitMQ … – Easy to duplicate state on nodes – Fail safe: Message will eventually be transferred – …and high latency is acceptable • Other reason to use Event Sourcing – Capture all changes to an application state as a sequence of events – Originates in Domain Driven Design – Also used as a log of actions (to replay, reverse etc) • Might end up with an Event-driven Architecture – Might add Complex Event Processing etc.
- 21. Implementing BASE Using NoSQL • Some NoSQL databases include replication • Example: CouchDB – Replication between nodes – Master-master replication using versioning – Trivial to set up – All nodes have the same data – Sharding only possible with additional proxies
- 22. More Sophisticated • Apache Cassandra • Each node is master for certain data • Data is replicated to N nodes • Data is read from R nodes • After a write W nodes must acknowledge • N,R,W are configurable
- 23. Different Parts Require Different Architecture Application • So far: Catalog – Data must be available on each node Catalog – Slight inconsistencies are OK – i.e. new item added to catalog • Stock information must be consistent – So customers are not disappointed Order – Might use caching-like structure • Orders are immediately send to the back end – No local storage at all • A lot more catalog browsing than ordering
- 24. More load on catalog -> Less load on order -> More instances Less instances Catalog Catalog Order No local Stock Stock data Database Database All send Cache Cache to backend Updates Stock Master
- 25. Handling Log Files • Business requirements – Need to measure hits on web pages – Need to measure hits for individual products etc. • Sounds like a batch – File in, statistics out • But: Data is globally distributed • Lots of data i.e. cannot be collected at a central place • Data should stay where it is • Some nodes might be offline or not available • Prefer incomplete answer over no answer at all
- 26. More Than CAP • CAP Theorem again • Consistency, Availability, Network Partitioning • You can only have two C • But: We want Availability • …and a flexible trade off between P A Consistency and Network Partitioning • Like Casssandra
- 27. Harvest and Yield • Yield: Probability of completing a request • Harvest: Fraction of data represented in the result • Harvest and Yield vs. CAP • Yield = 100% -> Availability • Harvest = 100% -> Consistency • Can be used to think about Cassandra configurations • Can also be used to execute some logic on all data • …and wait until enough harvest is there to answer a query • So: Send out a query to all log files • …and collect the results
- 28. Map / Reduce • Map: Apply a function to all data – Emit (item name, 1) for each log file line • Master sorts by item name • Reduce: Add all (item name, 1) to the total score Reduce • Map can be done on any node • Master collects data Map Map
- 29. Another Case Study • Financials • Build a Highly Available, High Throughput System, Low Latency System on Standard Hardware! • Just like Google and Amazon • Driver: Standard infrastructure – cheap and stable • Driver: Even more availability, throughput, scalability and lower latency • You will need to consider CAP, BASE, Harvest & Yield etc. • Very likely in a Private Cloud
- 30. Another Case Study • Random Project • Make deployment easier! • Make it easy to create test environment! • Driver: Business Agility and Developer Productivity • Will need to use automated installation + IaaS or PaaS • Might be in a Public or Private Cloud
- 31. Custom Self Service Portal
- 32. Conclusion • Current PaaS allow to run Enterprise applications unchanged • At one point you will need to change • CAP: Consistency, Availability, Partition Tolerance – choose two • Cloud: AP, no C • BASE: Basically Available, Soft State, Eventually Consistent • Can be implemented using Event Sourcing • …or a NoSQL persistence solution • Create multiple deployment artifacts to scale each part • Harvest / Yield: Fine grained CAP • Map / Reduce to run batches in the Cloud
- 33. Wir suchen Sie als ! Software-Architekt (m/w) ! Projektleiter (m/w) ! Senior Software Engineer (m/w) ! Kommen Sie zum Stand und gewinnen Sie ein iPad 2! jobs@adesso.de www.AAAjobs.de