1. The magic is in the glue XQuery+Cloud Daniela Florescu Oracle

2. 2 My personal history PhD in object-oriented query processing/optimization Loved the database theory and practice (relational, object-oriented, semi- structured) Got really interested in it, and thought it was important… ….then I joined Oracle.

3. 3 … after 4 years in Oracle Applications are the really important issue How to develop, deploy, maintain, evolve, customize Databases are a side effect Customers are educated to think they need them DB are only useful as part of a general application architecture Customer is the king If they don’t make $$$, you don’t either Customers are in pain building apps right now

4. 4 Agenda Current pain in building apps What can XQuery do for customers ? What can the Cloud do for customers ? How do we put them together ? How do XQuery+Cloud solve the problem ? Some open research problems

5. 5 Imagine I am a customer, I need to build a new app. 1. How much does it cost Cost of developing the app (salaries) Cost of deploying the app Hardware, software licenses, maintenance Loss of income because of mis-provisioning Do I have to pay up front? Is the cost proportional with the income ?

6. 6 Other questions ? 2. How fast can I deliver the app Quicker on the market then my competitors ? 3. How good the application is More customers for the app. => more income Acceptable operational characteristics ? 4. Can I adapt if something changes ? Operational characteristics Functionality 5. Can I customize the same app in a different vertical / different set of customers ? 6. Is there a risk in the technology ?

7. 7 Customers concerns Cost Time to market FlexibilityCustomizabilitySustainabilityRisk Often a tradeoff

8. 8 Different classes of customers Enterprise (e.g. Bank of America) CostSustainabilityRiskCustomizabilityFlexibility Time to market Government agency (eg. DoD) SustainabilityCost Time to market (?) Flexibility (?) CustomizabilityRisk Consumer (e.g Craiglist) Time to market CostFlexibilityCustomizabilitySustainabilityRisk

9. 9 Typical enterprise app stack Communication (XML, REST, WS) Application logic (Java, C#) DatabaseSQL) OracleIBMSAPMicrosoft

10. 10 Cost ? $$$$! Communication (XML, REST, WS) Application logic (Java, C#) DatabaseSQL) Cost of developing the app Cost of deploying the app (hardware, software licenses, maintenance) Loss of income because of mis- provisioning Do I have to pay up front? Is the cost proportional with the income ?

11. 11 Time to market ? Years! Communication (XML, REST, WS) Application logic (Java, C#) DatabaseSQL) 2. How fast can I deliver the app

12. 12 Flexibility ? Customizability? Hardly any ! Communication (XML, REST, WS) Application logic (Java, C#) DatabaseSQL) Can I adapt if something changes ? Operational characteristics Functionality Can I customise it to a different vertical? Oracle experience: for every $1M for Oracle app licenses, customers pay $2M to customize it. (SAP experience even worse :-)

13. 13 Two major evil points 1. Multi layer infrastructure 2. Schemas a pre-requisite New apps: Even the Oracle apps ! New platforms: Salesforce, GoogleApps, Facebook Communication ApplicationLogic(schema-less) Persistent (key, value) store (schema-less) XQuery a possible solution. putget

14. 14 Another evil point Lack of cost elasticity Cost proportional with income Lack of elasticity in performance Response time independent of # clients The Cloud is the beginning of a solution.

15. 15 Agenda Current pain in building apps What can XQuery do for customers ? What can the Cloud do for customers ? How do we put them together ? How do XQuery+Cloud solve the problem ? Some open research problems

16. 16 Why XML ? Covers all spectrum from structured data to textual information Schema independent Platform independent Continuity with the basic Internet infrastructure (URI, HTML, HTTP)

17. 17 What is XQuery ? A programming language for XML processing Functional in style Turing complete Contains:Navigation Declarative query and aggregation (FLWOR) Search (full text) Declarative updates TransformsScripting Streaming and windowing Error handling and second order expressions Packaging (modules) Has limitations (further)

18. 18 History and status Standard of the W3C Good and bad 10 years old 40 existing implementations Implemented in major databases Best implementations in open source If you have XML data, it is hard to avoid.

19. 19 Navigation fn:doc("catalog.xml") /items/item fn:doc("catalog.xml")/items//item fn:doc("catalog.xml")/items//*fn:doc("catalog.xml")/items/@item fn:doc("parts.xml")/parts/part[partno = $i/partno] $x/items/item

20. 20 FLWOR for $i in fn:doc("catalog.xml")/items/item, $p in fn:doc("parts.xml")/parts/part[partno = $i/partno], $s in fn:doc("suppliers.xml")/suppliers /supplier[suppno = $i/suppno] order by $p/description, $s/suppname return $ s Groupby, having, outerjoins, etc

21. 21 Creation of new information <descriptive-catalog> { for $i in fn:doc("catalog.xml")/items/item, $p in fn:doc("parts.xml")/parts/part[partno = $i/partno], $s in fn:doc("suppliers.xml")/suppliers /supplier[suppno = $i/suppno] order by $p/description, $s/suppname return { $p/description, $s/suppname, $i/price } { $p/description, $s/suppname, $i/price } } }</descriptive-catalog>

22. 22 Textual search $doc ftcontains ( ( "mustang" ftand ({("great", "excellent")} any word occurs at least 2 times) ) window 11 words ftand ftnot "rust" ) same paragraph

23. 23 Declarative updates for $p in /inventory/part let $deltap := $changes/part[partno eq $p/partno] let $deltap := $changes/part[partno eq $p/partno]return replace value of node $p/quantity with $p/quantity + $deltap/quantity

24. 24 Transforms let $oldx := /a/b/x return copy $newx := $oldx modify (rename node $newx as "newx", replace value of node $newx by $newx * 2) return ($oldx, $newx)

25. 25 Streams and windowing for sliding window $w in (2, 4, 6, 8, 10, 12, 14) start at $s when fn:true() only end at $e when $e - $s eq 2 return { $w } return { $w } Result of the above query: 2 4 6 2 4 6 4 6 8 4 6 8 6 8 10 6 8 10 8 10 12 8 10 12 10 12 14 10 12 14

26. 26 Scripting expressions block{ declare $a as xs:integer := 0; declare $b as xs:integer := 1; declare $c as xs:integer := $a + $b; declare $fibseq as xs:integer* := ($a, $b); while ($c < 100) { set $fibseq := ($fibseq, $c); set $a := $b; set $b := $c; set $c := $a + $b; };$fibseq;}

27. 27 Where can it be used in today’s architectures? Databases Middle tiers Information dispatch Transformation Data integration Browsers (see XQIB demo, WWW’09 paper) Mobile devices (XQuery on iPhone anyone ?)

28. 28 XQuery’s real potential Standalone programming language for information intensive applications Can build extremely rich applications ApplicationLogic(XQuery) XMLXML XML

29. 29 Why XQuery ? Because of XML Schema independent Continuity with basic Internet infrastructure Continuity structured data textual information XQuery’s own advantages XQuery’s own advantagesDeclarative Single layer code Open source friendly Extra Goodies Opportunity to rethink ACID transactions Unique opportunities for introspection Code and data migration 1. 1. Cost 2. 2. Time to market 3. 3. Flexibility 4. 4. Customizability 5. 5. Sustainability 6. 6. Risk

30. 30 Declarativity Small number of lines of code Development cost Time to market # bugs Easy to optimize automatically Easy to parallelize automatically Especially important in the cloud Easier to achieve elasticity in performance Easier to generate automatically Important for smart/non-developers UIs

31. 31 Declarativity, negative side 1. Less number of developers capable of writing such code 2. Easy to write, harder to read 3. Tools harder to make (e.g. debuggers) 4. Performance can be unstable Despite that, in the history of CS we evolve in the direction of declarativity Assembly, C, C++, Java, Haskell Cobol, SQL

32. 32 Rethink transactions and data consistency XQuery silent as ACID transactions go On purpose ! Are ACID transactions really needed ? Are they really enforced in Web apps ? No. Open research field Interaction of programming languages with new transactional models and new data consistency models

33. 33 Sigmod’08 Data consistency is something to optimize, not an absolute requirement Data consistency models [Tanembaum] Shared-Disk (Naïve approach) No concurrency control at all Eventual Consistency (Basic Protocol) Updates become visible any time and will persist No lost update on page level Atomicity All or no updates of a transaction become visible Monotonic reads, Read your writes, Monotonic writes,... Strong Consistency database-style consistency (ACID) via OCC Data consistency a la carte

34. 34 Introspection opportunities Closed world Everything is (or will be) XML Data, schemas, code, PULs, metadata, configs, runtime information Unique opportunity to: introspect at runtime all of them reason about them change them dynamically (not only data, but schemas, code and configuration) Open research field: Consequences on programming

35. 35 Why NOT XQuery XML is complicated XML Schema is hard/impossible to understand XQuery is complicated XQuery is incomplete (maybe research opport.?) Missing a standard persistent data model Missing DDL functionality (indexes, integrity constraints) Missing basic functionalities (e.g. eval, function overloading) Missing basic data modeling functionality (n:m relationships) XQuery lacks a standard environment (e.g. J2EE) (maybe research opport.?) No tools (debuggers, profilers) (maybe research opport.?) Performance is not clear yet (certainly research opport !) There are few XQuery developers (teaching opport )

36. 36 Agenda Current pain in building apps What can XQuery do for customers ? What can the Cloud do for customers ? How do we put them together ? How do XQuery+Cloud solve the problem ? Some open research problems

37. 37 What is Cloud Computing ? The „rental cars“ paradigm for computing Commoditization of (certain aspects of ) Computing CPU, storage, and network Goal 1: Reduction of Cost principle: fine-grained renting of resources „pay as you go“ (elasticity of cost) Goal 2: Simplification of Management potentially infinite/unbreakable computing resources potentially no administration Goal 3: Elasticity of performance Same resp time independently of workload Note: does not work yet for DB or apps

38. 38 Case Study: Amazon AWS EC2 : scalable virtual private servers using Xen. S3 : WS based storage for applications SQS : hosted message queue for web applications SimpleDB : the core functionality of a database Hadoop based functionality Similar providers: IBM Blue Cloud, Microsoft Azure, (GoogleApp engine)

39. 39 The limits of the (Amazon) Cloud Cloud Computing a great starting point Unfortunately, only a fraction of the stack Hardware DBMS Application Server Application Customization, Training,...

40. 40 Making use of the Cloud Solution 1 (conservative) Take an existing application (Java+SQL, etc) and try to make it run on the cloud (e.g. make Oracle run on AWS) Solution 2 (reactionary) Create an fresh new infrastructure, specially designed for Web apps requirements, to be deployed in the cloud BenefitRisk

41. 41 Solution 1 (conservative) take a traditional DBMS (e.g., Oracle, MySQL,...) install it on an EC2 instance use S3 or EBS as a persistent store Advantages traditional databases are available proven to work well; many tools people trained and confident with them Disadvantages traditional DBMS solve the wrong problem anyway (e.g. focus on consistency) traditional DBMS make the wrong assumptions (DB optimizers fail on virtualized hardware)

42. 42 Solution 2 (reactionary) Rethink the whole system architecture do NOT use a traditional DBMS and app server create new breed of application server (with DB) run application server on n EC2 instances use S3 + distributed consistency protocols Advantages and Disadvantages requires new breed of (immature) systems + tools solves the right problem and gets it right Examples: GoogleApps (Python in the cloud) Sausalito (www.28msec.com) (XQuery in the cloud)

43. 43 Agenda Current pain in building apps What can XQuery do for customers ? What can the Cloud do for customers ? How do we put them together ? How do XQuery+Cloud solve the problem ? Some open research problems

44. 44 XQuery + AWS Cloud Cookbook: Take an existing XQuery processor Partition the XML data on S3 Map REST calls to XQuery programs Run the XQuery programs on EC2 Use SQS for (asyncronous) updates Voila. The magic is in the glue (XQuery proc. + AWS ) Application Server + Web Server + Database integrated XQuery based application stack for Web- based apps fully SOA enabled all pre-configured and lean (ZERO admin)

45. 45 XQuery in the Cloud (connected)

46. 46 Customers concerns Cost Time to market FlexibilityCustomizabilitySustainability

47. 47 XQuery in the Cloud (no Server)

48. 48 XQuery in the Cloud (offline)

49. 49 Demo at www.28msec.com ! www.28msec.com Look at www.programmableweb.com for use cases ( consumer and enterprise mashups) www.programmableweb.com

50. 50 Competitors: Internet Web 2.0 Development Frameworks E.g., Ruby on Rails, PHP / LAMP,... Deployment in the cloud still problematic Google AppEngine, Facebook Apps Proprietary programming model (Python-based) Limited functionality Vendor lock-in, privacy issues Oracle on AWS, do-it-yourself on AWS limited functionality and/or scalability

51. 51 Competitors: Enterprise Salesforce AppExchange proprietary programming model Limited applications domain (CRM) Microsoft Azure.Net programming model manual configuration needed (recent offering, market adoption unclear) Virtualization Companies (e.g., VMWare) No offerings / expertise for data management Oracle (Grid, RAC) limited scalability, cost prohibitive

52. 52 Web 2.0 Support vs. Cloud Support Proprietary Standard Development Deployment Cloud Trad. XQuery+AWS AWS Azure Google App Engine, Facebook Ruby on Rails Oracle VMWare Cloud, Citrix Salesforce, Workday

53. 53 Agenda Current pain in building apps What can XQuery do for customers ? What can the Cloud do for customers ? How do we put them together ? How do XQuery+Cloud solve the problem ? Some open research problems

54. 54 Versions and variations Human mind does not like agreements We like our differences (for a good reason) Different ways to see: DataSchemasCode Current stack is imposing agreement unlike our own nature We have to come up with solutions that allow, welcome and exploit variations Darwinian, evolutionary approach to data, schema and code mutations

55. 55 Versions and variations Research problems: What is a (data, schema, code) variation ? What does it mean to run an app in the presence of variations ? How do you store (index, etc) variations ? How do you re-integrate them back into mainstream app (e.g. community voting ?) What is the correct lifecycle for data, schema, code that allows and maximally exploits variations ? Note: I have a easier time to think of a solution if the app is in XML/XQuery rather if the app is in Java+SQL (even Python)

56. 56 Conclusion XQuery in the cloud a serious alternative for some (large # and large $$) customers Nothing equivalent in the competition: How “solid” (standard, tested) this is Richness of applications Potential for optimization and parallelization Ease of porting to the cloud

57. 57 My advice My advice Keep the eye on the apps, not db Keep the customer in mind Rethink the entire stack Don’t be afraid to shake down existing ideas about how applications are supposed to work Thank you!