Design Guidelines for Large Message-based EAI Systems (A Case Study)

1. Design Guidelines for Large Message-based EAI Systems(A Case Study) Jim White Director of Training Intertech, Inc. St. Paul, MN jwhite@intertech.com

3. This Talk • Presents an EAI case study • A very large EAI system for a retail chain. • Identify issues and challenges encountered in the project • Identifies lessons learned and recommendations for your EAI projects. • Let’s you know others do have it as bad as you. • The story does have a happy ending • Maybe providing hope to the hopeless.

4. How many of you… • Are actively working on an EAI project? • Have been on an EAI project in the past? • Plan on being on an EAI project in the near future? • Have no idea what EAI is but it sounded like a good topic to help put me to sleep after lunch??

5. How many of you… • Are project architects? • Are technical project leads? • Are developers/designers of systems? • Are managers? • Are testers/QA/support folks? • Can’t remember after 4 days of the conference?

6. First Off – What’s EAI? • From Wikipedia – “…the integration of data between applications in a company.” • wMUsers – “technology that connects enterprise-wise systems… evolved to refer to technologies used to connect systems anywhere” • Hohpe/Woolf – “enterprise integration using messaging”

7. EAI & Messaging • “Enables data or commands to be sent across the network” • using a send and forget approach

8. Messaging How? • Message-oriented middleware (MOM) like that offered by • IBM WebSphere MQ • Microsoft BizTalk • TIBCO • WebMethods • SeeBeyond (now Sun owned) • Vitria • and others • Using … • Java Message Service (JMS) • Microsoft’s Message Queuing (MSMQ) and/or Messaging libraries in Microsoft .NET • Web services standards that support asynchronous Web services • WS-ReliableMessaging • Sun’s Java API for XML Messaging (JAXM) • Microsoft’s Web Services Extensions (WSE).

9. Why large EAI is different? • Messaging/EAI development  Web or other distributed app development • Especially when very large • Many new or significantly altered considerations • Requirement differences • Time and space needs • Process control/orchestration • Failure handling • Monitoring • Proprietary nature of vendor solutions • Support turnover • Staffing needs

10. The Case Study • A major retail chain has dozens of distribution centers • Each distribution center or warehouse services hundreds of stores (>1200 total stores). • Each distribution center is moving thousands of “cartons” (i.e. boxes) around the warehouse each day • Receiving them from trucks through dock doors. • Moving them with fork lifts to storage areas in the warehouse • Conveying them to “break down” areas for distribution to stores. • Conveying them down belts to storage areas or outbound trucks. • Moving them onto trucks that depart the warehouse.

11. The Case Study • A box is tracked via labels and bar code readers. • Some “reads” are manual and some are automated. • Generating literally hundreds of “events” per second per warehouse. • RFID was about to make create more events. • More “reads” from more points in the warehouse. • Potentially adding store “reads” to the event list.

12. The “System” Part I • The retail chain wanted all the data on events regarding the movement of cartons sent to HQ • Providing them with unparalleled real time information on inventory levels and product status. • Providing more accurate information for merchandise analyst and productivity monitoring for warehouse managers. Part II (Not germane to the discussion today) • Providing a Java Web application to nearly 10,000 users to access the data company wide. • Reports galore. • Some limited ad hoc query reporting.

13. Let’s do the math • >25 warehouses • Each generating ~15-20 carton events per second • Averaging 400 messages a second incoming at HQ • Peak around 1300 messages a second incoming at HQ • Data around an event ~200bytes/msg • 24x7x52 (31,449,600 seconds for those not counting) • = ~ 4-7GB a day

14. …and the math isn’t getting better • During Christmas time things were worse – much worse. • The organization wants to double its current size by 2010! • Oh yah…did I mention RFID was coming • Tripling or quadrupling the number of events

15. My Challenge • Design and implement a system to get the data from the warehouses to HQ • In near real time to support the reporting needs • Use whatever makes sense (to some degree – more later) • With a good size team (20-25 people in various roles)

16. My Background • 15 year “grizzled” veteran of software development. • 6 years of Java experience. • Author of a Java book. • Experienced architect, manager, mentor, trainer. • Eager to take on any software system challenge. • No experience in EAI! • An organization with limited EAI experience.

17. The “Perfect Storm” The size of the EAI project + abilities of the development team =

18. The Solution • Significant company resources and investment in SeeBeyond EAI product. • Put SeeBeyond at all the endpoints (warehouses and HQ). • All data would move through SeeBeyond. • SeeBeyond is Java based (also a company technology direction). • Write routing/minor processing code in Java in SeeBeyond. • Significant company resources and investment in Oracle RDBMS. • Oracle already at the warehouses • Obtain a “honking” big Oracle DB at HQ. • Use Oracle stored procedures for heavy lifting (data processing – report data preparation).

19. Solution Diagram Bundling 20-40 event messages at a time Ex: move this carton there, but have We gotten the “receive” carton msg yet? We have rec’d a carton – do we have the reference data for the product yet?

20. Problem #1 – We weren’t ready • As an architect, I was not aware how different an EAI messaging system is. • Asynchronous-everywhere nature • Had no patterns to follow (No – I had not read Hohpe/Woolf EAI book) • Did not have an awareness of the vendor landscape • Was easily talked into solutions by others. • My organization didn’t see how big it was • Had only implemented smaller EAI solutions • Finding good help was hard – and a critical step • Internally – lots of support but no experience • Contractors – lots of desire, but little implementation experience to the scale/level of effort

21. Getting Yourself Ready • Get yourself ready • Understand your options – all the three letter E’s (EAI, ETL, EII, EDR, etc.) • Read EAI patterns • Know the products (WBI, Vitria, Tibco, WebMethods, SeeBeyond, etc.) • Find people with real EAI experience • Experienced with systems matching the size of your app • Find people with product expertise • Find people with design/pattern expertise

22. EAI Patterns • Enterprise Integration Patterns: Hohpe/Woolf • Next Generation Application Integration: Linthicum • IT Architectures and Middleware: Britton

23. EAI Component Basics • A typical messaging system is comprised of the following parts. • Endpoints • Messages • Channels • Routers • Translators • Monitors

24. EAI Component Analogy

25. EAI Patterns • As the GOF pointed out in generic software, there are common behaviors in software systems. • They are powerful tools for communicating behavior. • They represent naturally occurring processes. • Are generally repetitive in nature, and lend themselves to reuse. • Each of the message components also has several patterns that represent common behaviors in a messaging system and encourage reuse.

26. Getting Resources Ready • Let the network engineers know of your plans • You are going to be using a significant amount of pipe. • Have you considered failover/load balancing? (comm lines around warehouses get cut on occasion) • Let the database engineers know of your plans • Terabytes of data to be stored and processed – where will it go? • Consider backup/recovery systems • Database logs/archiving • Performance tuning

27. Getting Support Ready • Support staffs will be lost at turnover • How many of your support shops really know … • How to manage application servers? • How to manage web applications effectively? • Can you expect them to be able to operate, maintain and support component based messaging systems? • Do they know what a message server or bus is? • Across a very distributed environment? • Get them trained early (in messaging infrastructure). • Have them help you design the monitoring tools and alert systems. • Work together to develop proactive systems checks and troubleshooting procedures.

28. Getting Others Ready • If your development team isn’t ready, what about… • Testing/QA teams? • Analyst? • Managers? • For example, finding experienced testers for asynchronous messaging systems is difficult. • They usually need intricate knowledge of the messaging subsystem monitors and admin capabilities.

29. Problem #2 – Proprietary EAI • EAI Products/Solutions are many. • EAI Standards are few. • EAI/ETL/EII/… market place is tumultuous • Sun has purchases SeeBeyond • IBM bought Ascential • Everyone calling their product an ESB (example on next page) • Products/Solutions have scale limits • Some they know about • Others they do not • Java alone does not make you platform independent.

30. Can you identify this product?? “…provides an award-winning messaging backbone for deploying your enterprise service bus (ESB) today as the connectivity layer of a service-orientated architecture (SOA).”

31. Examine Your Solution Options • See if what you already have would work. • There is a reason MQ has been around a long time. • Where possible consider tried, true and already deployed platforms • But again do the math and see if they can support the extra load. • In house support is probably better equipped (more in a bit) • Not everything has to travel by message. • Consider multiple/alternate technologies for parts of your solution. • ETL is great for certain parts of a large solution • There is a reason why products like Oracle are expensive (technologies like Oracle Replication – more in a bit). • Does, however, create more issues of timing.

32. What Travels by Message? • Consider multiple/alternate technologies for parts of your solution. • Replication of reference data • Bulk/batch transfers • Non-real time needs • ETL is great for certain parts of a large solution • Examine features in your DB/App Servers • There is a reason why products like Oracle are expensive (technologies like Oracle Replication – more in a bit). • How about those Message Beans in the app server? • This can, however, create more issues of timing.

33. Reference Data • In many applications, you need reference data on both ends of the messaging systems. • You can build a “replicating” message engine to treat this like other message data (not recommended). • Referential integrity becomes a real problem. • Consider issues of message timing (PR becomes the 51st state but messages with PR references start to arrive before the new state data does) • Use simple replication technologies where possible • ETL tools - if reference data changes only happen at certain times. • Technologies like Oracle Replication for real time (it can operate over a WAN).

34. Interoperability • We used Java, but… • Even when you use Java, how is it being applied? • Java running inside of proprietary components (like SeeBeyond eWays) does not make you portable. • Write component code that can be used by or incorporated by proprietary systems. • Under the covers, is the vendor using • JMS • JMX/SNMP • Web services/WS-Reliable Messaging/JAX-RPC • Etc…

35. Process outside the bus • Process outside the message bus/subsystem if you can • Let the bus focus on delivering the goods. • Too much processing time in the bus will create • Scalability problems • Monitoring problems • Possibly interoperability problems (especially when using proprietary technology/components) • Process with components that are • Flexible • easy to get at (and change) • interoperable (if possible) • and contain reusable business logic (if possible)

36. Problem #3 – Math we didn’t do • We didn’t do enough “math” up front. • We didn’t plan for failure/growth. • The messages moved slower than anticipated. • The message processing took more time than expected. • The amount of data was larger than expected.

37. Do the math and ask the tough ?’s • How much time its going to take to get a message from A to B • Test that estimate early. • Work with the business analysts to figure out how many messages need to be moved. • Make volume estimates part of the non-functional requirements gathering process. • Check that against the existing databases if possible. • How much data needs to be packaged, shipped, processed, stored? • Design the messages and calculate the size of the overall message (XML and all). • Calculate the rate and add up the total volume.

38. …and pad your answer! • Do you have room to spare?? • Can the messaging system handle that (on both ends)? • Can the consuming database handle that? • Can the hardware and network handle that? • Anticipate failure • What happens if something/anything goes down for an hour? • What happens if you go down for a day? • What happens if you have unexpected growth?

39. Problem #4 – Exception handling wasn’t • More considerations for failover and redundancy • Versus Web application • We did not plan on downtime • Unplanned system issues • Planned outages • We didn’t build in enough redundancy • Load balancing and • Failover were both after thoughts • All messages always correct all the time (NOT) • At first, we had no proper dead letter queuing • No proper exception processing • No means to properly see and react to issues • Many more points of failure and potential issues • More widely distributed

40. Design load balancing & failover upfront • Load balancing and failover must be accommodated • Like security, you need a multi-layered approach • Hardware (like Big IP) • Redundant message bus/message servers • Processing components • Database • EAI system throttling • How are you going to kick over to the failover systems (and return to regular systems)? • Without losing messages • Without causing timing problems in message deliver/receipt

41. Throttling • Throttling limits ("throttles") the number of requests it will respond to within a specified period of time. • Limits congestion. • Built into most good EAI solutions today. • Often overlooked and not used. • Used in messaging systems to ensure that no one part of the system is driven beyond its capacity or performance efficiently.

42. Throttling at the potential congestion point Throttle points. Potentially lots of messages, especially if the WAN goes down Congestion point

43. Space, space and more space • Plan on extra space for failure • A place for queued messages to sit if something goes down • Space in the DB or space in the message channels – or both • Consider the time lags for getting additional hardware bought, installed, and up and running • Plan on extra space for logs • You are going to want to keep log files around for a while. • Some problems take time to manifest to a point of awareness. • Devise an automated archive/clean up for logs. • No…not all EAI systems provide log clean up utilities.

44. Anticipate bad messages • Build a Dead Letter Queue (see EAI Patterns book). • Unless you have a simple system, you will have messages the system can’t handle • Improper format, wrong data, etc… • Build a means to capture and handle these • Less they clog your process. • Where do you put them? DB, other queue? • Who checks them (do you have a “one’s” issue or systemic problem?)

45. Message Repair • If possible, build a message triage mechanism to inspect, fix, resend DLQ’ed messages • This can be built/improved over time • More manual at first • Automated as you learn more. • Considerations • How are you going to clean up the error “droppings” (messages that are truly dead) • Consider a “retry” queue with varied strategies to retry messages that have failed. • Failure may be due to row locks or reference updates that are just microseconds away from completion. • Be cautious of when/why messages end up in the “dead letter” queue. • You don’t want it flooded because the DB is down.

46. Dead Letter Queue

47. Tools to Manage It/Monitor It • The multiple points of failures and issues of your systems make them complicated to manage and support. • Build in automated monitoring facilities and system health dashboards. • You need a one stop shop for • what’s up • what’s down • what’s queuing properly • what’s queuing too much, etc. • Consider the use of JMX or SNMP • It is probably already built into some of your infrastructure components. • Consider environment management for all phases, not just production. • Environment management for large dev teams across dev/test/stage was very laborious. • Compounded when other projects need to leverage the same systems. • Calculate system thresholds. • Provide automated alerts to the dashboard and email/page/etc. systems when they start to get close (not once they have been achieved).

48. Problem #5 – Change is Inevitable • The size and shape of our messages changed over time. • We had no way to deal effectively with change. • Consequently, new system versions/updates caused • Shutdown • Replace (sometimes transforming data to a new structure) • Restart • The real world was the only time we saw some situations • We had no effective test harness • Typically leading to ugly back outs

49. Version Strategy • EAI system stability/life span depends on the message structure. • Message structure is the hardest part to get exactly right up front. • When message formats need to change, this creates a real problem. The entire must be down, queues emptied, etc. • Consider version information in the message and routing/processing instructions in the bus. • More complicated system • Can also affect performance • Allows for dual operation (old and new systems) without failure and major down time. • Its going to happen – especially early – plan for it.

50. Version Routing