1. Buying Database Hardware Adam Backman – President White Star Software, LLC.

2. About the speaker President – White Star Software A leader in the Progress OpenEdge services sector for over 30 years Vice President – DBAppraise Managed database services backed up by experienced Progress OpenEdge professionals Author – Progress Software’s Expert Series Over 25 years of Progress OpenEdge experience – Support – Training – Consulting (Database and System configuration, management and tuning)

3. Agenda Understanding the process Understanding system resources Picking the right vendor Where to spend your money – CPU fast vs. many – Memory – can you ever have too much – Disk – where all the data starts – Network and other parts of the system Conclusion

4. Understanding the Process Know where you are now Important portions of the application – Core OLTP (call center, orders, shipping) – Subset of daily management tasks – Subset of weekly/monthly tasks Know your direction Plan your direction

5. Know where you are now Know your model of operation CPU centric – More application logic – More calculations Data centric – Request centric – MRP, Accounting inventory

6. Have data for comparison Timings – Just time critical items with a watch Frequently used functions Time critical portions of the application Performance Information – System level – SAR, iostat, vmstat – Progress Level – ProTop, promon, custom VST programs

7. Understanding system resources Supported architectures Understand your options Performance tradeoffs

8. Architectures supported by OpenEdge Database engine – Database with no portion of the application Host-based system – Database, clients and background all on one system Pure client/server – Database on one machine and clients on other machines n-tier architecture – Database and background on Machine A – AppServers on Machine B – Clients on individual machines

9. Understand your options Single large system vs. 2 or more smaller machines Virtualization Single platform or multi-platform Cloud vendors SAN vs. Direct attached storage Network considerations

10. Single large machine vs. 2 or more smaller machines Single large machine – Pros Highest potential performance by eliminating network layer Easier to manage as everything is in one place – Cons A single machine will have limited scalability Usually two mid-range systems are more cost effective than a single high-end system Potential license cost issues (CPU-Based pricing)

11. Single large machine vs. 2 or more smaller machines (cont.) Multi-machine – Pros Flexibility – ability to repurpose machines Scalability – ability to add additional machines to solution Recoverability – ability to use AppServer machine as the database engine – Cons Cost – duplication of items, power, maintnenace Adding network layer can hurt performance Management – more machines to manage Maintenance – more things to break

12. Purchase guidance Databases tend to use disk extensively – Spend on disk subsystem – Allow for a minimum of 10% of the database size for database buffers (-B memory) – Do not forget other memory allocations OS buffers can be reduced to 10% or less of total memory Applications are memory and CPU intensive – Generally better to buy fewer faster cores – Memory can greatly reduce I/O via –B -Bp -Bt, -mmax, … Examine your use cases for the machine and buy with both primary use and most likely alternative uses in mind

13. Purchase guidance Most people over spend on CPU You can have all the CPU in the world but it will do you no good unless you can get data to them efficiently People should focus on the performance “food chain” – Network – Disk – Memory – CPU Slower resources should be addressed before faster resources

14. Virtualization A thousand ways to go wrong and very few ways to do it right Everyone is doing it but why? – Ability to build new environments – Ability to recover quickly (part of a DR solution) – Potential for better resource saturation (unused CPU) Why not? – Performance – Complexity – Cost (VMWare is not free – More applications affected by an outage

15. Options: N-tier option Database engine – Fast Disk – Moderate memory (over 10% of DB + OS and extras) – Relatively little CPU AppServer machine – Internal disk – setup well but not crazy – Higher memory usage – CPU intensive Client machine – Web/Mobile – Desktops – Citrix/Windows terminal server

16. Cloud: Make it someone else’s problem

17. Cloud Watch for variable performance – Measure throughput (Disk and memory) – Measure compute capacity – Measure at different days/times Performance guaranty from vendor Iops/sec. vs. perception (real measurements)

18. Why is disk important CPU capacity doubles every 18 months Network bandwidth doubling every 12 months Memory is 37,000+ times faster than disk Disk (per disk I/O rate) fairly static (150 – 200 iops/sec.) Storage will generally cost more than servers and this is particularly true for database servers

19. Buy better storage Many disks – 150 iops/sec. per disk – Look at you buffer hit rate and total request load – Don’t forget temporary file I/O which can account for a significant percentage of your total I/O load Larger cache – Some systems require you to expand cache when you expand your storage but most don’t – Adding cache is akin to adding database buffers to a database SSD – save money buy fewer devices – SSDs are a real solution now and prices are competitive though not cheap when compared to conventional storage on a per GB basis

20. Do better disk configuration Still no RAID 5, No RAID S, No RAID 6, No RAID 7 RAID 10 still king for database storage – really there are a bunch of really cool stats to prove this out Large stripe widths – Performance improved with stripe width through 2MB Use best portion of rotating disk (rotating rust) – Using outer edge of disk will provide the best performance which may be as much as 15% better vs. inner portion of disk Even usage across all disks – Eliminate disk variance – Think of ALL sources of I/O (DB, BI, AI, Temp files, OS, …)

21. Storage Direct attached – Less expensive in most cases – Less complex – Single machine tuning OS and Array – High performance – Disks dedicated SAN – generalized business storage NAS – file optimized storage SAN – Purpose-built high performance Why SAN twice? There is a huge difference in SANs and you need to buy for your need not for their marketing

22. Direct-attached storage Pros – Not shared with other hosts (isolation is bliss) – Easier problem resolution – Massive controller throughput for little money – Cheaper to maintain Cons – Not shared with other hosts (no cost sharing)

23. SAN: Generalized business storage Pros – Best option in virtualized environment – Share one powerful storage system with many hosts – One stop storage system for all hosts Cons – High initial cost – Single point of failure unless array mirroring/clustering is in place – Not optimized to individual tasks – Complex

24. SAN: Purpose-built Pros: – Excellent performance – Additional control at array level – Massively scalable – Ability to dedicate resources to hosts – Reliable (fault tolerant) Cons – Single point of failure unless array mirroring is in place – Cost – Complexity

25. SAN monitoring More difficult as there are many moving parts Multiple hosts need to be monitored SAN needs to be monitored Monitoring data needs to be synchronized Work loads need to be balanced across hosts

26. NAS: file optimized storage Pros – Sharable across hosts – Generally cheaper than SAN – Good service for application files Cons – File optimized not block optimized – Not database optimized – Not client temporary file optimized

27. Network options Simple – Put a single quad card in the server and bind the ports for performance Moderate – Multiple cards bound with a two networks. One for Data and the other for client traffic Complex – Multiple machine – Multiple networks (vlan) – Dedicated networks for DB, replication, client traffic, AppServer

28. Network Try to use your network efficiently – -Mm 8192 to increase throughput – Remember to move to jumbo frames (client, server, switches, …) Move invasive processes to separate network – Backup – Replication – System syncronizations

29. Picking the right vendor – The less of two evils

30. Picking the right vendor Better support nearly always beats a better upfront price Look at quality of “local” support infrastructure – Response time (SLA) – In country – In the correct language Always comparison shop even if you “know” what you want – This keeps vendors honest – Choosing historic rivals helps drive down price Simplify to enhance support – Bundle Linux support under hardware contract – Single vendor simplicity

31. Paying for support Buy all support with the initial purchase Allows easier (capital) write-off Years 4+ of support can cost as much as the initial price if purchased later

32. Picking the wrong solution NetApp for database storage. Performance will be non-optimal (Performance will be HORRIBLE) NUMA Architecture – Good vendors make bad solutions – All CPUs allocated to a Progress domain must come from the same book/shelf/node – All Memory must meet the same criteria as CPU Using client/server for reporting – Kill the network access whenever possible – Use AppServer for complex OLTP

33. Where to spend your money Disks Storage SAN SSD Really, look at storage first then concern yourself with other trivial issues such as memory and CPU This is the problem over 9 out of 10 times

34. Questions, Comments, … 问题 Preguntas

35. THANK YOU Thank you for your time