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WebinarMethods and challenges to Scale out a MySQLDatabase                                   October 23, 2012
Agenda       1. Who We Are       2. The Scalability Problem       3. Methods and challenges to scale out a MySQL DB       ...
Who We Are    Presenters:                                     Paul Campaniello,                                           ...
Pain Points – The Scalability Problem• Thousands of new online and mobile  apps launching every day• Demand climbs for the...
Big Data = Big Scaling Needs       Big Data = Transactions + Interactions + Observations               Sensors/RFID/Device...
Scalability PainInfrastructureCost $                   Large                     You just lost                   Capital  ...
Methods and Challenges to Scale Out MySQL    • August 16 and September 20, 2012       – Scaling MySQL: ScaleUp versus Scal...
Scale Up Pros & ConsPros                                  ConsMay result in major performance       Tedious, never ending…...
The Database Engine is the Bottleneck...    • Every write operation is At Least 4 write operations inside the DB:       – ...
The Database Engine is the Bottleneck • Every write operation is At Least 4 write operations inside the DB:     – Data seg...
Scale Out (two methods)                                             Read                                             Write...
Read/Write Splitting • Good for scaling high session-volume reads • Limited in scaling high data-volume reads     – Replic...
Scale Out Features and Benefits     Feature                           Benefit     Replication lag-based routing     Improv...
Automatic Data Distribution • The ultimate way to scale • Provides significant performance improvements • The only way to ...
Scale Out Features and Benefits     Feature                                 Benefit     Parallel query execution          ...
Scale Out Provides Immediate & Tangible Value     Application Server            Database A    Standby A     Application Se...
Typical Scale Out (ScaleBase) Deployment     Application Server                           Database A    Standby A         ...
Choose Your Scale-out Path                              Data Distribution                              (Reads and writes) ...
Scaling Out Achieves Unlimited Scalability             160000             140000             120000             100000Thro...
Detailed Scale Out Case Studies     Nokia               AppDynamics             Mozilla           Solar Edge     • Device ...
Summary     • Database scalability is a significant problem         – App explosion, Big Data and mobile compound it     •...
Questions (please enter directly into the GTW side panel)617.630.2800www.ScaleBase.comdoron.levari@scalebase.compaul.campa...
Thank You23
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ScaleBase Webinar: Methods and Challenges to Scale Out a MySQL Database

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This webinar examines best practices around scaling MySQL databases.

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Transcript of "ScaleBase Webinar: Methods and Challenges to Scale Out a MySQL Database"

  1. 1. WebinarMethods and challenges to Scale out a MySQLDatabase October 23, 2012
  2. 2. Agenda 1. Who We Are 2. The Scalability Problem 3. Methods and challenges to scale out a MySQL DB 4. Customer ROI/Case Studies 5. Q & A (please type questions directly into the GoToWebinar side panel)2
  3. 3. Who We Are Presenters: Paul Campaniello, VP of Global Marketing 25 year technology veteran with marketing experience at Mendix, Lumigent, Savantis and Precise. Doron Levari, Founder A technologist and long-time veteran of the database industry. Prior to founding ScaleBase, Doron was CEO to Aluna.3
  4. 4. Pain Points – The Scalability Problem• Thousands of new online and mobile apps launching every day• Demand climbs for these apps and databases can’t keep up• App must provide uninterrupted access and availability• Database performance and scalability is critical4
  5. 5. Big Data = Big Scaling Needs Big Data = Transactions + Interactions + Observations Sensors/RFID/Devices Mobile Web User Generated Content Spatial & GPS Coordinates BIG DATAPetabytes User Click Stream Sentiment Social Interactions & Feeds Web Logs Dynamic Pricing Search Marketing WEB Offer History A/B Testing Affiliate NetworksTerabytes External Demographics Segmentation Customer Touches CRM Business Data Offer Details Support Contacts FeedsGigabytes HD Video, Audio, Images Behavioral ERP Purchase Detail Targeting Speech to Text Purchase Record Product/Service Logs Payment Record Dynamic Funnels SMS/MMSMegabytes Increasing Data Variety and Complexity 5 The 451 Group & Teradata
  6. 6. Scalability PainInfrastructureCost $ Large You just lost Capital customers Expenditure Predicted Demand Opportunity Traditional Cost Hardware Actual Demand Dynamic Scaling time 6
  7. 7. Methods and Challenges to Scale Out MySQL • August 16 and September 20, 2012 – Scaling MySQL: ScaleUp versus Scale Out: This webinar will examine best practices around scaling MySQL databases • Today – Methods and challenges to Scale out MySQL • November 15, 2012 – Catch 22 of read-write splitting • December 13, 2012 – Automated data distribution7
  8. 8. Scale Up Pros & ConsPros ConsMay result in major performance Tedious, never ending…improvementsMostly transparent to the application SQL modifications are not always an optionHW upscale is easy Expensive Requires unique skill set Requires downtime Limited. At one (near) point – the database engine itself becomes the bottleneck8
  9. 9. The Database Engine is the Bottleneck... • Every write operation is At Least 4 write operations inside the DB: – Data segment – Index segment – Undo segment – Transaction log • And Multiple Activities in the DB engine memory: – Buffer management – Locking – Thread locks/semaphores – Recovery tasks9
  10. 10. The Database Engine is the Bottleneck • Every write operation is At Least 4 write operations inside the DB: – Data segment – Index segment – Undo segment Now multiply – Transaction log by 10TB accessed by • And Multiple Activities in the DB engine memory: 10000 – Buffer management concurrent – Locking sessions – Thread locks/semaphores – Recovery tasks10
  11. 11. Scale Out (two methods) Read Write Read/Write1 Splitting Replication Automatic Data2 Distribution 11
  12. 12. Read/Write Splitting • Good for scaling high session-volume reads • Limited in scaling high data-volume reads – Replication of data-volume is costly – Queries balanced to the slave, still meet big data • Limited in for scaling writes • Home-grown implementations have drawbacks12
  13. 13. Scale Out Features and Benefits Feature Benefit Replication lag-based routing Improves data consistency and isolation Read stickiness after writes Ensure consistent and isolated database operation 100% compatible MySQL proxy Applications unmodified Standard MySQL tools and interfaces MySQL databases untouched Data is safe within MySQL InnoDB/MyISAM/any Real-time monitoring and alerts Simplify management, reduce TCO13
  14. 14. Automatic Data Distribution • The ultimate way to scale • Provides significant performance improvements • The only way to really improve read and also writes • Good for scaling high session-volume reads and writes • Good for scaling high data-volume reads and writes • Home-grown implementations have drawbacks14
  15. 15. Scale Out Features and Benefits Feature Benefit Parallel query execution Great performance of cross-db queries & maintenance commands Query result aggregation Support of sophisticated cross-db queries, even with ORDER BY, GROUP BY, LIMIT, Aggregate functions… Online data redistribution Flexibility: no need to over-provision No downtime 100% compatible MySQL proxy Applications unmodified Standard MySQL tools and interfaces MySQL databases untouched Data is safe within MySQL InnoDB/MyISAM/any Data distribution review and analysis Optimization of data distribution policy Data consistency verifier Validate system-wide data consistency Real-time monitoring and alerts Simplify management, reduce TCO15
  16. 16. Scale Out Provides Immediate & Tangible Value Application Server Database A Standby A Application Server Database B Standby B Database C Standby C BI Database D Standby D Management16
  17. 17. Typical Scale Out (ScaleBase) Deployment Application Server Database A Standby A ScaleBase Central Management Application Server Database B Standby B ScaleBase Data Traffic Manager Database C Standby C BI Database D Standby D Management17
  18. 18. Choose Your Scale-out Path Data Distribution (Reads and writes) Database Size Read/Write Splitting (Reads) 1 DB? Good for me! # of concurrent sessions18
  19. 19. Scaling Out Achieves Unlimited Scalability 160000 140000 120000 100000Throughput 84000 80000 Throughput (TPM) Total DB Size (MB) 60000 60000 # Connections 48000 40000 36000 24000 2500 20000 2000 12000 1500 1500 6000 1000 0 500 500 1 2 4 6 8 10 14 Number of Databases 19
  20. 20. Detailed Scale Out Case Studies Nokia AppDynamics Mozilla Solar Edge • Device Apps App • Next gen APM • New Product/ • Next Gen • Availability company Next Gen App/ Monitoring App • Scalability • Scalability for the AppStore • Massive Scale • Geo-clustering Netflix • Scalability • Monitors real implementation • Geo-sharding time data from • 100 Apps thousands of • 300 MySQL DB distributed systems20
  21. 21. Summary • Database scalability is a significant problem – App explosion, Big Data and mobile compound it • Scale Up helps somewhat, but Scale Out provides a longer term and more cost effective solution • ScaleBase has an effective scale out solution with a proven ROI – ScaleBase improves performance and requires NO changes to your existing infrastructure • Choose your scale-out path.... – ScaleBase is a platform that enables you to start with R/W splitting and grow into data distribution21
  22. 22. Questions (please enter directly into the GTW side panel)617.630.2800www.ScaleBase.comdoron.levari@scalebase.compaul.campaniello@scalebase.com22
  23. 23. Thank You23
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