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Scaling MySQL: Catch 22 of Read Write Splitting
The document discusses the challenges of database scalability in light of increasing app demands and big data. It presents Scalebase's solution of read/write splitting as a way to enhance database performance without requiring changes to existing infrastructure. Key benefits include improved resource utilization, faster read/write operations, and high availability with real-time monitoring.
Scaling MySQL: Catch 22 of Read Write Splitting
- 1. Webinar: Scaling MySQL Catch22 of Read Write Splitting January 17, 2013
- 2. Agenda 1. Who We Are 2. The Scalability Problem 3. How We Solve it with Read/Write Splitting 4. Customer ROI/Case Studies 5. Q & A (please type questions directly into the GoToWebinar side panel) 2
- 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. Who We Are ScaleBaseallows apps to cost-effectively scale to an infinite number of users, with NO disruption to the existing infrastructure 4
- 5. The ScaleBase DataTraffic Manager • Database Scalability – Scale out relational databases to unlimited users – Real-time elasticity • Database Availability – Enable high availability of all apps • Centralized Management – Removes complexity and provides a unified point of management for distributed database environments • Improves performance Requires NO changes to your existing infrastructure 5
- 6. 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 critical 6
- 7. Big Data =Big Scaling Needs Big Data = Transactions + Interactions + Observations Sensors/RFID/Devices Mobile Web User Generated Content Spatial & GPS Coordinates BIG DATA Petabytes User Click Stream Sentiment Social Interactions & Feeds Web Logs Dynamic Pricing Search Marketing WEB Offer History A/B Testing Affiliate Networks Terabytes External Demographics Segmentation Customer Touches CRM Business Data Offer Details Support Contacts Feeds Gigabytes HD Video, Audio, Images Behavioral ERP Purchase Detail Targeting Speech to Text Purchase Record Product/Service Logs Payment Record Dynamic Funnels SMS/MMS Megabytes Increasing Data Variety and Complexity 7 The 451 Group & Teradata
- 8. Scalability Pain Infrastructure Cost $ Large You just lost Capital customers Expenditure Predicted Demand Opportunity Traditional Cost Hardware Actual Demand Dynamic Scaling time 8
- 9. Ongoing “Scaling MySQL”Series • August 16 & September 20, 2012 – Scaling MySQL: ScaleUp versus Scale Out • October 23, 2012 – Methods and challenges to Scale out MySQL • December 13, 2012 – Benefits of Automatic Data Distribution • Today – Catch 22 of read-write splitting 9
- 10. The Database Engineis 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 tasks 10
- 11. The Database Engineis 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 tasks 11
- 12. So… Let’s getto work! • I’m growing, have more users, need to support more throughput thru scale-out • Solution: – Create replicated database servers – Distribute the sessions across those database servers. Read / Write splitting: – Reads use the slaves – Writes go to the master • Benefits: – Better resource consumption – Get more read throughput – Get more write throughput – Awesome! 12
- 13. Read / WriteSplitting Replication 13
- 14. If Done Alone… •Application code needs to be changed • Maintain 2 connection pools – Write – master – Reads – A blend of all slaves • Every flow, in its beginning, disclaims: – “I will do only reads” – “I will do reads and writes” – What’s the default? • Result: – Writing code, maintaining code – Maintaining database ops in the app: add/remove slaves, change IPs... – Master database is far more occupied than it should be – Reads are not well balanced – What if replication breaks? – Can I read stale data? 14
- 15. Read / WriteSplitting with ScaleBase • 0 code changes and 0 code maintenance • Reads run faster • Writes run faster • Better resource utilization/load balancing • Improved data consistency/transaction isolation • Built-in failover with high availability • Database aware, replication state aware, replication lag aware • Real time monitoring and alerts • Centralized management dashboard 15
- 16. Read / WriteSplitting Current Replication With ScaleBase Replication Application Experience 16
- 17. Read/Write Splitting Read Write APPLICATION / USERS WEB SERVERS Replication 17
- 18. Scale Out withAmazon AWS RDS Read Replica Current RDS Read Replicas With ScaleBase RDS Read Replicas Application Experience 18
- 19. Choose Your Scale-outPath Data Distribution Database Size Read/Write Splitting 1 DB? Good for me! # of concurrent sessions 19
- 20. Scaling Out AchievesUnlimited Scalability 160000 140000 120000 100000 Throughput 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 20
- 21. Detailed Scale OutCase Studies One of world's largest & most widely respected manufacturers of smart phones and telecommunications hardware & software 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 systems 21
- 22. Summary • Database scalability is a significant problem – App explosion, Big Data, Mobile • Scale Up helps somewhat, but Scale Out provides a long-term, cost-effective solution • ScaleBase has an effective Scale Out solution with a proven ROI – Improves performance & requires NO changes to your existing infrastructure • Choose your scale-out path.... – The ScaleBase platform enables you to start with R/W splitting and grow into automatic data distribution 22
- 23. Questions (please enterdirectly into the GTW side panel) 617.630.2800 www.ScaleBase.com doron.levari@scalebase.com paul.campaniello@scalebase.com 23
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