Cory Isaacson, CEO at CodeFutures, led this session at the RightScale User Conference 2010 in Santa Clara. Session Abstract: Database performance is the number one cause of poor application performance. Exacerbating the problem are cloud environments where I/O and bandwidth are generally slower and less predictable than in dedicated data centers. Database sharding is a highly effective method of removing the database scalability barrier, operating on top of proven RDBMS products such as MySQL and Postgres as well as the new NoSQL database platforms. In this session, you'll learn what it really takes to implement sharding, reliability, and end-to-end lifecycle management for your entire database environment. We'll present an example of seamlessly combining SQL and NoSQL databases into a unified infrastructure along with a case study that shows how effective database sharding allowed a high-volume social networking application to scale in the cloud.

1. Scaling your Database in the Cloud Fall 2010 Presented by: Cory Isaacson, CEO CodeFutures Corporation http://www.dbshards.com

2. Introduction Who I am Cory Isaacson, CEO of CodeFutures Providers of dbShards Author of Software Pipelines Partnerships: Rightscale The leading Cloud Management Platform myCloudWatcher Provider of cloud monitoring and management services Leaders in scalability, performance, high-availability and database solutions for the cloud… …based on real-world experience with dozens of cloud-based applications …social networking, gaming, data collection, mobile, analytics Objective is to provide useful experience you can apply to scaling (and managing) your database tier… …especially for high volume applications

3. Challenges of cloud computing Cloud provides highly attractive service environment Flexible, scales with need (up or down) No need for dedicated IT staff, fixed facility costs Pay-as-you-go model Cloud services occasionally fail Partial network outages Server failures… …by their nature cloud servers are “transient” Disk volume issues Cloud-based resources are constrained CPU I/O Rates… …the “Cloud I/O Barrier”

4. Typical Application Architecture

5. Scaling in the Cloud Scaling Load Balancers is easy Stateless routing to app server Can add redundant Load Balancers if needed DNS round-robin or intelligent routing for larger sites If one goes down… …failover to another Scaling Application Servers is easy Stateless Add or remove servers as need dictates If one goes down… …failover to another

6. Scaling in the Cloud Scaling the Database tier is hard “Stateful” by definition (and necessity…) Large, integrated data sets… 10s of GBs to TBs (or more…) Difficult to move, reload I/O dependent… …adversely affected by cloud service failures …and slow cloud I/O If one goes down… …ouch! Databases form the “last mile” of true application scalability Initially simple optimization produces the best result Implement a follow-on scalability strategy for long-term performance goals… …plus a high-availability strategy is a must

7. All CPUs wait at the same speed… The Cloud I/O Barrier

8. More Database scalability challenges Databases have many other challenges that limit scalability ACID compliance… …especially Consistency …user contention Operational challenges Failover Planned, unplanned Maintenance Index rebuild Restore Space reclamation Lifecycle Reliable Backup/Restore Monitoring Application Updates Management

9. Database slowdown is not linear…

10. Challenges apply to all types of databases Traditional RDBMS (MySQL, Postgres, Oracle…) I/O bound Multi-user, lock contention High-availability Lifecycle management In-memory Databases (NoSQL, Caching, Specialty…) Reliability Limits of a single server …and a single thread Data dumps to disk High-availability Lifecycle Management No matter what the technology, big databases are hard to manage… …elastic scaling is a real challenge …degradation from growth in size and volume is a certainty Application-specific database requirements add to the challenge …database design is key… …balance performance vs. convenience vs. data size

11. The Laws of Databases Law #1: Small Databases are fast… Law #2: Big Databases are slow… Law #3: Keep databases small

12. What is the answer? Database sharding is the only effective method for achieving scale, elasticity, reliability and easy management… …regardless of your database technology

13. What is Database Sharding? “Horizontal partitioning is a database design principle whereby rows of a database table are held separately... Each partition forms part of a shard, which may in turn be located on a separate database server or physical location.” Wikipedia

14. The key to Database Sharding…

15. Database Sharding Architecture

16. Database Sharding Architecture

17. Database Sharding… the results

18. Why does Database Sharding work? Maximize CPU/Memory per database instance… …as compared to database size No contention between servers Locking, disk, memory, CPU Allows for intelligent parallel processing… …Go Fish queries across shards Keep CPUs busy and productive

19. Breaking the Cloud I/O Barrier

20. Database types Traditional RDBMS Proven SQL language… …although ORM can change that Durable, transactional, dependable… …but inflexible NoSQL Typically in-memory… …the real speed advantage Various flavors… Key/Value Store Document database Hybrid

21. Black box vs. Application-Aware Sharding Both utilize sharding on a data key… …typically modulus on a value or consistent hash Black box sharding is automatic… …attempts to evenly shard across all available servers …no developer visibility or control …can work acceptably for simple, non-indexed NoSQL data stores …easily supports single-row/object results Application-Aware sharding is defined by the developer… …selective sharding of large data …explicit developer control and visibility …tunable as the database grows and matures …more efficient for result sets and searchable queries

22. How Database Sharding works for NoSQL

23. How Database Sharding works for SQL Primary Shard Table Shard Child Tables Global Tables

24. How Database Sharding works for SQL

25. What about Cross-Shard result sets?

26. More on Cross-Shard result sets… Black Box approach requires “scatter gather” for multi-row/object result set… …common with NoSQL engines …forces use of denormalized lists …must be maintained by developer/application code …Map Reduce processing helps with this (non-realtime) Application-Aware provides access to meaningful result sets of related data… …aggregation, sort easier to perform …logical search operations more natural

27. What about High-Availability? Can you afford to take your databases offline: …for scheduled maintenance? …for unplanned failure? …can you accept some lost transactions? By definition Database Sharding adds failure points to the data tier A proven High-Availability strategy is a must… …system outages…especially in the cloud …planned maintenance…a necessity for all database engines

29. No rapid failover for continuous operation

30. Maintenance difficult in production environments

32. …3 active-active instances for in-memory databases

33. Support for…

34. …fail-down

35. …failover

36. …maintenance

37. …automated backup/restore

39. Database Sharding…elastic shards Expand the number of shards… …divide a single shard into N new shards Contract the number of shards… …consolidate N shards into a single shard Due to large data sizes, this takes time… …regardless of data architecture Requires High-Availability to ensure no downtime… …perform scaling on live replica

40. Database Sharding…the future Ability to leverage proven database engines… …SQL …NoSQL …Caching Allow developers to select the best database engine for a given set of application requirements… …seamless context-switching within the application …use the API of choice Improved management… …monitoring …configuration “on-the-fly” …dynamic elastic shards based on demand

41. Database Sharding summary Database Sharding is the most effective tool for scaling your database tier in the cloud… …or anywhere Use Database Sharding for any type of engine… …SQL …NoSQL …Cache Use the best engine for a given application requirement… …avoid the “one-size-fits-all” trap …each engine has its strengths to capitalize on Ensure your High-Availability is proven and bulletproof… …especially critical on the cloud …must support failure and maintenance

42. Questions/Answers Cory Isaacson CodeFutures Corporation cory.isaacson@codefutures.com http://www.dbshards.com