RightScale Webinar: How RightScale Architects Its Databases (for Worldwide Scale, HA and DR Scenarios)
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The document discusses how RightScale architected its databases for global scale, high availability (HA), and disaster recovery (DR) scenarios, utilizing a combination of RDBMS (MySQL) and NoSQL (Cassandra) technologies. It outlines the data taxonomy, the various data types used, and their applications within different systems while emphasizing the need for scalability and fault isolation. The presentation concludes with insights on using multiple database technologies to optimize performance and reliability across cloud environments.
RightScale Webinar: How RightScale Architects Its Databases (for Worldwide Scale, HA and DR Scenarios)
- 1. How RightScale Architects Its Databases (for Worldwide Scale, HA and DR Scenarios) January 30, 2013 Watch the recording of this webinar #rightscale
- 2. 2# Your Panel Today Presenting • Rafael H. Saavedra, VP Engineering, RightScale • Josep Blanquer, Chief Architect, RightScale Q&A • Jared Marcell, Account Manager, RightScale • David Manriquez, Account Manager, RightScale Please use the “Questions” window to ask questions any time! #rightscale
- 3. 3# Menu Intro Data Taxonomy Data Storage Design Scale, HA and DR Conclusion #rightscale
- 4. 4# Intro: Expectations and scope What this is and what is not • IS a talk about: • how RightScale has designed and implemented its backing datastores • …for a few of the most representative internal systems • …with the rationale behind it • Is NOT a talk about • RightScale’s overall architecture • Nodes or hosts, it’s about Systems • RightScale’s data modeling Note: Most of the design is implemented and in production but some of the most advanced things that are still in beta, or are still being worked on #rightscale
- 5. 5# Intro: Tools and Technologies • RightScale uses a mix of RDBMS and NoSQL technologies: • MySQL , Cassandra and S3 (for backups and archiving) • Transactionality: • MySQL: strong ACID properties • Cassandra: no Atomicity, eventually Consistent, some Isolation, Durable • Availability: • MySQL: async replication. Master-SlaveN or Master-Master • Cassandra: Distributed, master-less, highly-replicated (multi-DC) • Sharding: • MySQL: no explicit inter-node tools. (Sharding done by application) • Cassandra: partitions data internally across nodes. #rightscale
- 6. 6# Glossary: Examples we will use Marketplace Assets Configuration data objects that are RightScripts user-generated, private or shared ServerTemplates Resource data that drives automation and Tags reporting Data used to communicate recent events and Events news feeds to users Data that records actions and states of external Cloud Polling and Gateway API-linked services Data used to locate and transport messages Routing across instances and/or our services Infrastructure monitoring data recorded and Monitoring presented on behalf of users #rightscale
- 7. 7# Taxonomy of RightScale’s Data Representative systems with different data semantics: Global Objects Marketplace Assets Dashboard Objects Audits Tags Recent Events Cloud Polling Data Routing Data Monitoring/Syslog #rightscale
- 8. 8# Taxonomy of RightScale’s Data Representative systems with different data semantics: Global Objects Marketplace Assets Common across accounts: Users Dashboard Objects Account Plans Audits Settings MultiCloud Marketplace: Tags Published Assets Recent Events Sharing Groups … Cloud Polling Data Routing Data Monitoring/Syslog #rightscale
- 9. 9# Taxonomy of RightScale’s Data Representative systems with different data semantics: Global Objects Marketplace Assets Dashboard Objects Audits Private to each account: Tags Deployments Imported assets Recent Events Alert Specifications Server Inputs Cloud Polling Data Audit Routing Data Tags User Events Monitoring/Syslog … #rightscale
- 10. 10# Taxonomy of RightScale’s Data Representative systems with different data semantics: Global Objects Marketplace Assets Dashboard Objects Audits Private to each account: Tags Cloud resource states (cache) Cloud credentials Recent Events Cloud Polling Data Routing Data Monitoring/Syslog #rightscale
- 11. 11# Taxonomy of RightScale’s Data Representative systems with different data semantics: Global Objects Marketplace Assets Dashboard Objects Audits Private to each account: Tags Instance agents location Recent Events Core agents location Agent action registry Cloud Polling Data … Routing Data Monitoring/Syslog #rightscale
- 12. 12# Taxonomy of RightScale’s Data Representative systems with different data semantics: Global Objects Marketplace Assets Dashboard Objects Audits Tags Recent Events Private to each account: Cloud Polling Data Collected metric data Collected syslog data Routing Data … Monitoring/Syslog #rightscale
- 13. 13# Taxonomy of RightScale’s Data Which data do we need? Global Objects X-acct • Data for all accounts Marketplace Assets • Data for a single account Dashboard Objects Data shared between accounts Audits Tags Data scope and containment Account Recent Events Cloud Polling Data Data required within scope of a single account Routing Data Monitoring/Syslog #rightscale
- 14. 14# Taxonomy of RightScale’s Data Who uses the data? Global Objects • Users through the Dash/API Marketplace Assets • Instances from the Cloud Users Dashboard Objects Audits Data close to the Users Tags Recent Events Data Placement Cloud Polling Data Instances Routing Data Data close to the Cloud Monitoring/Syslog #rightscale
- 15. 15# Taxonomy of RightScale’s Data Who uses the data? Proximity to User vs. Cloud Which data do we need? Scope of data available Global Objects X-acct Close to user Marketplace Assets Globally accessible data Users Dashboard Objects Audits Close to user Tags Account-shardable data Account Recent Events Cloud Polling Data Instances Close to cloud resources Routing Data Account-shardable* data Monitoring/Syslog #rightscale
- 16. 16# X-Account Account Users Instances #rightscale
- 17. 17# X-Account Why custom? More control • Multiple sources Users global • Individual columns Custom replication • Apply transformations • Smart re-sync features Global: MySQL • ACID semantics • Master-Slave replication Instances #rightscale
- 18. 18# X-Account Account Data archive: S3 S3 • Low read rate tags • Globally accessible Users global dash audit Other systems: Cassandra events • Simpler Key-Value access • Great scalability • Great replica control • High write availability Dashboard: MySQL • Time-to-live expiration as cache • ACID semantics • Rows tagged by account • Master-SlaveN replication • Slave reads Instances • Rows tagged by account #rightscale
- 19. 19# X-Account Account S3 tags tags Users global dash audit dash audit events events So we can horizontally scale our dashboard by partitioning objects based on account groups: Clusters Instances #rightscale
- 20. 20# Account S3 S3 S3 tags tags tags Cluster 1 Cluster 3 Cluster dash audit dash audit … dash audit N event event event s s s Features: Users • 1 cluster: N accounts • 1 account: 1 home RightScale Accounts • Migratable accounts Benefits: • Great horizontal growth Account Set 2 • Better failure isolation Account Set 1 • Independent scale • Load rebalancing • Versionable code • Differentiated service #rightscale
- 21. 21# X-Account Account S3 tags tags Users global dash audit dash audit events events polling Instances monitor routing #rightscale
- 22. 22# X-Account Account S3 tags dash And partition our cloud objects based on the cloud Users global audit the instances of an account run on: events Islands polling polling Instances monitor monitor routing routing #rightscale
- 23. 23# Account polling polling polling polling Polling Clouds: MySQL Monitoring: Custom • Master-Slave replication • Replicated files Island N Island 1 Island 2 • Can port to NoSQL easily monitor monitor monitor • Backup to S3 monitor • Mostly a resource cache • Archive to S3 • But cloud partitionable routing routing routing routing Instances Features: • 1 instance: 1 home island • 1 Island can serve N clouds • Core Agents: global data Routing: Cassandra • Simpler Key-Value access Benefits: • Very high availability • Close to cloud resources Services co-located • Services co-located Great scalability • Good failure isolation Services co-located with resources • with resources Great replica control • As good resources with as cloud • Plus cross DC replication* • Good scale: global replicas across Cassandra DCs Cloud 1 Cloud 2 Cloud N #rightscale
- 24. 24# Account S3 S3 S3 tags tags tags Cluster 1 Cluster 3 Users Cluster dash audit dash audit … dash audit N event event event s s s Differentif the cloud What Geographies where the cluster is deployed on… polling Fails? polling polling Instances Island N Island 1 Island 2 monitor monitor monitor routing routing routing Different Clouds #rightscale
- 25. 25# Account Sister Clusters S3 S3 S3 tags tags tags Cluster 1 Cluster 3 Users Cluster audit Full replica dash dash audit … dash audit N event event event s s s Features: • Each master has an extra remote slave • Each cluster in a pair is a DC replica of the other’s polling polling polling Instances localring Island N Island 1 Island 2 monitor At Disaster Recovery time: monitor monitor • Apps are told to start serving an extra shard routing • No need to provision more infrastructure to recover routing routing (try to avoid since everybody is on the same boat) • New resources can be allocated over time to help offload existing ones #rightscale
- 26. 26# Conclusions • Shown that RightScale uses multiple database technologies • RDBMS – MySQL for the ACID semantics and ‘queryability’ • Using a Master to N-Slaves for RO scale, and quick failure recovery • And ReadOnly Provisioning – To increase RO availability and scale remote systems • NoSQL: Cassandra for Availability and Scalability • for higher Read/Write availability within a cluster • For fully replicated regions across the globe (for Read/Write!) • Shown how RightScale uses them in different techniques • It partitions resource data into Islands based on cloud proximity • Can achieve in-cloud polling,and keep monitoring/syslog data storage next to instances • Can provide routing availability, colocated with instances for any world region • It partitions core data into Clusters based on account groups • To scale the core horizontally, and independently and achieve account isolation/differentiation • Enhances fault isolation: Assigning accounts to Clusters deployed away their cloud resources • It maintains cluster pairs (sister sites) • To recover from full cloud region failures • It doesn’t require massive amounts of new resources to recover #rightscale
- 27. 27# Next Steps Contact RightScale (866) 720-0208 1. Learn: Building Scalable Applications [email protected] in the Cloud Whitepaper www.rightscale.com www.rightscale.com/whitepapers 2. Analyze: Deployment review of your environment The next big RightScale Community Event! www.rightscale.com/contact April 25-26 in San Francisco www.RightScaleCompute.com 3. Try: Free Edition •Attend technical breakout sessions www.rightscale.com/free •Get RightScale training •Talk with RightScale customers •Ask questions at the Expert Bar #rightscale
Editor's Notes
- #3: Telcos built point-2-point networks for their customers