Architecting for the Cloud

Seth Proctor, CTO @technicallyseth

What’s unique about “cloud”?

Cloud architecture   On-demand

  Scale-out for capacity & availability   Public infrastructure; dynamic provisioning

  Flexible   Commodity   Hybrid (public & private)

  Simple   Monitoring & management   Platform APIs and automation

  Resilient

Why a different architecture?

  Greater capacity   Cost-effectiveness   Higher availability and better failure-handling   Lower latencies for global deployment

Challenges

  Distribution brings challenges   Lots of failures happen with frequency   More difficult to get a global view   Security & data lifecycle is harder   Everything else about “distributed computing”

  Still, we can scale most layers   Load-balancers & name services at the top   Horizontally-scaled app servers   Caches & CDNs for content   Redundant disks and object stores

Scaling the database is the real challenge

Migrating to “the cloud”

  Modern architectures are commodity, on-demand and virtualized   Enterprise applications need availability and transactions   If cloud-scaling breaks global consistency migration isn’t possible

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Traditional database design

  RDBMS architectures start at the disk   Vertical scale follows   Caching helps, but often breaks consistency   HA systems become very expensive

  Schema & operation is hard to evolve   Hard to harness commodity infrastructure   Not designed to scale-out

Common options

  Replication   Active-passive or (gulp) multi-master   Replicated data but visible delays & conflict

Sharding   Split one database into many sub-sets   More capacity but hard to evolve and relate

  Abandon consistency   Push correctness & conflict to the application   Simpler core architecture but painful for

applications and hard to reconcile failures

Consistency

Side-effects

  Applications are tied to deployment   A key motivator for dev-ops   Complex for on-demand changes, failures

  More, independent pieces   Harder to interpret failures   Complexity

Global operation

  Many motivations   Disaster Recovery   Lower-latency for distributed users   Data access & storage residency rules

  Trade-offs between latencies and safety   Storage may be a separate concern from interaction

The database is not the disk

Evolution of “operational”

  Hybrid Tasks   Hybrid analytics for real-time insight   Document & SQL for flexibility   Graph views to ask hypothetical questions

and model and track lifecycle

  These imply that …   Data-sets are larger   Access and cache patterns are variable   Latencies have more impact

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Global requirements

  Global Operation   Active in multiple locations & globally

consistent

  Data Residence & Governance   Where is your data on-disk and in-memory

  SLAs as Policy for Automation   Reactive or proactive   Resilient

  Multiple Models

Cloud is the evolution from “client-server” to “distributed”

Distributed Database Designs

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Approach Shared Disk Shared-Nothing/Sharded

Synchronous Replication

Durable Distributed Cache

Key Idea Sharing a file system. Independent databases for disjoint subsets of

data.

Committing data transactionally to multiple

locations before returning.

Replicating data in memory on-demand.

Topology

Example Oracle RAC DB2 Pure Scale

MySQL Cluster and most NoSQL/NewSQL

solutions Google F1

A Durable, Distributed Cache

  Caching puts a database in-memory   Optimizations focus on memory, not disk   Caches are transient, on-demand and

hierarchical by nature

  Distribution means independence   Equivalent peers that coordinate to provide a

single logical entity   Drives service resiliency

  Durability provides safety   Decisions about replication, location and

resource allocation are operational

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Peer to Peer Architecture

P

P P

S3Disk , ...

P

P NuoDB Database Peer Process

Provisioned, Manageable Resources

Peer to Peer Communications

SQL Client

Management Client

SQL Front-EndSQL Optimizer

Transaction Handling

Object CachingObject Coordination

Durability

P

NuoDB is designed for

  Global operations   On-demand capacity   Continuous availability   Policy-driven deployment   Multi-tenancy   Multiple models

NuoDB is a single, logical service with global consistency

Scaling YCSB

Scaling DBT-2

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Summary

  Look for distributed architectures with on-demand capabilities   Layer & abstract to support evolution and react gracefully to failures   Assume your needs will evolve; plan with scale in mind

http://dev.nuodb.com

Questions?