summit 2013 spring rob hirschfeld migrations v1

Rob Hirschfeld

Dell, Distinguished Engineer

http://lifeatthebar.com

• This session could repeat a lot from last summit

• http://www.openstack.org/summit/san-diego-2012/openstack-summit-

sessions/presentation/getting-from-folsom-to-grizzly-a-devops-upgrade-

pattern

• Interoperability & Reference Architecture

• Reference Architecture w/ Heat (Tuesday @ 11:00)

• Interop Panel (Tuesday @ 5:20)

• Upgrade Projects

• https://wiki.openstack.org/wiki/Upgrade-with-minimal-downtime

• https://wiki.openstack.org/wiki/Grenade

• The “Problem“ with Migration

• Paths to Nirvana (or Roads to Perdition)

• Alternatives

• An Opinion

• Discussion

http://learn.genetics.utah.edu/content/begin/cells/organelles/

F G

H

• OpenStack has 3 month release major/minor cycle

• Major version every 6 months

• Minor version (but important) 3 & 6 months after release

• Lots of Changes

• Bugs are fixed

• Operating Systems upgrade

• New technologies appear

• Whole projects are split off

• We expect operators to

• Keep systems running

• Never loose data

• And… Stay up to date http://cdn2.arkive.org

sockeye-salmon-predated-by-grizzly-bear-on-migration-upstream.jpg

• What are we upgrading? • OpenStack - Yes!

• Dependent packages - Probably?

• Base OS - Maybe?

• What is the state during the "in-between" time? • Infrastructure downtime?

• VM downtime? VM Reboot? Controlled/Informed?

• Availability Windows?

• What contingency plans? • Dry run? Maybe.

• Recover by going backwards? Maybe.

• What level of safety and trust do you need? • Assure data integrity?

• Assure Infrastructure Integrity?

• Maintain Security?

• How long can the migration take? • Big bang move or gradual migrate?

• How will my API consumers/ecosystem cope?

• Can Keystone Grizzly work with Folsom Nova???

• What about futures? G.1 to G.2? H to I?

• Can I skip versions? Jump from G to I? http://www.publicdomainpictures.net

Steep Steps by Peter Griffin

• Beginning Answers

• Distros will manage dependencies and packaging

• We can’t lose data or compromise security

• Infrastructure state and integrity will vary by solution

• Assumption of Staging

• Some managed environment (not a manual deploy)

• Staging/test environment to get "familiar" with the problem.

• Maintenance window for production - limits scope of change

• Step-wise changes are OK (big bang is not required)

• We can make trade-offs to defray expensive requirements

• Beyond Assumptions… Paradigm Shifts

• There are shared best practices

• Upgrades can be automated in a sharable way

http://www.theemailadmin.com/wp-content/uploads/2012/09/GFI229-hot-water-migration.jpg

All the nodes update to the latest code

in a short time window

• Details: 1. Cookbooks include update (instead of install) directives.

2. Control upstream package point (e.g. apt-update when appropriate)

3. Force chef-client run

4. Now at new level

• Considerations • Pros: Potentially fast, continuous operation

• Cons: Don't mess up, it is your production environment

• Scope: Security updates

• Code Assumptions:

• System can function through service restarts.

• Underlying data models don't change or migrate appropriately.

Nodes migrate in staged groups

• Details:

1. Choose subset of machines and quiesce them.

2. Update set

3. Freeze state (by tenant)

4. Migrate service/tenant content

5. Repurpose after complete.

• Considerations

• Pros: Safer, more controlled, and can move tenants as needed

• Cons: Takes longer, still has cut-over point, but less open

http://allgodscrittersgotrhythm.blogspot.com/2010_08_01_archive.html

Nodes changed individually by a system-wide

orchestration that supports components of multiple versions

• Details

1. Components must be able to straddle versions

2. Orchestration updates core components to new version

3. System as a whole queiseces and is validated (requires self test)

4. Orchestration individually migrates components (return to step 3)

• Considerations

• Pros: Creates a highly resilient system that handles higher rate of change

• Cons: More complex to create and maintain

http://www.grizzlycentral.com/forum/grizzly-tire-wheel-combos/1204-upgrade-tires-grizzly.html

• Orchestration (not just deployment automation)

• Awareness of physical layout is required

• Must respect fault zones to sustain HA

• Proximity of resources matters for migration

• Networking transitions are essential

• Collaboration with development teams is essential

• Components must support current and previous

• Upgrade plan must be baked into configuration and tested

• Upgrade dependencies must be 1) clear and 2) minimized

• HA complicates upgrades

• Upgrade can be detected as a failure

• HA system must be able to bridge versions

• Partial features were confusing

• We wanted to get ahead on upgrade

• It looked like dev jumped to Grizzly

• Good news:

• Some testing of upgrade

• Folsom to Grizzly ops was pretty smooth

• Bad news:

• Grizzly is more complex (more moving parts)

• Missing multi-node upgrade validation

DB DB

Msg Bus Msg Bus

Compute Compute

Client Client

Dashboard Dashboard Cinder Cinder

Quantum Quantum

Glance Glance

Keystone Keystone

Oslo Oslo

Celimeter Celimeter

Nova Nova

• Fault Tolerance on BOTH SIDES AND VERSIONS

• Same Version = EASY

• Backwards Version = HARD

• Forward Version = IMPOSSIBLE

Keystone

Havana Easy

Keystone

Grizzly

Nova

Havana

• We want to limit need to sustain old services

• New versions should support past APIs

• API consumers can migrate in steps

Ideally, we’d server AND client would be multi-version

Keystone

Havana

Step 3

Keystone

Grizzly

API

Nova

Havana

Nova

Grizzly

Ste

p 2

• Size Matters

• Big Steps = Release Based

• Small Steps = Commit Based

• Small steps are digest

• Easier to test small steps

• Incur less technical debt

• Expose issues to developers while code is fresh

• Large steps create risk

• More combinations to test

• More changes at one time

• Difficult to fix design issues

G H

Small Step vs Large

Serv

er

vs C

lient

Big Bang!

Continuous

Deploy

Staged

Upgrade

Rolling

Upgrade

Protocol

Stepping

Protocol

Driven

Parallel

Operation

Forced Client

Migration

Continuous

Deploy

Staged

Upgrade

Rolling

Upgrade

Protocol

Driven

Parallel

Operation

Forced Client

Migration

Protocol

Stepping

Big Bang!

Small Step vs Large

Serv

er

vs C

lient

Continuous

Deploy

Staged

Upgrade

Rolling

Upgrade

Protocol

Driven

Parallel

Operation

Forced Client

Migration

Protocol

Stepping

Big Bang!

Small Step vs Large

Serv

er

vs C

lient

• Servers & agents must be version tolerant

• Clients protocols must be testable and documented

• Ensure non-destructive migration

• Fast-fail on client, but version tolerant on server

• Require Expectation that servers will migrate need to be built into the system! Servers must be adopting latest protocols or clients will not follow.

• Servers must test legacy clients/protocols! We must have tests!

• We must be able to find and upgrade legacy clients

• Deployment Upstream Cookbooks/Modules

• Best Practice Discussions

• Code for Upgradeability

• Crowbar Collaboration

• Upgrade is a FEATURE!

• Orchestration + Chef

• Pull from Source Deployments

• System Discovery

• Networking Configuration

• Operating System Install

http://farm3.static.flickr.com/2561/3891653055_262410bc31.jpg