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Container orchestration from theory to practice

"Join Laura Frank and Stephen Day as they explain and examine technical concepts behind container orchestration systems, like distributed consensus, object models, and node topology. These concepts build the foundation of every modern orchestration system, and each technical explanation will be illustrated using SwarmKit and Kubernetes as a real-world example. Gain a deeper understanding of how orchestration systems work in practice and walk away with more insights into your production applications."

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Stephen Day & Laura Frank Tacho
Container Orchestration
from Theory to Practice
@stevvooe
Container enthusiast
Stephen Day
@rhein_wein
Director of Engineering, CloudBees
Laura Tacho
● Understanding the SwarmKit object model
● Node topology
● Distributed consensus and Raft
Agenda
An open source framework for building
orchestration systems
github.com/docker/swarmkit
SwarmKit
containerd
kubernetes
docker
swarmkitorchestration
container runtime
user tooling
The Old Way
Cluster
https://en.wikipedia.org/wiki/Control_theory
Orchestration

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Container orchestration from theory to practice

  • 1. v Stephen Day & Laura Frank Tacho Container Orchestration from Theory to Practice
  • 3. ● Understanding the SwarmKit object model ● Node topology ● Distributed consensus and Raft Agenda
  • 4. An open source framework for building orchestration systems github.com/docker/swarmkit SwarmKit
  • 7. A control system for your cluster ClusterO - Δ S D D = Desired State O = Orchestrator C = Cluster S = State Δ = Operations to converge S to D https://en.wikipedia.org/wiki/Control_theory Orchestration
  • 8. Convergence A functional view D = Desired State O = Orchestrator C = Cluster St = State at time t f(D, St-1 , C) → St | min(St -D)
  • 9. The Problem Low Observability High Observability Failure Process State User Input Observability and Controllability
  • 11. Data Model Requirements ● Represent difference in cluster state ● Maximize observability ● Support convergence ● Do this while being extensible and reliable
  • 12. Show me your data structures and I’ll show you your orchestration system
  • 13. Services ● Express desired state of the cluster ● Abstraction to control a set of containers ● Enumerates resources, network availability, placement ● Leave the details of runtime to container process ● Implement these services by distributing processes across a cluster Node 1 Node 2 Node 3
  • 14. message ServiceSpec { // Task defines the task template this service will spawn. TaskSpec task = 2 [(gogoproto.nullable) = false]; // UpdateConfig controls the rate and policy of updates. UpdateConfig update = 6; // Service endpoint specifies the user provided configuration // to properly discover and load balance a service. EndpointSpec endpoint = 8; } Service Spec Protobuf Example
  • 15. message Service { ServiceSpec spec = 3; // UpdateStatus contains the status of an update, if one is in // progress. UpdateStatus update_status = 5; // Runtime state of service endpoint. This may be different // from the spec version because the user may not have entered // the optional fields like node_port or virtual_ip and it // could be auto allocated by the system. Endpoint endpoint = 4; } Service Protobuf Example
  • 17. Prepare: setup resources Start: start the task Wait: wait until task exits Shutdown: stop task, cleanly Task Model Runtime
  • 18. message TaskSpec { oneof runtime { NetworkAttachmentSpec attachment = 8; ContainerSpec container = 1; } // Resource requirements for the container. ResourceRequirements resources = 2; // RestartPolicy specifies what to do when a task fails or finishes. RestartPolicy restart = 4; // Placement specifies node selection constraints Placement placement = 5; // Networks specifies the list of network attachment // configurations (which specify the network and per-network // aliases) that this task spec is bound to. repeated NetworkAttachmentConfig networks = 7; } Task Spec Protobuf Example
  • 19. message Task { // Spec defines the desired state of the task as specified by // the user. // The system will honor this and will *never* modify it. TaskSpec spec = 3 [(gogoproto.nullable) = false]; // DesiredState is the target state for the task. It is set to // TaskStateRunning when a task is first created, and changed // to TaskStateShutdown if the manager wants to terminate the // task. This field is only written by the manager. TaskState desired_state = 10; TaskStatus status = 9 [(gogoproto.nullable) = false]; } Task Protobuf Example
  • 20. Orchestrator Task Model Atomic Scheduling Unit of SwarmKit Object Current State Spec Desired State Task0 Task1 … Taskn Scheduler
  • 21. type Deployment struct { // Specification of the desired behavior of the Deployment. // +optional Spec DeploymentSpec // Most recently observed status of the Deployment. // +optional Status DeploymentStatus } Kubernetes Parallel Concepts
  • 22. Declarative - SwarmKit $ docker network create -d overlay backend vpd5z57ig445ugtcibr11kiiz $ docker service create -p 6379:6379 --network backend redis pe5kyzetw2wuynfnt7so1jsyl $ docker service scale serene_euler=3 serene_euler scaled to 3 $ docker service ls ID NAME REPLICAS IMAGE COMMAND pe5kyzetw2wu serene_euler 3/3 redis
  • 23. Declarative - Kubernetes $ kubectl create deployment --image redis redis deployment.extensions "redis" created $ kubectl scale deployment redis --replicas=3 deployment.extensions "redis" scaled $ kubectl rollout status deploy redis deployment "redis" successfully rolled out
  • 25. And now with jelly!
  • 28. Only one component of the system can write to a field Field Ownership Consistency
  • 29. message Task { TaskSpec spec = 3; string service_id = 4; uint64 slot = 5; string node_id = 6; TaskStatus status = 9; TaskState desired_state = 10; repeated NetworkAttachment networks = 11; Endpoint endpoint = 12; Driver log_driver = 13; } Owner User Orchestrator Allocator Scheduler Shared Task Protobuf Example
  • 30. State Owner < Assigned Manager >= Assigned Worker Task Status Field Handoff
  • 31. Worker Pre-Run Preparing Manager Terminal States Task State New Allocated Assigned Ready Starting Running Complete Shutdown Failed Rejected
  • 32. Observability and Controllability The Problem Low Observability High Observability Failure Process State User Input
  • 33. v Node Topology in SwarmKit
  • 34. We’ve got a bunch of nodes… now what? Task management is only half the fun. These tasks can be scheduled across a cluster for HA!
  • 35. We’ve got a bunch of nodes… now what? Your cluster needs to communicate!
  • 36. Not the mechanism, but the sequence and frequency Three scenarios important to us now • Node registration • Workload dispatching • State reporting Manager <-> Worker Communication
  • 37. Most approaches take the form of two patterns • Push, where the Managers push to the Workers • Pull, where the Workers pull from the Managers Manager <-> Worker Communication
  • 38. 3 - Payload 1 - Register 2 - Discover Registration & Payload Push Model Pull Model Payload
  • 39. Pros Provides better control over communication rate - Managers decide when to contact Workers Cons Requires a discovery mechanism - More failure scenarios - Harder to troubleshoot Pros Simpler to operate - Workers connect to Managers and don’t need to bind - Can easily traverse networks - Easier to secure - Fewer moving parts Cons Workers must maintain connection to Managers at all times Push Model Pull Model
  • 40. Fetching logs Attaching to running pods via kubectl Port forwarding Manager Kubernetes
  • 41. Work dispatching in batches Next heartbeat timeout Manager Worker SwarmKit
  • 43. v Rate Control in SwarmKit
  • 44. Rate Control: Heartbeats Manager dictates heartbeat rate to Workers Rate is configurable (not by end user) Managers agree on same rate by consensus via Raft Managers add jitter so pings are spread over time (avoid bursts) Ping? Pong! Ping me back in 5.2 seconds
  • 45. Rate Control: Workloads Worker opens a gRPC stream to receive workloads Manager can send data whenever it wants Manager will send data in batches Changes are buffered and sent in batches of 100 or every 100 ms, whichever occurs first Adds little delay (at most 100ms) but drastically reduces amount of communication Give me work to do 100ms - [Batch of 12 ] 200ms - [Batch of 26 ] 300ms - [Batch of 32 ] 340ms - [Batch of 100] 360ms - [Batch of 100] 460ms - [Batch of 42 ] 560ms - [Batch of 23 ]
  • 47. ● Backbone of distributed consensus in etcd and SwarmKit (and other places!) The Raft Consensus Algorithm
  • 48. SwarmKit and Kubernetes don’t make the decision about how to handle leader election and log replication, Raft does! github.com/kubernetes/kubernetes/tree/master/vendor/github.com/coreos/etcd/raft github.com/docker/swarmkit/tree/master/vendor/github.com/coreos/etcd/raft The Raft Consensus Algorithm
  • 49. secretlivesofdata.com Want to know more about Raft? The Raft Consensus Algorithm
  • 50. Single Source of Truth ● SwarmKit implements Raft directly, instead of via etcd ● The logs live in /var/lib/docker/swarm ● Or /var/lib/etcd/member/wal ● Easy to observe (and even read)
  • 52. v in SwarmKit Manager <> Worker Communication
  • 53. Communication Leader FollowerFollower Worker can connect to any reachable Manager Followers will forward traffic to the Leader
  • 54. Reducing Network Load Followers multiplex all workers to the Leader using a single connection Backed by gRPC channels (HTTP/2 streams) Reduces Leader networking load by spreading the connections evenly Example: On a cluster with 10,000 workers and 5 managers, each will only have to handle about 2,000 connections. Each follower will forward its 2,000 workers using a single socket to the leader. Leader FollowerFollower
  • 55. Leader FollowerFollower Leader Failure (Raft!) Upon Leader failure, a new one is elected All managers start redirecting worker traffic to the new one Transparent to workers
  • 56. Leader Election (Raft!) Follower FollowerLeader Upon Leader failure, a new one is elected All managers start redirecting worker traffic to the new one Transparent to workers
  • 57. Manager Failure - Manager 1 Addr - Manager 2 Addr - Manager 3 Addr Manager sends list of all managers’ addresses to Workers When a new manager joins, all workers are notified Upon manager failure, workers will reconnect to a different manager Leader FollowerFollower
  • 58. Leader FollowerFollower Manager Failure (Worker POV) Manager sends list of all managers’ addresses to Workers When a new manager joins, all workers are notified Upon manager failure, workers will reconnect to a different manager
  • 59. Manager Failure (Worker POV) Leader FollowerFollower Reconnect to random manager Manager sends list of all managers’ addresses to Workers When a new manager joins, all workers are notified Upon manager failure, workers will reconnect to a different manager
  • 61. v Presence Scalable presence in a distributed system
  • 62. Presence Swarm still handles node management, even if you use the Kubernetes scheduler Manager Leader commits Worker state (Up or Down) into Raft • Propagates to all managers via Raft • Recoverable in case of leader re-election
  • 63. Presence ● Heartbeat TTLs kept in Manager Leader memory ○ Otherwise, every ping would result in a quorum write ○ Leader keeps worker<->TTL in a heap (time.AfterFunc) ○ Upon leader failover workers are given a grace period to reconnect ■ Workers considered Unknown until they reconnect ■ If they do they move back to Up ■ If they don’t they move to Down
  • 65. Sequencer ● Every object in the store has a Version field ● Version stores the Raft index when the object was last updated ● Updates must provide a base Version; are rejected if it is out of date ● Provides CAS semantics ● Also exposed through API calls that change objects in the store
  • 66. Versioned Updates Consistency service := getCurrentService() spec := service.Spec spec.Image = "my.serv/myimage:mytag" update(spec, service.Version)
  • 67. Sequencer Original object: Service ABC Version = 189 Spec Replicas = 4 Image = registry:2.3.0 ... Raft index when it was last updated
  • 68. Sequencer Service ABC Spec Replicas = 4 Image = registry:2.3.0 ... Version = 189 Service ABC Spec Replicas = 4 Image = registry:2.4.0 ... Version = 189 Update request:Original object:
  • 69. Sequencer Service ABC Spec Replicas = 4 Image = registry:2.3.0 ... Version = 189 Original object: Service ABC Spec Replicas = 4 Image = registry:2.4.0 ... Version = 189 Update request:
  • 70. Sequencer Service ABC Spec Replicas = 4 Image = registry:2.4.0 ... Version = 190 Updated object:
  • 71. Sequencer Service ABC Spec Replicas = 4 Image = registry:2.4.0 ... Version = 190 Service ABC Spec Replicas = 5 Image = registry:2.3.0 ... Version = 189 Update request:Updated object:
  • 72. Sequencer Service ABC Spec Replicas = 4 Image = registry:2.4.0 ... Version = 190 Service ABC Spec Replicas = 5 Image = registry:2.3.0 ... Version = 189 Update request:Updated object:
  • 75. #TODO - Rate control (Kubernetes) - Where is push vs pull used in k8s? - Object Model: Field ownership - TLS in k8s - Kubernetes node visualization (if using a stack deploy, does it show on the docker visualizer)