HASHICORP

Deploying and Discovering at Scale with Consul & Nomad

HASHICORP

Jon Benson @jm_benson

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Agenda

• Nomad Overview

• Nomad Architecture

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Agenda

• Consul Overview

• Consul Architecture

• Prepared Queries

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Agenda

• Demo

• Questions!

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Advantages of a Scheduler

Higher Resource Utilization

Decouple Work from Resources

Better Quality of Service

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Advantages of a Scheduler

Bin Packing

Over-Subscription

Job Queueing

Higher Resource Utilization

Decouple Work from Resources

Better Quality of Service

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Advantages of a Scheduler

Abstraction

API Contracts

Standardization

Higher Resource Utilization

Decouple Work from Resources

Better Quality of Service

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Advantages of a Scheduler

Priorities

Resource Isolation

Pre-emption

Higher Resource Utilization

Decouple Work from Resources

Better Quality of Service

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NomadHASHICORP

Cluster Scheduler

Easily Deploy Applications

Job Specification

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example.nomad

# Define our simple redis jobjob "redis" {

# Run only in us-east-1 datacenters = ["us-east-1"]

# Define the single redis task using Docker task "redis" { driver = "docker"

config { image = "redis:latest" }

resources { cpu = 500 # Mhz memory = 256 # MB network { mbits = 10 dynamic_ports = ["redis"] } } }}

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Job Specification

Declares what to run

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Job Specification

Nomad determines where and manages how to run

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Job Specification

Abstract work from resources

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Higher Resource Utilization

Decouple Work from Resources

Better Quality of Service

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Multi-Datacenter

Multi-Region

Flexible Workloads

Job Priorities

Bin Packing

Large Scale

Operationally Simple

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Thousands of regions

Tens of thousands of clients per region

Thousands of jobs per region

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Built on Experience

gossip consensus

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• Cluster Management

• Gossip Based (P2P)

• Membership

• Failure Detection

• Event System

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• Gossip Protocol

• Large Scale

• Production Hardened

• Operationally Simple

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• Service configuration and discovery

• Monitoring at scale

• High-availability

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• Service configuration and discovery

• Monitoring at scale

• High-availability

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• Service configuration and discovery

• Monitoring at scale

• High-availability

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• Service configuration and discovery

• Monitoring at scale

• High-availability

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• Multi-Datacenter

• Raft Consensus

• Large Scale

• Production Hardened

• Coordination (Locking)

• Central Servers + Distributed Clients

• Network Tomography

• Prepared Queries

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Prepared Queries

• Multiple instances of a given service exist in multiple

datacenters

• Clients can talk to any of them, and always prefer the

instances with lowest latency

• Policies can change, desire to not have the clients know

the details of how to locate a healthy service

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Prepared Queries

• New query namespace, similar to services

• Register queries to answer for parts of this namespace

• Clients use APIs, or “.query.consul” DNS lookups to run

queries

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gossip consensus

Mature Libraries

Design Patterns

No Scheduling Logic

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Built on Research

gossip consensus

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Inspired by Google Omega

Optimistic Concurrency

Internal State and Coordination

Service and Batch workloads

Pluggable Architecture

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Single Region Architecture

SERVER SERVER SERVER

CLIENT CLIENT CLIENTDC1 DC2 DC3

FOLLOWER LEADER FOLLOWER

REPLICATIONFORWARDING

REPLICATIONFORWARDING

RPC RPC RPC

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Multi Region Architecture

SERVER SERVER SERVERFOLLOWER LEADER FOLLOWER

REPLICATIONFORWARDING

REPLICATION

REGION B GOSSIP

REPLICATION REPLICATIONFORWARDING

REGION FORWARDING

REGION A

SERVERFOLLOWER

SERVER SERVERLEADER FOLLOWER

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Region is Isolation Domain

1-N Datacenters Per Region

Flexibility to do 1:1 (Consul)

Scheduling Boundary

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Data Model

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Evaluations ~= State Change Event

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Create / Update / Delete JobNode Up / Node Down

Allocation Failed

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External Event

Evalua?on Crea?on

Evalua?on Queuing

Evalua?on Processing

Op?mis?c Coordina?on

State Updates

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Server Architecture

Omega Class Scheduler

Pluggable Logic

Internal Coordination and State

Multi-Region / Multi-Datacenter

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Client Architecture

Broad OS Support

Host Fingerprinting

Pluggable Drivers

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Fingerprinting

Operating System

Hardware

Applications

Environment

Type Examples

Kernel, OS, Versions

CPU, Memory, Disk

Java, Docker, Consul

AWS, GCE

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Fingerprinting

Constrain Placement and Bin Pack

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Fingerprinting

“Task Requires Linux, Docker, and PCI-Compliant Hardware” expressed as Constraints

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Fingerprinting

“Task needs 512MB RAM and 1 Core” expressed as Resource Ask

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Drivers

Execute Tasks Provide Resource Isolation

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Containerized

Virtualized

Standalone

Docker

Qemu / KVM

Java Jar

Static Binaries

Rocket

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Containerized

Virtualized

Standalone

Docker

Rocket

Windows Server Containers

Qemu / KVM

Hyper-V

Xen

Java Jar

Static Binaries

C#

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Workload Flexibility:

Schedulers

Fingerprints

Drivers

Job Specification

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Operational Simplicity:

Single Binary

No Dependencies

Highly Available

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Cluster Scheduler

Easily Deploy Applications

Job Specification

NomadHASHICORP

Higher Resource Utilization

Decouple Work from Resources

Better Quality of Service

NomadHASHICORP

• Million Container Challenge

• hashicorp.com/c1m.html

• github.com/hashicorp/c1m

• Nomad 0.4

• Volume support across drivers

• Advanced networking

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Thanks!We’ll do a quick demo then answer questions…

Jon Benson @jm_benson