container 2 - jax finance 2018 · pdf file- rapid deployment - dependency ... - spreadsheet...

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Container 2.0Container: check! But what about persistent data, big data or fast data?!

@unterstein @joerg_schad @dcos @jaxdevops

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Jörg SchadDistributed Systems Engineer

@joerg_schad

Johannes UntersteinDistributed Applications Engineer

@unterstein

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In the beginning there was a big

Monolith

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Hardware

Operating System

Application

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COMPUTERS

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noun | ˈmīkrō/ /ˈsərvəs/ :

an approach to application development in which a large application is built as a suite of modular services. Each module supports a specific business goal and uses a simple, well-defined interface to communicate with other modules.*

Microservices are designed to be flexible, resilient, efficient, robust, and individually scalable.

*From whatis.com

OVERVIEW

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Operating System

Operating System

Operating System

ServiceApp ServiceServiceAppApp

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MICROSERVICES- Polyglot- Single Responsibility- Smaller Teams- Utilization- Machine

types/groups- Dependency hell

Machine

Infrastructure

Machine Machine

ServiceService ServiceServiceServiceService

© Gerard Julien/AFP

Run everything in containers!

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ServiceApp ServiceServiceAppApp

OS

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CONTAINERS- Rapid deployment- Dependency

vendoring- Container image

repositories- Spreadsheet

scheduling

OS OS

Machine

Infrastructure

Machine Machine

Container Runtime Container Runtime Container Runtime

ServiceService ServiceServiceServiceService

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Demo 1Chuck Norris Jokes via HTTP

Code @https://git.io/vXUoy

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ServiceApp ServiceServiceAppApp

OS

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CONTAINERS- Rapid deployment- Dependency

vendoring- Container image

repositories- Spreadsheet

scheduling

OS OS

Machine

Infrastructure

Machine Machine

Container Runtime Container Runtime Container Runtime

ServiceService ServiceServiceServiceService

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CONTAINER SCHEDULING

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RESOURCE MANAGEMENT

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SERVICE MANAGEMENT

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CONTAINERSCHEDULING

RESOURCE MANAGEMENT

SERVICE MANAGEMENT

- Load Balancing- Readiness Checking

CONTAINER ORCHESTRATION

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CONTAINERSCHEDULING

- Placement- Replication/Scaling- Resurrection- Rescheduling- Rolling Deployment- Upgrades- Downgrades- Collocation

RESOURCE MANAGEMENT

- Memory- CPU- GPU- Volumes- Ports- IPs- Images/Artifacts

SERVICE MANAGEMENT

- Labels- Groups/Namespaces- Dependencies- Load Balancing- Readiness Checking

CONTAINER ORCHESTRATION

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Orc

hest

ratio

n

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Machine Infrastructure

Web Apps & Services

Scheduling

Resource Management

Container Runtime

Machine & OS

Service Management

CONTAINERORCHESTRATION

Machine & OS Machine & OS

Container Runtime Container Runtime

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MapReduce is crunching Data

Meanwhile...

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DATA PROCESSING AT HYPERSCALE

EVENTSUbiquitous data streams from connected devices

INGEST

Apache Kafka

STORE

Apache Spark

ANALYZE

Apache Cassandra

ACT

Akka

Ingest millions of events per second

Distributed & highly scalable database

Real-time and batch process data

Visualize data and build data driven applications

DC/OS

Sensors

Devices

Clients

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DATA PROCESSING AT HYPERSCALE

EVENTSUbiquitous data streams from connected devices

INGEST

Apache Kafka

STORE

Apache Spark

ANALYZE

Apache Cassandra

ACT

Akka

Ingest millions of events per second

Distributed & highly scalable database

Real-time and batch process data

Visualize data and build data driven applications

DC/OS

Sensors

Devices

Clients

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● Apache Storm● Apache Spark● Apache Samza● Apache Flink● Apache Apex● Concord● cloud-only: AWS Kinesis,

Google Cloud Dataflow

STREAM PROCESSING

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Micro-Batching

EXECUTION MODEL

Native Streaming

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DATA PROCESSING AT HYPERSCALE

EVENTSUbiquitous data streams from connected devices

INGEST

Apache Kafka

STORE

Apache Spark

ANALYZE

Apache Cassandra

ACT

Akka

Ingest millions of events per second

Distributed & highly scalable database

Real-time and batch process data

Visualize data and build data driven applications

DC/OS

Sensors

Devices

Clients

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Datastores

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Key-Value

Data Model

GraphRelational Document● Schema● SQL ● Foreign

Keys/Joins● OLTP/OLAP

● Simple● Scalable● Cache

FilesTime-Series● Complex

relations● Social Graph● Recommend

ation● Fraud

detections

● Schema-Less● Semi-structu

red queries● Product

catalogue● Session data

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Datacenter

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NAIVE APPROACH

Typical Datacentersiloed, over-provisioned servers,

low utilization

Industry Average12-15% utilization

mySQL

microservice

Cassandra

Spark/Hadoop

Kafka

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MULTIPLEXING OF DATA, SERVICES, USERS, ENVIRONMENTS

Typical Datacentersiloed, over-provisioned servers,

low utilization

Mesos, DC/OSautomated schedulers, workload multiplexing onto the

same machines

mySQL

microservice

Cassandra

Spark/Hadoop

Kafka

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• A top-level Apache project• A cluster resource

negotiator• Scalable to 10,000s of

nodes• Fault-tolerant, battle-tested• An SDK for distributed apps• Native Docker support

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Apache Mesos

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Datacenter Operating System (DC/OS)

Distributed Systems Kernel (Mesos)

DC/OS ENABLES MODERN DISTRIBUTED APPS

Big Data + Analytics EnginesMicroservices (in containers)

Streaming

Batch

Machine Learning

Analytics

Functions & Logic

Search

Time Series

SQL / NoSQL

Databases

Modern App Components

Any Infrastructure (Physical, Virtual, Cloud)31

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THEBASICS

DC/OS is … ● 100% open source (ASL2.0)

+ A big, diverse community● An umbrella for ~30 OSS projects

+ Roadmap and designs+ Docs and tutorials

● Not limited in any way● Familiar, with more features

+ Networking, Security, CLI, UI, Service Discovery, Load Balancing, Packages, ...

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THEUNIVERSE

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Demo 2Scaling DC/OS with Chuck Norris

Code @https://git.io/vXUoy

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● Default Sandbox

Simple to use, Task failures

● Local Persistent Volumes

Task failures, (permanent) Node failures

● Distributed File System/External Storage

Node failures, non-local writes

Container Storage

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DC/OS SDKFor Stateful Services

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● a State Machine for each task:

● state kept in zookeeper

● framework runs event loop

● and handles state changes

State Machine on Mesos

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Simple things should be simple; complicated things should be complicated

~1,000 LOC

~100 LOC

10K+ LOC

● Simple YAML interface● Default, customizable behaviors● Most services probably fall here

● Need to be a service expert● Need to write a little Java code● Don’t need to understand DC/OS

● Can’t use the defaults● Don't mind writing a lot of Java ● Willing to understand DC/OS

CustomPlans & Strategies

Build Your Own Schedulerwith commons libraries

Defaults

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Simple things should be simple; complicated things should be complicated

~1,000 LOC

~100 LOC

10K+ LOC

● Simple YAML interface● Default, customizable behaviors● Most services probably fall here

● Need to be a service expert● Need to write a little Java code● Don’t need to understand DC/OS

● Can’t use the defaults● Don't mind writing a lot of Java ● Willing to understand DC/OS

CustomPlans & Strategies

Build Your Own Schedulerwith commons libraries

Defaults

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name: "helloworld"principal: "helloworld-principal"zookeeper: master.mesos:2181api-port: 8080pods: helloworld: count: 5 tasks: server: goal: RUNNING cmd: "echo hello world >> hello-container-path/output && sleep 1000" cpus: 1 mem: 256 volumes: - path: "hello-container-path" type: ROOT size: 5000

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Hello-World: YAML

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Keep it running!

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Monitoring- Collecting metrics- Routing events- Downstream processing

- Alerting- Dashboards- Storage (long-term retention)

Logging- Scopes- Local vs. Central- Security considerations

Keep it running

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Maintenance - Cluster Upgrades- Cluster Resizing- Capacity Planning- User & Package Management- Networking Policies- Auditing- Backups & Disaster Recovery

Troubleshooting- Debugging

- Services- System

- Tracing- Chaos Engineering

Keep it running

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Questions?

Code: https://git.io/vXUoyPsssssssst …… we are hiring!

http://grnh.se/ie76ru