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

© 2016 Mesosphere, Inc. All Rights Reserved. 2

Jörg SchadDistributed Systems Engineer

@joerg_schad

Johannes UntersteinDistributed Applications Engineer

@unterstein


In the beginning there was a big

Monolith

© 2017 Mesosphere, Inc. All Rights Reserved.

Hardware

Operating System

Application

4

COMPUTERS


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


Operating System

Operating System

Operating System

ServiceApp ServiceServiceAppApp

6

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!



OS

8

CONTAINERS- Rapid deployment- Dependency

vendoring- Container image

repositories- Spreadsheet

scheduling

OS OS

Machine

Infrastructure

Machine Machine

Container Runtime Container Runtime Container Runtime



Demo 1Chuck Norris Jokes via HTTP

Code @https://git.io/vXUoy

https://git.io/vXUoy






OS

10

CONTAINERS- Rapid deployment- Dependency

vendoring- Container image

repositories- Spreadsheet

scheduling

OS OS

Machine

Infrastructure

Machine Machine

Container Runtime Container Runtime Container Runtime



CONTAINER SCHEDULING

11


RESOURCE MANAGEMENT

12


SERVICE MANAGEMENT

13


CONTAINERSCHEDULING

RESOURCE MANAGEMENT

SERVICE MANAGEMENT

- Load Balancing- Readiness Checking

CONTAINER ORCHESTRATION


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


Orc

hest

ratio

n

16

Machine Infrastructure

Web Apps & Services

Scheduling

Resource Management

Container Runtime

Machine & OS

Service Management

CONTAINERORCHESTRATION

Machine & OS Machine & OS

Container Runtime Container Runtime


MapReduce is crunching Data

Meanwhile...


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




INGEST

Apache Kafka

STORE

Apache Spark

ANALYZE

Apache Cassandra

ACT

Akka





DC/OS

Sensors

Devices

Clients


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

Google Cloud Dataflow

STREAM PROCESSING


Micro-Batching

EXECUTION MODEL

Native Streaming




INGEST

Apache Kafka

STORE

Apache Spark

ANALYZE

Apache Cassandra

ACT

Akka





DC/OS

Sensors

Devices

Clients


Datastores


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


Datacenter


NAIVE APPROACH

Typical Datacentersiloed, over-provisioned servers,

low utilization

Industry Average12-15% utilization

mySQL

microservice

Cassandra

Spark/Hadoop

Kafka


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


• 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

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


Demo 2Scaling DC/OS with Chuck Norris

Code @https://git.io/vXUoy






● 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


DC/OS SDKFor Stateful Services


● a State Machine for each task:

● state kept in zookeeper

● framework runs event loop

● and handles state changes

State Machine on Mesos


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


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


Keep it running!


Monitoring- Collecting metrics- Routing events- Downstream processing

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

Logging- Scopes- Local vs. Central- Security considerations

Keep it running


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


Questions?

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

http://grnh.se/ie76ru








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

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