Google Tech Talk with Dr. Eric Brewer in Korea Apr.27.2015

Google Tech Talk.Containers: What, Why and HowGoogle Cloud Innovation

Dr. Eric BrewerVP of InfrastructureApril 27, 2015

Google confidential │ Do not distribute




Management MobileDeveloper Tools

Compute

Networking

Big Data

Storage


“Cloud Native” ApplicationsMiddle of a great transition• unlimited “ethereal” resources in the Cloud• an environment of services not machines• thinking in APIs and co-designed services• high availability offered and expected

(this is how Google already works internally)


Google has been developing and using containers to manage our applications for over 10 years.

Images by Connie Zhou

2B launched per week● simplifies management● performance isolation● efficiency

Google confidential │ Do not distribute Google confidential │ Do not distribute

What are containers?


VMs vs. Containers

Physical Processor

Virtual Processor

Operating System

Libraries

User Code PrivateCopy

Shared

Virtual Machines

Physical Processor

Virtual Processor

Operating System

Libraries

User Code

Containers

ISA

syscall

Containers: less overhead, enable more “magic”


Merging Two Kinds of ContainersDocker• It’s about packaging• Control:

• packages• versions• (some config)

• Layered file system• => Prod matches testing

Linux Containers• It’s about isolation

… performance isolation• not security isolation

… use VMs for that• Manage CPUs, memory,

bandwidth, …• Nested groups


Google Platform Layering

Infrastructure: Machines

App Engine: Language-based

Containers: Process-based

GCE

Kubernetes

GKE

GAE

Easy to use,Flexible


Why do we use them?


Why ContainersEase of development:

• User makes jobs based on containers; we schedule those jobs for them• They don’t worry about machines or the OS

Utilization (much more efficient)• We pack many containers per machine• Mix or live services and batch workloads• Batch fills in the “holes”

Ease of Operation: fewer staff per job• E.g. all jobs use latest security patches• More shared code among projects (shared services and code)


How do we use them?


Services ModelEach app lives in an environment of shared services

• storage, monitoring, logging• master election, deployment, testing

Services are updated independently• Multiple versions running at a time

• Required for “canary” testing -- deploy new version a little bit• => Need version numbers

• Usually new versions are backward compatible• Sometimes not => must eventually update ALL clients• Running multiple versions gives clients some time to update


Borg Scheduler

Borg is our primary scheduler for the last 10 years• Schedules over a single very large cluster (10,000+ machines)• Starts, moves, restarts container-based jobs• Also handles some logging, monitoring• Users don’t worry about where their jobs run

Recent paper on Borg:

https://research.google.com/pubs/pub43438.html


Kubernetes

• Container orchestrator • Runs Docker containers• Supports multiple cloud and bare-metal environments• Inspired and informed by Google’s experiences and

internal systems• Open source, written in Go

Manage applications, not machines

κυβερνήτης: Greek for “pilot” or “helmsman of a ship” the open source cluster manager from Google


Kubernetes: Higher level of Abstraction

Don’t Worry About• OS details• Packages — no conflicts• Machine sizes (much)• Mixing languages• Port conflicts

Think About• Composition of services• Load-balancing• Names of services• State management• Monitoring and Logging• Upgrading


Kubernetes Partners


Open sourced in June 2014

Google launched Google Container Engine (GKE)• Full managed and hosted Kubernetes• https://cloud.google.com/container-engine/

Roadmap:• https://github.com/GoogleCloudPlatform/kubernetes/blob/master/docs/roadmap.md

Working towards v1.0 over next few months• Key use cases: Redis, Memcache, MySQL, Mongo, Cassandra

Kubernetes Status & Plans


Evolution is the Real ValueApps Structured as Independent Microservices● Encapsulated state with APIs (like “objects”)● Mixture of languages● Mixture of teams

Services are Abstract● A “Service” is just a long-lived abstract name● Varied implementations over time (versions)● Kubernetes routes to the right implementation


Pod

/data

Containers:• Handle package dependencies• Different versions, same machine• No “DLL hell”

Managing Dependencies

python 3.4.2glibc 2.21

MyService

python 2.7.9glibc 2.19

MySQL Pods:• Co-locate containers• Shared volumes• IP address, independent port space• Unit of deployment, migration


Dependencies: Services

Services:• Replicated pods

• Source pod is a template• Auto-restart member pods• Abstract name (DNS)• IP address for the service

• in addition to the members• Load balancing among replicas

Load Balancer

Service IP


Example: Rolling Upgrade with Labels

Servers:

Labels:frontend

v1.2

frontend

v1.2

frontend

v1.2

frontend

v1.2

frontend

v1.3

frontend

v1.3

frontend

v1.3

frontend

v1.3

frontend

Replication Controller

replicas: 4

v1.2

Replication Controller

replicas: 1

v1.3

replicas: 3 replicas: 2replicas: 3replicas: 2replicas: 1 replicas: 4replicas: 0


SummaryA new path for Cloud Native applications:• Collection of independent (micro) services• Each service evolves on its own

• Scale as needed• Update as needed• Mix versions as needed

• Pods are a building block• Template for service members• Group containers and volumes• Dedicated IP and thus port space

• Containers simplify deployment

Latest Google Innovation

2012 20132002 2004 2006 2008 2010

Cloud Dataflow

History of Big Data at GoogleWhy Cloud Dataflow?

MapReduce

GFS Big Table

Dremel

Pregel

Flume

Colossus

SpannerMillWheel

Time to answer some Big Data questions

1M Devices

16.6K Events/sec

43B Events/month

518B Events/year

What was the average viewing time over the

past 7 days, compared to the last year?

How many active viewers did I have in the

last minute?

How many sales were made in the last

hour due to advertising conversion?

“One” approach

1M Devices

16.6K Events/sec

43B Events/month

518B Events/month

Big Data on Google Cloud

BigQueryHigh throughput ingestion, store and query petabytes

DataflowStream & batch

processing, unified and simplified

Pub/SubScalable, flexible, and

globally available messaging

Fully Managed, No-Ops Services

Capture Process Analyze

Cloud Dataflow

Cloud Dataflow is a collection of SDKs for

building batch or streaming parallelized

data processing pipelines.

Cloud Dataflow is a fully managed & elastic service

for executing optimized parallelized data processing

pipelines.

Benefits of Cloud Dataflow

❯ No Ops - truly elastic data processing for the cloud• On demand resource allocation w/intelligent auto-scaling• Automated worker optimization

❯ Unified model for batch & stream based processing • Flexible I/O support• Fine grained correctness primitives

❯ Open sourced SDK @ github• Java 7 today @ /GoogleCloudPlatform/DataflowJavaSDK• Python 2 in progress• Scala @/darkjh/scalaflow & /jhlch/scala-dataflow-dsl• Spark runner@ /cloudera/spark-dataflow• Flink runner @ /dataArtisans/flink-dataflow

https://github.com/jhlch/scala-dataflow-dsl

Optimizing Your Time To Answer

More time to dig into your data

Programming

Resource provisioning

Performance tuning

Monitoring

ReliabilityDeployment & configuration

Handling Growing Scale

Utilization improvements

Data Processing with Cloud DataflowTypical Data Processing

Programming

• Movement

• Filtering

• Enrichment

• Shaping

• Reduction

• Batch computation

• Continuous computation

• Composition

• External orchestration

• Simulation

Where might you use Cloud Dataflow?

AnalysisETL Orchestration

The tension and polarity of Big Data

AccuracySpeed

Cost control Complexity

Time to answer

Let’s build something

1M Devices

16.6K Events/sec

43B Events/month

518B Events/month

Cloud Pub/Sub Cloud Dataflow BigQuery

How many active viewers did I have in the last minute?


1M Devices

16.6K Events/sec

43B Events/month

518B Events/month

Cloud Dataflow BigQuery



1M Devices

16.6K Events/sec

43B Events/month

518B Events/month

Cloud Dataflow



1M Devices

16.6K Events/sec

43B Events/month

518B Events/month

Cloud Dataflow SDK+


Thank you!http://cloud.google.com

Date post:	14-Aug-2015
Category:	Technology
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Google Tech Talk with Dr. Eric Brewer in Korea Apr.27.2015

Technology