Денис Баталов, Принципы построения высоконагруженных...

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What your users want…


Fast, performant experience



Always on, accessible anywhere




Personalized and rich applicaIon





Lots of new features all of the

Ime



Ime



Powerful web applicaIons

Building powerful web applicaIons

Rule 2: Service requests as fast as possible

Rule 1: Service all web requests

Rule 3: Handle requests at any scale

Rule 4: Simplify architecture with services

Rule 5: Automate operaIonal management

Rule 6: Leverage unique cloud properIes

DNS ApplicaIon Data

Rule 1: Service all web requests a) Make sure requests get to your ‘front door’

DNS ApplicaIon Data Request



a) Make sure requests get to your ‘front door’ Rule 1: Service all web requests


…then this is irrelevant

Clients can’t resolve you?



“100% Available”

SLA


Route53

Feature Details

Global Supported from AWS global edge locaIons for fast and reliable domain name resoluIon

Scalable AutomaIcally scales based upon query volumes

Latency based rou5ng Supports resoluIon of endpoints based upon latency, enabling mulI-‐region applicaIon delivery

Integrated Integrates with other AWS services allowing Route 53 to front load balancers, S3 and EC2

Secure Integrates with IAM giving fine grained control over DNS record access

hhp://aws.amazon.com/route53/sla

a) Make sure requests get to your ‘front door’


Rule 1: Service all web requests a)  Make sure requests get to your ‘front door’ b)  Make sure you open the door when they arrive

Route53

Region



ElasIc Load

Balancer Region

Availability Zone

Availability Zone

Availability Zone

Availability Zone

Route53

a)  Make sure requests get to your ‘front door’ b)  Make sure you open the door when they arrive

ElasIc load balancing MulI-‐availability zone MulI-‐region

Region



Region

a)  Make sure requests get to your ‘front door’ b)  Make sure you open the door when they arrive c)  Have the data to form a response

ElasIc Load

Balancer Region

Availability Zone

Availability Zone

Availability Zone

Availability Zone

Route53

Region



Region

ElasIc Load

Balancer

Route53

Region

Availability Zone

Availability Zone

Availability Zone

Availability Zone

a)  Make sure requests get to your ‘front door’ b)  Make sure you open the door when they arrive c)  Have the data to form a response

MulI-‐AZ RDS (Master-‐slave) Inter-‐region replicaIon Read-‐replicas

Rule 2: Service requests as fast as possible a)  Choose the fastest route

Region A

Route53

Region B

Request

Rule 2: Service requests as fast as possible a)  Choose the fastest route

Region A

Route53

Region B

16ms 92ms

Request


Region A

Route53

Region B

16ms

Request Region A DNS entry

a)  Choose the fastest route

Rule 2: Service requests as fast as possible a)  Choose the fastest route b)  Offload your applicaIon servers

London

Paris

NY

Served from S3 /images/*

3

Served from EC2 *.php

2

Single CNAME www.mysite.com

1

CloudFront World-‐wide content distribu1on network Easily distribute content to end users with low latency, high data transfer speeds, and no commitments.

Without CloudFront EC2 webservers/app servers loaded by user requests


With CloudFront Load of user requests pushed into CloudFront, EC2 cluster can scale down

Offload Scale Down



Respon

se Tim

e

Server Load

Respon

se Tim

e

Server

Load

Respon

se Tim

e

Server

Load

No CDN CDN for StaIc

Content

CDN for StaIc & Dynamic Content

Offload Scale Down

a)  Choose the fastest route b)  Offload your applicaIon servers

Rule 2: Service requests as fast as possible a)  Choose the fastest route b)  Offload your applicaIon servers c)  Cache it if you can

ElasICache Memcached compa1ble caching layer

Serve frequently requested & slow changing data from scalable cache clusters

Reduce load on database and other servers

Rule 2: Service requests as fast as possible a)  Choose the fastest route b)  Offload your applicaIon servers c)  Cache it if you can d)  Single digit latencies where it mahers

Scale Database Que

ry Perform

ance

Desired consistency, predictability


Scale Database Que

ry Perform

ance


Actual degraded

performance with scale


Scale Database Que

ry Perform

ance


Actual degraded

performance with scale

Management problems

Data sharding Data caching Provisioning

Cluster management Fault management


Scale Database Que

ry Perform

ance

Dynamo DB Query Performance

RelaIonal Database Query

Performance

DynamoDB

Low latency Large scale Zero admin

Predictable performance


Scale Database Que

ry Perform

ance

Dynamo DB Query Performance DynamoDB

Low latency Large scale Zero admin

Predictable performance Average single-‐digit milliseconds server side

latencies

Runs on solid state drives, and is built to maintain consistent, fast latencies at any scale

Rule 3: Handle requests at any scale a)  Scale up

VerIcal Scaling From $0.02/hr

Basic unit of compute capacity Range of CPU, memory & local disk op1ons 17 Instance types available, from micro through cluster compute to SSD backed

Scale up with ElasIc Compute Cloud (EC2)

Rule 3: Handle requests at any scale a)  Scale up b)  Scale out

Trigger auto-scaling policy

as-create-auto-scaling-group MyGroup --launch-configuration MyConfig --availability-zones eu-west-1a --min-size 4 --max-size 200

Auto-‐scaling Automa1c re-‐sizing of compute clusters based upon demand

Manually

Send an API call or use CLI to launch/terminate instances – Only need to

specify capacity change (+/-‐)

By Schedule

Scale up/down based on date and Ime

a)  Scale up b)  Scale out

By Policy

Scale in response to changing condiIons, based on user configured real-‐Ime

monitoring and alerts

Auto-‐Rebalance

Instances are automaIcally launched/terminated to ensure the applicaIon is

balanced across mulIple Azs


Manually

Send an API call or use CLI to launch/terminate instances – Only need to

specify capacity change (+/-‐)

By Schedule

Scale up/down based on date and Ime PreempIve manual scaling of capacity

e.g. before a marke1ng event add 10 more instances

Regular scaling up and down of instances

e.g. scale from 0 to 2 to process SQS messages every night or double capacity

on a Friday night

a)  Scale up b)  Scale out

By Policy

Scale in response to changing condiIons, based on user configured real-‐Ime

monitoring and alerts

Auto-‐Rebalance

Instances are automaIcally launched/terminated to ensure the applicaIon is

balanced across mulIple Azs


Dynamic scale based upon custom metrics

e.g. SQS queue depth, Average CPU load, ELB latency

Maintain capacity across availability zones

e.g. Instance availability maintained in event of AZ becoming unavailable

Rule 3: Handle requests at any scale a)  Scale up b)  Scale out c)  Dial it up

ElasIc Block Store Provisioned IOPS up to 4000 per EBS

volume Predictable performance for demanding workloads such as

databases

DynamoDB Provisioned read/write performance per

table Predictable high performance scaled via

console or API

“AWS gave us the flexibility to bring a massive amount of capacity online in a short period of

Ime and allowed us to do so in an operaIonally straighvorward way.

AWS is now Shazam’s cloud provider of choice,”

Jason Titus,

CTO

DynamoDB: over 500,000 writes per

second

Amazon EMR: more than 1 million writes

per second

Your Business

70%

On-‐Premise Infrastructure

30%

Managing All of the “UndifferenIated Heavy Li{ing”


AWS Cloud-‐Based

Infrastructure

Your Business

More Time to Focus on Your Business

Configuring Your Cloud Assets

70%

30% 70%

On-‐Premise Infrastructure

30%

Managing All of the “UndifferenIated Heavy Li{ing”


RelaIonal Database Service Database-‐as-‐a-‐Service

No need to install or manage database instances

Scalable and fault tolerant configura1ons

DynamoDB Provisioned throughput NoSQL database

Fast, predictable performance

Fully distributed, fault tolerant architecture

Use RDS for databases

Use DynamoDB for high performance key-‐

value DB


Amazon SQS

Processing task/processing trigger

Processing results

Amazon SQS Reliable, highly scalable, queue service

for storing messages as they travel

between instances

Task A

Task B (Auto-‐scaling)

Task C

2

3

1

Simple Workflow Reliably coordinate processing steps

across applica1ons

Integrate AWS and non-‐AWS resources Manage distributed state in complex

systems

Push inter-‐process workflows into the cloud with SWF

Reliable message queuing without

addiIonal so{ware


Cloud Search Elas1c search engine based upon

Amazon A9 search engine

Fully managed service with sophis1cated feature set

Scales automa1cally

Document Server

Results

Search Server

Don’t install search so{ware, use CloudSearch

Process large volumes of data cost effecIvely

with EMR

ElasIc MapReduce Elas1c Hadoop cluster

Integrates with S3 & DynamoDB

Leverage Hive & Pig analy1cs scripts Integrates with instance types such as

spot


“Amazon CloudSearch is a game-‐changing product that has allowed us to deliver powerful new search capabiliIes. Our customers can now find what they are looking for faster and more

easily than ever before…

….We saved many months of re-‐architecture and development Ime by going with Amazon

CloudSearch”

Don MacAskill CEO & Chief Geek

SmugMug

Compute

Storage

Security Scaling

Database

Networking Monitoring

Messaging

Workflow

DNS Load Balancing

Backup CDN

Rule 5: Automate operaIonal management a)  Everything is programmable

Access everything via CLI, API or

Console

Achieve the highest levels of automaIon

sophisIcaIon with ease

Rule 5: Automate operaIonal management a)  Everything is programmable b)  Think disposable, one click deployments

Cloud FormaIon Automate crea1on of ‘stacks’ in a repeatable way

Scrip1ng framework for AWS resource crea1on

Feature Details

PlaCorm support Support for AWS resources from EC2 to IAM

Resource crea5on Creates AWS resources behind the scenes and reports on progress

Declara5ve Specify stacks in JSON format and source control your environments

Customizable Drive stack creaIon with paramaters

Rule 5: Automate operaIonal management a)  Everything is programmable b)  Think disposable, one click deployments c)  Design for failure, implement self healing

Customize instance startup

Get instances to ask ‘who am I?’ quesIon on startup and be configured dynamically upon

being asnwered

Maintain capacity of instances

Using a minimum pool size will maintain

capacity in the event of instance failures

Know what’s going on, take automated acIons

Use CloudWatch standard and custom metrics to create

alarms.

Respond with automated administraIon acIons

Bootstrapping Auto-scaling Cloud Watch

Rule 5: Automate operaIonal management a)  Everything is programmable b)  Think disposable, one click deployments c)  Design for failure, implement self healing

Rule 6: Leverage unique cloud properIes a)  OpImize costs with instance types

Unix/Linux instances start at $0.02/hour

Pay as you go for compute power

Low cost and flexibility

Pay only for what you use, no up-‐front commitments or long-‐term contracts

Use Cases:

Applica1ons with short term, spiky, or

unpredictable workloads;

Applica1on development or tes1ng

On-demand instances

1-‐ or 3-‐year terms

Pay low up-‐front fee, receive significant hourly discount

Low Cost / Predictability

Helps ensure compute capacity is available

when needed

Use Cases:

Applica1ons with steady state or predictable usage

Applica1ons that require reserved capacity, including disaster recovery

Reserved instances

Bid on unused EC2 capacity

Spot Price based on supply/demand, determined automaIcally

Cost / Large Scale, dynamic workload handling

Use Cases:

Applica1ons with flexible start and end 1mes

Applica1ons only feasible at very low compute prices

Spot instances


0

1000

2000

3000

4000

5000

6000

7000

Reserved Instances

On Demand

Spot


a)  OpImize costs with instance types b)  Get insight fast with ElasIc MapReduce


ElasIc MapReduce Managed, elas1c Hadoop cluster Integrates with S3 & DynamoDB

Leverage Hive & Pig analy1cs scripts

Integrates with instance types such as spot

Feature Details

Scalable Use as many or as few compute instances running Hadoop as you want. Modify the number of instances while your job flow is running

Integrated with other services

Works seamlessly with S3 as origin and output. Integrates with DynamoDB

Comprehensive Supports languages such as Hive and Pig for defining analyIcs, and allows complex definiIons in Cascading, Java, Ruby, Perl, Python, PHP, R, or C++

Cost effec5ve Works with Spot instance types

Monitoring Monitor job flows from with the management console

a)  OpImize costs with instance types b)  Get insight fast with ElasIc MapReduce

ElasIc MapReduce

Code Name node

Output S3 + SimpleDB

S3 + DynamoDB

ElasIc cluster

HDFS Queries + BI

Via JDBC, Pig, Hive

Input data


Features powered by Amazon ElasIc MapReduce:

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Review highlights Auto complete as you type on search

Search spelling sugges1ons Top searches

Ads

200 ElasIc MapReduce jobs per day Processing 3TB of data

“With AWS, our developers can now do things they couldn’t before…

…Our systems team can focus their energies on other

challenges.”

Dave Marin Search and data-‐mining engineer

Cluster compute instances Implement HVM process execu1on Intel® Xeon® E5-‐2670 processors 10 Gigabit Ethernet

Cluster Compute

80 EC2 Compute Units

60GB RAM

3TB Local Disk

Network placement groups Cluster instances deployed in a ‘Placement Group’ enjoy low

latency, full bisec1on 10 Gbps bandwidth

10Gbps

Rule 6: Leverage unique cloud properIes a)  OpImize costs with instance types b)  Get insight fast with ElasIc MapReduce c)  Create a supercomputer backend when you need it




Ime



With AWS

ElasIc uIlity capacity

✔


Ime



With AWS


✔ Highly available global coverage

✔


Ime


With AWS



✔


Agility & automated operaIons

✔

With AWS



✔

Agility & automated operaIons

✔ Cost effecIve storage, big data &

analyIcs ✔

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@awsoblako @dbatalov

Денис Баталов, Принципы построения высоконагруженных...

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