theanatomyofthegooglearchitecturefinalv1-1-091210035101-phpapp02

8/3/2019 theanatomyofthegooglearchitecturefinalv1-1-091210035101-phpapp02

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GOOGLE TALK

Ed Austin 12-09-09


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Pre PresentationThe Google Philosophy (according to ed)

Jedis build their own lightsabres (the MS Eat your own Dog Food)

Parallelize Everything

Distribute Everything (to atomic level if possible) Compress Everything (CPU cheaper than bandwidth)

Secure Everything (you can never be too paranoid)

Cache (almost) Everything

Redundantize Everything (in triplicate usually)

Latency is VERY evil


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The Anatomy of the Google

ArchitectureThe unofficial Version

V1.0 November 2009

Ed Austin{ed, edik} @i-dot.com


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Section I The Basic Glue

SERVER HARDWARE

RHEL 2.6.X PAE

RACK

INTERIOR NETWORK IPv6

GFS / GFS II

BigTable MapreduceBigTable

Chubby Lock

GOOGLE APP

ENGINE

Python, Java, C++,Sawzall, other

DC

GOOGLE APPSSEARCHINDEX

CRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ

1. Exterior Network (Perimeter Architecture)

2. Data Centre

3. Rack Characteristics

4. Core Server Hardware

5. Operating System Implementation

6. Interior Network Architecture


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THE PERIMETER

How does your data enter the Google empire?


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Perimeter Network Security (as known)

DNS Load Balanced splits traffic (country, .com multiple DNS, other X1)to FW

Firewall filters traffic (http/s, smtp,pop etc)

Netscalar Load Balancers take Request from FW blocks DOS attacks,ping floods (DOS) blocks non IPv4/6 and none 80/443 ports and http

multiplexes (limited caching capability) User Request forwarded to Squid (Reverse Proxy) probably HUGE

cache (Petabytes?)

If not in Cache forwarded to GWS (Custom C++ Web Server) now notusing Custom apache?

GWS sends the Request to appropriate internal (Cell) servers

Request is processed

exterior https via thawte certs

Dedicated Crawler Architecture se arate from other infrastructure

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTableMapreduce

BigTable

Chubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPS

SEARCHINDEX

CRAWL

GMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ

CellInterior Network

GFS II etc

Firewall80/443

DNSLoad Balanced (.COM = 3, UK only one)

[ed@d800 ~]$ dig google.com.....

;; ANSWER SECTION:google.com. 223 IN A 74.125.45.100google.com. 223 IN A 74.125.53.100

google.com. 223 IN A 74.125.67.100

[ed@d800 ~]$

NetScalarhttp multiplexing

SquidReverse Proxy

GWSWeb Server Farm

FirewallDMZPerimeter

Client Browser80/443

Possible Search Traffic PathBased upon Known Technologies employededge routing not shown/instances not shown


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PERIMETER NETWORK CACHING

-Uses Squid Reverse Proxy

-Perimeter Cache hit rates 30-60% = Huge!

- Dependent on search complexity/user preferences/traffic type

- All Image Thumbnails caches, much Multimedia cached- Expensive common queries cached (common words i.e. Obama,

edinburgh) as they require significant back-end processing.

- On cache flush/update big latency spike and capacity drop- Index servers need to do significant work to rebuild cache

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable MapreduceBigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPS

SEARCHINDEX

CRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQSquid

Reverse Proxy

80/443 80/443


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THE DATA CENTRE

Where do they store all that Data?


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Worldwide Data Centres

Where is Google Located?

Last estimated were 36 Data Centers, 300+ GFSII Clusters andupwards of 800K machines.

US (#1) Europe (#2) Asia (#3) South America/Russia (#4)

Australia on Hold

Future: Taiwan, Malaysia, Lithuania, and Blythewood, South Carolina.

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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The Modular Data Centre

Standard Google Modular DC (Cell) holds 1160 Servers / 250KWPower Consumption in 30 racks (40U).

This is the Atomic Data Centre Building Block of Google.

A Data Centre would consist of 100s of Modular Cells.DC architecture then being the aggregation of smaller Cell level infrastructures intheir own container some being pure GFS, other BT, other Map, some mixed etc.

MDCs can also be deployed autonomously at the Perimeter(stand alone).

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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THE RACK

How is a server stored in the Data Centre?


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Google Rack (GOOG rack)

Why interesting?

The rack Implementation!

EVERYTHING custom!

Mini Server Size

Old Servers are Custom 1U

New Servers are 2U...

again a custom design

seem 1/3 width of a normal 2U Server

40U/80U Custom Racks (50% each side)

DesignHuge Heating and Power Issues

Optimized Motherboards

Work closely with HW MB developers

Have their own HW builds

specified to component level

Servers expected to be expendable

build redundancy on top of failure

Motherboard directly mounted into Rack

servers have no casing - just bare boards

assist with heat dispersal issues

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable

Mapreduce

BigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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THE HARDWARE

Millions of exactly what?


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Server Hardware

2U Low-Cost (but not slow) Commodity Servers

2009 Currently 2-Way, Dual Core/16GB/1-2TB +- Standard

Both Intel/AMD Chipsets 1 NIC 2 USB Looks like they RAID1/mirror the disks for better I/O - read performance

SATA 7.2K/10K/15K drives? 8 x 2GB DDR3 ECC

Standard HW Build (Several HW Build Versions at any one time) Currently at 7Gen Build (1G 2005 was probably Dual Core/SMP)

Each Server 12V Battery Backup and can run autonomously without external power (lasts 20-30s?)

Work closely with chip manufacturers to improve design/reduce power custom Intel chips that canwithstand higher heat factors than generic versions

YEAR Average Server Specification

1999/2000 PII/PIII 128MB+

2003/2004 Celeron 533, PIII 1.4 SMP, 2-4GB DRAM, Dual XEON 2.0/1-4GB/40-160GB IDE - SATA Disks via Silicon Images SATA 3114/SATA 3124

2006 Dual Opteron/Working Set DRAM(4GB+)/2x400GB IDE (RAID0?)

2009 2-Way/Dual Core/16GB/1-2TB SATA

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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THE OPERATING SYSTEM

The Core Software on each of those servers


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OPERATING SYSTEM

-100% Redhat Linux Based since 1998 inception

- RHEL (Why not CentOS?)

- 2.6.X Kernel- PAE- Custom glibc.. rpc... ipvs...- Custom FS (GFS II)- Custom Kerberos- Custom NFS

- Custom CUPS- Custom gPXE bootloader- Custom EVERYTHING.....

Kernel/Subsystem Modificationstcmallocreplaces glibc 2.3 mallocmuchfaster! works very well with threads...rpcthe rpc layer extensively modified to provide > perf increase < latency

(52%/40%)

Significantly modified Kernel and Subsystems all IPv6 enabled

Use Python as the primary scripting languageDeploy Ubuntu internally (likely for the Desktop) also Chrome OS base

Easily the Worlds largest installed Linux base

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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THE INTERIOR NETWORK

How does your datatravel around the Google empire?


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INTERIOR NETWORK

ROUTING PROTOCOL

Internal network is IPv6 (exterior machines can bereached using IPv6)

Heavily Modified Version of OSPF as the IRP

Intra-rack network is 100baseT

Inter-rack network is 1000baseTInter-DC network pipes unknown but veryfast

Technology:

Juniper, Cisco, Foundry, HP, routers and

switches

Software:

ipvs (ip virtual server)

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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THE MAJOR GLUE

The three foundation blocks of GooglesSecret Sauce


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Section II Googles Major Glue

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II


Chubby Lock

GOOGLE APPENGINE


DC


CRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ 1. Google File System Architecture GFS II

2. Google Database - Bigtable

3. Google Computation - Mapreduce

4. Google Scheduling - GWQ


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GOOGLE FILE SYSTEM

Manages the underlying Data on behalf of the upperlayers and ultimately the applications


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FILE SYSTEM I GFS v1

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTableMapreduce

BigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ

The GFS II cell is Googles fundamental building block everything can belayered on top of this

Consists of (Highly distributed Linux based) Master Servers and Chunk Servers

Chunk Servers serve the Data in 64MB Chunks to the client directly via Master arbitration

DATA REDUNDANCY/FAULT TOLERANCE?Triplicate Copies of Chunks are kept often in other clusters / DCChunks can be pulled from outside the DC! Expensive.... And try not to do!However apps built on top of GFS/BT do this on an ad-hoc basis (i.e. Gmail)

On Chunk loss the Master handles the Recovery by sourcing a chunk copy

Data is compressed using BMDiff/Zippy

Chunk Server Fault-Tolerance achieved by Heart-beat to the Master (I am alive..)

Master Failure was problematic for Google (finally down from 2 minutes to 10 seconds)


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FILE SYSTEM I GFS II

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTableMapreduce

BigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ

GFS II Colossus Version 2 improves in many ways (is a complete

rewrite)

Elegant Master Failover (no more 2s delays...)

Chunk Size is now 1MB likely to improve latency for serving data other than Indexing fo

example GMail this was the rationale behind the change

Master can store more Chunk Metadata (therefore more chunks addressable up to 100million) = also more Chunk Servers

However according to Google Engineer they have only ever lost one 64MB chunk (in GFS I)during its entire production deployment (2004 2008?) so assumed extremely reliable


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GOOGLE DATABASE

Accesses the underlying Data on behalf of the upperlayers and ultimately the applications


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Bigtable I - Introduction

What is it?

Googles Database Implementation since 1994

Used internally for all large scale (Search, Indexing, GMail etc)

Similar to a sharded Database implemention

GOALS

Huge Scalability to many PBs (Web Database currently around 40 Billion URLs)

Tight Latency

Highly efficient scans over Textual Data

Fault Tolerant

Load Balancable

Eliminate Googles dependency on an external provider

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable

Mapreduce

BigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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Bigtable II

How is Data Referenced?

Distributed Multi-Dimensional Sparse Map

Simple addressing model using a triple:

(row, column, {timestamp}) -> cell contents

ROWS

- Rows (arbitrary length usually 10-100 Bytes Max


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Bigtable III Table StructureStudying contents: columnshows three versions ofcontents of a page (current,cached and ?)presumably all othercolumns are timestampedso could be used in acomparitive way (such asanchor increase/decrease)OTF in SERPS alg mustuse a combo of TimeSt diffbetween n(=3 rest garbagecollected) page Versionsand crawled anchors -what else does table hold?Possibly PR (or OTF) andother search related

weightings

Google keeps much moreinfo for ranking purposesthan it did in 1999

Webtable hinted at 100columns+!

How do page units affect

the URL reversal of theURL bigtable?-Does a Tables TabletsCross a Clustersnamespace (yes if unifiedelse no?)

ENG uk.co.bbc.news

language:ROW

10-100 Bytes


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Bigtable IV

How tables are broken down in storage ?

For example Webtable is billions of pages!

-Large Tables broken (split) into tablets at row boundaries

-Tablets discontiguous (assists in fault-tolerance) spread over DC but try tokeep one copy in same rack

-Tablet Size is approximately 100-200MB of compressed Data

-Load Balanced migrate tablets from heavily loaded machines to lightly loadedones

- Heavily used tablets probably stay in working set (cached)

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTableMapreduce

BigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPS

SEARCHINDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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GOOGLE MAPREDUCE

Computes the underlying Data on behalf of theapplications


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Mapreduce I

Map Reduction can be seen as a way to exploit massive parallelismby breaking a task down into constituent parts and executing onmultiple processors

The Major Functions are MAP & REDUCE (with a number of intermediatsteps)

MAP Break task down into parallel stepsREDUCE Combine results into final output

Shown is a 2-pipeline Map Reduction (There are 24 Map Reductions in the indexing pipeline)Mappers & Reducers usually run on separate processors (90% loss of reducers job still completed!)

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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Mapreduce II

LANGUAGE BINDINGS

C++, Java, Python, Sawzall

DEPLOYED

Implemented 2004 before this MySQL?

STATISTICS

-In September 2009 Google ran 3,467,000 MR Jobs with an average 475 sec completion timeaveraging 488 machines per MR and utilising 25.5K Machine years

-Technique extensively used by Yahoo with Hadoop (similar architecture to Google) and Facebook(since 06 multiple Hadoop clusters, one being 2500CPU/1PB with HBase).

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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Chubby Lock

Googles Distributed File Locking Service for Bigtable

-Provides Mutex Support for Data Access (atomic access to column data)

- Used to synchronize access to shared resources

- Consists of a Master and Slaves (designated by election)

- Failover consists of a Slave replacing the functionality of a Master

-- Also servers as an ultra-fast high availability File Server for small fines (100s bytes)

- Provides an ACL for tablet authentication (row and column data)

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTableMapreduceBigTable

Chubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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GOOGLE WORKQUEUE

Provides Resource Management for the ComputationalJobs


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GWQ Google Workqueue

Batch Submission/Scheduler System

-Software to submit Mapreduce Jobs to a Cell/Cluster

-Arbitrates (process priorities) Schedules, Allocates Resources, process failover,Reports status, collects results

- Often Workqueue overlaid on a GFS Cluster- i.e. GFS cluster not computational bound jobs also

seems to match co-locate tasks near data = just diskI/O not Network I/O (on the Chunk Server?)

- Workqueue can manage many tens of thousands of machines

Launched via API or command line (sawzall example shown)

saw --program code.szl --workqueue testing--input_files /gfs/cluster1/2005-02-0[1-7]/submits.* \--destination /gfs/cluster2/$USER/output@100

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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Section III Some more Glue

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II


Chubby Lock

GOOGLE APPENGINE


DC


CRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ

1. Languages employed

2. Development Environment

3. Google App Engine

4. Network Security

5. Future Google Architecture Advances

6. Odds n Sods

7. DIY Google


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DEVELOPMENT LANGUAGES

- Initially Python, Java, C++

Usual Suspects

- Sawzall (since 2006)

- equivalent to Hadoops Pig Latin- written in C++ - interpreted bytecode output JITd

An internal Procedural language employed to solve map reduction problems. Thefew published Google papers employ Sawzall in the algorithm examples. Runs in theMap phase, Aggregators run in the Reduce phase (from each Sawzall Map instance)to get the final output.

- Transparent Parallelization no specialist Distrib Sys KnowledgeRequired (Good for developer)- Simple Datatypes 64-bit signed int, float, string, byte and a few unique such astime- Much STR regexp support

- Compound Types arrays, tuples- typesafed (and declarations) similar to Pascal (Probably an LL(1) lang?)- similar to Algol, C Syntax (no pointers though!)- No Processing of exceptions (no exception handlers)- Shorter than corresponding C++ code by a factor of 10

Early versions could not write into Bigtable. Now implemented?

Output sometimes pipelined into MySQL for further analysis

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable MapreduceBigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPS

SEARCHINDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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GOOGLE APP ENGINEUsing Application Platform technology stack

Allows a developer to leverage components of Google Technology (butnot necessarily primary Infrastructure i.e. The usual business resources)

-Supports Python, Java

- Bigtable support (via GQL)

-Uses GFS as underlying FS usual Fault-tolerance/Load-balancing

-Task Queue similar to GWQ?

-Code exposed to Google

- No support for subprocess spawning more importantly none of the

google mapreduce library made available- isolates computational aspects to single servers but the I/O is probably thegoogle standard implementation underneath- therefore computationally intensive tasks more problematic= keeping your resource usage under controlSERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II


Chubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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Security

Rack Board Level (possible scenario)

gPXE on the board goes through DHCP/tftp sequence to pull over an encryptedimage (this is not expensive as is done once per boot and boots are not usual)

Image is pulled from a Secure Image Distribution Server (and held encrypted onthese)

Once at the board end the image is OTF decrypted and booted as normal RHEL

02/09 Google Engineer didnt dispute this and seemed to concur adding that in-core encryption might be a possibility (R/T decryption might not be thatexpensive) this possibily means cryptology is used throughout the lifetime ofthe image including components outside the working-set but sensitive parts ofthe in-core OS (OTF decrypted)

Enterprise

Kerberos is used throughout the enterprise

They have an Automated issuance system for SSL certificates, used by internal

(secure) infrastructure to validate https/TLS and generic SSL connections.

Complete internal network encryption unlikely due to latency introduced?

Likely that one of the reasons failover between DCs problematic is the latency

introduced due to the expense of Wide Area Encryption (essential)

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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Google Future Architecture

- 99%ile latency for all data


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Odds n Sods

borg google technology/architecture (is a cluster..)Borg: a hybrid protocol for scalable application-level multicast in peer-to-peer networks(WAN multimedia steaming)

data cube google technology

Have a global loadbalancer assume load balances across a unified namespaceprobably worldwide

gmail designers implemented application level failover to move your session toan alternate DC in a seamless fashion to the end user.Probably all Google Apps will be able to migrate to an alternate DC cell (the application,and its GFS data if need be)

MySQL isused for back-end sys admin stuff (high availability master-slaveimplementations) and post Bigtable processing

Remote employee access is via VPN

Sys Admins maintain 5 and 30 minute SLAs so on the ball

Has its own internal archive.org equiv.

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable Mapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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BUILD YOUR OWN GOOGLE

The Basic Open Source Tools

The Google Stack (vs Yahooish/Open Source)


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The Google Stack (vs Yahooish/Open Source)

SERVER HARDWARE SERVER HARDWARE

RHEL 2.6.X PAE CentOS 2.6.X PAE

RACK RACK

INTERIOR NETWORK IPv6 INTERIOR NETWORK IPv6

GFS / GFS II HDFS (hadoop)

Hadoop Framework

MapreduceHbase (Bigtable equiv.)

Mapreduce

BigTableChubby Lock

Pig Latin, Python, PHP, Java ....anything


CLIENT APPLICATION

DC DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Conceptual Overview

Google vs. Open Source

Architecture

Open Source(Yahooish)Architecture

Exterior Network Exterior Network

GWQ Job Tracker

Googles

Secret Sauce

Hadoop

Open Source(Other Tools such as crawlers, indexers readily available)

BigTable

Python, Java,C++,

APP ENGINE

Task Queue


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END

(Thankyou)


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DIY GOOGLE

What you require:Preferably 2 Machines + 100BT

CentOS/RHEL(squid)ApacheHadoop (HDFS, Mapreduce, Pig, HBase)HDFS

bmdiff/zippy compression libraryGoogle glibc/tcmalloc perftools

Supporting stuff JRE etc

Browser with Search Box

pig mr call to scan a few filesprint results

SERVER HARDWARE

CentOS 2.6.X PAE

RACK


HDFS (hadoop)

Hadoop FrameworkMapreduce

Hbase (Bigtable equiv.)

Python, PHP, Java .... anything

CLIENT APPLICATION

DC


Exterior Network

Job Tracker (Work Queue equiv.)


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DIY GOOGLE

Install Hadoop and Pig on ClusterInstall eclipse and dependenciesInstall PigPen for eclipse and configure to cluster (NFS)

SERVER HARDWARE

CentOS 2.6.X PAE

RACK


HDFS (hadoop)

Hadoop FrameworkMapreduce

Hbase (Bigtable equiv.)

Python, PHP, Java .... anything

CLIENT APPLICATION

DC


Exterior Network

Job Tracker (Work Queue equiv.)


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TEMPLATE

- IPv6 enablement started 2008 (2009 finished?)

- IRP OSPFGoogle authored RFC points towards OSPF

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II


Chubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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DEVELOPMENT ENVIRONMENT bits&bobs

A rare shot of some concrete google internal stuff (this of a GFS Master Server code executionfound as a perftools profiling example)

Agile Methodologies Used (development iterations, teamwork, collaboration, and process adaptabilitythroughout the life-cycle of the project)

Libraries are the predominant way of building programs

An infrastructure handles versioning of applications so they can be release without a fear of breakingthings = roll out with minimal QA

- Internal Code uses replacement libraries- Google as youd expect rewrites everything!- Hungarian Notation?- Work in small teams 3-5 peoplelikely few scutters know the big picture

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTable

Mapreduce

BigTableChubby Lock

GOOGLE APPENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

Exterior Network

GWQ


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Internal Linux development and deployment

Served as technical lead of team responsible for customizing and deploying Linux to internal systems and workstations.

Fixed bugs and added enterprise features to several Linux components, including NFS, Kerberos, CUPS. All relevant patches werepushed to upstream maintainers, and most are in current released distributions.

Developed and maintained systems to automate installation, updates, and upgrades of Linux systems.

Developed IPv6 support for Linux load-balancing (ipvs).

Managed several interns and contractors.

loadbalancing user accounts within a datacenter, and coordinating with the global loadbalancer, which uses linear programming tooptimally allocate users. In particular, this avoids "shared fate" risks and reduces latency and costs incurred due to excessive transatlantic

data traffic. Learned Sketchup so as to document the four dimensional data structures effectively The testing, evalulation, deployment, operations, and maintenance of Netscaler load balancers.

automated Apache configuration reloader

gPXE open-source network booting software

GWS custom C++ webserver = not apache?

Google 02/09 talk example was a Cluster is 30 racks (I believe this refers to Google). At a 40U rack 40Ux30racks = 1200 =approximately a MDC can assume each MDC is a Cluster/cell at architectural level

Google engineer stated a DC is a collection of Modular Units(MDCs?) the picture (not above) illustrated suggested this.


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Some Pre Presentation Information

1 Million GB = 1000TB = 1 PB (x 1000 = 1 EXABYTE) Internet Archive is around 3PB (2009)

CLEAN UP BEFORE all the poorly sourced stuff

Add lock service to bt to all slides Google rack server on rack page SSTable

Google PROFITS US $16M A DAY


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Pre Presentation Disclaimer

Put together in a week from knowing zero about Google

I am not associated with Google

Numbers are approximate but certainly are ball-park Googleoften delivers contradictory figures and uses many terms for

some items - cell/cluster scheduler/workqueue (obfuscation?)

Googles philosophy/paranoia of tell as little as possible (pausingpresenters and sideways answers) makes it hard to fill in some(significant) gapsinferences are sometimes drawn (in red)

Google seem to design absolutely EVERYTHING themselvesfrom HW MB build, Racks, Switches(?), Software... So its hard tofind sources of information beyond broad concepts


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Bigtable VI

-

Latest (or at least since 2006..)

-Increased Scalability (across Namespace/Datacenters)- i.e. Tablets spread over DCs for a table but expensive

(both computationally and financially!)

-Service Clusters (?)

-Multiple Bigtable Clusters replicated throughout DC

Current Status

- Many Hundreds may be thousands of Bigtable Cells- Late 2009 stated 500 Bigtable clusters

- At minimum scaled to many thousands of machinesper cell in production

- Cells manage Managing 3-figure TB data (0.X PB)

SERVER HARDWARE

RHEL 2.6.X PAE

RACK


GFS / GFS II

BigTableMapreduce

BigTableChubby Lock

GOOGLE APP

ENGINE


DC

GOOGLE APPSSEARCH

INDEXCRAWLGMAIL...

Architecture

Python. Java.C++

GWQ

theanatomyofthegooglearchitecturefinalv1-1-091210035101-phpapp02

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