The Anatomy Of The Google Architecture Fina Lv1.1

GOOGLE TALK
Ed Austin 12-09-09

Pre Presentation The 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

The Anatomy of the Google Architecture “The unofficial Version“ V1.0 November 2009 Ed Austin {ed, edik} @i-dot.com

Section I – The Basic Glue 1. Exterior Network (Perimeter Architecture) 2. Data Centre 3. Rack Characteristics 4. Core Server Hardware 5. Operating System Implementation 6. Interior Network Architecture

THE PERIMETER How does your data enter the Google empire?

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 not using Custom apache?
GWS sends the Request to appropriate internal ( Cell ) servers
Request is processed
exterior https via thawte certs
Dedicated Crawler Architecture separate from other infrastructure

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

THE DATA CENTRE Where do they store all that Data?

Worldwide Data Centres Where is Google Located? Last estimated were 36 Data Centers, 300+ GFSII Clusters and upwards of 800K machines. US (#1) – Europe (#2) – Asia (#3) – South America/Russia (#4) Australia – on Hold Future : Taiwan, Malaysia, Lithuania, and Blythewood, South Carolina.

The Modular Data Centre Standard Google Modular DC (Cell) holds 1160 Servers / 250KW Power Consumption in 30 racks (40U). This is the “Atomic“ Data Centre Building Block of Google. A Data Centre would consist of 100‘s of Modular Cells. DC architecture then being the aggregation of smaller Cell level infrastructures in their own container – some being pure GFS, other BT, other Map, some mixed etc. MDC‘s can also be deployed autonomously at the Perimeter (stand alone).

THE RACK How is a server stored in the Data Centre?

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)
Design
Huge 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

THE HARDWARE Millions of exactly what?

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 can withstand 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

THE OPERATING SYSTEM The Core Software on each of those servers

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 Modifications
tcmalloc – replaces glibc 2.3 malloc – much faster! works very well with threads... rpc – the 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 language Deploy Ubuntu internally (likely for the Desktop) – also Chrome OS base Easily the Worlds largest installed Linux base

THE INTERIOR NETWORK How does your datatravel around the Google empire?

INTERIOR NETWORK ROUTING PROTOCOL Internal network is IPv6 (exterior machines can be reached using IPv6) Heavily Modified Version of OSPF as the IRP Intra-rack network is 100baseT Inter-rack network is 1000baseT Inter-DC network pipes unknown but very fast Technology: Juniper, Cisco, Foundry, HP, routers and switches Software: ipvs (ip virtual server)

THE MAJOR GLUE The three foundation blocks of Googles Secret Sauce

Section II – Googles Major Glue 1. Google File System Architecture – GFS II 2. Google Database - Bigtable 3. Google Computation - Mapreduce 4. Google Scheduling - GWQ

GOOGLE FILE SYSTEM Manages the underlying Data on behalf of the upper layers and ultimately the applications

FILE SYSTEM I – GFS v1 The GFS II cell is Googles fundamental building block – everything can be layered 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 / DC Chunks 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)

FILE SYSTEM I – GFS II 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 – for example GMail – this was the rationale behind the change Master can store more Chunk Metadata (therefore more chunks addressable up to 100 million) = 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

GOOGLE DATABASE Accesses the underlying Data on behalf of the upper layers and ultimately the applications

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 PB‘s (Web Database currently around 40 Billion URL‘s) Tight Latency Highly efficient scans over Textual Data Fault Tolerant Load Balancable Eliminate Googles dependency on an external provider

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 <=64KB) - Rows stored lexographically - example row (URL)) COLUMNS - example column (contents:, PR, anchor1: ..) TIMESTAMP (OPTIONAL?) - timestamp (various API func args, i.e. “ALL“, “LATEST“) .

Bigtable III – Table Structure
Studying contents: column shows three versions of contents of a page (current, cached and ?) – presumably all other columns are timestamped so could be used in a comparitive way (such as anchor increase/decrease) OTF in SERPS – alg must use a combo of TimeSt diff between n(=3 rest garbage collected) page Versions and crawled anchors - what else does table hold? Possibly PR (or OTF) and other search related weightings
Google keeps much more info for ranking purposes than it did in 1999
Webtable hinted at 100 columns+!
How do page units affect the URL reversal of the URL bigtable?
Does a Tables Tablets Cross a Clusters namespace (yes if unified else no?)
C++ Bigtable Mutate of some Anchors //open table Table *T=OpenOrDie(“/ bigtable/web/bigtable “); //write new anchor and delete old anchor RowMutation r1(T,“uk.co.bbc.news“); r1.Set (“anchor:www.abc.org“,“CNN“); r1.Delete (“anchor:www.def.com“); Operation op; Apply (&op, &r1); //atomic mutate to the columns Other primitives such as DeleteCells(), DeleteRow(), Scanner (read arbitrary cells in a row)

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 to keep one copy in same rack
Tablet Size is approximately 100-200MB of compressed Data
Load Balanced – migrate tablets from heavily loaded machines to lightly loaded ones
Heavily used tablets probably stay in working set (cached)

GOOGLE MAPREDUCE Computes the underlying Data on behalf of the applications

Mapreduce I Map Reduction can be seen as a way to exploit massive parallelism by breaking a task down into constituent parts and executing on multiple processors The Major Functions are MAP & REDUCE (with a number of intermediatary steps) MAP Break task down into parallel steps REDUCE 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!)

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 time averaging 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).

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 (100‘s bytes)
Provides an ACL for tablet authentication (row and column data)

GOOGLE WORKQUEUE Provides Resource Management for the Computational Jobs

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 disk I/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

Section III – Some more Glue 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

DEVELOPMENT LANGUAGES - Initially Python, Java, C++ Usual Suspects - Sawzall (since 2006) - equivalent to Hadoops Pig Latin - written in C++ - interpreted bytecode output JIT‘d An internal Procedural language employed to solve map reduction problems. The few published Google papers employ Sawzall in the algorithm examples. Runs in the Map phase, Aggregators run in the Reduce phase (from each Sawzall Map instance) to get the final output. - Transparent Parallelization – no specialist Distrib Sys Knowledge Required (Good for developer) - Simple Datatypes 64-bit signed int, float, string, byte and a few unique such as time - 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

GOOGLE APP ENGINE Using “Application Platform technology stack“

Allows a developer to leverage components of Google Technology (but not 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 the google standard implementation underneath - therefore computationally intensive tasks more problematic = keeping your resource usage under control

Security Rack Board Level (possible scenario) gPXE on the board goes through DHCP/tftp sequence to pull over an encrypted image (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 on these) Once at the board end the image is OTF decrypted and booted as normal RHEL 02/09 Google Engineer didn‘t dispute this and seemed to concur adding that in-core encryption might be a possibility (R/T decryption might not be that expensive) – this possibily means cryptology is used throughout the lifetime of the image – including components outside the working-set but sensitive parts of the 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 DC‘s problematic is the latency introduced due to the expense of Wide Area Encryption (essential)

Google Future Architecture
- 99%ile latency for all data <50ms is a key speed metric
Single global namespace
“ Spanning multiple data centers is still an unsolved problem. Most websites are in one and at most two data centers. How to fully distribute a website across a set of data centers. ”
Spanner
Dynamic Load Balancing of upwards of 10M Servers between Data Centers
- “automatic, dynamic world-wide placement of data & computation to minimize latency or cost.”
Allegedly used to reduce heat issues at DC‘s by moving the load when the heat issue becomes a problem at the new chillerless DC‘s (i.e. Belgium DC) – not using chillers introducess significant savings.
- Translation Servers (automatic translation of documents)
- GDrive Servers

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 namespace – probably worldwide gmail designers implemented application level failover to move your session to an 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 is used for back-end sys admin stuff (high availability master-slave implementations) and post Bigtable processing Remote employee access is via VPN Sys Admins maintain 5 and 30 minute SLA’s – so on the ball Has its own internal archive.org equiv.

BUILD YOUR OWN GOOGLE The Basic Open Source Tools

The Google Stack (vs Yahoo‘ish/Open Source)

END (Thankyou)

DIY GOOGLE What you require: Preferably 2 Machines + 100BT CentOS/RHEL (squid) Apache Hadoop (HDFS, Mapreduce, Pig, HBase) HDFS bmdiff/zippy compression library Google glibc/tcmalloc – perftools Supporting stuff – JRE etc Browser with Search Box pig mr call to scan a few files print results

DIY GOOGLE Install Hadoop and Pig on Cluster Install eclipse and dependencies Install PigPen for eclipse and configure to cluster (NFS)

TEMPLATE - IPv6 enablement started 2008 (2009 finished?) - IRP OSPF Google authored RFC points towards OSPF

DEVELOPMENT ENVIRONMENT bits&bobs A rare shot of some concrete google internal stuff (this of a GFS Master Server code execution found as a perftools profiling example) Agile Methodologies Used ( development iterations, teamwork, collaboration, and process adaptability throughout 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 breaking things = roll out with minimal QA - Internal Code uses replacement libraries - Google as you‘d expect rewrites everything! - Hungarian Notation? - Work in small teams 3-5 people – likely few scutters know ‘‘the big picture“

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 were pushed 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 to optimally 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 (MDC‘s?) – the picture (not above) illustrated suggested this.

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

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 – Google often delivers contradictory figures and uses many terms for some items - cell /cluster – scheduler/ workqueue (obfuscation?)
Googles philosophy/paranoia of tell as little as possible (pausing presenters and sideways answers) makes it hard to fill in some (significant) gaps – inferences are sometimes drawn (in red)
Google seem to design absolutely EVERYTHING themselves – from HW MB build, Racks, Switches(?), Software... So its hard to find sources of information beyond broad concepts

Bigtable VI
Latest (or at least since 2006..)
Increased Scalability (across Namespace/Datacenters)
i.e. Tablets spread over DC‘s 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 machines per cell in production - Cells manage Managing 3-figure TB data (0.X PB)

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The Anatomy Of The Google Architecture Fina Lv1.1

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