Vineet Gupta | GM – Software Engineering | Directihttp://www.vineetgupta.com

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Intelligent People. Uncommon Ideas.

• 22M+ users• Dozens of DB servers• Dozens of Web servers• Six specialized graph database servers to

run recommendations engine

Source: http://highscalability.com/digg-architecture

• 1 TB / Day• 100 M blogs indexed / day• 10 B objects indexed / day• 0.5 B photos and videos• Data doubles in 6 months• Users double in 6 months

Source: http://www.royans.net/arch/2007/10/25/scaling-technorati-100-million-blogs-indexed-everyday/

• 2 PB Raw Storage• 470 M photos, 4-5 sizes each• 400 k photos added / day• 35 M photos in Squid cache (total)• 2 M photos in Squid RAM• 38k reqs / sec to Memcached• 4 B queries / day

Source: http://mysqldba.blogspot.com/2008/04/mysql-uc-2007-presentation-file.html

• Virtualized database spans 600 production instances residing in 100+ server clusters distributed over 8 datacenters

• 2 PB of data• 26 B queries / day• 1 B page views / day• 3 B API calls / month• 15,000 App servers

Source: http://highscalability.com/ebay-architecture/

• 450,000 low cost commodity servers in 2006• Indexed 8 B web-pages in 2005• 200 GFS clusters (1 cluster = 1,000 – 5,000

machines)• Read / write thruput = 40 GB / sec across a

cluster• Map-Reduce

o 100k jobs / dayo 20 PB of data processed / dayo 10k MapReduce programs

Source: http://highscalability.com/google-architecture/

• Data Size ~ PB• Data Growth ~ TB / day• No of servers – 10s to 10,000• No of datacenters – 1 to 10• Queries – B+ / day

Host

App Server

DB Server

RAMCPUCPU

CPURAM

RAM

Sunfire X4640 M28 x 6-core 2.6 GHz

$ 27k to $ 170k

PowerEdge R200Dual core 2.8 GHz

Around $ 550

• Increasing the hardware resources on a host• Pros

o Simple to implemento Fast turnaround time

• Conso Finite limito Hardware does not scale linearly (diminishing

returns for each incremental unit)o Requires downtimeo Increases Downtime Impacto Incremental costs increase exponentially

T1, T2, T3, T4

App Layer

T1, T2, T3, T4

App Layer

T1, T2, T3, T4

T1, T2, T3, T4

T1, T2, T3, T4

T1, T2, T3, T4

• Each node has its own copy of data• Shared Nothing Cluster

• Read : Write = 4:1o Scale reads at cost of writes!

• Duplicate Data – each node has its own copy

• Master Slaveo Writes sent to one node, cascaded to others

• Multi-Mastero Writes can be sent to multiple nodeso Can lead to deadlockso Requires conflict management

Master

App Layer

Slave Slave Slave Slave

• n x Writes – Async vs. Sync• SPOF• Async - Critical Reads from Master!

Master

App Layer

Master Slave Slave Slave

• n x Writes – Async vs. Sync• No SPOF• Conflicts! O(N2) or O(N3) resolution

Write

Read

Write

Read

Write

Read

Write

Read

Write

Read

Write

Read

Write

Read

Per Server:• 4R, 1W• 2R, 1W• 1R, 1W

• Vertical Partitioningo Divide data on tables / columnso Scale to as many boxes as there are tables or

columnso Finite

• Horizontal Partitioningo Divide data on rowso Scale to as many boxes as there are rows!o Limitless scaling

T1, T2, T3, T4,

T5

App Layer

T3

App Layer

T4 T5T2T1

• Facebook - User table, posts table can be on separate nodes

• Joins need to be done in code (Why have them?)

T3

App Layer

T4 T5T2T1First million rows

T3 T4 T5T2T1Second million rows

T3 T4 T5T2T1Third million rows

• Value Basedo Split on timestamp of postso Split on first alphabet of user name

• Hash Basedo Use a hash function to determine cluster

• Lookup Mapo First Come First Serveo Round Robin

Source: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.1495

• In distributed systems, much weaker forms of consistency are often acceptable, e.g.,o Only a few (or even one) possible writers of data,

and/oro Read-mostly data (seldom modified), and/oro Stale data may be acceptable

• Eventual consistencyo If no updates take place for a long time, all

replicas will eventually become consistent• Implementation

o Need only ensure updates eventually reach all of the replicated copies of the data

• Monotonic Readso If a node sees a version x at time t, it will never see an older

version at a later time

• Monotonic Writeso A write operation by a process on a data item x is completed

before any successive write operation on x by the same process

• Read your writeso The effect of a write operation by a process on data item x

will always be seen by a successive read operation on x by the same process

• Writes follow Readso Write occurs on a copy of x that is at least as recent as the

last copy read by the process

• Many Kinds of Computing are “Append-Only”o Lots of observations are made about the world

Debits, credits, Purchase-Orders, Customer-Change-Requests, etc

o As time moves on, more observations are added You can’t change the history but you can add new

observations• Derived Results May Be Calculated

o Estimate of the “current” inventoryo Frequently inaccurate

• Historic Rollups Are Calculatedo Monthly bank statements

• 5 joins for 1 query!o Do you think FB would do this?o And how would you do joins with partitioned

data?• De-normalization removes joins• But increases data volume

o However disk is cheap and getting cheaper• And can lead to inconsistent data

o But only if we do UPDATEs and DELETEs

• Normalization’s Goal Is Eliminating Update Anomalieso Can Be Changed Without “Funny Behavior”o Each Data Item Lives in One Place

Emp #

Emp NameMgr

#Mgr

NameEmp Phone Mgr Phone

47 Joe 13 Sam5-1234 6-9876

18 Sally 38 Harry3-3123 5-6782

91 Pete 13 Sam2-1112 6-9876

66 Mary 02 Betty5-7349 4-0101

Classic problemwith de-normalization

Can’t updateSam’s phone #since there aremany copies

De-normalization isOK if you aren’t going to update!

Source: http://blogs.msdn.com/pathelland/

• Partitioning for scalingo Replication for availability

• No ACID transactions• No JOINs• Immutable data

o No cascaded UPDATEs and DELETEs

• Partitioning – for R/W scaling• Replication – for availability• Versioning – for immutable data• Eventual Consistency• Error detection and handling

• Google – BigTable• Amazon – Dynamo• Facebook – Cassandra (BigTable +

Dynamo)• LinkedIn – Voldemort (similar to Dynamo)• Many more

• Tens of millions of customers served at peak times

• Tens of thousands of servers• Both customers and servers distributed

world wide

http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html • Eventually consistent data store• Always writable• Decentralized• All nodes have the same responsibilities

• Similar to Chordo Each node gets an ID from the space of keyso Nodes are arranged in a ringo Data stored on the first node clockwise of the

current placement of the data key• Replication

o Preference lists of N nodes following the associated node

• A problem with the Chord schemeo Nodes placed randomly on ringo Leads to uneven data & load distribution

• In Dynamoo “Virtual” nodeso Each physical node has multiple virtual nodes

More powerful machines have more virtual nodeso Distribute virtual nodes across the ring

• Updates generate a new timestampo Vector clocks are used

• Eventual consistencyo Multiple versions of the same object might co-

exist• Syntactic Reconciliation

o System might be able to resolve conflicts automatically

• Semantic Reconciliationo Conflict resolution pushed to application

• Request arrives at a node (coordinator)o Ideally the node responsible for the particular keyo Else forwards request to the node responsible for

that key and that node will become the coordinator

• The first N healthy and distinct nodes following the key position are considered for the request

• Application defineso N = total number of participating nodeso R = number of nodes required for successful

Reado W = number of nodes required for successful

write• R + W > N gives quorum

• Writeso Requires generation of a new vector clock by

coordinatoro Coordinator writes locallyo Forwards to N nodes, if W-1 respond then the

write was successful• Reads

o Forwards to N nodes, if R-1 respond then forwards to user

o Only unique responses forwardedo User handles merging if multiple versions exist

• Sloppy Quorumo Read write ops performed on first N healthy

nodeso Increases availability

• Hinted Handoffo If node in preference list is not available, send

replica to a node further down in the listo With a hint containing the identity of the original

nodeo The receiving node keeps checking for the

originalo If the original becomes available, transfers replica

to it

• Replica Synchronizationo Synchronize with another

nodeo Each node maintains a

separate Merkel tree for each key range it hosts

o Nodes exchange roots of trees for common key-ranges

o Quickly determine divergent keys by comparing hashes

• Ring Membershipo Membership is explicit to avoid re-balancing of

partition assignmento Use background gossip to build 1-hop DHTo Use external entity to bootstrap the system to avoid

partitioned rings• Failure Detection

o Node A finds node B unreachable (for servicing a request)

o A uses other nodes to service requests and periodically checks B

o A does not assume B to have failedo No globally consistent view of failure (because of

explicit ring membership)

• Application Configurable (N, R, W)• Every node is aware of the data hosted by

its peerso requiring the gossiping of the full routing table

with other nodeso scalability is limited by this to a few hundred

nodeso hierarchy may help to overcome the limitation

• Typical configuration for the Dynamo (N, R, W) is (3, 2, 2)

• Some implementations vary (N, R, W)o Always write might have W=1 (Shopping Cart)o Product catalog might have R=1 and W=N

• Response requirement is 300ms for any request (read or write)

• Consistency vs. Availabilityo 99.94% one versiono 0.00057% twoo 0.00047% threeo 0.00009% four

• Server-driven or Client-driven coordinationo Server-driven

uses load balancers forwards requests to desired set of nodes

o Client-driven 50% faster requires the polling of Dynamo membership updates the client is responsible for determining the appropriate nodes to

send the request to

• Successful responses (without time-out) 99.9995%

• Enormous data (and high growth)o Traditional solutions don’t work

• Distributed databaseso Lots of interesting work happening

• Great time for young programmers!o Problem solving ability

Intelligent People. Uncommon Ideas.

Licensed under Creative Commons Attribution Sharealike Noncommercial