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Intelligent People. Uncommon Ideas.,[object Object],Handling Data in Mega Scale Web Apps(lessons learnt @ Directi),[object Object],Vineet Gupta | GM – Software Engineering | Directi,[object Object],http://vineetgupta.spaces.live.com,[object Object],Licensed under Creative Commons Attribution Sharealike Noncommercial,[object Object]
Outline,[object Object],Characteristics,[object Object],App Tier Scaling,[object Object],Replication,[object Object],Partitioning,[object Object],Consistency,[object Object],Normalization,[object Object],Caching,[object Object],Data Engine Types,[object Object]
Not Covering,[object Object],Offline Processing (Batching / Queuing),[object Object],Distributed Processing – Map Reduce,[object Object],Non-blocking IO,[object Object],Fault Detection, Tolerance and Recovery,[object Object]
Outline,[object Object],Characteristics,[object Object],App Tier Scaling,[object Object],Replication,[object Object],Partitioning,[object Object],Consistency,[object Object],Normalization,[object Object],Caching,[object Object],Data Engine Types,[object Object]
How Big Does it Get,[object Object],22M+ users,[object Object],Dozens of DB servers,[object Object],Dozens of Web servers,[object Object],Six specialized graph database servers to run recommendations engine,[object Object],Source:http://highscalability.com/digg-architecture,[object Object]
How Big Does it Get,[object Object],1 TB / Day,[object Object],100 M blogs indexed / day,[object Object],10 B objects indexed / day,[object Object],0.5 B photos and videos,[object Object],Data doubles in 6 months,[object Object],Users double in 6 months,[object Object],Source:http://www.royans.net/arch/2007/10/25/scaling-technorati-100-million-blogs-indexed-everyday/,[object Object]
How Big Does it Get,[object Object],2 PB Raw Storage,[object Object],470 M photos, 4-5 sizes each,[object Object],400 k photos added / day,[object Object],35 M photos in Squid cache (total),[object Object],2 M photos in Squid RAM,[object Object],38k reqs / sec to Memcached,[object Object],4 B queries / day,[object Object],Source:http://mysqldba.blogspot.com/2008/04/mysql-uc-2007-presentation-file.html,[object Object]
How Big Does it Get,[object Object],Virtualized database spans 600 production instances residing in 100+ server clusters distributed over 8 datacenters,[object Object],2 PB of data,[object Object],26 B SQL queries / day,[object Object],1 B page views / day,[object Object],3 B API calls / month,[object Object],15,000 App servers,[object Object],Source:http://highscalability.com/ebay-architecture/,[object Object]
How Big Does it Get,[object Object],450,000 low cost commodity servers in 2006,[object Object],Indexed 8 B web-pages in 2005,[object Object],200 GFS clusters (1 cluster = 1,000 – 5,000 machines),[object Object],Read / write thruput = 40 GB / sec across a cluster,[object Object],Map-Reduce,[object Object],100k jobs / day,[object Object],20 PB of data processed / day,[object Object],10k MapReduce programs,[object Object],Source:http://highscalability.com/google-architecture/,[object Object]
Key Trends,[object Object],Data Size ~ PB,[object Object],Data Growth ~ TB / day,[object Object],No of servers – 10s to 10,000,[object Object],No of datacenters – 1 to 10,[object Object],Queries – B+ / day,[object Object],Specialized needs – more / other than RDBMS,[object Object]
Outline,[object Object],Characteristics,[object Object],App Tier Scaling,[object Object],Replication,[object Object],Partitioning,[object Object],Consistency,[object Object],Normalization,[object Object],Caching,[object Object],Data Engine Types,[object Object]
Host,[object Object],RAM,[object Object],CPU,[object Object],CPU,[object Object],RAM,[object Object],CPU,[object Object],RAM,[object Object],App Server,[object Object],DB Server,[object Object],Vertical Scaling (Scaling Up),[object Object]
Big Irons,[object Object],Sunfire E20k,[object Object],PowerEdge SC1435,[object Object],36x 1.8GHz processors,[object Object],Dualcore 1.8 GHz processor,[object Object],$450,000 - $2,500,000,[object Object],Around $1,500,[object Object]
Vertical Scaling (Scaling Up),[object Object],Increasing the hardware resources on a host,[object Object],Pros,[object Object],Simple to implement,[object Object],Fast turnaround time,[object Object],Cons,[object Object],Finite limit,[object Object],Hardware does not scale linearly (diminishing returns for each incremental unit),[object Object],Requires downtime,[object Object],Increases Downtime Impact,[object Object],Incremental costs increase exponentially,[object Object]
Host,[object Object],Host,[object Object],App Server,[object Object],DB Server,[object Object],Vertical Partitioning of Services,[object Object]
Vertical Partitioning of Services,[object Object],Split services on separate nodes,[object Object],Each node performs different tasks,[object Object],Pros,[object Object],Increases per application Availability,[object Object],Task-based specialization, optimization and tuning possible,[object Object],Reduces context switching,[object Object],Simple to implement for out of band processes,[object Object],No changes to App required,[object Object],Flexibility increases,[object Object],Cons,[object Object],Sub-optimal resource utilization,[object Object],May not increase overall availability,[object Object],Finite Scalability,[object Object]
Horizontal Scaling of App Server,[object Object],Web Server,[object Object],Load Balancer,[object Object],Web Server,[object Object],DB Server,[object Object],Web Server,[object Object]
Horizontal Scaling of App Server,[object Object],Add more nodes for the same service,[object Object],Identical, doing the same task,[object Object],Load Balancing,[object Object],Hardware balancers are faster,[object Object],Software balancers are more customizable,[object Object]
The problem - State,[object Object],Web Server,[object Object],User 1,[object Object],Load Balancer,[object Object],Web Server,[object Object],DB Server,[object Object],User 2,[object Object],Web Server,[object Object]
Sticky Sessions,[object Object],Web Server,[object Object],User 1,[object Object],Load Balancer,[object Object],Web Server,[object Object],DB Server,[object Object],User 2,[object Object],Web Server,[object Object],Asymmetrical load distribution,[object Object],Downtime,[object Object]
Central Session Store,[object Object],Web Server,[object Object],User 1,[object Object],Load Balancer,[object Object],Web Server,[object Object],Session Store,[object Object],User 2,[object Object],Web Server,[object Object],SPOF,[object Object],Reads and Writes generate network + disk IO,[object Object]
Clustered Sessions,[object Object],Web Server,[object Object],User 1,[object Object],Load Balancer,[object Object],Web Server,[object Object],User 2,[object Object],Web Server,[object Object]
Clustered Sessions,[object Object],Pros,[object Object],No SPOF,[object Object],Easier to setup,[object Object],Fast Reads,[object Object],Cons,[object Object],n x Writes,[object Object],Increase in network IO with increase in nodes,[object Object],Stale data (rare),[object Object]
Sticky Sessions with Central Store,[object Object],Web Server,[object Object],User 1,[object Object],Load Balancer,[object Object],Web Server,[object Object],DB Server,[object Object],User 2,[object Object],Web Server,[object Object]
More Session Management,[object Object],No Sessions,[object Object],Stuff state in a cookie and sign it!,[object Object],Cookie is sent with every request / response,[object Object],Super Slim Sessions,[object Object],Keep small amount of frequently used data in cookie,[object Object],Pull rest from DB (or central session store),[object Object]
Sessions - Recommendation,[object Object],Bad,[object Object],Sticky sessions,[object Object],Good,[object Object],Clustered sessions for small number of nodes and / or small write volume,[object Object],Central sessions for large number of nodes or large write volume,[object Object],Great,[object Object],No Sessions!,[object Object]
App Tier Scaling - More,[object Object],HTTP Accelerators / Reverse Proxy,[object Object],Static content caching, redirect to lighter HTTP,[object Object],Async NIO on user-side, Keep-alive connection pool,[object Object],CDN,[object Object],Get closer to your user,[object Object],Akamai, Limelight,[object Object],IP Anycasting,[object Object],Async NIO,[object Object]
Scaling a Web App,[object Object],App-Layer,[object Object],Add more nodes and load balance!,[object Object],Avoid Sticky Sessions,[object Object],Avoid Sessions!!,[object Object],Data Store,[object Object],Tricky! Very Tricky!!!,[object Object]
Outline,[object Object],Characteristics,[object Object],App Tier Scaling,[object Object],Replication,[object Object],Partitioning,[object Object],Consistency,[object Object],Normalization,[object Object],Caching,[object Object],Data Engine Types,[object Object]
Replication = Scaling by Duplication,[object Object],App Layer,[object Object],T1, T2, T3, T4,[object Object]
Replication = Scaling by Duplication,[object Object],App Layer,[object Object],T1, T2, T3, T4,[object Object],T1, T2, T3, T4,[object Object],T1, T2, T3, T4,[object Object],T1, T2, T3, T4,[object Object],T1, T2, T3, T4,[object Object],Each node has its own copy of data,[object Object],Shared Nothing Cluster,[object Object]
Replication,[object Object],Read : Write = 4:1,[object Object],Scale reads at cost of writes!,[object Object],Duplicate Data – each node has its own copy,[object Object],Master Slave,[object Object],Writes sent to one node, cascaded to others,[object Object],Multi-Master,[object Object],Writes can be sent to multiple nodes,[object Object],Can lead to deadlocks,[object Object],Requires conflict management,[object Object]
Master-Slave,[object Object],App Layer,[object Object],Master,[object Object],Slave,[object Object],Slave,[object Object],Slave,[object Object],Slave,[object Object],n x Writes – Async vs. Sync,[object Object],SPOF,[object Object],Async -  Critical Reads from Master!,[object Object]
Multi-Master,[object Object],App Layer,[object Object],Master,[object Object],Master,[object Object],Slave,[object Object],Slave,[object Object],Slave,[object Object],n x Writes – Async vs. Sync,[object Object],No SPOF,[object Object],Conflicts!,[object Object]
Replication Considerations,[object Object],Asynchronous,[object Object],Guaranteed, but out-of-band replication from Master to Slave,[object Object],Master updates its own db and returns a response to client,[object Object],Replication from Master to Slave takes place asynchronously,[object Object],Faster response to a client ,[object Object],Slave data is marginally behind the Master,[object Object],Requires modification to App to send critical reads and writes to master, and load balance all other reads,[object Object],Synchronous,[object Object],Guaranteed, in-band replication from Master to Slave,[object Object],Master updates its own db, and confirms all slaves have updated their db before returning a response to client,[object Object],Slower response to a client ,[object Object],Slaves have the same data as the Master at all times,[object Object],Requires modification to App to send writes to master and load balance all reads,[object Object]
Replication Considerations,[object Object],Replication at RDBMS level,[object Object],Support may exists in RDBMS or through 3rd party tool,[object Object],Faster and more reliable,[object Object],App must send writes to Master, reads to any db and critical reads to Master,[object Object],Replication at Driver / DAO level,[object Object],Driver / DAO layer ensures ,[object Object],writes are performed on all connected DBs,[object Object],Reads are load balanced,[object Object],Critical reads are sent to a Master,[object Object],In most cases RDBMS agnostic,[object Object],Slower and in some cases less reliable,[object Object]
Diminishing Returns,[object Object],Per Server:,[object Object],4R, 1W,[object Object],2R, 1W,[object Object],1R, 1W,[object Object],Read,[object Object],Read,[object Object],Read,[object Object],Write,[object Object],Write,[object Object],Write,[object Object],Read,[object Object],Read,[object Object],Read,[object Object],Read,[object Object],Write,[object Object],Write,[object Object],Write,[object Object],Write,[object Object]
Outline,[object Object],Characteristics,[object Object],App Tier Scaling,[object Object],Replication,[object Object],Partitioning,[object Object],Consistency,[object Object],Normalization,[object Object],Caching,[object Object],Data Engine Types,[object Object]
Partitioning = Scaling by Division,[object Object],Vertical Partitioning,[object Object],Divide data on tables / columns,[object Object],Scale to as many boxes as there are tables or columns,[object Object],Finite,[object Object],Horizontal Partitioning,[object Object],Divide data on rows,[object Object],Scale to as many boxes as there are rows!,[object Object],Limitless scaling,[object Object]
Vertical Partitioning,[object Object],App Layer,[object Object],T1, T2, T3, T4, T5,[object Object],Note: A node here typically represents a shared nothing cluster,[object Object]
Vertical Partitioning,[object Object],App Layer,[object Object],T3,[object Object],T4,[object Object],T5,[object Object],T2,[object Object],T1,[object Object],Facebook - User table, posts table can be on separate nodes,[object Object],Joins need to be done in code (Why have them?),[object Object]
Horizontal Partitioning,[object Object],App Layer,[object Object],T3,[object Object],T4,[object Object],T5,[object Object],T2,[object Object],T1,[object Object],First million rows,[object Object],T3,[object Object],T4,[object Object],T5,[object Object],T2,[object Object],T1,[object Object],Second million rows,[object Object],T3,[object Object],T4,[object Object],T5,[object Object],T2,[object Object],T1,[object Object],Third million rows,[object Object]
Horizontal Partitioning Schemes,[object Object],Value Based,[object Object],Split on timestamp of posts,[object Object],Split on first alphabet of user name,[object Object],Hash Based,[object Object],Use a hash function to determine cluster,[object Object],Lookup Map,[object Object],First Come First Serve,[object Object],Round Robin,[object Object]
Outline,[object Object],Characteristics,[object Object],App Tier Scaling,[object Object],Replication,[object Object],Partitioning,[object Object],Consistency,[object Object],Normalization,[object Object],Caching,[object Object],Data Engine Types,[object Object]
CAP Theorem,[object Object],Source:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.1495,[object Object]
Transactions,[object Object],Transactions make you feel alone,[object Object],No one else manipulates the data when you are,[object Object],Transactional serializability,[object Object],The behavior is as if a serial order exists,[object Object],Source:http://blogs.msdn.com/pathelland/,[object Object],Slide 46,[object Object]
Life in the “Now”,[object Object],Transactions live in the “now” inside services,[object Object],Time marches forward,[object Object],Transactions commit ,[object Object],Advancing time,[object Object],Transactions see the committed transactions,[object Object],A service’s biz-logic lives in the “now”,[object Object],Source:http://blogs.msdn.com/pathelland/,[object Object],Slide 47,[object Object]
Sending Unlocked Data Isn’t “Now”,[object Object],Messages contain unlocked data,[object Object],Assume no shared transactions,[object Object],Unlocked data may change,[object Object],Unlocking it allows change,[object Object],Messages are not from the “now”,[object Object],They are from the past,[object Object],There is no simultaneity at a distance!,[object Object],[object Object]
 Knowledge travels at speed of light
 By the time you see a distant object it may have changed!
 By the time you see a message, the data may have changed!Services, transactions, and locks bound simultaneity!,[object Object],[object Object]
 Simultaneity only inside a transaction!
 Simultaneity only inside a service!Source:http://blogs.msdn.com/pathelland/,[object Object],Slide 48,[object Object]
Outside Data: a Blast from the Past,[object Object],All data from distant stars is from the past,[object Object],[object Object]
 The sun may have blown up 5 minutes ago
 We won’t know for 3 minutes more…All data seen from a distant service is from the “past”,[object Object],By the time you see it, it has been unlocked and may change,[object Object],Each service has its own perspective,[object Object],Inside data is “now”; outside data is “past”,[object Object],My inside is not your inside; my outside is not your outside,[object Object],This is like going from Newtonian to Einstonian physics,[object Object],[object Object]
 Instant knowledge
 Classic distributed computing: many systems look like one
 RPC, 2-phase commit, remote method calls…
 In Einstein’s world, everything is “relative” to one’s perspective
 Today: No attempt to blur the boundarySource:http://blogs.msdn.com/pathelland/,[object Object],Slide 49,[object Object]
Versions and Distributed Systems,[object Object],Can’t have “the same” dataat many locations,[object Object],Unless it isa snapshot,[object Object],Changing distributed dataneeds versions,[object Object],Creates asnapshot…,[object Object],Source:http://blogs.msdn.com/pathelland/,[object Object]
Subjective Consistency,[object Object],Given what I know here and now, make a decision,[object Object],Remember the versions of all the data used to make this decision,[object Object],Record the decision as being predicated on these versions,[object Object],Other copies of the object may make divergent decisions,[object Object],Try to sort out conflicts within the family,[object Object],If necessary, programmatically apologize,[object Object],Very rarely, whine and fuss for human help,[object Object],Subjective Consistency,[object Object], Given the information I have at hand, make a decision and act on it !,[object Object], Remember the information at hand !,[object Object],Ambassadors Had Authority,[object Object],Back before radio, it could be months between communication with the king.  Ambassadors would make treaties and much more... They had binding authority.  The mess was sorted out later!,[object Object],Source:http://blogs.msdn.com/pathelland/,[object Object]
Eventual Consistency,[object Object],Eventually, all the copies of the object share their changes,[object Object],“I’ll show you mine if you show me yours!”,[object Object],Now, apply subjective consistency:,[object Object],“Given the information I have at hand, make a decision and act on it!”,[object Object],Everyone has the same information, everyone comes to the same conclusion about the decisions to take…,[object Object],Eventual Consistency,[object Object],[object Object]
 Everyone sharing their knowledge leads to the same result...This is NOT magic; it is a design requirement !,[object Object],Idempotence, commutativity, and associativity of the operations(decisions made) are all implied by this requirement,[object Object],Source:http://blogs.msdn.com/pathelland/,[object Object]
Outline,[object Object],Characteristics,[object Object],App Tier Scaling,[object Object],Replication,[object Object],Partitioning,[object Object],Consistency,[object Object],Normalization,[object Object],Caching,[object Object],Data Engine Types,[object Object]
Why Normalize?,[object Object],Classic problemwith de-normalization,[object Object],Can’t updateSam’s phone #since there aremany copies,[object Object],Emp #,[object Object],Emp Name,[object Object],Mgr #,[object Object],Mgr Name,[object Object],Emp Phone,[object Object],47,[object Object],Joe,[object Object],13,[object Object],Sam,[object Object],5-1234,[object Object],18,[object Object],Sally,[object Object],38,[object Object],Harry,[object Object],3-3123,[object Object],91,[object Object],Pete,[object Object],13,[object Object],Sam,[object Object],2-1112,[object Object],66,[object Object],Mary,[object Object],02,[object Object],Betty,[object Object],5-7349,[object Object],Mgr Phone,[object Object],6-9876,[object Object],5-6782,[object Object],6-9876,[object Object],4-0101,[object Object],Normalization’s Goal Is Eliminating Update Anomalies,[object Object],Can Be Changed Without “Funny Behavior”,[object Object],Each Data Item Lives in One Place,[object Object],De-normalization is,[object Object],OK if you aren’t going to update!,[object Object],Source:http://blogs.msdn.com/pathelland/,[object Object]
Eliminate Joins,[object Object]
Eliminate Joins,[object Object],6 joins for 1 query!,[object Object],Do you think FB would do this?,[object Object],And how would you do joins with partitioned data?,[object Object],De-normalization removes joins,[object Object],But increases data volume,[object Object],But disk is cheap and getting cheaper,[object Object],And can lead to inconsistent data,[object Object],If you are lazy,[object Object],However this is not really an issue,[object Object]
“Append-Only” Data,[object Object],Many Kinds of Computing are “Append-Only”,[object Object],Lots of observations are made about the world,[object Object],Debits, credits, Purchase-Orders, Customer-Change-Requests, etc,[object Object],As time moves on, more observations are added,[object Object],You can’t change the history but you can add new observations,[object Object],Derived Results May Be Calculated,[object Object],Estimate of the “current” inventory,[object Object],Frequently inaccurate,[object Object],Historic Rollups Are Calculated,[object Object],Monthly bank statements,[object Object]
Databases and Transaction Logs,[object Object],Transaction Logs Are the Truth,[object Object],High-performance & write-only,[object Object],Describe ALL the changes to the data,[object Object],Data-Base  the Current Opinion,[object Object],Describes the latest value of the data as perceived by the application,[object Object],Log,[object Object],DB,[object Object],The Database Is a Caching of the Transaction Log !,[object Object],It is the subset of the latest committed values represented in  the transaction log…,[object Object],Source:http://blogs.msdn.com/pathelland/,[object Object]
We Are Swimming in a Sea of Immutable Data ,[object Object],Source:http://blogs.msdn.com/pathelland/,[object Object]
Outline,[object Object],Characteristics,[object Object],App Tier Scaling,[object Object],Replication,[object Object],Partitioning,[object Object],Consistency,[object Object],Normalization,[object Object],Caching,[object Object],Data Engine Types,[object Object]

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  • 50. By the time you see a distant object it may have changed!
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  • 55. The sun may have blown up 5 minutes ago
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  • 58. Classic distributed computing: many systems look like one
  • 59. RPC, 2-phase commit, remote method calls…
  • 60. In Einstein’s world, everything is “relative” to one’s perspective
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