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Building a Scalable Architecture for Web Apps - Part I (Lessons Learned @ Directi) <ul><li>By Bhavin Turakhia </li></ul><ul><li>CEO, Directi </li></ul><ul><li>( http://www.directi.com | http://wiki.directi.com | http:// careers.directi.com ) </li></ul>Licensed under Creative Commons Attribution Sharealike Noncommercial

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Agenda <ul><li>Why is Scalability important </li></ul><ul><li>Introduction to the Variables and Factors </li></ul><ul><li>Building our own Scalable Architecture (in incremental steps) </li></ul><ul><ul><li>Vertical Scaling </li></ul></ul><ul><ul><li>Vertical Partitioning </li></ul></ul><ul><ul><li>Horizontal Scaling </li></ul></ul><ul><ul><li>Horizontal Partitioning </li></ul></ul><ul><ul><li>… etc </li></ul></ul><ul><li>Platform Selection Considerations </li></ul><ul><li>Tips </li></ul>

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Why is Scalability Important in a Web 2.0 world <ul><li>Viral marketing can result in instant successes </li></ul><ul><li>RSS / Ajax / SOA </li></ul><ul><ul><li>pull based / polling type </li></ul></ul><ul><ul><li>XML protocols - Meta-data > data </li></ul></ul><ul><ul><li>Number of Requests exponentially grows with user base </li></ul></ul><ul><li>RoR / Grails – Dynamic language landscape gaining popularity </li></ul><ul><li>In the end you want to build a Web 2.0 app that can serve millions of users with ZERO downtime </li></ul>

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The Variables <ul><li>Scalability - Number of users / sessions / transactions / operations the entire system can perform </li></ul><ul><li>Performance – Optimal utilization of resources </li></ul><ul><li>Responsiveness – Time taken per operation </li></ul><ul><li>Availability - Probability of the application or a portion of the application being available at any given point in time </li></ul><ul><li>Downtime Impact - The impact of a downtime of a server/service/resource - number of users, type of impact etc </li></ul><ul><li>Cost </li></ul><ul><li>Maintenance Effort </li></ul>High : scalability, availability, performance & responsiveness Low : downtime impact, cost & maintenance effort

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The Factors <ul><li>Platform selection </li></ul><ul><li>Hardware </li></ul><ul><li>Application Design </li></ul><ul><li>Database/Datastore Structure and Architecture </li></ul><ul><li>Deployment Architecture </li></ul><ul><li>Storage Architecture </li></ul><ul><li>Abuse prevention </li></ul><ul><li>Monitoring mechanisms </li></ul><ul><li>… and more </li></ul>

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Lets Start … <ul><li>We will now build an example architecture for an example app using the following iterative incremental steps – </li></ul><ul><ul><li>Inspect current Architecture </li></ul></ul><ul><ul><li>Identify Scalability Bottlenecks </li></ul></ul><ul><ul><li>Identify SPOFs and Availability Issues </li></ul></ul><ul><ul><li>Identify Downtime Impact Risk Zones </li></ul></ul><ul><ul><li>Apply one of - </li></ul></ul><ul><ul><ul><li>Vertical Scaling </li></ul></ul></ul><ul><ul><ul><li>Vertical Partitioning </li></ul></ul></ul><ul><ul><ul><li>Horizontal Scaling </li></ul></ul></ul><ul><ul><ul><li>Horizontal Partitioning </li></ul></ul></ul><ul><ul><li>Repeat process </li></ul></ul>

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Step 1 – Lets Start … Appserver & DBServer

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Step 2 – Vertical Scaling Appserver, DBServer CPU CPU RAM RAM

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Step 2 - Vertical Scaling <ul><li>Introduction </li></ul><ul><ul><li>Increasing the hardware resources without changing the number of nodes </li></ul></ul><ul><ul><li>Referred to as “Scaling up” the Server </li></ul></ul><ul><li>Advantages </li></ul><ul><ul><li>Simple to implement </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Finite limit </li></ul></ul><ul><ul><li>Hardware does not scale linearly (diminishing returns for each incremental unit) </li></ul></ul><ul><ul><li>Requires downtime </li></ul></ul><ul><ul><li>Increases Downtime Impact </li></ul></ul><ul><ul><li>Incremental costs increase exponentially </li></ul></ul>Appserver, DBServer CPU CPU RAM RAM CPU CPU RAM RAM

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Step 3 – Vertical Partitioning (Services) AppServer DBServer <ul><li>Introduction </li></ul><ul><ul><li>Deploying each service on a separate node </li></ul></ul><ul><li>Positives </li></ul><ul><ul><li>Increases per application Availability </li></ul></ul><ul><ul><li>Task-based specialization, optimization and tuning possible </li></ul></ul><ul><ul><li>Reduces context switching </li></ul></ul><ul><ul><li>Simple to implement for out of band processes </li></ul></ul><ul><ul><li>No changes to App required </li></ul></ul><ul><ul><li>Flexibility increases </li></ul></ul><ul><li>Negatives </li></ul><ul><ul><li>Sub-optimal resource utilization </li></ul></ul><ul><ul><li>May not increase overall availability </li></ul></ul><ul><ul><li>Finite Scalability </li></ul></ul>

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Understanding Vertical Partitioning <ul><li>The term Vertical Partitioning denotes – </li></ul><ul><ul><li>Increase in the number of nodes by distributing the tasks/functions </li></ul></ul><ul><ul><li>Each node (or cluster) performs separate Tasks </li></ul></ul><ul><ul><li>Each node (or cluster) is different from the other </li></ul></ul><ul><li>Vertical Partitioning can be performed at various layers (App / Server / Data / Hardware etc) </li></ul>

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Step 4 – Horizontal Scaling (App Server) AppServer AppServer AppServer Load Balancer DBServer <ul><li>Introduction </li></ul><ul><ul><li>Increasing the number of nodes of the App Server through Load Balancing </li></ul></ul><ul><ul><li>Referred to as “Scaling out” the App Server </li></ul></ul>

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Understanding Horizontal Scaling <ul><li>The term Horizontal Scaling denotes – </li></ul><ul><ul><li>Increase in the number of nodes by replicating the nodes </li></ul></ul><ul><ul><li>Each node performs the same Tasks </li></ul></ul><ul><ul><li>Each node is identical </li></ul></ul><ul><ul><li>Typically the collection of nodes maybe known as a cluster (though the term cluster is often misused) </li></ul></ul><ul><ul><li>Also referred to as “Scaling Out” </li></ul></ul><ul><li>Horizontal Scaling can be performed for any particular type of node (AppServer / DBServer etc) </li></ul>

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Load Balancer – Hardware vs Software <ul><li>Hardware Load balancers are faster </li></ul><ul><li>Software Load balancers are more customizable </li></ul><ul><li>With HTTP Servers load balancing is typically combined with http accelerators </li></ul>

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Load Balancer – Session Management <ul><li>Sticky Sessions </li></ul><ul><ul><li>Requests for a given user are sent to a fixed App Server </li></ul></ul><ul><ul><li>Observations </li></ul></ul><ul><ul><ul><li>Asymmetrical load distribution (especially during downtimes) </li></ul></ul></ul><ul><ul><ul><li>Downtime Impact – Loss of session data </li></ul></ul></ul>AppServer AppServer AppServer Load Balancer Sticky Sessions User 1 User 2

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Load Balancer – Session Management <ul><li>Central Session Store </li></ul><ul><ul><li>Introduces SPOF </li></ul></ul><ul><ul><li>An additional variable </li></ul></ul><ul><ul><li>Session reads and writes generate Disk + Network I/O </li></ul></ul><ul><ul><li>Also known as a Shared Session Store Cluster </li></ul></ul>AppServer AppServer AppServer Load Balancer Session Store Central Session Storage

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Load Balancer – Session Management <ul><li>Clustered Session Management </li></ul><ul><ul><li>Easier to setup </li></ul></ul><ul><ul><li>No SPOF </li></ul></ul><ul><ul><li>Session reads are instantaneous </li></ul></ul><ul><ul><li>Session writes generate Network I/O </li></ul></ul><ul><ul><li>Network I/O increases exponentially with increase in number of nodes </li></ul></ul><ul><ul><li>In very rare circumstances a request may get stale session data </li></ul></ul><ul><ul><ul><li>User request reaches subsequent node faster than intra-node message </li></ul></ul></ul><ul><ul><ul><li>Intra-node communication fails </li></ul></ul></ul><ul><ul><li>AKA Shared-nothing Cluster </li></ul></ul>AppServer AppServer AppServer Load Balancer Clustered Session Management

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Load Balancer – Session Management <ul><li>Sticky Sessions with Central Session Store </li></ul><ul><ul><li>Downtime does not cause loss of data </li></ul></ul><ul><ul><li>Session reads need not generate network I/O </li></ul></ul><ul><li>Sticky Sessions with Clustered Session Management </li></ul><ul><ul><li>No specific advantages </li></ul></ul>Sticky Sessions AppServer AppServer AppServer Load Balancer User 1 User 2

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Load Balancer – Session Management <ul><li>Recommendation </li></ul><ul><ul><li>Use Clustered Session Management if you have – </li></ul></ul><ul><ul><ul><li>Smaller Number of App Servers </li></ul></ul></ul><ul><ul><ul><li>Fewer Session writes </li></ul></ul></ul><ul><ul><li>Use a Central Session Store elsewhere </li></ul></ul><ul><ul><li>Use sticky sessions only if you have to </li></ul></ul>

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Load Balancer – Removing SPOF <ul><li>In a Load Balanced App Server Cluster the LB is an SPOF </li></ul><ul><li>Setup LB in Active-Active or Active-Passive mode </li></ul><ul><ul><li>Note: Active-Active nevertheless assumes that each LB is independently able to take up the load of the other </li></ul></ul><ul><ul><li>If one wants ZERO downtime, then Active-Active becomes truly cost beneficial only if multiple LBs (more than 3 to 4) are daisy chained as Active-Active forming an LB Cluster </li></ul></ul>AppServer AppServer AppServer Load Balancer Active-Passive LB Load Balancer AppServer AppServer AppServer Load Balancer Active-Active LB Load Balancer Users Users

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Step 4 – Horizontal Scaling (App Server) DBServer <ul><li>Our deployment at the end of Step 4 </li></ul><ul><li>Positives </li></ul><ul><ul><li>Increases Availability and Scalability </li></ul></ul><ul><ul><li>No changes to App required </li></ul></ul><ul><ul><li>Easy setup </li></ul></ul><ul><li>Negatives </li></ul><ul><ul><li>Finite Scalability </li></ul></ul>Load Balanced App Servers

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Step 5 – Vertical Partitioning (Hardware) DBServer <ul><li>Introduction </li></ul><ul><ul><li>Partitioning out the Storage function using a SAN </li></ul></ul><ul><li>SAN config options </li></ul><ul><ul><li>Refer to “Demystifying Storage” at http://wiki.directi.com -> Dev University -> Presentations </li></ul></ul><ul><li>Positives </li></ul><ul><ul><li>Allows “Scaling Up” the DB Server </li></ul></ul><ul><ul><li>Boosts Performance of DB Server </li></ul></ul><ul><li>Negatives </li></ul><ul><ul><li>Increases Cost </li></ul></ul>SAN Load Balanced App Servers

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Step 6 – Horizontal Scaling (DB) DBServer <ul><li>Introduction </li></ul><ul><ul><li>Increasing the number of DB nodes </li></ul></ul><ul><ul><li>Referred to as “Scaling out” the DB Server </li></ul></ul><ul><li>Options </li></ul><ul><ul><li>Shared nothing Cluster </li></ul></ul><ul><ul><li>Real Application Cluster (or Shared Storage Cluster) </li></ul></ul>DBServer DBServer SAN Load Balanced App Servers

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Shared Nothing Cluster <ul><li>Each DB Server node has its own complete copy of the database </li></ul><ul><li>Nothing is shared between the DB Server Nodes </li></ul><ul><li>This is achieved through DB Replication at DB / Driver / App level or through a proxy </li></ul><ul><li>Supported by most RDBMs natively or through 3 rd party software </li></ul>DBServer Database DBServer Database DBServer Database Note: Actual DB files maybe stored on a central SAN

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Replication Considerations <ul><li>Master-Slave </li></ul><ul><ul><li>Writes are sent to a single master which replicates the data to multiple slave nodes </li></ul></ul><ul><ul><li>Replication maybe cascaded </li></ul></ul><ul><ul><li>Simple setup </li></ul></ul><ul><ul><li>No conflict management required </li></ul></ul><ul><li>Multi-Master </li></ul><ul><ul><li>Writes can be sent to any of the multiple masters which replicate them to other masters and slaves </li></ul></ul><ul><ul><li>Conflict Management required </li></ul></ul><ul><ul><li>Deadlocks possible if same data is simultaneously modified at multiple places </li></ul></ul>

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Replication Considerations <ul><li>Asynchronous </li></ul><ul><ul><li>Guaranteed, but out-of-band replication from Master to Slave </li></ul></ul><ul><ul><li>Master updates its own db and returns a response to client </li></ul></ul><ul><ul><li>Replication from Master to Slave takes place asynchronously </li></ul></ul><ul><ul><li>Faster response to a client </li></ul></ul><ul><ul><li>Slave data is marginally behind the Master </li></ul></ul><ul><ul><li>Requires modification to App to send critical reads and writes to master, and load balance all other reads </li></ul></ul><ul><li>Synchronous </li></ul><ul><ul><li>Guaranteed, in-band replication from Master to Slave </li></ul></ul><ul><ul><li>Master updates its own db, and confirms all slaves have updated their db before returning a response to client </li></ul></ul><ul><ul><li>Slower response to a client </li></ul></ul><ul><ul><li>Slaves have the same data as the Master at all times </li></ul></ul><ul><ul><li>Requires modification to App to send writes to master and load balance all reads </li></ul></ul>

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Replication Considerations <ul><li>Replication at RDBMS level </li></ul><ul><ul><li>Support may exists in RDBMS or through 3 rd party tool </li></ul></ul><ul><ul><li>Faster and more reliable </li></ul></ul><ul><ul><li>App must send writes to Master, reads to any db and critical reads to Master </li></ul></ul><ul><li>Replication at Driver / DAO level </li></ul><ul><ul><li>Driver / DAO layer ensures </li></ul></ul><ul><ul><ul><li>writes are performed on all connected DBs </li></ul></ul></ul><ul><ul><ul><li>Reads are load balanced </li></ul></ul></ul><ul><ul><ul><li>Critical reads are sent to a Master </li></ul></ul></ul><ul><ul><li>In most cases RDBMS agnostic </li></ul></ul><ul><ul><li>Slower and in some cases less reliable </li></ul></ul>

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Real Application Cluster <ul><li>All DB Servers in the cluster share a common storage area on a SAN </li></ul><ul><li>All DB servers mount the same block device </li></ul><ul><li>The filesystem must be a clustered file system (eg GFS / OFS) </li></ul><ul><li>Currently only supported by Oracle Real Application Cluster </li></ul><ul><li>Can be very expensive (licensing fees) </li></ul>DBServer SAN DBServer DBServer Database

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Recommendation <ul><li>Try and choose a DB which natively supports Master-Slave replication </li></ul><ul><li>Use Master-Slave Async replication </li></ul><ul><li>Write your DAO layer to ensure </li></ul><ul><ul><li>writes are sent to a single DB </li></ul></ul><ul><ul><li>reads are load balanced </li></ul></ul><ul><ul><li>Critical reads are sent to a master </li></ul></ul>DBServer DBServer DBServer Writes & Critical Reads Other Reads Load Balanced App Servers

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Step 6 – Horizontal Scaling (DB) <ul><li>Our architecture now looks like this </li></ul><ul><li>Positives </li></ul><ul><ul><li>As Web servers grow, Database nodes can be added </li></ul></ul><ul><ul><li>DB Server is no longer SPOF </li></ul></ul><ul><li>Negatives </li></ul><ul><ul><li>Finite limit </li></ul></ul>Load Balanced App Servers DB Cluster DB DB DB SAN

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Step 6 – Horizontal Scaling (DB) <ul><li>Shared nothing clusters have a finite scaling limit </li></ul><ul><ul><li>Reads to Writes – 2:1 </li></ul></ul><ul><ul><li>So 8 Reads => 4 writes </li></ul></ul><ul><ul><li>2 DBs </li></ul></ul><ul><ul><ul><li>Per db – 4 reads and 4 writes </li></ul></ul></ul><ul><ul><li>4 DBs </li></ul></ul><ul><ul><ul><li>Per db – 2 reads and 4 writes </li></ul></ul></ul><ul><ul><li>8 DBs </li></ul></ul><ul><ul><ul><li>Per db – 1 read and 4 writes </li></ul></ul></ul><ul><li>At some point adding another node brings in negligible incremental benefit </li></ul>Reads Writes DB1 DB2

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Step 7 – Vertical / Horizontal Partitioning (DB) <ul><li>Introduction </li></ul><ul><ul><li>Increasing the number of DB Clusters by dividing the data </li></ul></ul><ul><li>Options </li></ul><ul><ul><li>Vertical Partitioning - Dividing tables / columns </li></ul></ul><ul><ul><li>Horizontal Partitioning - Dividing by rows (value) </li></ul></ul>Load Balanced App Servers DB Cluster DB DB DB SAN

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Vertical Partitioning (DB) <ul><li>Take a set of tables and move them onto another DB </li></ul><ul><ul><li>Eg in a social network - the users table and the friends table can be on separate DB clusters </li></ul></ul><ul><li>Each DB Cluster has different tables </li></ul><ul><li>Application code or DAO / Driver code or a proxy knows where a given table is and directs queries to the appropriate DB </li></ul><ul><li>Can also be done at a column level by moving a set of columns into a separate table </li></ul>App Cluster DB Cluster 1 Table 1 Table 2 DB Cluster 2 Table 3 Table 4

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Vertical Partitioning (DB) <ul><li>Negatives </li></ul><ul><ul><li>One cannot perform SQL joins or maintain referential integrity (referential integrity is as such over-rated) </li></ul></ul><ul><ul><li>Finite Limit </li></ul></ul>App Cluster DB Cluster 1 Table 1 Table 2 DB Cluster 2 Table 3 Table 4

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Horizontal Partitioning (DB) <ul><li>Take a set of rows and move them onto another DB </li></ul><ul><ul><li>Eg in a social network – each DB Cluster can contain all data for 1 million users </li></ul></ul><ul><li>Each DB Cluster has identical tables </li></ul><ul><li>Application code or DAO / Driver code or a proxy knows where a given row is and directs queries to the appropriate DB </li></ul><ul><li>Negatives </li></ul><ul><ul><li>SQL unions for search type queries must be performed within code </li></ul></ul>App Cluster DB Cluster 1 Table 1 Table 2 Table 3 Table 4 DB Cluster 2 Table 1 Table 2 Table 3 Table 4 1 million users 1 million users

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Horizontal Partitioning (DB) <ul><li>Techniques </li></ul><ul><ul><li>FCFS </li></ul></ul><ul><ul><ul><li>1 st million users are stored on cluster 1 and the next on cluster 2 </li></ul></ul></ul><ul><ul><li>Round Robin </li></ul></ul><ul><ul><li>Least Used (Balanced) </li></ul></ul><ul><ul><ul><li>Each time a new user is added, a DB cluster with the least users is chosen </li></ul></ul></ul><ul><ul><li>Hash based </li></ul></ul><ul><ul><ul><li>A hashing function is used to determine the DB Cluster in which the user data should be inserted </li></ul></ul></ul><ul><ul><li>Value Based </li></ul></ul><ul><ul><ul><li>User ids 1 to 1 million stored in cluster 1 OR </li></ul></ul></ul><ul><ul><ul><li>all users with names starting from A-M on cluster 1 </li></ul></ul></ul><ul><ul><li>Except for Hash and Value based all other techniques also require an independent lookup map – mapping user to Database Cluster </li></ul></ul><ul><ul><li>This map itself will be stored on a separate DB (which may further need to be replicated) </li></ul></ul>

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Step 7 – Vertical / Horizontal Partitioning (DB) Lookup Map <ul><li>Our architecture now looks like this </li></ul><ul><li>Positives </li></ul><ul><ul><li>As App servers grow, Database Clusters can be added </li></ul></ul><ul><li>Note: This is not the same as table partitioning provided by the db (eg MSSQL) </li></ul><ul><li>We may actually want to further segregate these into Sets, each serving a collection of users (refer next slide </li></ul>Load Balanced App Servers DB Cluster DB DB DB DB Cluster DB DB DB SAN

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Step 8 – Separating Sets Lookup Map Lookup Map Global Redirector Global Lookup Map SET 1 – 10 million users SET 2 – 10 million users <ul><li>Now we consider each deployment as a single Set serving a collection of users </li></ul>Load Balanced App Servers DB Cluster DB DB DB DB Cluster DB DB DB SAN Load Balanced App Servers DB Cluster DB DB DB DB Cluster DB DB DB SAN

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Creating Sets <ul><li>The goal behind creating sets is easier manageability </li></ul><ul><li>Each Set is independent and handles transactions for a set of users </li></ul><ul><li>Each Set is architecturally identical to the other </li></ul><ul><li>Each Set contains the entire application with all its data structures </li></ul><ul><li>Sets can even be deployed in separate datacenters </li></ul><ul><li>Users may even be added to a Set that is closer to them in terms of network latency </li></ul>

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Step 8 – Horizontal Partitioning (Sets) App Servers Cluster DB Cluster SAN Global Redirector SET 1 DB Cluster App Servers Cluster DB Cluster SAN SET 2 DB Cluster <ul><li>Our architecture now looks like this </li></ul><ul><li>Positives </li></ul><ul><ul><li>Infinite Scalability </li></ul></ul><ul><li>Negatives </li></ul><ul><ul><li>Aggregation of data across sets is complex </li></ul></ul><ul><ul><li>Users may need to be moved across Sets if sizing is improper </li></ul></ul><ul><ul><li>Global App settings and preferences need to be replicated across Sets </li></ul></ul>

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Step 9 – Caching <ul><li>Add caches within App Server </li></ul><ul><ul><li>Object Cache </li></ul></ul><ul><ul><li>Session Cache (especially if you are using a Central Session Store) </li></ul></ul><ul><ul><li>API cache </li></ul></ul><ul><ul><li>Page cache </li></ul></ul><ul><li>Software </li></ul><ul><ul><li>Memcached </li></ul></ul><ul><ul><li>Teracotta (Java only) </li></ul></ul><ul><ul><li>Coherence (commercial expensive data grid by Oracle) </li></ul></ul>

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Step 10 – HTTP Accelerator <ul><li>If your app is a web app you should add an HTTP Accelerator or a Reverse Proxy </li></ul><ul><li>A good HTTP Accelerator / Reverse proxy performs the following – </li></ul><ul><ul><li>Redirect static content requests to a lighter HTTP server (lighttpd) </li></ul></ul><ul><ul><li>Cache content based on rules (with granular invalidation support) </li></ul></ul><ul><ul><li>Use Async NIO on the user side </li></ul></ul><ul><ul><li>Maintain a limited pool of Keep-alive connections to the App Server </li></ul></ul><ul><ul><li>Intelligent load balancing </li></ul></ul><ul><li>Solutions </li></ul><ul><ul><li>Nginx (HTTP / IMAP) </li></ul></ul><ul><ul><li>Perlbal </li></ul></ul><ul><ul><li>Hardware accelerators plus Load Balancers </li></ul></ul>

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Step 11 – Other cool stuff <ul><li>CDNs </li></ul><ul><li>IP Anycasting </li></ul><ul><li>Async Nonblocking IO (for all Network Servers) </li></ul><ul><li>If possible - Async Nonblocking IO for disk </li></ul><ul><li>Incorporate multi-layer caching strategy where required </li></ul><ul><ul><li>L1 cache – in-process with App Server </li></ul></ul><ul><ul><li>L2 cache – across network boundary </li></ul></ul><ul><ul><li>L3 cache – on disk </li></ul></ul><ul><li>Grid computing </li></ul><ul><ul><li>Java – GridGain </li></ul></ul><ul><ul><li>Erlang – natively built in </li></ul></ul>

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Platform Selection Considerations <ul><li>Programming Languages and Frameworks </li></ul><ul><ul><li>Dynamic languages are slower than static languages </li></ul></ul><ul><ul><li>Compiled code runs faster than interpreted code -> use accelerators or pre-compilers </li></ul></ul><ul><ul><li>Frameworks that provide Dependency Injections, Reflection, Annotations have a marginal performance impact </li></ul></ul><ul><ul><li>ORMs hide DB querying which can in some cases result in poor query performance due to non-optimized querying </li></ul></ul><ul><li>RDBMS </li></ul><ul><ul><li>MySQL, MSSQL and Oracle support native replication </li></ul></ul><ul><ul><li>Postgres supports replication through 3 rd party software (Slony) </li></ul></ul><ul><ul><li>Oracle supports Real Application Clustering </li></ul></ul><ul><ul><li>MySQL uses locking and arbitration, while Postgres/Oracle use MVCC (MSSQL just recently introduced MVCC) </li></ul></ul><ul><li>Cache </li></ul><ul><ul><li>Teracotta vs memcached vs Coherence </li></ul></ul>

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Tips <ul><li>All the techniques we learnt today can be applied in any order </li></ul><ul><li>Try and incorporate Horizontal DB partitioning by value from the beginning into your design </li></ul><ul><li>Loosely couple all modules </li></ul><ul><li>Implement a REST-ful framework for easier caching </li></ul><ul><li>Perform application sizing ongoingly to ensure optimal utilization of hardware </li></ul>

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Questions?? bhavin.t@directi.com http://directi.com http://careers.directi.com Download slides: http://wiki.directi.com