Building a Scalable Architecture for Web App

nullBuilding a Scalable Architecture for Web Apps - Part I (Lessons Learned @ Directi)Building a Scalable Architecture for Web Apps - Part I (Lessons Learned @ Directi)By Bhavin Turakhia CEO, Directi (http://www.directi.com | http://wiki.directi.com | http://careers.directi.com)Licensed under Creative Commons Attribution Sharealike NoncommercialAgendaAgendaWhy is Scalability important Introduction to the Variables and Factors Building our own Scalable Architecture (in incremental steps) Vertical Scaling Vertical Partitioning Horizontal Scaling Horizontal Partitioning … etc Platform Selection Considerations TipsWhy is Scalability Important in a Web 2.0 worldWhy is Scalability Important in a Web 2.0 worldViral marketing can result in instant successes RSS / Ajax / SOA pull based / polling type XML protocols - Meta-data > data Number of Requests exponentially grows with user base RoR / Grails – Dynamic language landscape gaining popularity In the end you want to build a Web 2.0 app that can serve millions of users with ZERO downtimeThe VariablesThe VariablesScalability - Number of users / sessions / transactions / operations the entire system can perform Performance – Optimal utilization of resources Responsiveness – Time taken per operation Availability - Probability of the application or a portion of the application being available at any given point in time Downtime Impact - The impact of a downtime of a server/service/resource - number of users, type of impact etc Cost Maintenance EffortHigh: scalability, availability, performance & responsiveness Low: downtime impact, cost & maintenance effortThe FactorsThe FactorsPlatform selection Hardware Application Design Database/Datastore Structure and Architecture Deployment Architecture Storage Architecture Abuse prevention Monitoring mechanisms … and moreLets Start …Lets Start …We will now build an example architecture for an example app using the following iterative incremental steps – Inspect current Architecture Identify Scalability Bottlenecks Identify SPOFs and Availability Issues Identify Downtime Impact Risk Zones Apply one of - Vertical Scaling Vertical Partitioning Horizontal Scaling Horizontal Partitioning Repeat processStep 1 – Lets Start …Step 1 – Lets Start …Appserver & DBServerStep 2 – Vertical ScalingStep 2 – Vertical ScalingAppserver, DBServerCPUCPURAMRAMStep 2 - Vertical ScalingStep 2 - Vertical ScalingIntroduction Increasing the hardware resources without changing the number of nodes Referred to as “Scaling up” the Server Advantages Simple to implement Disadvantages Finite limit Hardware does not scale linearly (diminishing returns for each incremental unit) Requires downtime Increases Downtime Impact Incremental costs increase exponentiallyAppserver, DBServerCPUCPURAMRAMCPUCPURAMRAMStep 3 – Vertical Partitioning (Services)Step 3 – Vertical Partitioning (Services)AppServerDBServerIntroduction Deploying each service on a separate node Positives Increases per application Availability Task-based specialization, optimization and tuning possible Reduces context switching Simple to implement for out of band processes No changes to App required Flexibility increases Negatives Sub-optimal resource utilization May not increase overall availability Finite ScalabilityUnderstanding Vertical PartitioningUnderstanding Vertical PartitioningThe term Vertical Partitioning denotes – Increase in the number of nodes by distributing the tasks/functions Each node (or cluster) performs separate Tasks Each node (or cluster) is different from the other Vertical Partitioning can be performed at various layers (App / Server / Data / Hardware etc)Step 4 – Horizontal Scaling (App Server)Step 4 – Horizontal Scaling (App Server)AppServerAppServerAppServerLoad BalancerDBServerIntroduction Increasing the number of nodes of the App Server through Load Balancing Referred to as “Scaling out” the App ServerUnderstanding Horizontal ScalingUnderstanding Horizontal ScalingThe term Horizontal Scaling denotes – Increase in the number of nodes by replicating the nodes Each node performs the same Tasks Each node is identical Typically the collection of nodes maybe known as a cluster (though the term cluster is often misused) Also referred to as “Scaling Out” Horizontal Scaling can be performed for any particular type of node (AppServer / DBServer etc)Load Balancer – Hardware vs SoftwareLoad Balancer – Hardware vs SoftwareHardware Load balancers are faster Software Load balancers are more customizable With HTTP Servers load balancing is typically combined with http acceleratorsLoad Balancer – Session ManagementLoad Balancer – Session ManagementSticky Sessions Requests for a given user are sent to a fixed App Server Observations Asymmetrical load distribution (especially during downtimes) Downtime Impact – Loss of session dataAppServerAppServerAppServerLoad BalancerSticky SessionsUser 1User 2Load Balancer – Session ManagementLoad Balancer – Session ManagementCentral Session Store Introduces SPOF An additional variable Session reads and writes generate Disk + Network I/O Also known as a Shared Session Store ClusterAppServerAppServerAppServerLoad BalancerSession StoreCentral Session StorageLoad Balancer – Session ManagementLoad Balancer – Session ManagementClustered Session Management Easier to setup No SPOF Session reads are instantaneous Session writes generate Network I/O Network I/O increases exponentially with increase in number of nodes In very rare circumstances a request may get stale session data User request reaches subsequent node faster than intra-node message Intra-node communication fails AKA Shared-nothing ClusterAppServerAppServerAppServerLoad BalancerClustered Session ManagementLoad Balancer – Session ManagementLoad Balancer – Session ManagementSticky Sessions with Central Session Store Downtime does not cause loss of data Session reads need not generate network I/O Sticky Sessions with Clustered Session Management No specific advantages Sticky SessionsAppServerAppServerAppServerLoad BalancerUser 1User 2Load Balancer – Session ManagementLoad Balancer – Session ManagementRecommendation Use Clustered Session Management if you have – Smaller Number of App Servers Fewer Session writes Use a Central Session Store elsewhere Use sticky sessions only if you have toLoad Balancer – Removing SPOFLoad Balancer – Removing SPOFIn a Load Balanced App Server Cluster the LB is an SPOF Setup LB in Active-Active or Active-Passive mode Note: Active-Active nevertheless assumes that each LB is independently able to take up the load of the other 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 ClusterAppServerAppServerAppServerLoad BalancerActive-Passive LBLoad BalancerAppServerAppServerAppServerLoad BalancerActive-Active LBLoad BalancerUsersUsersStep 4 – Horizontal Scaling (App Server)Step 4 – Horizontal Scaling (App Server)DBServerOur deployment at the end of Step 4 Positives Increases Availability and Scalability No changes to App required Easy setup Negatives Finite ScalabilityStep 5 – Vertical Partitioning (Hardware)Step 5 – Vertical Partitioning (Hardware)DBServerIntroduction Partitioning out the Storage function using a SAN SAN config options Refer to “Demystifying Storage” at http://wiki.directi.com -> Dev University -> Presentations Positives Allows “Scaling Up” the DB Server Boosts Performance of DB Server Negatives Increases CostSANStep 6 – Horizontal Scaling (DB)Step 6 – Horizontal Scaling (DB)DBServerIntroduction Increasing the number of DB nodes Referred to as “Scaling out” the DB Server Options Shared nothing Cluster Real Application Cluster (or Shared Storage Cluster)DBServerDBServerSANShared Nothing ClusterShared Nothing ClusterEach DB Server node has its own complete copy of the database Nothing is shared between the DB Server Nodes This is achieved through DB Replication at DB / Driver / App level or through a proxy Supported by most RDBMs natively or through 3rd party softwareDBServer DatabaseDBServer DatabaseDBServer DatabaseNote: Actual DB files maybe stored on a central SANReplication ConsiderationsReplication ConsiderationsMaster-Slave Writes are sent to a single master which replicates the data to multiple slave nodes Replication maybe cascaded Simple setup No conflict management required Multi-Master Writes can be sent to any of the multiple masters which replicate them to other masters and slaves Conflict Management required Deadlocks possible if same data is simultaneously modified at multiple placesReplication ConsiderationsReplication ConsiderationsAsynchronous Guaranteed, but out-of-band replication from Master to Slave Master updates its own db and returns a response to client Replication from Master to Slave takes place asynchronously Faster response to a client Slave data is marginally behind the Master Requires modification to App to send critical reads and writes to master, and load balance all other reads Synchronous Guaranteed, in-band replication from Master to Slave Master updates its own db, and confirms all slaves have updated their db before returning a response to client Slower response to a client Slaves have the same data as the Master at all times Requires modification to App to send writes to master and load balance all readsReplication ConsiderationsReplication ConsiderationsReplication at RDBMS level Support may exists in RDBMS or through 3rd party tool Faster and more reliable App must send writes to Master, reads to any db and critical reads to Master Replication at Driver / DAO level Driver / DAO layer ensures writes are performed on all connected DBs Reads are load balanced Critical reads are sent to a Master In most cases RDBMS agnostic Slower and in some cases less reliableReal Application ClusterReal Application ClusterAll DB Servers in the cluster share a common storage area on a SAN All DB servers mount the same block device The filesystem must be a clustered file system (eg GFS / OFS) Currently only supported by Oracle Real Application Cluster Can be very expensive (licensing fees)DBServer SANDBServerDBServerDatabaseRecommendationRecommendationTry and choose a DB which natively supports Master-Slave replication Use Master-Slave Async replication Write your DAO layer to ensure writes are sent to a single DB reads are load balanced Critical reads are sent to a masterDBServerDBServerDBServerWrites & Critical ReadsOther ReadsStep 6 – Horizontal Scaling (DB)Step 6 – Horizontal Scaling (DB)Our architecture now looks like this Positives As Web servers grow, Database nodes can be added DB Server is no longer SPOF Negatives Finite limitStep 6 – Horizontal Scaling (DB)Step 6 – Horizontal Scaling (DB)Shared nothing clusters have a finite scaling limit Reads to Writes – 2:1 So 8 Reads => 4 writes 2 DBs Per db – 4 reads and 4 writes 4 DBs Per db – 2 reads and 4 writes 8 DBs Per db – 1 read and 4 writes At some point adding another node brings in negligible incremental benefitReadsWritesDB1DB2Step 7 – Vertical / Horizontal Partitioning (DB)Step 7 – Vertical / Horizontal Partitioning (DB)Introduction Increasing the number of DB Clusters by dividing the data Options Vertical Partitioning - Dividing tables / columns Horizontal Partitioning - Dividing by rows (value) Vertical Partitioning (DB)Vertical Partitioning (DB)Take a set of tables and move them onto another DB Eg in a social network - the users table and the friends table can be on separate DB clusters Each DB Cluster has different tables Application code or DAO / Driver code or a proxy knows where a given table is and directs queries to the appropriate DB Can also be done at a column level by moving a set of columns into a separate tableApp ClusterDB Cluster 1 Table 1 Table 2DB Cluster 2 Table 3 Table 4Vertical Partitioning (DB)Vertical Partitioning (DB)Negatives One cannot perform SQL joins or maintain referential integrity (referential integrity is as such over-rated) Finite LimitApp ClusterDB Cluster 1 Table 1 Table 2DB Cluster 2 Table 3 Table 4Horizontal Partitioning (DB)Horizontal Partitioning (DB)Take a set of rows and move them onto another DB Eg in a social network – each DB Cluster can contain all data for 1 million users Each DB Cluster has identical tables Application code or DAO / Driver code or a proxy knows where a given row is and directs queries to the appropriate DB Negatives SQL unions for search type queries must be performed within codeApp ClusterDB Cluster 1 Table 1 Table 2 Table 3 Table 4DB Cluster 2 Table 1 Table 2 Table 3 Table 41 million users1 million usersHorizontal Partitioning (DB)Horizontal Partitioning (DB)Techniques FCFS 1st million users are stored on cluster 1 and the next on cluster 2 Round Robin Least Used (Balanced) Each time a new user is added, a DB cluster with the least users is chosen Hash based A hashing function is used to determine the DB Cluster in which the user data should be inserted Value Based User ids 1 to 1 million stored in cluster 1 OR all users with names starting from A-M on cluster 1 Except for Hash and Value based all other techniques also require an independent lookup map – mapping user to Database Cluster This map itself will be stored on a separate DB (which may further need to be replicated)Step 7 – Vertical / Horizontal Partitioning (DB)Step 7 – Vertical / Horizontal Partitioning (DB)Lookup MapOur architecture now looks like this Positives As App servers grow, Database Clusters can be added Note: This is not the same as table partitioning provided by the db (eg MSSQL) We may actually want to further segregate these into Sets, each serving a collection of users (refer next slideStep 8 – Separating SetsStep 8 – Separating SetsLookup MapLookup MapGlobal RedirectorGlobal Lookup MapSET 1 – 10 million usersSET 2 – 10 million usersNow we consider each deployment as a single Set serving a collection of usersCreating SetsCreating SetsThe goal behind creating sets is easier manageability Each Set is independent and handles transactions for a set of users Each Set is architecturally identical to the other Each Set contains the entire application with all its data structures Sets can even be deployed in separate datacenters Users may even be added to a Set that is closer to them in terms of network latencyStep 8 – Horizontal Partitioning (Sets)Step 8 – Horizontal Partitioning (Sets)App Servers ClusterDB ClusterSANGlobal RedirectorSET 1DB ClusterApp Servers ClusterDB ClusterSANSET 2DB ClusterOur architecture now looks like this Positives Infinite Scalability Negatives Aggregation of data across sets is complex Users may need to be moved across Sets if sizing is improper Global App settings and preferences need to be replicated across SetsStep 9 – CachingStep 9 – CachingAdd caches within App Server Object Cache Session Cache (especially if you are using a Central Session Store) API cache Page cache Software Memcached Teracotta (Java only) Coherence (commercial expensive data grid by Oracle)Step 10 – HTTP AcceleratorStep 10 – HTTP AcceleratorIf your app is a web app you should add an HTTP Accelerator or a Reverse Proxy A good HTTP Accelerator / Reverse proxy performs the following – Redirect static content requests to a lighter HTTP server (lighttpd) Cache content based on rules (with granular invalidation support) Use Async NIO on the user side Maintain a limited pool of Keep-alive connections to the App Server Intelligent load balancing Solutions Nginx (HTTP / IMAP) Perlbal Hardware accelerators plus Load BalancersStep 11 – Other cool stuffStep 11 – Other cool stuffCDNs IP Anycasting Async Nonblocking IO (for all Network Servers) If possible - Async Nonblocking IO for disk Incorporate multi-layer caching strategy where required L1 cache – in-process with App Server L2 cache – across network boundary L3 cache – on disk Grid computing Java – GridGain Erlang – natively built inPlatform Selection ConsiderationsPlatform Selection ConsiderationsProgramming Languages and Frameworks Dynamic languages are slower than static languages Compiled code runs faster than interpreted code -> use accelerators or pre-compilers Frameworks that provide Dependency Injections, Reflection, Annotations have a marginal performance impact ORMs hide DB querying which can in some cases result in poor query performance due to non-optimized querying RDBMS MySQL, MSSQL and Oracle support native replication Postgres supports replication through 3rd party software (Slony) Oracle supports Real Application Clustering MySQL uses locking and arbitration, while Postgres/Oracle use MVCC (MSSQL just recently introduced MVCC) Cache Teracotta vs memcached vs CoherenceTipsTipsAll the techniques we learnt today can be applied in any order Try and incorporate Horizontal DB partitioning by value from the beginning into your design Loosely couple all modules Implement a REST-ful framework for easier caching Perform application sizing ongoingly to ensure optimal utilization of hardwareQuestions?? bhavin.t@directi.com http://directi.com http://careers.directi.com Download slides: http://wiki.directi.com Questions?? bhavin.t@directi.com http://directi.com http://careers.directi.com Download slides: http://wiki.directi.com