Building a Scalable Architecture for Web Apps - Part I (Lessons Learned @ Directi)

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

2. Agenda • Why 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 • Tips Creative Commons Sharealike Attributions Noncommercial

3. Why is Scalability Important in a Web 2.0 world • Viral 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 downtime Creative Commons Sharealike Attributions Noncommercial

4. The Variables • Scalability - 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 Effort High: scalability, availability, performance & responsivenessLow: downtime impact, cost & maintenance effort Creative Commons Sharealike Attributions Noncommercial

5. The Factors • Platform selection • Hardware • Application Design • Database/Datastore Structure and Architecture • Deployment Architecture • Storage Architecture • Abuse prevention • Monitoring mechanisms • … and more Creative Commons Sharealike Attributions Noncommercial

6. 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 process Creative Commons Sharealike Attributions Noncommercial

7. Step 1 – Lets Start … Appserver & DBServer Creative Commons Sharealike Attributions Noncommercial

8. Step 2 – Vertical Scaling CPU Appserver, DBServer CPU RAM RAM Creative Commons Sharealike Attributions Noncommercial

9. Step 2 - Vertical Scaling • Introduction • 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 exponentially CPU Appserver, DBServer CPU CPU CPU RAM RAM RAM RAM Creative Commons Sharealike Attributions Noncommercial

10. Step 3 – Vertical Partitioning (Services) • Introduction • 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 Scalability AppServer DBServer Creative Commons Sharealike Attributions Noncommercial

11. Understanding Vertical Partitioning • The 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) Creative Commons Sharealike Attributions Noncommercial

12. Step 4 – Horizontal Scaling (App Server) • Introduction • Increasing the number of nodes of the App Server through Load Balancing • Referred to as “Scaling out” the App Server Load Balancer AppServer AppServer AppServer DBServer Creative Commons Sharealike Attributions Noncommercial

13. Understanding Horizontal Scaling • The 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) Creative Commons Sharealike Attributions Noncommercial

14. Load Balancer – Hardware vs Software • Hardware Load balancers are faster • Software Load balancers are more customizable • With HTTP Servers load balancing is typically combined with http accelerators Creative Commons Sharealike Attributions Noncommercial

15. Load Balancer – Session Management Sticky Sessions • Sticky 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 data User 1 User 2 Load Balancer AppServer AppServer AppServer Creative Commons Sharealike Attributions Noncommercial

16. Load Balancer – Session Management Central Session Storage • Central Session Store • Introduces SPOF • An additional variable • Session reads and writes generate Disk + Network I/O • Also known as a Shared Session Store Cluster Load Balancer AppServer AppServer AppServer Session Store Creative Commons Sharealike Attributions Noncommercial

17. Load Balancer – Session Management • Clustered 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 Cluster Clustered Session Management Load Balancer AppServer AppServer AppServer Creative Commons Sharealike Attributions Noncommercial

18. Load Balancer – Session Management Sticky Sessions • Sticky 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 User 1 User 2 Load Balancer AppServer AppServer AppServer Creative Commons Sharealike Attributions Noncommercial

19. Load Balancer – Session Management • Recommendation • 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 to Creative Commons Sharealike Attributions Noncommercial

20. Load Balancer – Removing SPOF Active-Passive LB • In 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 Cluster Users Load Balancer Load Balancer AppServer AppServer AppServer Active-Active LB Users Load Balancer Load Balancer AppServer AppServer AppServer Creative Commons Sharealike Attributions Noncommercial

21. Step 4 – Horizontal Scaling (App Server) Load Balanced App Servers • Our deployment at the end of Step 4 • Positives • Increases Availability and Scalability • No changes to App required • Easy setup • Negatives • Finite Scalability DBServer Creative Commons Sharealike Attributions Noncommercial

22. Step 5 – Vertical Partitioning (Hardware) Load Balanced App Servers • Introduction • 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 Cost DBServer SAN Creative Commons Sharealike Attributions Noncommercial

23. Step 6 – Horizontal Scaling (DB) Load Balanced App Servers • Introduction • 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) DBServer DBServer DBServer SAN Creative Commons Sharealike Attributions Noncommercial

24. Shared Nothing Cluster • Each 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 software DBServer DBServer DBServer Database Database Database Note: Actual DB files maybe stored on a central SAN Creative Commons Sharealike Attributions Noncommercial

25. Replication Considerations • Master-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 places Creative Commons Sharealike Attributions Noncommercial

26. Replication Considerations • Asynchronous • 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 reads Creative Commons Sharealike Attributions Noncommercial

27. Replication Considerations • Replication 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 reliable Creative Commons Sharealike Attributions Noncommercial

28. Real Application Cluster • All 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 DBServer DBServer SAN Database Creative Commons Sharealike Attributions Noncommercial

29. Recommendation Load Balanced App Servers • Try 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 master DBServer DBServer DBServer Writes & Critical Reads Other Reads Creative Commons Sharealike Attributions Noncommercial

30. Step 6 – Horizontal Scaling (DB) Load Balanced App Servers DB Cluster DB DB DB SAN • Our architecture now looks like this • Positives • As Web servers grow, Database nodes can be added • DB Server is no longer SPOF • Negatives • Finite limit Creative Commons Sharealike Attributions Noncommercial

31. Step 6 – Horizontal Scaling (DB) Reads • 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 benefit Writes DB1 DB2 Creative Commons Sharealike Attributions Noncommercial

32. Step 7 – Vertical / Horizontal Partitioning (DB) Load Balanced App Servers DB Cluster DB DB DB SAN • Introduction • Increasing the number of DB Clusters by dividing the data • Options • Vertical Partitioning - Dividing tables / columns • Horizontal Partitioning - Dividing by rows (value) Creative Commons Sharealike Attributions Noncommercial

33. 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 table App Cluster DB Cluster 1 Table 1 Table 2 DB Cluster 2 Table 3 Table 4 Creative Commons Sharealike Attributions Noncommercial

34. Vertical Partitioning (DB) • Negatives • One cannot perform SQL joins or maintain referential integrity (referential integrity is as such over-rated) • Finite Limit App Cluster DB Cluster 1 Table 1 Table 2 DB Cluster 2 Table 3 Table 4 Creative Commons Sharealike Attributions Noncommercial

35. 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 code 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 Creative Commons Sharealike Attributions Noncommercial

36. 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) Creative Commons Sharealike Attributions Noncommercial

37. Step 7 – Vertical / Horizontal Partitioning (DB) Load Balanced App Servers DB Cluster DB Cluster DB DB DB DB DB DB SAN • Our 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 slide Lookup Map Creative Commons Sharealike Attributions Noncommercial

38. Step 8 – Separating Sets Load Balanced App Servers Load Balanced App Servers DB Cluster DB Cluster DB Cluster DB Cluster DB DB DB DB DB DB DB DB DB DB DB DB SAN SAN • Now we consider each deployment as a single Set serving a collection of users Global Lookup Map Global Redirector Lookup Map Lookup Map SET 1 – 10 million users SET 2 – 10 million users Creative Commons Sharealike Attributions Noncommercial

39. Creating Sets • The 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 latency Creative Commons Sharealike Attributions Noncommercial

40. Step 8 – Horizontal Partitioning (Sets) • Our 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 Sets Global Redirector App Servers Cluster App Servers Cluster DB Cluster DB Cluster DB Cluster DB Cluster SAN SAN SET 1 SET 2 Creative Commons Sharealike Attributions Noncommercial

41. Step 9 – Caching • Add 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) Creative Commons Sharealike Attributions Noncommercial

42. Step 10 – HTTP Accelerator • If 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 Balancers Creative Commons Sharealike Attributions Noncommercial

43. Step 11 – Other cool stuff • CDNs • 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 in Creative Commons Sharealike Attributions Noncommercial

44. Platform Selection Considerations • Programming 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 Coherence Creative Commons Sharealike Attributions Noncommercial

45. Tips • All 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 hardware Creative Commons Sharealike Attributions Noncommercial

46. Questions??bhavin.t@directi.com http://directi.comhttp://careers.directi.comDownload slides: http://wiki.directi.com