class: center, middle # RDBMS Scaling and Service Oriented Architectures __CS291A__ Dr. Bryce Boe November 7, 2019 --- # Outline * Database Scaling Motivation * Sharding * Service Oriented Architectures * Separating Reads from Writes --- # Motivation You have your web application running on EC2. It is becoming increasingly popular and performance is degrading. > What do you do? --- # Handling Concurrent Requests .left-column40[ You have deployed application servers that can serve many concurrent requests. But as your site's popularity continues to grow that is not sufficient. > What do you do? ] .right-column60[ ![Concurrent Requests](server_process_pool.png) ] --- # Vertical Scaling .center[ ![Vertical Scaling](vertical_scaling.png) ] You have increased your instance sizes and handled more load. However, as the popularity continues to grow you are no longer able to continue scaling vertically. > What do you do? --- # Horizontal Scaling .left-column30[ You have introduced a load balancer that distributes traffic across a pool of application servers. Nevertheless, as the traffic continues to increase, additional horizontal scaling of the application servers does not solve the problem. > What do you do? ] .right-column70[ ![Horizontal Scaling](load_balanced_topology.png) ] --- # Caching You have properly configured HTTP caching such that unnecessary requests never happen. You are also caching heavyweight database operations. But it is still not enough to handle the increased load of your popular site. > What do you do? --- # SQL Structure and Query Optimization You have reduced the number of queries your application servers makes to the database. You have used `EXPLAIN` to discover indexes to add, and to ensure your most common SQL queries are as optimal as possible. However, your database is still a bottleneck. > What do you do? --- # Database Vertical Scaling > Is your database swapping (out of memory)? -- Scale up the amount of memory. > Is your database load too high? -- Scale up the CPU. > Are neither of those working? > What do you do? --- # Database Horizontal Scaling? With application servers we are able to scale horizontally by adding additional machines and introducing a load balancer to access them. > Can we do the same with the database? ![Horizontal Database Idea](horizontal_database_idea.png) --- # Non-trivial DB Horizontal Scaling > Can we horizontally scale the database by adding more database servers and > accessing them through a load balancer? Unfortunately, it's not that simple. Horizontal scaling works great for _stateless_ services. However, the database contains the state of our application, thus is not trivial to horizontally scale. --- # Problem: R(X), W(X), R(X) ![Database Horizontal Scaling Problem](database_horizontal_scaling_problem.png) --- # Scaling Relational Databases In a future lecture we will discuss scaling the data layer using non-relational databases (NoSQL). Today we will discuss in what ways we __can__ horizontally scale a relational database. --- # Three Techniques Each of the following techniques partitions RDBMS (Relational Database Management System) in a different way: * Sharding * Service Oriented Architectures (SOA) * Separating Reads from Writes --- # AKF Cube ![AKF Cube](akf_cube.png) Reference: [http://akfpartners.com/techblog/2008/05/08/splitting-applications-or-services-for-scale/](http://akfpartners.com/techblog/2008/05/08/splitting-applications-or-services-for-scale/) --- class: center inverse middle # Sharding --- # Sharding: Idea Take a single database and split (__shard__) it up into multiple smaller databases such that everything still works. -- > How do we handle joins across sharded data? --- # Sharding: Joins Any particular database join connects a small part of your database. However, transitively, database joins could connect everything together. ## E.g. Demo App * Any comment is only related to its parent (if not top-level), its children (replies), and its submission. * Submissions relate to each other through communities. * Transitively, all of these relationships could be joined across. --- # Sharding: Separating Data .left-column40[ Sharding a database requires finding some partition of your data that ideally produces unrelated (not joined across) _shards_. Once separated (_sharded_), your application cannot utilize the database to join across them. If you need to perform operations across sharded data, you will need to do it at the application level. Consider the performance trade-offs. Could you shard another way? ] .right-column60[ ![Sharding](sharding_app_server.png) ] --- # Sharding: Similar Data Sharding involves splitting data of the same type (e.g., the rows of the tables). For instance if we wanted to shard our `Comments` table into two partitions, we could store comments belonging to half the submissions in _shard1_, and those belonging to the other half in _shard2_. > What is not sharding? -- Separating tables into their own databases is not sharding. While this approach may work for some applications, the ability to join across tables is lost. --- # Finding the Data Assume we have sharded the data for our application. We need some sort of mapping to determine where to find that data. > How can we find what shard our data is on? --- # Finding the Shard ## At the application server layer? > How would we implement this? ## At the load balancer? > How would we implement this? ## Across multiple load balancers? > How would we implement this? --- # At the App Server .left-column40[ Each application server contains a configuration that informs it of where each database is (IP address, DNS name) and how to map data to the database. The mapping can be arbitrarily complex. The mapping itself may even be stored in a database. ] .right-column60[ ![App Server Sharding](sharding_app_server.png) ] --- # At the load balancer The load balancer could be configured to route requests to the app servers that are configured to _talk_ to the right database. Such mappings are limited by knowledge that the load balancer can inspect: * Resource URI * Headers * Request Payload .center[![Load Balancer Sharding](sharding_load_balancer.png)] --- # Across Load Balancers .left-column40[ Host names (DNS) can be configured to point to the correct load balancer for a given request. Examples: * en.wikipedia.org vs. es.wikipedia.org (language based sharding) * google.com vs. google.co.uk (location based sharding) * na6.salesforce.com vs. naX.salesforce.com (customer based sharding) __Note__: The above examples could involve only a single load balancer. ] .right-column60[ ![Sharding Across Load Balancers](sharding_region.png) ] --- # Finding Data: Trade-offs The approaches we just described are vary from providing more flexibility to providing more scalability. * App Server (most flexible) * Load Balancer * DNS (most scalable) --- # Sharding and Growth Ideally the number of partitions increase as the usage of your application increases. Example: If each customer's data can be partitioned from the others, then doubling the number of customers doubles the number of shards. --- # Email Example The data that represent one user's email conceptually requires no relation to the data representing other users' email. When a request arrives associated with a particular user, the server applies some mapping function to determine which database the user's data is located on. Should the email provider need to take down a database, they can relocate the sharded data to another database, and update the mapping with little disruption. --- # Demo App Example * Users can create and view communities. * Users can create submissions in these communities. * Each submission has a tree of comments. > How can we shard this application? --- # Sharding Demo App ## By User? This would have difficulty as logged in users will want to see communities, submissions, and comments made by other logged in users. -- ## By Submission? This would make generating submission lists for a single community difficult. -- ## By Community? Obtaining a community list will be more difficult, but cleanly partitioning comments and submissions by their community is doable. --- # Community Sharding > How could we make community based sharding work? We can use information in the url with any of the three load balancing approaches. * http://ucsb.demo.com (community sub-domain) * http://demo.com/ucsb (community path) Either the application server connects to the right database for the `ucsb` community, or DNS/loadbalancer directs the request to an application server that always talks to the `ucsb` containing database. --- # Community Sharding Success .left-column[ ![Sharding Submission View](sharding_submission_view.png) ] .right-column[ ![Sharding New Submission View](sharding_new_submission_view.png) ] .center.clear[ ![Sharding New Comment View](sharding_new_comment_view.png) ] --- # Community Sharding Difficulty .left-column40[ * The global list of submissions. * List of submissions by user. * List of comments by user. > What can we do to resolve these issues? ] .right-column60[ ![Sharding Global Submission View](sharding_index_view.png) ] --- # Solving Sharding Problems * Modify the user interface such that the difficult to shard page does not exist. > Can you get by with only providing the list of communities? * Alternatively, periodically run an expensive background job keep a semi-up-to-date global submission list aggregating results from across databases. > Why might you not want to trigger this as part of a request? --- # Sharding in Rails Rails 6 has built-in support for sharding: https://guides.rubyonrails.org/active_record_multiple_databases.html ```ruby def index ... ActiveRecord::Base.connected_to(database: :customer1) ... end end ``` Sharding could previously be accomplished via: https://github.com/thiagopradi/octopus --- # Sharding Trade-offs ## Strengths * If you genuinely have zero relations across shards, this scaling path is very powerful. * Sharding works best when partitions grow with usage. -- ## Weaknesses * Sharding can inhibit feature development. That is your application may be perfectly shardable today, but future features may change that. * Not easy to retroactively add sharding to an existing application. * Transactions across shards do not exist. * Consistent DB snapshots across shards do not exist. --- class: center inverse middle # Service Oriented Architecture (SOA) --- # Service Oriented Architecture _Sharding_ partitions data of the same type into separate, unrelated groups. _Service Oriented Architectures_ (SOA) do something different. They partition both the data and the code based on type and function. Like sharding, no relations will cross these partitions. --- # SOA Stack The primary concept behind SOA is having many focused mini-applications. Each of these focused mini-applications is called a service. When a front-end application server needs data to satisfy a request, instead of speaking to a database, it will request data from the appropriate service. .center[![Service Oriented Architecture](soa_stack.png)] --- # SOA Functions Each service is broken out by logical function. E.g.: * Users service that handles authentication and authorization * Billing service that handles credit cards and subscriptions * Account subsystem that tracks invoices .center[![Service Oriented Architecture](soa_stack.png)] --- # SOA Communications With _sharding_ the application server often only talks to a single shard per request. With _SOA_ the front-end application server may communicate with many distinct services, and some of those services may talk to a handful of other services. .center[![Service Oriented Architecture](soa_stack.png)] --- # Benefits of SOA With SOA the deployment of services is decoupled. That means that each can be updated and scaled independently of the remainder of the system. This decoupling can provide isolated outages (billing is down for 5 minutes). Services lend themselves well to maintenance by a single development team thus minimizing conflicts between teams that would otherwise collectively work on a single monolithic application. .center[![Service Oriented Architecture](soa_stack.png)] --- # SOA and the Demo App > How could we divide the Demo App into services? .center[![Demo App](demo_submissions_index.png)] --- # SOA and the Demo App 1. Comments service can track comments and replies for each submission. -- 2. Submissions service can be responsible for all the links that are submitted. -- 3. Communities service can store the list of communities along with their creator. -- 4. Users service can manage the users in the system. --- # Demo App SOA Code Before ```ruby class CommunitiesController < ApplicationController def create if current_user.allowed_to_create_community? Community.create!(params) render :show and return end render :new, status: :unprocessable_entity end end ``` -- After ```ruby class CommunitiesController < ApplicationController def create user = UsersService.get_user_from_session(cookie) if user.allowed_to_create_community? CommunitiesService.create_submission!(params['title'], params['community'], user_id) render :show and return end render :new, status: :unprocessable_entity end end ``` --- # SOA Implementation SOA is mostly implemented by JSON over HTTP. RESTful APIs are common. > Why JSON/HTTP/REST? It's very easy to do. In fact, rails makes you do work in order to __not__ have a JSON API (assuming you are using `rails generate`). JSON APIs over HTTP are easily discovered. This permits (but doesn't necessitate) less documentation as the API may be intuitive enough to use without knowing much more than the endpoints and their input. Using JSON/HTTP permits a shared technology stack. Rails on the front-end application servers, and Rails on the servers hosting the services. --- # Alternatives to JSON/HTTP/Rest The primary issue with JSON/HTTP/REST is performance. For high-performance internal APIs there are a few excellent options: * Google Protocol Buffers * Apache Thrift (developed by Facebook) High performance APIs often trade flexibility for performance. For instance they may require strongly typed data and as a result require more documentation. --- # SOA at Amazon Jeff Bezos (Amazon CEO) circa 2002 (according to Steve Yegge) 1. All teams will henceforth expose their data and functionality through service interfaces. -- 2. Teams must communicate with each other through these interfaces. -- 3. There will be no other form of inter-process communication allowed: no direct linking, no direct reads of another team's data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network. -- 4. It doesn't matter what technology they use. HTTP, Corba, Pubsub, custom protocols -- doesn't matter. Bezos doesn't care. -- 5. All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions. -- 6. Anyone who doesn't do this will be fired. -- Archived Source: https://gist.github.com/breadtk/bf3591a1ec181b62b659b68411694a06 --- # SOA Trade-offs ## Strengths * Small encapsulated code-bases * Scales well as application size scales * Scales well as the number of teams scale -- ## Weaknesses * May not scale with the number of users (e.g., increased load to authentication service) * Transactions across services do not exist * Consistent DB snapshots across services do not exist * Application logic required to join data --- class: center inverse middle # Separating Reads from Writes --- # Database Operations ![Graph of Database Reads and Writes](database_reads_writes.png) This graph shows significantly more _reads_ than _writes_. This may be the case for your application. --- # Database Horizontal Scaling .left-column[ In general, a relational database is hard to horizontally scale. However, when limited to a read-only copies, databases are very easy to horizontally scale. * Set up separate machines to act as __read replicas__ * Whenever any transaction commits to the _primary_ database, send a copy to each _replica_ and apply it ] .right-column[![Database Primary Replica](database_master_replica.png)] --- # Database Replication .left-column40[![Database Primary Replica](database_master_replica.png)] .right-column60[ The sending of data from the primary to its replicas (replication) can happen either synchronously or asynchronously. ## Synchronous When a transaction is committed to mater, the primary sends the transaction to its replicas and waits until applied by all before completing. ## Asynchronous When a transaction is committed to the primary, the primary sends the transaction to its replicas but does not wait to see if the transaction is applied.] --- # Database Replication Trade-offs .left-column[ > What are the advantages of waiting until writes are applied to all replicas? ] .right-column[![Database Primary Replica](database_master_replica.png)] --- # Database Replication Trade-offs .left-column[ > What are the advantages of waiting until writes are applied to all replicas? Consistency. Subsequent read requests will see changes. > What are the disadvantages of waiting until writes are applied to all replicas? ] .right-column[![Database Primary Replica](database_master_replica.png)] --- # Database Replication Trade-offs .left-column[ > What are the advantages of waiting until writes are applied to all replicas? Consistency. Subsequent read requests will see changes. > What are the disadvantages of waiting until writes are applied to all replicas? Performance. There may be many read replicas to apply changes to. ] .right-column[![Database Primary Replica](database_master_replica.png)] --- # Database Replication Levels .left-column[![Database Primary Replica](database_master_replica.png)] .right-column[ ## Statement-level Similar to streaming the journal from the primary to its replicas. ## Block-level Instead of sending the SQL statements to the replicas, send the consequences of those statements. > What are the advantages of each? ] --- # Database Statement-level Replication Statement-level is faster than block-level, with a catch. An SQL statement is generally more compact than its consequences. ```sql UPDATE txns SET amount=5; ``` The above query acts on all rows which may require a lot of data to transmit the consequences. However, SQL statements must now be deterministic: ```sql UPDATE txns SET amount=5, updated_at=NOW(); ``` > What is the value of `NOW()`? Such values must be communicated from the primary to its replicas. --- # PostgreSQL Replication > PostgreSQL streaming replication is asynchronous by default. If the primary > server crashes then some transactions that were committed may not have been > replicated to the standby server, causing data loss. The amount of data loss > is proportional to the replication delay at the time of failover. > When requesting synchronous replication, each commit of a write transaction > will wait until confirmation is received that the commit has been written to > the transaction log on disk of both the primary and standby server. The only > possibility that data can be lost is if both the primary and the standby > suffer crashes at the same time. This can provide a much higher level of > durability, though only if the sysadmin is cautious about the placement and > management of the two servers. Waiting for confirmation increases the user's > confidence that the changes will not be lost in the event of server crashes > but it also necessarily increases the response time for the requesting > transaction. The minimum wait time is the roundtrip time between primary to > standby. https://www.postgresql.org/docs/9.1/warm-standby.html#SYNCHRONOUS-REPLICATION --- # Demo App and Read Replicas The following pages would be served from read replicas: .center[ ![Global Submission View](small_demo_index_view.png) ![Submissions View](sharding_submission_view.png) ] --- # Demo App DB Primary Pages The controllers associated with the actions from following pages (create) would need to talk to the primary database. .center[ .left-column[![Demo New Submission View](demo_half_new_submission_view.png)] .right-column[![Demo New Comment View](demo_half_new_comment_view.png)] ] --- # Rails Read Replica Support Rails 6 has first-class support for read replicas now. "Automatic switching" must be explictly configured and enabled: > Automatic switching allows the application to switch from the primary to > replica or replica to primary based on the HTTP verb and whether there was a > recent write. -- > If the application is receiving a POST, PUT, DELETE, or PATCH request the > application will automatically write to the primary. For the specified time > after the write the application will read from the primary. For a GET or HEAD > request the application will read from the replica unless there was a recent > write. -- > Rails guarantees "read your own write" and will send your GET or HEAD request > to the primary if it's within the delay window. By default the delay is set > to 2 seconds. You should change this based on your database > infrastructure. Rails doesn't guarantee "read a recent write" for other users > within the delay window and will send GET and HEAD requests to the replicas > unless they wrote recently. --- # Trade-offs of Read Replicas ## Strengths For applications with a high read-to-write ratio: * the load on the primary database can be dramatically reduced. * read replicas can be horizontally scaled (even with a load balancer) -- ## Weaknesses Application developer needs to think about reads that affect writes vs. reads that do not affect writes as such dependent reads should occur in the same transaction as the write. --- # At AppFolio ## High usage of sharding * Each customer's data is stored in separate logical database. * Those logical databases can be moved between distinct physical database servers as needed. ## Medium use of SOA * Some functionality broken out into services, but the primary benefit is for scaling engineers than scaling load. ## Low use of Read vs. Write distinction * We utilize read replicas for backup and offline analysis. --- # Summary Horizontal scaling of relational databases is hard. There is no silver bullet, but by combining sharding, SOA, and read replicas you can get very far. For applications that need to scale writes beyond what RDBMS can offer, you need non-relational databases.