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

Vertical Scaling

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

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)?

Database Vertical Scaling

Is your database swapping (out of memory)?

Scale up the amount of memory.

Is your database load too high?

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?

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.

Note: Horizontally scaling databases in this way works fine for read-only operations.

Problem: R(X), W(X), R(X)

Techniques for Scaling Relational Databases

Each of the following techniques partitions RDBMS (Relational Database Management System) in a different way:

• Sharding

• Service Oriented Architectures (SOA)

• Separating Reads from Writes

Partitioning (Sharding): Idea

Take a single database and split/partition/shard it up into multiple smaller databases such that everything still works.

Partitioning (Sharding): Idea

Take a single database and split/partition/shard it up into multiple smaller databases such that everything still works.

How do we handle joins across partitioned data?

Partitioning: 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.

Partitioning: Separating Data

Partitioning: Similar Data

Partitioning involves splitting data of the same type (e.g., the rows of the tables).

For instance if we wanted to split our Comments table into two partitions, we could store comments belonging to half the submissions in partition1, and those belonging to the other half in partition2.

Partitioning: Similar Data

Partitioning involves splitting data of the same type (e.g., the rows of the tables).

For instance if we wanted to split our Comments table into two partitions, we could store comments belonging to half the submissions in partition1, and those belonging to the other half in partition2.

What is not partitioning?

Partitioning: Similar Data

Partitioning involves splitting data of the same type (e.g., the rows of the tables).

For instance if we wanted to split our Comments table into two partitions, we could store comments belonging to half the submissions in partition1, and those belonging to the other half in partition2.

What is not partitioning?

Separating tables into their own databases is not partitioning. While this approach may work for some applications, the ability to join across tables is lost.

Finding the Data

Assume we have partitioned the data for our application.

How can we find what partition our data is on?

We need some sort of mapping to determine where to find that data.

Finding the Partition

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

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

Across Load Balancers

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)

Partitioning 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 partitions.

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 are located in.

Should the email provider need to take down a database, they can relocate the partitioned 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 partition this application?

Partitioning 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.

Partitioning 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.

Partitioning 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 Partitioning

How could we make community based paritioning work?

We can use information in the url with any of the partitioning 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 Partitioning Success

Community Partitioning Difficulty

Solving Partitioning Problems

• Modify the user interface such that the difficult to partition page does not exist.

  Can you get by with only providing the list of communities?

• Alternatively, periodically run an expensive background job to keep a semi-up-to-date global submission list aggregating results from across databases.

Partitioning in Rails

Rails 6+ has built-in support for partitioning: https://guides.rubyonrails.org/active_record_multiple_databases.html

def index
    ...
    ActiveRecord::Base.connected_to(database: :customer1)
        ...
    end
end

Partitioning Trade-offs - Summary

Strengths

• If you genuinely have zero relations across partitions, this scaling path is very powerful.

• Partitioning works best when partitions grow with usage.

Partitioning Trade-offs - Summary

Strengths

• If you genuinely have zero relations across partitions, this scaling path is very powerful.

• Partitioning works best when partitions grow with usage.

Weaknesses

• Partitioning can inhibit feature development. That is your application may be perfectly partitionable today, but future features may change that.

• Not easy to retroactively add partitioning to an existing application.

• Transactions across partitions do not exist.

• Consistent DB snapshots across partitions do not exist.

Service Oriented Architecture

Partitioning splits 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 with partitioning, no joins can automatically be performed across these partitions.

SOA Stack

The primary concept behind SOA is having many focused mini-applications (microservices).

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.

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

SOA Communications

With partitioning or sharding the application server often only talks to a single partition 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.

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.

SOA and the Demo App

How could we divide the Demo App into services?

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

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

Demo App SOA Code

Before

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

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

Modes of Dataflow

• Via Databases

• Via Service Calls (REST, GraphQL, gRPC, Thrift)

• Via Async message passing (message brokers: RabbitMQ, ActiveMQ, Apache Kafka, Amazon SQS, Google Pub/Sub, etc.)

Source: Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

SOA Trade-offs

Strengths

• Small encapsulated code-bases

• Scales well as application size scales

• Scales well as the number of teams scale

SOA Trade-offs

Strengths

• Small encapsulated code-bases

• Scales well as application size scales

• Scales well as the number of teams scale

Weaknesses

• Transactions across services do not exist

• Consistent DB snapshots across services do not exist

• Application logic required to join data

• Some service may grow disproportionately to others

  • sharding / partitioning might still be neccessary

Database Operations

This graph shows significantly more reads than writes. This may be the case for your application.

Database Horizontal Scaling

Database Replication

Database Replication Trade-offs

Database Replication Trade-offs

Database Replication Trade-offs

Database Replication Levels

Database Statement-level Replication

Statement-level is faster than block-level, with a catch.

An SQL statement is generally more compact than its consequences.

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:

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:

Demo App DB Primary Pages

The controllers associated with the actions from following pages (create) would need to talk to the primary database.

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.

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 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)

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.

• DNS is used to route customer to their application server

Medium use of SOA

• Some functionality broken out into services, but the primary benefit is for scaling engineers rather 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.