Overview of Discord's data platform that daily processes petabytes of data and trillion points



924 views Backend System Design



To handle trillions of data points and petabytes of data every single day, Discord needs a simple yet robust Data Platform.

Here’s a quick overview of their arch and key design decisions 👇‍

What is a Data Platform?

A data platform comprises a set of services that ensures data is replicated from various databases, put in central storage, and making it available for consumption by internal teams, and services.

Discord needs to analyze the data to

  • make strategic business decisions
  • power their machine learning models
  • understand how people are using their product

Why replicate data in one place?

  • data is split across microservices
  • firing queries that span multiple databases infeasible
  • each microservice has its own flavor of database (SQL and NoSQL)

For example, firing queries across orders (MongoDB) and payments (MySQL) to get the items that generated the most revenue.

Discrod’s Data Platform - Derived

Discord uses Google’s BigQuery as its Data Warehouse (a place to keep and query large volumes of data). The data is stored, processed, and consumed across 3 layers

  1. Transactional Layer
  2. Core Tables
  3. Derived Tables

Let’s understand each layer in detail.

Transactional Layer

The transactional layer comprises the transactional databases used in powering the microservices. These databases typically act as a source of truth for the services.

Microservices are free to choose the flavor of the database - SQL or NoSQL to power their usecase.

Core Tables

The core layer holds the series of tables in BigQuery that are populated using the transactional layer.

Data pipelines replicate the data from various transactional databases, like MongoDB, MySQL, etc into a set of structured core tables, and become the input for the subsequent Derived Layer.

Derived Tables

Derived Tables are the actual consumable tables created from a set of core tables. Each team can create its own set of derived tables by joining a set of core tables as per their need.

Each derived table is essentially a SQL query on core tables. The specified SQL query is fired periodically to join and replicate the unprocessed data into a derived table.

Each derived table has its own configuration file that holds

  • columns of the derived table
  • schedule and window
  • partition key, cluster key,
  • dataset and SQL query

A replication strategy is also specified in the YAML file that implies if the output of the SQL query should append to, merge with, or replace the existing derived data.

A separate K8S pod is run for each derived table that ensures an isolated continuous data replication to the derived tables.

Thus, each team can define its own set of derived tables using just a SQL query, enabling teams to make data-driven decisions.


Arpit Bhayani

Arpit's Newsletter

CS newsletter for the curious engineers

❤️ by 21000+ readers

If you like what you read subscribe you can always subscribe to my newsletter and get the post delivered straight to your inbox. I write essays on various engineering topics and share it through my weekly newsletter.




Other essays that you might like


Overview of Discord's data platform that daily processes petabytes of data and trillion points

924 views 54 likes 2022-11-14

When a company scales, they adopt microservices and each service typically gets its own independent database. With data ...

How Airbnb designed and scaled its central authorization system - Himeji

2206 views 98 likes 2022-11-07

Authorization plays a critical role in ensuring that the platform is not abused. For example, Instagram ensures that if ...

How Gojek masks and keeps users' phone numbers secure at scale?

2572 views 152 likes 2022-10-31

Do hyperlocal companies like Uber, Ola, Swiggy, Gojek, Zomato, etc share our phone numbers with the delivery people or t...

The architecture of Yelp's in-house Search Engine - nrtSearch

2193 views 81 likes 2022-10-24

Elasticsearch is a great search engine, but Yelp was not happy with its performance, so they built their own HTTP layer ...


Be a better engineer

A set of courses designed to make you a better engineer and excel at your career; no-fluff, pure engineering.


Paid Courses

System Design Masterclass

A masterclass that helps you become great at designing scalable, fault-tolerant, and highly available systems.

1000+ learners

Details →

Redis Internals

Learn internals of Redis by re-implementing some of the core features in Golang.

28+ learners

Details →

Free Courses

Designing Microservices

A free playlist to help you understand Microservices and their high-level patterns in depth.

17+ learners

Details →

GitHub Outage Dissections

A free playlist to help you learn core engineering from outages that happened at GitHub.

67+ learners

Details →

Hash Table Internals

A free playlist to help you understand the internal workings and construction of Hash Tables.

25+ learners

Details →

BitTorrent Internals

A free playlist to help you understand the algorithms and strategies that power P2P networks and BitTorrent.

42+ learners

Details →

Topics I talk about

Being a passionate engineer, I love to talk about a wide range of topics, but these are my personal favourites.





  • v13.7.5
  • © Arpit Bhayani, 2022

Powered by this tech stack.