How Pinterest Scaled to 11 Million Users with a Small Engineering Team by Adopting Scalable Technologies

Core Concepts

Pinterest scaled its platform from 0 to tens of billions of monthly page views in just two years by adopting a simple, scalable technology stack and a phased approach to database sharding.

Abstract

Pinterest's scaling journey can be divided into four distinct phases:

1. The Age of Finding Yourself: This phase was characterized by rapid prototyping and evolving product requirements, managed by a small engineering team.

2. The Age of Experimentation: Exponential user growth demanded rapid scaling, leading to the adoption of numerous technologies. However, this resulted in a complex, fragile system.

3. The Age of Maturity: This phase involved a conscious simplification of their architecture, focusing on mature, scalable technologies like MySQL, Memcache, and Redis. Instead of adding to the tech stack, Pinterest invested the money into growing what was working well.

4. The Age of Return: With the right architecture in place, Pinterest continued its growth trajectory simply by scaling horizontally, validating its choices.

Pinterest prioritized technologies that were reliable, well-understood, and could easily scale with their growing user base, such as:

• MySQL: A robust and mature relational database management system known for its stability and a wide community of users.
• Memcache: A simple, high-performance system for caching frequently accessed data in memory.
• Redis: A versatile data store capable of handling various data structures and offering flexibility in persistence and replication.
• Solr: A search engine platform chosen for its quick implementation and ability to handle large numbers of small documents and queries.

When it came to scaling the database, Pinterest chose sharding over clustering due to its relative simplicity and their negative experiences with clustering during the "Age of Experimentation." Sharding involved partitioning data into smaller chunks and placing each chunk on a dedicated server, which provided a more predictable and manageable approach.

The shift to sharding was a phased process, including eliminating database joins, denormalizing data, and aggressive caching to maintain performance. While sharding offered a more manageable approach, it came with challenges, such as scripting for data migration, implementing application-level logic for data consistency, and reporting across multiple shards.

Overall, Pinterest's scaling journey offers valuable lessons for anyone architecting systems for growth, including the importance of simplicity, prioritizing scalability over features, and designing for horizontal growth.

Stats

Pinterest scaled from 0 to tens of billions of monthly page views in just two years.
Pinterest's engineering team consisted of only 6 engineers during this rapid scaling period.

Quotes

"In one case they bring in a new secondary. At about 80% the secondary says it's primary and the primary goes to secondary and you've lost 20% of the data. Losing 20% of the data is worse than losing all of it because you don't know what you've lost."
"All data (pins, boards, etc) for a user is collocated on the same shard. Huge advantage. Rendering a user profile, for example, does not take multiple cross shard queries. It's fast."

Key Insights Distilled From

How Pinterest Scaled to 11 Million Users With Only 6 Engineers

by at machine-learning-made-si... 05-13-2024

https://machine-learning-made-simple.medium.com/how-pinterest-scaled-to-11-million-users-with-only-6-engineers-a0f62cea62b8

Deeper Inquiries

How did Pinterest's engineering team manage to scale the platform with such a small team?

Pinterest's engineering team managed to scale the platform with only six engineers by prioritizing simplicity, scalability, and strategic technology choices. They went through distinct phases of evolution, focusing on rapid prototyping, experimentation, simplification of architecture, and horizontal scaling. By choosing reliable and well-understood technologies like MySQL, Memcache, Redis, and Solr, they ensured easy maintenance, troubleshooting, and scalability. Additionally, they opted for sharding over clustering for database distribution, sacrificing some database features for increased control and simplicity at the application level. This phased approach allowed for incremental implementation and thorough validation, minimizing user impact during the transition.

What were the key trade-offs and challenges Pinterest faced when adopting a sharded database architecture, and how did they address them?

When adopting a sharded database architecture, Pinterest faced trade-offs and challenges such as scripting for migration, application logic for data consistency, schema modifications, and reporting hurdles. Transferring vast amounts of data to the sharded infrastructure proved time-consuming, highlighting the need for robust scripting tools. The lack of database-level joins and transactions required developers to implement logic for maintaining data consistency within the application layer. Altering the database schema across all shards and generating reports across multiple shards also posed challenges. Pinterest addressed these issues by carefully planning schema modifications, implementing application-level logic for data consistency, and developing processes to aggregate results for reporting purposes.

What other technology or architectural decisions did Pinterest make that contributed to their rapid scaling success, and how could these principles be applied to other web applications?

In addition to adopting a sharded database architecture, Pinterest's success in rapid scaling can be attributed to their emphasis on simplicity, scalability, and strategic technology choices. They prioritized technologies like MySQL, Memcache, Redis, and Solr for their reliability, scalability, and ease of maintenance. By choosing straightforward and well-understood technologies, they simplified troubleshooting and reduced the risk of unforeseen problems. Furthermore, their focus on horizontal growth allowed them to add more resources as their user base expanded. These principles can be applied to other web applications by prioritizing simplicity in technology choices, sacrificing some features for scalability, and designing for horizontal growth. By embracing these principles, web applications can navigate the challenges of growth and scalability more effectively.