How Pinterest Scaled to 11 Million Users with a Small Engineering Team

Core Concepts

Pinterest successfully scaled its platform to 11 million users with only 6 engineers by adopting a pragmatic, phased approach to architecture simplification and horizontal scaling.

Abstract

The article discusses how Pinterest scaled its platform from 0 to 10s of billions of page views per month in just two years, despite having a small engineering team of only 6 people. The scaling journey can be divided into four distinct phases:

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

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.

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.

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

The core technologies that Pinterest relied on were MySQL, Memcache, and Redis. These were chosen for their reliability, scalability, and ease of maintenance. Pinterest also had to make a critical choice between database clustering and sharding. They ultimately opted for sharding due to the complexity and issues they faced with clustering during the "Age of Experimentation."
The migration to a sharded architecture was a phased process, which involved eliminating database joins, denormalizing data, and implementing aggressive caching. This approach allowed for incremental implementation and thorough validation at each stage.
While sharding offered a more manageable approach, it came with its own challenges, such as scripting for data migration, implementing application-level logic for data consistency, and reporting across multiple shards. Pinterest addressed these challenges through careful planning and the development of robust tooling.
The key takeaways from Pinterest's scaling journey include the importance of simplicity, prioritizing scalability over database features, and designing for horizontal growth. By embracing these principles, Pinterest was able to successfully navigate the challenges of explosive user growth with a small engineering team.

Stats

Pinterest scaled to 11 million users with only 6 engineers.
Pinterest grew from 0 to 10s of billions of page views per month in just two years.

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 medium.com 05-15-2024

https://medium.com/codex/how-pinterest-scaled-to-11-million-users-with-only-6-engineers-a0f62cea62b8

Deeper Inquiries

How did Pinterest's approach to scaling differ from other web companies that experienced rapid growth during the same period?

Pinterest's approach to scaling differed from other web companies in several key ways. Firstly, Pinterest prioritized simplicity and reliability in their technology choices, opting for well-understood and easily scalable technologies like MySQL, Memcache, and Redis. This focus on simplicity helped them avoid the pitfalls of overly complex systems that some other companies faced.
Additionally, Pinterest made a conscious effort to simplify their architecture during the "Age of Maturity" phase, focusing on mature technologies rather than constantly adding new ones. This strategic decision allowed them to invest resources into growing what was working well, rather than constantly chasing the latest tech trends.
Moreover, Pinterest's decision to choose sharding over clustering for database scaling set them apart. While clustering offers automatic scaling and high availability, Pinterest experienced issues with cluster management and data rebalancing during their experimentation phase. Sharding, on the other hand, provided a more predictable and manageable approach, aligning with Pinterest's goal of simplicity and control.

What were the key factors that enabled Pinterest to successfully scale with such a small engineering team?

Several key factors enabled Pinterest to successfully scale with only six engineers. Firstly, their focus on simplicity and reliability in technology choices allowed the small team to manage and maintain the system effectively. By prioritizing well-understood technologies like MySQL, Memcache, and Redis, Pinterest reduced the complexity of their system, making it easier for a small team to handle.
Additionally, Pinterest's phased approach to migrating to a sharded architecture played a crucial role in their success. By carefully eliminating joins, implementing ID-based sharding, and conducting the migration during a feature freeze, Pinterest ensured a smooth transition without disrupting user experience. This meticulous planning and execution were essential for a small team to handle such a significant architectural change.
Furthermore, Pinterest's willingness to sacrifice some database features for scalability, as well as their emphasis on horizontal growth, allowed them to scale efficiently with limited resources. By designing their system for scalability and focusing on incremental improvements, Pinterest maximized the impact of their small engineering team.

How might Pinterest's scaling strategy and technology choices need to evolve as the platform continues to grow and diversify its offerings?

As Pinterest continues to grow and diversify its offerings, their scaling strategy and technology choices may need to evolve to meet new challenges. One potential area of evolution could be in handling increased data volume and complexity. As the platform expands, Pinterest may need to revisit their sharding strategy and consider more advanced data partitioning techniques to ensure efficient data management.
Moreover, with the introduction of new features and services, Pinterest may need to evaluate the scalability of their existing technologies and potentially introduce new tools or platforms to support the growing demands. This could involve exploring cloud-based solutions, advanced caching mechanisms, or even machine learning algorithms to optimize performance and user experience.
Additionally, as Pinterest expands globally, they may need to consider geographical data distribution and resilience to datacenter outages. This could involve implementing multi-region architectures or leveraging content delivery networks to ensure fast and reliable access for users worldwide.
Overall, Pinterest's scaling strategy and technology choices will need to adapt to the evolving needs of the platform, balancing scalability, reliability, and performance as they continue to grow and diversify their offerings.