As a data driven company, Pinterest relies heavily on A/B experiments to make product decisions. How efficiently we run these experiments affects how quickly we can iterate. We’ll cover all aspects of experimentation from configuration management, instrumentation, data processing, metrics tracking, realtime monitoring and rolling out technology and processes across the company. Additionally, as we grow our experimentation culture and our service becomes more real-time, we’ve built stateful online services on replicated RocksDB. With RocksDB, we’ve created high throughput and low latency online systems operating on multiple large online updated and/or offline generated datasets. These systems have powered many Pinterest products, including personalized Pin scoring, Pin impression tracking, Picked For You feed, and ads delivery tracking. We’ll discuss the technical challenges and approaches we’ve used, as well as the common architecture and shared components of replicated RocksDB based systems. The design philosophy and implementation details of one of the core components, RocksDB replicator will be also presented.