Lyft uses Apache Cassandra as one of their primary datastores. Cassandra at Lyft supports various use cases, including:
- User Data Storage: Lyft uses Cassandra to store user data such as user profiles, payment details, and ride history. This data is critical to the functioning of the platform and requires high availability and scalability.
- Configurations: Lyft uses Cassandra to store configuration data such as feature flags and service configurations. Cassandra allows them to store this data in a distributed, fault-tolerant manner that can be easily updated and accessed by their microservices.
- Tracking Ride Data: Lyft uses Cassandra to track ride data such as ride requests, driver assignments, and ride completion data. This data is critical to the real-time functioning of the platform and requires a highly scalable and performant database.
- Real-time Analytics: Lyft uses Cassandra to store and analyze data in real-time, such as user behavior and ride patterns. This data helps them make data-driven decisions and improve the overall customer experience.
- Fraud Detection: Lyft uses Cassandra to store and analyze data related to fraudulent activities such as credit card fraud. Cassandra’s ability to handle large volumes of data and provide real-time analytics helps Lyft detect and prevent fraud.
Overall, Cassandra’s scalability, availability, and fault-tolerance make it an ideal database for Lyft’s complex and high-volume use cases.