Founder Xprtis, Helping College Graduates Get Hired | Early at, Two Public, One Acquired, Startups | Two Decades of Building Distributed Systems
The "better" approach between read-optimized and write-optimized databases strongly depends on the specific use case. Traditional relational databases excel in scenarios where data consistency and integrity are paramount. They typically provide strong ACID guarantees, making them ideal for financial transactions, inventory management, and other applications where read and write operations are required to be accurate and consistent. On the other hand, LSM tree-based databases, like the one discussed in the document, are typically write-optimized. They excel in high-volume, write-intensive workloads as they utilize a log-structured approach that minimizes write amplification and optimizes for write speeds. These databases are often used in logging, event data management, and other scenarios where data is written more frequently than it is read or updated. However, this doesn't mean LSM tree-based databases are not capable of efficient reads. By sorting data on key before timestamp and using background compaction to limit read amplification, LSM trees can also provide efficient read operations. But they might not provide the same level of consistency guarantees as traditional databases. We used LSMT for our home grown distributed KV system Juno at PayPal that served 5M operations/sec at peak back then. Typical Issue is not read vs write but the concurrency management and if there are hot keys with multiple updates….
