- transaction size and duration limitations. I can almost understand the limitation on large write transactions, but the same size limitation applies to read transactions. If you’re doing a large range read, you may not know whether your range will reach the 10MB limit, and thus raise an exception.
- the storage backends seem less impressive than the marketing leads you to believe. The default memory backend is obviously too limited to use in production, and the “ssd” backend turns out just to be built on top of the Btree code from SQLite. Besides that, the documentation warns against using the ssd backend on macOS. Isn’t that a bit strange, considering who owns foundationdb??
- while testing, I found that it was impossible to shrink a cluster. If you add a second storage node just to test that the distributed stuff works correctly, you can’t reduce it back to a single node without destroying the entire database and starting over. If it’s possible to run everything on one node, it should be possible to shrink a cluster back to a single node.
- the storage backends have a crazy amount of write amplification (something like 3x, according to the docs). The foundationdb folks should focus on improving the underlying storage, for instance by building on lmdb or RocksDB or something. For my toy app, I abstracted my data access to use either lmdb (for local testing) or foundationdb (for production), but I ultimately ended up just using lmdb because I didn’t want to deal with fdb’s limitations and operational unknowns.
- another weird fdb limitation: the best single threaded latency you’ll get is supposedly around 1ms for small reads. The docs suggest you can achieve much better performance by scaling the cluster and number of clients. That may be true, but some applications may want high single-threaded performance. (Something like lmdb can achieve tens of thousands of reads per second)