I noticed that all the write benchmarks in https://apple.github.io/foundationdb/benchmarking.html are for random writes. Is write throughput affected by highly-sequential writes (e.g. - time series) vs random writes? How do you avoid hot-spotting on recent ranges?
How efficient are range deletes?
On https://apple.github.io/foundationdb/performance.html I read "The memory engine is optimized for datasets that entirely fit in memory, with secondary storage used for durable writes but not reads." I'd like some clarification:
(1) Which memory does "entirely fit in memory" refer to? A single machine? Or SingleNodeMemory * Nodes / ReplicationFactor?
(2) If only recently-written data is likely to be queried, and all recently-written data fits entirely in memory, is that sufficient? If so, would an unexpected query of old data cause a huge impact on write throughput?
(3) What is the structure/format of the data stored on disk? How is it updated?
I'm wondering how well this could be used for time series data. I saw mention here that wavefront uses FoundationDB for this, but would like more details if any are available.