That said, this can be solved by changing the level of abstraction. If you tightly coupled the execution and storage engine as a single module, which is not intrinsically dependent on the type of data model, it would allow high-performance multi-use within reasonable constraints. Albeit with a very different API than a storage engine. But that is not a thing that exists in open source AFAIK.
The loss from separating storage and execution may not matter for databases where operational efficiency is not paramount (e.g. because the scale is too small for it to matter) but many database engine designers are not going to take that use case limiting performance hit because the CapEx/OpEx implications are large.
The performance gains at the distributed level seem to rely on either parallelized compute (a-la MapReduce) or over-indexing (a-la ElasticSearch/GIN) rather than the sort of efficiency gains that were made at the level of optimizing between cpu/cache/memory/disk. I think modern problems regarding distributed databases are concerned with the time complexity not exploding as the size of the data does.
The distributed databases in open source are a bit of a biased sample as they tend to copy architectures from each other and are a trailing indicator of the state-of-the-art. For example, you virtually never see facilities for individual nodes to self-orchestrate parallel query execution (this is almost always centralized), which entirely changes how you would go about scaling out e.g. join operations or mixed workload. Building this facility typically requires a tightly coupled storage and execution engine, but open source systems are strongly biased toward decoupling these things for expediency. Open source database architecture is trapped in a local minima.
This is what we are doing with FaunaDB; Fauna's own native semantics are essentially a unified intermediate representation of a variety of high level query languages, forthcoming.
But by far the biggest advantage is operational. Your DB is now stateless! Nodes can die and be replaced in seconds, because they don't store any data; this makes designs like Bigtable's much more practical. You can quickly scale up the processing to deal with spikes without spending hours re-replicating. Operating the storage layer is still inherently challenging, but just has to be figured out once for all your DBs and other processing systems.
While performance can take a hit, this can be mitigated by local caching and good internal networking. It isn't able to meet the lowest levels of latency (sub ms), but in practice it works great for web applications.
Though later it went stateless.
TiDB has a similar architecture: https://www.pingcap.com/docs/architecture/
See more https://docs.datomic.com/on-prem/storage.html#storage-servic...
https://www.brentozar.com/archive/2019/01/how-azure-sql-db-h...
I sketched out a lot of architecture diagrams in that, showing how it differs from traditional RDBMS’s, and I tried to keep the post approachable for database folks from different platforms.
I hope they fix it