I'd like to know how its model of transaction isolation works given that reads and writes are claimed to be independent.
It seems as though a 'transaction' is defined as an atomic set of updates, but doesn't involve reads.
It seems as though a 'transaction' is defined as an atomic set of updates, but doesn't involve reads.
Any MVCC-style model allows full concurrency between readers and writers. The bigger problem is managing concurrency between conflicting writers in what amounts to a distributed database system. None of the material on Datomic's website explains how they intend to tackle that issue, which seems especially tricky with their model of distributed peers. All they say is that the Transactor is responsible for globally linearizing transactions and that this is better than existing models. However, if there is a genuine conflict, the loose coupling among peers seems to make the problem much worse than existing models, not better.
I'd love to know more details.
Update:
1. you can do synchronous transactions.
http://datomic.com/docs/javadoc/datomic/Connection.html#tran...
2. transactions can include data functions.
"The database can be extended with data functions that expand into other data functions, or eventually bottom out as assertions and retractions. A set of assertions/ retractions/functions, represented as data structures, is sent to the transactor as a transaction, and either succeeds or fails all together, as one would expect."
That describes many methods of optimistic concurrency control, but it doesn't answer my question of how this supposed to work in practice with high write contention, the higher latency of a distributed peer model, the long-running transactions the video mentions (or maybe that remark only applied to long-running queries), etc. My point being, if the distributed transaction problem was easily solved by sprinkling on optimistic multi-versioning concurrency control, it would have been solved a long time ago. There must be some special sauce they're not mentioning.
Thus, Datomic is well suited for applications that require write consistency and read scalability.
However, that doesn't mean it has slow writes - it should still do writes at least on a par with any traditional transactional database, and probably a good deal faster since it's append-only.
I'd also like to know more about how the app-side caching works. If I've got a terabyte of User records and want to query for all users of a certain type, does a terabyte of data get sent over the wire, cached, and queried locally? Only the fields I ask for? Something else?
2. The database is oriented around 'datoms', which are an entity/attribute/value/time. Each of these has its own (hierarchical) indexes, so you only end up pulling the index segments you need to fulfill a given query. You'd only pull 1TB if your query actually encompassed all the data you had.