6 ms·

Hey, one of the core architects of Datahike here. As a Clojure company we are also super happy that Nubank made Clojure and Datomic much more credible with this

by whilo 6y ago

Hey, one of the core architects of Datahike here. As a Clojure company we are also super happy that Nubank made Clojure and Datomic much more credible with this move. While Datomic is obviously much more mature, it is important to understand that we have a different scope in our goals than Datomic. Datomic is mostly built as a more convenient backend database for corporate environments and is highly tailored towards AWS and a business environment where the costs of operating and depending on these cloud services is acceptable, which is only the most profitable, but small, slice of the whole market. Even when Datomic gets open-sourced it will not be automatically built with other than these design goals in mind. Our goals on the other hand go even beyond thinking about this backend market; we want to use Datalog really as a distributed systems environment and extend Datahike to all endpoints including the browser and IOT development: https://www.youtube.com/watch?v=A2CZwOHOb6U https://www.youtube.com/watch?v=A2CZwOHOb6U Our main intention was never to just build an open-source Datomic. But it made too much sense not to do it as a first step. In fact we also really hope that Datomic will be open-sourced such that we can merge our efforts. But given the current governance model of Clojure and Datomic we do not yet foresee that open-sourcing Datomic alone would address a large section of our plans. We are ahead of Datomic already in a few areas: We have funded development for ClojureScript support going for instance and in comparison to Datomic all our efforts where from the beginning aimed at this, we in fact provide more a set of libraries and abstractions that can stand on their own and that you can compose in different ways than having a top-down design that we then unbundle into libraries. This made it much easier for us to evolve and reuse our stack despite the pivot we did from replikativ to Datahike. Regarding maturity we have worked hard during quarantine to address some of our pain points: 1. We significantly improved our write throughput and Datomic performance is in reach (close to release), https://github.com/replikativ/datahike/pull/201 https://github.com/replikativ/datahike/pull/201 2. We have a first version of our server API available and will extend this in the next months to provide Datomic-style local querying https://github.com/replikativ/datahike-server/ https://github.com/replikativ/datahike-server/ 3. We recently provided Java bindings https://lambdaforge.io/2020/05/25/java-api.html https://lambdaforge.io/2020/05/25/java-api.html Over the last year we also built a cooperative that has more than 5 people working on this full-time and we aim to grow even faster next year and really bring Datalog to the community and the masses. If Datomic gets properly open-sourced, we will get there even faster.

synthc 6y ago
Thanks for clarifying. I agree that Datomic is expensive to run and that open sourcing it would not improve this overnight, a lightweight and cost-effective alternative would be great. How will you handle security when accessing a Datahike backend from the browser? I've used Datomic from the browser indirectly in the past for internal tools, using a custom rest api to run the queries, but for external access it was not clear how to limit the queries to the parts of the database the user had permission to view. What are your plans for IOT development? I found that Datomic is not a good fit for timeseries data, does Datahike offer any advantages?
- whilo 6y ago
  Interesting, honestly speaking we have not thought about time series data a lot yet, but I think we should be able to provide custom indices and extend Datalog with more efficient query primitives, if this is necessary. Can you elaborate a bit? I have used HDF5 binary blobs for tensors of experimental recordings (parameter evolution in spiking neural networks) in Datomic a few years ago and it is definitely possible to integrate external index data structures, but eventually the query engine will need to be aware of how to join them efficiently. W.r.t. security, our current approach is to shard access rights and encryption on a database level and just provide many databases, one for each user. This is obviously not the most space efficient, but the most general approach. If users can share access keys and data we can also do structural sharing between these instances and factorize further. We envision doing joins potentially over dozens of distributed Datahike instances in a global address space during single queries. Since the indices are amortized data structures it does not make too much sense to encrypt chunks of them for different users as this defeats the optimality guarantees of B+-trees, i.e. you could have very bad scan behaviour over huge ranges of encrypted Datoms. How have you tried to partition the data? This is an interesting problem. We can also expose the datahike-server query endpoint directly and you can write static checks for access right restrictions. We only do this so far to limit the usage of builtin functions to safe ones, but you could also go ahead and do the same for more complex access controls. Some work in this direction for Datahike has also been done here: https://github.com/theronic/eacl https://github.com/theronic/eacl Doing this openly on the internet will also require a resource model to fend of denial of service attacks, fortunately Datalog engines can have powerful query planners and we can restrict our runtime to limited budgets as well.

by whilo 6y ago

synthc 6y ago

Thanks for clarifying. I agree that Datomic is expensive to run and that open sourcing it would not improve this overnight, a lightweight and cost-effective alternative would be great. How will you handle security when accessing a Datahike backend from the browser? I've used Datomic from the browser indirectly in the past for internal tools, using a custom rest api to run the queries, but for external access it was not clear how to limit the queries to the parts of the database the user had permission to view. What are your plans for IOT development? I found that Datomic is not a good fit for timeseries data, does Datahike offer any advantages?

whilo 6y ago
Interesting, honestly speaking we have not thought about time series data a lot yet, but I think we should be able to provide custom indices and extend Datalog with more efficient query primitives, if this is necessary. Can you elaborate a bit? I have used HDF5 binary blobs for tensors of experimental recordings (parameter evolution in spiking neural networks) in Datomic a few years ago and it is definitely possible to integrate external index data structures, but eventually the query engine will need to be aware of how to join them efficiently. W.r.t. security, our current approach is to shard access rights and encryption on a database level and just provide many databases, one for each user. This is obviously not the most space efficient, but the most general approach. If users can share access keys and data we can also do structural sharing between these instances and factorize further. We envision doing joins potentially over dozens of distributed Datahike instances in a global address space during single queries. Since the indices are amortized data structures it does not make too much sense to encrypt chunks of them for different users as this defeats the optimality guarantees of B+-trees, i.e. you could have very bad scan behaviour over huge ranges of encrypted Datoms. How have you tried to partition the data? This is an interesting problem. We can also expose the datahike-server query endpoint directly and you can write static checks for access right restrictions. We only do this so far to limit the usage of builtin functions to safe ones, but you could also go ahead and do the same for more complex access controls. Some work in this direction for Datahike has also been done here: https://github.com/theronic/eacl https://github.com/theronic/eacl Doing this openly on the internet will also require a resource model to fend of denial of service attacks, fortunately Datalog engines can have powerful query planners and we can restrict our runtime to limited budgets as well.