Congratulations on the funding, I've been following the project for a while (your tech blog is great) and am especially fond of the spin-off project Badger[1]. I'd like to ask a few questions and make a few "content requests" for the tech blog if I may, since I see @campoy is here and @mrjn will probably stop by later =)

Some things I'd love to read about on your blog

1 - I'd love to read more on the local information retrieval strategies you use for Badger, especially if you have use cases that are comparable to sequential reads (something that Kafka does, but with a structure optimized for magnetic drives) or ISAM style indexation (traditional SQL databases), and how you leverage the metadata byte to speed things up (this has been touched on only superficially on the blog afaik) along with other specialized features/operations Badger supports;

2 - Some insights on how Dgraph's data model influenced the design of Badger, and what parts you decided to explicitly generalize for other use cases, and what use case constraints have you purposefully decided on (aside from the obvious ones like SSD optimized, etc);

3 - Any progress you might have made regarding why some queries in Badger take longer than in RocksDB. That so far has been the biggest cliffhanger in the tech blog, I've been waiting for the sequel to that one for some time now :)

It's difficult to find good resources where people go in depth on these database engineering topics, but your blog is a very good one and a joy to read. Thanks for not simplifying things and keeping it very technical!

Some questions relevant to the post and Dgraph itself

4 - When I (and I assume many others) see "Enterprise Features" on databases that is kind of a turn off. That usually correlates to keeping separate codebases that are being manually kept in sync (which has problems on it's own), and it sometimes feels like it's gatekeeping some pretty crucial features (e.g. better backups) which make people either accept a more vulnerable architectural situation, or makes it unsuitable for a few use cases for lack of such features. I do understand and respect the need to make money to keep Dgraph being awesome, but have you considered something like the CockroachDB approach (selling support + mandating a license to sell the DB as a managed service)? If you did, why did you turn it down and what would make you reconsider it? On a second note from a maintenance perspective, how are you planning to handle the "feature flagging" and "repo syncing" of community and enterprise editions?

5 - As someone who's considered using Dgraph in the past, something that I found interesting is that graph databases in some use cases alleviate some of the same data modelling woes that Datomic does[2][3]. How would you diff them apart and what would you say they differently excel at?

6 - Graph databases sit in a spectrum of use cases, where some people want a system to work similar to a traditional RDBMS but with more flexible/powerful data modelling capabilities, and some people might want to load up big graph-heavy datasets for analytics purposes (i.e. loading a giant product catalog to map product relationships and either do a bunch of batch queries to feed a secondary dataset or keep it live for realtime queries on a recommender system). How would you place Dgraph and it's strengths/weaknesses in this spectrum? What alternative solutions would you recommend for the places where Dgraph would be unsuited for?

Sorry for writing too many questions (which will probably take up a lot of time to answer) but many of these things (especially on the second half) are not explicitly said anywhere, and are valuable knowledge for making an informed decision on a database product.

[1] https://github.com/dgraph-io/badger

[2] https://augustl.com/blog/2018/datomic_look_at_all_the_things...

[3] https://augustl.com/blog/2018/datomic_look_at_all_the_things...