3 ms·

I agree. And I saw how averse companies were to graph databases because of the perception that they are "not reliable." So, we built Dgraph with the same concep

by mrjn 8y ago

I agree. And I saw how averse companies were to graph databases because of the perception that they are "not reliable." So, we built Dgraph with the same concepts as Bigtable and Google Spanner, i.e. horizontal scalability, synchronous replication, ACID transactions, etc. Once built, we engaged Kyle and got Jepsen testing done on Dgraph. In fact, Dgraph is the first graph database to be Jepsen tested. http://jepsen.io/analyses/dgraph-1-0-2 http://jepsen.io/analyses/dgraph-1-0-2 (all pending issues are now resolved). Dgraph is now being used as a primary DB in many companies in production (including Fortune 500), which to me is an incredible milestone.

geuszb 8y ago
Can you share more about the kinds of use cases and qps that your customers have for dgraph?
- mrjn 8y ago
  Dgraph usages are quite wide-spread. It is being used for typical graph cases, like recommendation engines, real-time fraud detection, adtech, fintech uses, etc. The design is very well suited for just building apps (scalable, flexible schema) as well, given the early choices to use GraphQL (modified) as the query lang and JSON as the response. So, we see even Fortune 500 companies using Dgraph to build mobile apps. Most open source users use the simplest 2-node cluster, but we easily see enterprise customers use 6-node (High Availability) cluster or 12-node cluster (HA + Sharding). Given synchronous replication, query throughput can scale out linearly as you add more replicas/machines (each replica can reply without worrying about issues with typical eventual consistency models. Dgraph provides linearizable reads). Write throughput wise, Dgraph can sustain XXX,XXX records/sec in the live path (and millions in the offline path). See my recent commit: https://github.com/dgraph-io/dgraph/commit/b7189935e6ec93aec04762321252b70f98466985 https://github.com/dgraph-io/dgraph/commit/b7189935e6ec93aec... Some recent public usage mentions of Dgraph: https://github.com/intuit/katlas https://github.com/intuit/katlas https://twitter.com/pg_kansas/status/1096260809171353600 https://twitter.com/pg_kansas/status/1096260809171353600
  - geuszb 8y ago
    Thanks!

by mrjn 8y ago

geuszb 8y ago

Can you share more about the kinds of use cases and qps that your customers have for dgraph?

mrjn 8y ago
Dgraph usages are quite wide-spread. It is being used for typical graph cases, like recommendation engines, real-time fraud detection, adtech, fintech uses, etc. The design is very well suited for just building apps (scalable, flexible schema) as well, given the early choices to use GraphQL (modified) as the query lang and JSON as the response. So, we see even Fortune 500 companies using Dgraph to build mobile apps. Most open source users use the simplest 2-node cluster, but we easily see enterprise customers use 6-node (High Availability) cluster or 12-node cluster (HA + Sharding). Given synchronous replication, query throughput can scale out linearly as you add more replicas/machines (each replica can reply without worrying about issues with typical eventual consistency models. Dgraph provides linearizable reads). Write throughput wise, Dgraph can sustain XXX,XXX records/sec in the live path (and millions in the offline path). See my recent commit: https://github.com/dgraph-io/dgraph/commit/b7189935e6ec93aec04762321252b70f98466985 https://github.com/dgraph-io/dgraph/commit/b7189935e6ec93aec... Some recent public usage mentions of Dgraph: https://github.com/intuit/katlas https://github.com/intuit/katlas https://twitter.com/pg_kansas/status/1096260809171353600 https://twitter.com/pg_kansas/status/1096260809171353600
- geuszb 8y ago
  Thanks!