Thanks for your feedback over the last couple of months. This release addresses some of the main pain points of using Dgraph .
Custom encoding: Go implementation in net/rpc vs grpc and why we switched
At Dgraph , we aim to build a low latency, distributed graph database. This means our data is distributed among nodes in the cluster. Executing a query means multiple nodes are communicating with each other. To keep our latency of communication low, we use a new form of serialization library called Flatbuffers. > What sets FlatBuffers apart is that it represents hierarchical data in a flat binary buffer in such a way that it can still be accessed directly without parsing/unpacking, while also still supporting data structure evolution (forwards/backwards compatibility). > The only memory needed to access your data is that of the buffer.
Can it really scale?
In this post, we’ll look at how Dgraph performs on varying the number of nodes in the cluster, specs of the machine and load on the server to answer the ultimate question: Can it really scale?
Wisemonk: A slackbot to move discussions from Slack to Discourse
Then there was the fact that we had so many channels and direct messages and group chats. It multiplexed my brain and left me in a constant state of anxiety, feeling that I needed to always be on guard. — Dave Teare, Curing Our Slack Addiction