Local-first software aims to empower the user at the edge. User operations should be accepted locally at the users’ node with negligible latency and only asynchronously propagated to other nodes or the cloud infrastructure when connectivity conditions permit. This considerably impacts overall metadata size for non-trivial data types, as is the case of counters and causal CRDTs. Here we explore this problem and point to possible solutions.

My research interests cover data management in eventual consistent settings, distributed data aggregation and causality tracking. In the last years I have collaborated with my co-authors in the development of data summary mechanisms such as Scalable Bloom Filters, causality tracking for dynamic settings with Interval Tree Clocks and Dotted Version Vectors and in predictable eventual consistency with Conflict-Free Replicated Data Types. My recent work has been applied in the Riak distributed database and in Akka distributed data, and is running in production systems serving millions of users worldwide.