A key goal has been to avoid ingestion bottlenecks that are otherwise typical when indexing late-arriving temporal data. This is partly achieved by having a very simple EAV index structure over fast local KV stores like RocksDB and LMDB (as opposed to a complex distributed storage architecture with vastly more moving pieces).
As for query performance, the indexes have been designed so that graph traversals are efficient regardless of how much transaction time or valid time history is stored for the entire database of entities. One trick to this is the use of "Morton space filling curves": https://github.com/juxt/crux/blob/master/crux-core/src/crux/...
A key goal has been to avoid ingestion bottlenecks that are otherwise typical when indexing late-arriving temporal data. This is partly achieved by having a very simple EAV index structure over fast local KV stores like RocksDB and LMDB (as opposed to a complex distributed storage architecture with vastly more moving pieces).
As for query performance, the indexes have been designed so that graph traversals are efficient regardless of how much transaction time or valid time history is stored for the entire database of entities. One trick to this is the use of "Morton space filling curves": https://github.com/juxt/crux/blob/master/crux-core/src/crux/...
This is a great talk about the design and internals from ClojurTRE 2019 if you're curious: https://www.youtube.com/watch?v=YjAVsvYGbuU