Re:Datomic Cloud
by valw on Thursday January 18, 2018 @04:34AM
(#16175834)
> ...it is not appropriate for a system that has performance requirements
This is an unreasonably simplistic approach to assessing performance. A useful approach would be to describe what workloads Datomic may or may not be suited for. Having used Datomic (On-Prem) 2 years in production, here's my take on it:
1. For a system that writes a lot (eventually leading to 10 billion datoms or having high peaks of write throughput), Datomic is not a great fit, because it has a single writer thread and does quite a bit of indexing.
2. Reads are horizontally scalable, and because Datomic's semantic allow for pervasive and reliable cacheing, reads can handle a huge load, and have low latency for OLTP-style queries.
3. Datalog is relatively slow for aggregations, and offers no facilities for trading accuracy for speed: if you need big, low-latency aggregations, you should offload it to a specialized store like ElasticSearch. This is especially easy to do with Datomic, because Datomic makes it trivial to implement change detection, in-real time if needed (see Log API and txReportQueue)
4. When writes are overwhelmed and become unavailable, reads stay available, which is an awesome situation to be in.
This is an unreasonably simplistic approach to assessing performance. A useful approach would be to describe what workloads Datomic may or may not be suited for. Having used Datomic (On-Prem) 2 years in production, here's my take on it:
1. For a system that writes a lot (eventually leading to 10 billion datoms or having high peaks of write throughput), Datomic is not a great fit, because it has a single writer thread and does quite a bit of indexing. 2. Reads are horizontally scalable, and because Datomic's semantic allow for pervasive and reliable cacheing, reads can handle a huge load, and have low latency for OLTP-style queries. 3. Datalog is relatively slow for aggregations, and offers no facilities for trading accuracy for speed: if you need big, low-latency aggregations, you should offload it to a specialized store like ElasticSearch. This is especially easy to do with Datomic, because Datomic makes it trivial to implement change detection, in-real time if needed (see Log API and txReportQueue) 4. When writes are overwhelmed and become unavailable, reads stay available, which is an awesome situation to be in.