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# Project  ## Goals  - to apply ideas from combining and abstract locks to distributed transactions  - avoid hotspots automatically by splitting/replicating on the fly  - use a rigorous   - more generalized notion of commutative ops make transactions more efficient (allow them to commit even while operating on replicas, more concurrency)  ## Hypotheses  - asynchronous phasing/reconciliation will perform alright and reduce the overhead of waiting  - exposing more mergeable operations will allow more concurrency and therefore greater performance  - these techniques will allow us to enforce strong consistency with low cost, provided your operations are designed well  ## Implementation  - How to do splitting?  - *Complete replicas:* Keep track of operations, combine them into more efficient update, and send to all replicas  - don't have to collapse replicas just to do ops that need global state  - after sharing, everyone has the complete "log", so we can service all requests locally  - is this too much more communication? seems like we can expect that hot records will continue to be hot, so we should keep them split...  - all replicas have to know about all other replicas... (maybe we can do as a tree?)  - *Splits:* Essentially "empty" versions of original data structure, with defined semantics for merging into the original  - could be done with a Monoid-like interface  - limited operations, anything requiring concrete state must wait  - *Proxies:* Custom logic for how to take in operations, combine them, and spit out an answer.  - allows for annihilation  - saves from sending state of large object at splitting time  - data structure maintainer must implement by hand?  ## Evaluation  - *Goals*  - show that this approach does better than OCC/contending  -   -   - graph database  - must we support multi-node transactions? is it worth it?  - transactions:  - read queries (over small parts of the graph)  - add edge & update vertices on both side