_find_dropper(self, ts: 'TaskState', candidates: 'set[WorkerState] | None', pending_drop: 'set[WorkerState]') -> 'WorkerState | None'

Returns

The worker with the highest memory usage (downstream of pending replications and

drops), or None if no eligible candidates are available.

Choose a worker to drop its replica of an in-memory task among a set of candidates. If candidates is None, default to all workers in the cluster. Regardless, workers that either do not hold a replica or are already scheduled to drop theirs at the end of this AMM iteration are not considered. This method also ensures that a key will not lose its last replica.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /distributed/active_memory_manager.py#244
type: <class 'function'>
Commit: