distributed 2021.10.0

Parameters
replicate(self, comm=None, keys=None, n=None, workers=None, branching_factor=2, delete=True, lock=True)

This performs a tree copy of the data throughout the network individually on each piece of data.

Parameters

keys: Iterable :

list of keys to replicate

n: int :

Number of replications we expect to see within the cluster

branching_factor: int, optional :

The number of workers that can copy data in each generation. The larger the branching factor, the more data we copy in a single step, but the more a given worker risks being swamped by data requests.

Replicate data throughout cluster

See Also

Scheduler.rebalance

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/scheduler.py#6416
type: <class 'function'>
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