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Alex Auvolat 2021-02-25 11:23:22 +01:00
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doc/Load_Balancing.md
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@ -2,30 +2,37 @@ I have conducted a quick study of different methods to load-balance data over di
### Requirements
- good balancing: two nodes that have the same announced capacity should receive close to the same number of items
- multi-datacenter: the replicas of a partition should be distributed over as many datacenters as possible
- minimal disruption: when adding or removing a node, as few partitions as possible should have to move around
- *good balancing*: two nodes that have the same announced capacity should receive close to the same number of items
- *multi-datacenter*: the replicas of a partition should be distributed over as many datacenters as possible
- *minimal disruption*: when adding or removing a node, as few partitions as possible should have to move around
- *order-agnostic*: the same set of nodes (associated with a datacenter name
and a capacity) should always return the same distribution of partition
replicas, independently of the order in which nodes were added/removed (this
is to keep the implementation simple)
### Methods
#### Naive multi-DC ring walking strategy
This strategy can be used with any ring-linke algorithm to make it aware of the *multi-datacenter* requirement:
This strategy can be used with any ring-like algorithm to make it aware of the *multi-datacenter* requirement:
- the ring is a list of positions, each associated with a single node in the cluster
- look up position of item on ring
- select the node for that position
- go clockwise, skipping nodes that:
- we halve already selected
- are in a datacenter of a node we have selected, except if we already have nodes from all available datacenters
- are in a datacenter of a node we have selected, except if we already have nodes from all possible datacenters
In this way the selected nodes will always be distributed over
`min(n_datacenters, n_replicas)` different datacenters, which is the best we
can do.
This method was implemented in the first iteration of Garage, with the basic
ring construction that consists in associating `n_token` random positions to
each node.
This method was implemented in the first version of Garage, with the basic
ring construction from Dynamo DB that consists in associating `n_token` random positions to
each node (I know it's not optimal, the Dynamo paper already studies this).
#### Better rings
@ -43,7 +50,7 @@ To solve this, we want to apply a second method for partitionning our dataset:
I have studied two ways to do the attribution, in a way that is deterministic:
- Custom: take `argmin_node(hash(node, partition_number))`
- Min-hash: for each partition, select node that minimizes `hash(node, partition_number)`
- MagLev: see [here](https://blog.acolyer.org/2016/03/21/maglev-a-fast-and-reliable-software-network-load-balancer/)
MagLev provided significantly better balancing, as it guarantees that the exact