204 lines
8.3 KiB
Markdown
204 lines
8.3 KiB
Markdown
+++
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title = "Load balancing data"
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weight = 10
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+++
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# Load Balancing Data (planned for version 0.2)
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**This is being yet improved in release 0.5. The working document has not been updated yet, it still only applies to Garage 0.2 through 0.4.**
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I have conducted a quick study of different methods to load-balance data over different Garage nodes using consistent hashing.
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## Requirements
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- *good balancing*: two nodes that have the same announced capacity should receive close to the same number of items
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- *multi-datacenter*: the replicas of a partition should be distributed over as many datacenters as possible
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- *minimal disruption*: when adding or removing a node, as few partitions as possible should have to move around
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- *order-agnostic*: the same set of nodes (each associated with a datacenter name
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and a capacity) should always return the same distribution of partition
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replicas, independently of the order in which nodes were added/removed (this
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is to keep the implementation simple)
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## Methods
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### Naive multi-DC ring walking strategy
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This strategy can be used with any ring-like algorithm to make it aware of the *multi-datacenter* requirement:
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In this method, the ring is a list of positions, each associated with a single node in the cluster.
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Partitions contain all the keys between two consecutive items of the ring.
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To find the nodes that store replicas of a given partition:
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- select the node for the position of the partition's lower bound
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- go clockwise on the ring, skipping nodes that:
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- we halve already selected
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- are in a datacenter of a node we have selected, except if we already have nodes from all possible datacenters
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In this way the selected nodes will always be distributed over
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`min(n_datacenters, n_replicas)` different datacenters, which is the best we
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can do.
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This method was implemented in the first version of Garage, with the basic
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ring construction from Dynamo DB that consists in associating `n_token` random positions to
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each node (I know it's not optimal, the Dynamo paper already studies this).
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### Better rings
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The ring construction that selects `n_token` random positions for each nodes gives a ring of positions that
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is not well-balanced: the space between the tokens varies a lot, and some partitions are thus bigger than others.
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This problem was demonstrated in the original Dynamo DB paper.
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To solve this, we want to apply a better second method for partitionning our dataset:
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1. fix an initially large number of partitions (say 1024) with evenly-spaced delimiters,
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2. attribute each partition randomly to a node, with a probability
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proportionnal to its capacity (which `n_tokens` represented in the first
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method)
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For now we continue using the multi-DC ring walking described above.
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I have studied two ways to do the attribution of partitions to nodes, in a way that is deterministic:
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- Min-hash: for each partition, select node that minimizes `hash(node, partition_number)`
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- MagLev: see [here](https://blog.acolyer.org/2016/03/21/maglev-a-fast-and-reliable-software-network-load-balancer/)
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MagLev provided significantly better balancing, as it guarantees that the exact
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same number of partitions is attributed to all nodes that have the same
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capacity (and that this number is proportionnal to the node's capacity, except
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for large values), however in both cases:
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- the distribution is still bad, because we use the naive multi-DC ring walking
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that behaves strangely due to interactions between consecutive positions on
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the ring
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- the disruption in case of adding/removing a node is not as low as it can be,
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as we show with the following method.
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A quick description of MagLev (backend = node, lookup table = ring):
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> The basic idea of Maglev hashing is to assign a preference list of all the
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> lookup table positions to each backend. Then all the backends take turns
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> filling their most-preferred table positions that are still empty, until the
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> lookup table is completely filled in. Hence, Maglev hashing gives an almost
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> equal share of the lookup table to each of the backends. Heterogeneous
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> backend weights can be achieved by altering the relative frequency of the
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> backends’ turns…
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Here are some stats (run `scripts/simulate_ring.py` to reproduce):
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```
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##### Custom-ring (min-hash) #####
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#partitions per node (capacity in parenthesis):
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- datura (8) : 227
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- digitale (8) : 351
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- drosera (8) : 259
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- geant (16) : 476
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- gipsie (16) : 410
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- io (16) : 495
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- isou (8) : 231
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- mini (4) : 149
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- mixi (4) : 188
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- modi (4) : 127
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- moxi (4) : 159
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Variance of load distribution for load normalized to intra-class mean
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(a class being the set of nodes with the same announced capacity): 2.18% <-- REALLY BAD
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Disruption when removing nodes (partitions moved on 0/1/2/3 nodes):
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removing atuin digitale : 63.09% 30.18% 6.64% 0.10%
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removing atuin drosera : 72.36% 23.44% 4.10% 0.10%
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removing atuin datura : 73.24% 21.48% 5.18% 0.10%
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removing jupiter io : 48.34% 38.48% 12.30% 0.88%
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removing jupiter isou : 74.12% 19.73% 6.05% 0.10%
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removing grog mini : 84.47% 12.40% 2.93% 0.20%
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removing grog mixi : 80.76% 16.60% 2.64% 0.00%
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removing grog moxi : 83.59% 14.06% 2.34% 0.00%
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removing grog modi : 87.01% 11.43% 1.46% 0.10%
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removing grisou geant : 48.24% 37.40% 13.67% 0.68%
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removing grisou gipsie : 53.03% 33.59% 13.09% 0.29%
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on average: 69.84% 23.53% 6.40% 0.23% <-- COULD BE BETTER
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--------
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##### MagLev #####
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#partitions per node:
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- datura (8) : 273
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- digitale (8) : 256
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- drosera (8) : 267
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- geant (16) : 452
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- gipsie (16) : 427
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- io (16) : 483
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- isou (8) : 272
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- mini (4) : 184
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- mixi (4) : 160
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- modi (4) : 144
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- moxi (4) : 154
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Variance of load distribution: 0.37% <-- Already much better, but not optimal
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Disruption when removing nodes (partitions moved on 0/1/2/3 nodes):
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removing atuin digitale : 62.60% 29.20% 7.91% 0.29%
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removing atuin drosera : 65.92% 26.56% 7.23% 0.29%
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removing atuin datura : 63.96% 27.83% 7.71% 0.49%
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removing jupiter io : 44.63% 40.33% 14.06% 0.98%
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removing jupiter isou : 63.38% 27.25% 8.98% 0.39%
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removing grog mini : 72.46% 21.00% 6.35% 0.20%
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removing grog mixi : 72.95% 22.46% 4.39% 0.20%
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removing grog moxi : 74.22% 20.61% 4.98% 0.20%
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removing grog modi : 75.98% 18.36% 5.27% 0.39%
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removing grisou geant : 46.97% 36.62% 15.04% 1.37%
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removing grisou gipsie : 49.22% 36.52% 12.79% 1.46%
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on average: 62.94% 27.89% 8.61% 0.57% <-- WORSE THAN PREVIOUSLY
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```
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### The magical solution: multi-DC aware MagLev
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Suppose we want to select three replicas for each partition (this is what we do in our simulation and in most Garage deployments).
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We apply MagLev three times consecutively, one for each replica selection.
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The first time is pretty much the same as normal MagLev, but for the following times, when a node runs through its preference
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list to select a partition to replicate, we skip partitions for which adding this node would not bring datacenter-diversity.
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More precisely, we skip a partition in the preference list if:
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- the node already replicates the partition (from one of the previous rounds of MagLev)
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- the node is in a datacenter where a node already replicates the partition and there are other datacenters available
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Refer to `method4` in the simulation script for a formal definition.
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```
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##### Multi-DC aware MagLev #####
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#partitions per node:
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- datura (8) : 268 <-- NODES WITH THE SAME CAPACITY
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- digitale (8) : 267 HAVE THE SAME NUM OF PARTITIONS
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- drosera (8) : 267 (+- 1)
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- geant (16) : 470
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- gipsie (16) : 472
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- io (16) : 516
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- isou (8) : 268
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- mini (4) : 136
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- mixi (4) : 136
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- modi (4) : 136
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- moxi (4) : 136
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Variance of load distribution: 0.06% <-- CAN'T DO BETTER THAN THIS
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Disruption when removing nodes (partitions moved on 0/1/2/3 nodes):
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removing atuin digitale : 65.72% 33.01% 1.27% 0.00%
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removing atuin drosera : 64.65% 33.89% 1.37% 0.10%
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removing atuin datura : 66.11% 32.62% 1.27% 0.00%
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removing jupiter io : 42.97% 53.42% 3.61% 0.00%
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removing jupiter isou : 66.11% 32.32% 1.56% 0.00%
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removing grog mini : 80.47% 18.85% 0.68% 0.00%
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removing grog mixi : 80.27% 18.85% 0.88% 0.00%
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removing grog moxi : 80.18% 19.04% 0.78% 0.00%
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removing grog modi : 79.69% 19.92% 0.39% 0.00%
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removing grisou geant : 44.63% 52.15% 3.22% 0.00%
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removing grisou gipsie : 43.55% 52.54% 3.91% 0.00%
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on average: 64.94% 33.33% 1.72% 0.01% <-- VERY GOOD (VERY LOW VALUES FOR 2 AND 3 NODES)
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```
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