capistrant opened a new pull request, #16335:
URL: https://github.com/apache/druid/pull/16335

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   ### Description
   
   <!-- Describe the goal of this PR, what problem are you fixing. If there is 
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   <!-- Describe your patch: what did you change in code? How did you fix the 
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   #### EnumeratedDistribution Balancer
   
   I have implemented a new BalancerStrategy implementation called 
EnumeratedDistribution. This Balancer Uses 
[org.apache.commons.math3.distribution.EnumeratedDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/EnumeratedDistribution.html#sample(int,%20T[]))
 to decide what servers to load segments to and what server to move a segment 
to. For choosing which server(s) to drop a segment from, it does not use this 
probabilistic sampling, but instead just orders the servers by % utilized in 
order to drop from the most "full" candidate server first.
   
   The overarching goal of the balancer is to drive a cluster's storage 
distribution towards uniform disk utilization % across historical servers.
   
   It differs from the diskNormalized balancer in that this balancer is 
completely disconnected from the cost balancer. It is strictly assigning 
segment destinations by probabilistic sampling that EnumeratedDistribution uses.
   
   ##### Replicating a segment
   
   Assign probabilities to every candidate server. The probability input for 
server s is a float between 0 and 1. The probability for a server correlates to 
it's utilization %. The most utilized server in the cluster has the lowest 
probability assigned and the least utilized server has the highest probability 
assigned.
   
   This is where the impl gets a little funky compared to the other Balancers. 
The other balancers return an Iterator over all of the candidate servers after 
they have been sorted according to the balancer algorithm. For this new 
balancer, it is less straightforward. We sample the set of candidates (with 
probability) a number of times that is equal to the size of the candidate list. 
We then de-dup the list of sampled servers since it will likely - by design - 
have picked the higher probability servers multiple times. This means the 
return list to the caller is not going to have every candidate. The feelin is 
that this shouldn't be a problem when you have a meaningful number of 
historicals since the number of replicas is likely fairly low. However, the 
method signature could be changed to define a minimum number of servers 
returned if we needed to guarantee that N servers were in the iterable that the 
caller receives.
   
   ##### Moving a segment
   
   Assign probabilities to every candidate server. The probability input for an 
individual server is a float between 0 and 1 that correlates to it's disk 
utilization %. The most utilized server in the cluster has the lowest 
probability assigned and the least utilized server has the highest probability 
assigned.
   
   Sample from the set of candidate servers with the aforementioned 
probabilities in mind (EnumeratedDistribution library handles all this for us). 
Return the sampled server.
   
   ##### Dropping a segment
   
   Order the candidate servers in descending order by segment cache space 
utilization % and return an iterable over them. This pushes the cluster to drop 
from the "most full" servers first, working towards the ultimate goal of 
converging on a uniform utilization % across historical servers.
   
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   Add a new BalancerStrategy called `EnumeratedDistributionBalancerStrategy`. 
This balancer uses 
[org.apache.commons.math3.distribution.EnumeratedDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/EnumeratedDistribution.html#sample(int,%20T%5B%5D))
 to make segment assignment, move, and drop decisions using probabilities that 
correlate to how much available segment storage a server has remaining. This 
balancer will converge historical servers to a uniform level of storage 
consumption (expressed as a % of available storage utilized on each server). To 
enable this balancer, set `druid.coordinator.balancer.strategy` to 
`enumeratedDistribution`.
   
   
   <hr>
   
   ##### Key changed/added classes in this PR
    * `EnumeratedDistributionBalancerStrategy`
   
   <hr>
   
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   This PR has:
   
   - [ ] been self-reviewed.
   - [X] added documentation for new or modified features or behaviors.
   - [X] a release note entry in the PR description.
   - [X] added Javadocs for most classes and all non-trivial methods. Linked 
related entities via Javadoc links.
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wherever would not be obvious for an unfamiliar reader.
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ensuring the threshold for [code 
coverage](https://github.com/apache/druid/blob/master/dev/code-review/code-coverage.md)
 is met.
   - [ ] added integration tests.
   - [X] been tested in a test Druid cluster.
   


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