eric-maynard opened a new pull request, #21:
URL: https://github.com/apache/polaris-tools/pull/21
This implements a new scenario, `WeightedWorkloadOnTreeDataset`, that
supports the configuration of multiple **distributions** over which to weight
reads & writes against the catalog.
Compared with `ReadUpdateTreeDataset`, this allows us to understand how
performance changes when reads or writes frequently hit the same tables.
### Sampling
The distributions are defined in the config file like so:
```
# Distributions for readers
# Each distribution will have `count` threads assigned to it
# mean / variance describe the properties of the normal distribution
# Readers will read a random table in the table space based on sampling
# Default: [{ count = 8, mean = 0.3, variance = 0.0278 }]
readers = [
{ count = 8, mean = 0.3, variance = 0.0278 }
]
```
`count` is simply the number of threads which will sample from the
distribution, while `mean` and `variance` describe the Gaussian distribution to
sample from. These values are generally expected to fall between 0 and 1.0 and
when they don't the distribution will be repeatedly **resampled**.
For an extreme example, refer to the following:
<img width="400" alt="Screenshot 2025-04-30 at 1 27 43 AM"
src="https://github.com/user-attachments/assets/d77e98f1-7a94-463d-be82-0c47bbda92a1";
/>
In this case, about 50% of samples should fall below 0.0 and therefore be
resampled.
Once a value between 0 and 1 is obtained, this is mapped to a table, where
1.0 is the highest table (e.g. T_2048) in the tree dataset and 0.0 is T_0.
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