  In the fields of big data, AI, and streaming processing, we work with large
amounts of data from multiple sources. Due to memory and network limitations,
we process data streams on distributed systems to alleviate computational and
network loads. When data streams with non-uniform distributions are processed,
we often observe overloaded partitions due to the use of simple hash
partitioning. To tackle this imbalance, we can use dynamic partitioning
algorithms that require a sampling algorithm to precisely estimate the
underlying distribution of the data stream. There is no standardized way to
test these algorithms. We offer an extensible ranking framework with benchmark
and hyperparameter optimization capabilities and supply our framework with a
data generator that can handle concept drifts.
  Our work includes a generator for dynamic micro-bursts that we can apply to
any data stream. We provide algorithms that react to concept drifts and compare
those against the state-of-the-art algorithms using our framework.
