This adds an initial Dataset.stats() framework for debugging dataset performance. At a high level, execution stats for tasks (e.g., CPU time) are attached to block metadata objects. Datasets have stats objects that hold references to these stats and parent dataset stats (this avoids stats holding references to parent datasets, allowing them to be gc'ed). Similarly, DatasetPipelines hold stats from recently computed datasets.
Currently only basic ops like map / map_batches are instrumented. TODO placeholders are left for future PRs.
block splitting and makes it off by default. This makes it easier to debug problems potentially related to this feature. Criteria for enabling by default:
- We're confident all nightly tests pass (currently, there may be an issue with large-scale groupby with block splitting).
- We're confident lineage-based reconstruction can work with block splitting.
The default block size of 500MiB seems too low for some common workloads, e.g. shuffling 500GB. This creates 1000 blocks which means 1 million intermediate shuffle objects until we implement #20500.
This PR adds support for automatic block splitting on read and map transforms, to keep block size bounded to ~500MiB. This avoids potential OOM situations where a map task may consume too much intermediate Python heap memory, or too much object store shared memory for one block.
