Notable improvements:
* A separate aggregation pass is no longer required.
* The user can opt to produce only aggregated data
(resuing in a much smaller data set).
* Large portion of the number cruching in Jupyter is now done in C
through the rich DataFrames API.
* Added support for comparisons against the "median" validator
performance in the network.
The new format is based on compressed CSV files in two channels:
* Detailed per-epoch data
* Aggregated "daily" summaries
The use of append-only CSV file speeds up significantly the epoch
processing speed during data generation. The use of compression
results in smaller storage requirements overall. The use of the
aggregated files has a very minor cost in both CPU and storage,
but leads to near interactive speed for report generation.
Other changes:
- Implemented support for graceful shut downs to avoid corrupting
the saved files.
- Fixed a memory leak caused by lacking `StateCache` clean up on each
iteration.
- Addressed review comments
- Moved the rewards and penalties calculation code in a separate module
Required invasive changes to existing modules:
- The `data` field of the `KeyedBlockRef` type is made public to be used
by the validator rewards monitor's Chain DAG update procedure.
- The `getForkedBlock` procedure from the `blockchain_dag.nim` module
is made public to be used by the validator rewards monitor's Chain DAG
update procedure.
Zahary Karadjov
tersec