01ca415721
Currently, computed hash keys are stored in a separate column family with respect to the MPT data they're generated from - this has several disadvantages: * A lot of space is wasted because the lookup key (`RootedVertexID`) is repeated in both tables - this is 30% of the `AriKey` content! * rocksdb must maintain in-memory bloom filters and LRU caches for said keys, doubling its "minimal efficient cache size" * An extra disk traversal must be made to check for existence of cached hash key * Doubles the amount of files on disk due to each column family being its own set of files Here, the two CFs are joined such that both key and data is stored in `AriVtx`. This means: * we save ~30% disk space on repeated lookup keys * we save ~2gb of memory overhead that can be used to cache data instead of indices * we can skip storing hash keys for MPT leaf nodes - these are trivial to compute and waste a lot of space - previously they had to present in the `AriKey` CF to avoid having to look in two tables on the happy path. * There is a small increase in write amplification because when a hash value is updated for a branch node, we must write both key and branch data - previously we would write only the key * There's a small shift in CPU usage - instead of performing lookups in the database, hashes for leaf nodes are (re)-computed on the fly * We can return to slightly smaller on-disk SST files since there's fewer of them, which should reduce disk traffic a bit Internally, there are also other advantages: * when clearing keys, we no longer have to store a zero hash in memory - instead, we deduce staleness of the cached key from the presence of an updated VertexRef - this saves ~1gb of mem overhead during import * hash key cache becomes dedicated to branch keys since leaf keys are no longer stored in memory, reducing churn * key computation is a lot faster thanks to the skipped second disk traversal - a key computation for mainnet can be completed in 11 hours instead of ~2 days (!) thanks to better cache usage and less read amplification - with additional improvements to the on-disk format, we can probably get rid of the initial full traversal method of seeding the key cache on first start after import All in all, this PR reduces the size of a mainnet database from 160gb to 110gb and the peak memory footprint during import by ~1-2gb. |
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|---|---|---|
| .. | ||
| .gitignore | ||
| README.md | ||
| block-import-stats.py | ||
| check_copyright_year.sh | ||
| make_dist.sh | ||
| make_states.sh | ||
| print_version.nims | ||
| requirements.in | ||
| requirements.txt | ||
README.md
Utility scripts
block-import-stats.py
This script compares outputs from two nimbus import --debug-csv-stats, a
baseline and a contender.
To use it, set up a virtual environment:
# Create a venv for the tool
python -m venv stats
. stats/bin/activate
pip install -r requirements.txt
python block-import-stats.py
- Generate a baseline version by processing a long range of blocks using
nimbus import - Modify your code and commit to git (to generate a unique identifier for the code)
- Re-run the same import over the range of blocks of interest, saving the import statistics to a new CSV
- Pass the two CSV files to the script
By default, the script will skip block numbers below 500k since these are mostly unintersting.
See -h for help text on running the script.
Testing a particular range of blocks
As long as block import is run on similar hardware, each run can be saved for future reference using the git hash.
The block import can be run repeatedly with --max-blocks to stop after
processing a number of blocks - by copying the state at that point, one can
resume or replay the import of a particular block range
See make_states.sh for such an example.