XRPL Validation Tracker

Four modules are provided:

1. The aggregator combines multiple websocket subscription streams into a single outgoing websocket stream.
2. db_writer stores XRP Ledger data in relational databases. Since this code is in testing, only sqlite3 is supported now. Better database support is needed for production.
3. supplemental_data provides data from manifests, TOML files, and published UNL(s).
4. ws_client is used to connect to remote websocket servers and is used by both the aggregator and db_writer modules.

The aggregator is designed for more flexible use with a wide range of streams. The db_writer is currently specific to 'validation' and 'ledger' subscription streams, and support for additional streams can be easily added as needed.

The aggregator code is structured to provide multiple layers of redundancy. For example, four servers could use the aggregator code to subscribe to 5-10 XRP Ledger nodes. Two additional servers could then use the db_writer, which already depends on the aggregator, to subscribe to the previously mentioned four servers. This schema provides redundancy at both the data aggregation and database ingestion stages.

Installing & Requirements

• All modules require websockets

• db_writer and supplemental_data require sqlite3

  • supplemental_data also requires pytomlpp, and aiohttp

• supplemental_data requires xrpl-unl-manager, which must be manually downloaded.

• pip install -r requirements.txt automatically installs the required packages

  • xrpl-unl-manager must be downloaded manually
  • The requirements.txt is generated using pip freeze in Python 3.8
  • It is possible older package versions will also work

At this time, all dependencies can be installed inside a Python3 virtual environment using: pip install -r requirements.txt && git clone https://github.com/antIggl/xrpl-unl-manager.git && mv xrpl-unl-manager ./xrpl_validation_tracker/xrpl_unl_manager

Development is tested on Python 3.6, 3.7, & 3.8.

Running the Software

1. Install dependencies
2. Navigate to the xrpl_validation_tracker directory
3. Adjust the settings in settings_aggregator.py, settings_db_writer.py, and settings_supplemental.py
4. Run python3 run_tracker.py using the '-a', '-d', and/or 's' flags to specify which module(s) to run (there is not a flag to run ws_client, as it is a dependency for other modules).

All three modules modules can be run on the same system and started simultaneously. A Python multiprocessing bug can inhibit clean shutdown via keyboard interrupt, and users are encouraged to check for orphaned processes if keyboard interrupt must be invoked multiple times.

Querying the database

The database can be queried using standard sqlite3.

Query validators whose TOML files are verified: sqlite3 validations.sqlite3 'SELECT * FROM master_keys WHERE toml_verified IS 1 ORDER BY domain ASC;'

Query validators with verified domains: sqlite3 validations.sqlite3 'SELECT * FROM master_keys WHERE domain IS NOT NULL ORDER BY domain ASC;' | cat >> keys_domain.txt

Query the transaction count in ledger(s) matching a given sequence: sqlite3 validations.sqlite3 'SELECT txn_count from ledgers WHERE sequence is 61809888;'

Query the number of entries in the validation_stream table: sqlite3 validations.sqlite3 'select Count(*) FROM validation_stream;'

Query the number of dUNL validators: sqlite3 validations.sqlite3 'SELECT Count(*) FROM master_keys WHERE dunl IS 1;'

Given that sqlite3 is not ideal for production, there is a need for additional scripts that interface with more robust databases.

To Do Items

1. Add support (translate queries) for Postgres or another production database
2. Improve database structure, consolidate queries, & index the tables
3. Improve the websocket server ws_server in the aggregator - accept headers, subscribe messages, etc.
4. API access - mimic Data API v2 + live validation stream subscription (notify missing) - consider developing a db_reader package to retrieve requests.
5. Multiple "To-do" items are noted in comments throughout the code.
6. Change logging to % format
7. Daemonize
8. Fix errors with multiprocessing when exiting using keyboard interrupt
9. Support multiple published UNLs & verify published UNL signatures
10. Check attestations in TOMLs
11. Move this list to Issues
12. Add ephemeral_key column to validation_stream DB
13. Add 'first_seen' columns to master and ephemeral key DBs
14. Verify manifest signatures
15. Find & deal with blocking in ws_server, process_data, & others
16. Write a setup.py script for xrpl-unl-manager?
17. Track 'cookies' field in the validation stream to check if multiple validators have the same validation key

Thoughts

1. Identify main chain through an aggregated ledger subscription stream - use this to verify hash, index, and time
2. Trie or rrdtool?