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[CIP-20] Update Query & Test (#30)
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This PR finalizes the DB query based on the progress made in [rewards script](https://github.com/cowprotocol/solver-rewards) -- specifically that query which was finalized [here](cowprotocol/solver-rewards#203)

Its a bit gross that the orderbook query lives in two places (for the time being). At some point we will want to expose this as an API endpoint (on a constrained `block_range`) directly via the orderbook. However, until we have landed on the correct query (sufficient to reproduce solver rewards) we will maintain this externally - in two places.

The tests introduced here are both for the DB extraction result and then the transformation phase to JSON before upload.

Closes #31
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@bh2smith
bh2smith authored Mar 20, 2023
1 parent 752697c commit edffd47
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23 changes: 21 additions & 2 deletions src/fetch/orderbook.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,15 @@
from src.utils import open_query

REORG_THRESHOLD = 65
NUMERICAL_COLUMNS = [
"execution_cost",
"surplus",
"fee",
"uncapped_payment_eth",
"capped_payment",
"winning_score",
"reference_score",
]


class OrderbookEnv(Enum):
Expand Down Expand Up @@ -101,9 +110,19 @@ def get_batch_rewards(cls, block_range: BlockRange) -> DataFrame:
.replace("{{start_block}}", str(block_range.block_from))
.replace("{{end_block}}", str(block_range.block_to))
)
data_types = {"block_number": "int64"}
data_types = {
# According to this: https://stackoverflow.com/a/11548224
# capitalized int64 means `Optional<Integer>` and it appears to work.
"block_number": "Int64",
"block_deadline": "int64",
}
barn, prod = cls._query_both_dbs(cow_reward_query, data_types)

# Solvers do not appear in both environments!
assert set(prod.solver).isdisjoint(set(barn.solver)), "solver overlap!"
return pd.concat([prod, barn])
# Ensure numerical types.
combined_df = pd.concat([prod, barn])
for number_col in NUMERICAL_COLUMNS:
combined_df[number_col] = pd.to_numeric(combined_df[number_col])

return combined_df
11 changes: 7 additions & 4 deletions src/models/batch_rewards_schema.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
from dataclasses import dataclass
from typing import Any

import pandas
from pandas import DataFrame


Expand All @@ -18,19 +19,21 @@ def from_pdf_to_dune_records(cls, rewards_df: DataFrame) -> list[dict[str, Any]]
"""Converts Pandas DataFrame into the expected stream type for Dune"""
return [
{
"block_number": int(row["block_number"]),
"block_number": int(row["block_number"])
if not pandas.isna(row["block_number"])
else None,
"tx_hash": row["tx_hash"],
"solver": row["solver"],
"block_deadline": int(row["block_deadline"]),
"data": {
# All the following values are in WEI.
"reward_eth": str(row["reward_eth"]),
"uncapped_payment_eth": str(row["uncapped_payment_eth"]),
"capped_payment": str(row["capped_payment"]),
"execution_cost": str(row["execution_cost"]),
"surplus": str(row["surplus"]),
"fee": str(row["fee"]),
"winning_score": str(row["winning_score"]),
"reference_score": str(row["reference_score"]),
# TODO - Not sure yet how to parse this bytea[]
# Will need to experiment with this.
"participating_solvers": row["participating_solvers"],
},
}
Expand Down
161 changes: 95 additions & 66 deletions src/sql/orderbook/batch_rewards.sql
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Original file line number Diff line number Diff line change
@@ -1,68 +1,97 @@
WITH observed_settlements AS (
SELECT
-- settlement
tx_hash,
solver,
-- settlement_observations
block_number,
effective_gas_price * gas_used AS execution_cost,
surplus,
fee,
-- auction_transaction
auction_id
FROM settlement_observations so
JOIN settlements s
ON s.block_number = so.block_number
AND s.log_index = so.log_index
JOIN auction_transaction at
ON s.tx_from = at.tx_from
AND s.tx_nonce = at.tx_nonce
WHERE block_number > {{start_block}} AND block_number <= {{end_block}}
),
WITH observed_settlements AS (SELECT
-- settlement
tx_hash,
solver,
s.block_number,
-- settlement_observations
effective_gas_price * gas_used AS execution_cost,
surplus,
fee,
-- auction_transaction
at.auction_id
FROM settlement_observations so
JOIN settlements s
ON s.block_number = so.block_number
AND s.log_index = so.log_index
JOIN auction_transaction at
ON s.tx_from = at.tx_from
AND s.tx_nonce = at.tx_nonce
JOIN settlement_scores ss
ON at.auction_id = ss.auction_id
WHERE ss.block_deadline > {{start_block}}
AND ss.block_deadline <= {{end_block}}),

reward_data AS (
SELECT
-- observations
tx_hash,
coalesce(
solver,
-- This is the winning solver (i.e. last entry of participants array)
participants[array_length(participants, 1)]
) as solver,
-- Right-hand terms in coalesces below represent the case when settlement
-- observations are unavailable (i.e. no settlement corresponds to reported scores).
-- In particular, this means that surplus, fee and execution cost are all zero.
-- When there is an absence of settlement block number, we fall back
-- on the block_deadline from the settlement_scores table.
coalesce(block_number, block_deadline) as block_number,
coalesce(execution_cost, 0) as execution_cost,
coalesce(surplus, 0) as surplus,
coalesce(fee, 0) as fee,
surplus + fee - reference_score AS payment,
-- scores
winning_score,
reference_score,
-- participation
participants
FROM settlement_scores ss
-- If there are reported scores,
-- there will always be a record of auction participants
JOIN auction_participants ap
ON os.auction_id = ap.auction_id
-- outer joins made in order to capture non-existent settlements.
LEFT OUTER JOIN observed_settlements os
ON os.auction_id = ss.auction_id
)
auction_participation as (SELECT ss.auction_id,
array_agg(
concat('0x', encode(participant, 'hex')) ORDER BY participant
) as participating_solvers
FROM auction_participants
JOIN settlement_scores ss
ON auction_participants.auction_id = ss.auction_id
WHERE block_deadline > {{start_block}}
AND block_deadline <= {{end_block}}
GROUP BY ss.auction_id),
reward_data AS (SELECT
-- observations
tx_hash,
ss.auction_id,
-- TODO - Assuming that `solver == winner` when both not null
-- We will need to monitor that `solver == winner`!
coalesce(solver, winner) as solver,
block_number as settlement_block,
block_deadline,
case
when block_number is not null and block_number > block_deadline then 0
else coalesce(execution_cost, 0) end as execution_cost,
case
when block_number is not null and block_number > block_deadline then 0
else coalesce(surplus, 0) end as surplus,
case
when block_number is not null and block_number > block_deadline then 0
else coalesce(fee, 0) end as fee,
-- scores
winning_score,
reference_score,
-- auction_participation
participating_solvers
FROM settlement_scores ss
-- If there are reported scores,
-- there will always be a record of auction participants
JOIN auction_participation ap
ON ss.auction_id = ap.auction_id
-- outer joins made in order to capture non-existent settlements.
LEFT OUTER JOIN observed_settlements os
ON os.auction_id = ss.auction_id),
reward_per_auction as (SELECT tx_hash,
settlement_block,
block_deadline,
solver,
execution_cost,
surplus,
fee,
surplus + fee - reference_score as uncapped_payment_eth,
-- Uncapped Reward = CLAMP_[-E, E + exec_cost](uncapped_payment_eth)
LEAST(GREATEST(-10000000000000000, surplus + fee - reference_score),
10000000000000000 + execution_cost) as capped_payment,
winning_score,
reference_score,
participating_solvers
FROM reward_data)

SELECT
block_number,
concat('0x', encode(tx_hash, 'hex')) as tx_hash,
concat('0x', encode(solver, 'hex')) as solver,
execution_cost,
surplus,
fee,
surplus + fee - reference_score as reward_eth,
winning_score,
reference_score,
participants as participating_solvers
FROM reward_data

SELECT settlement_block as block_number,
block_deadline,
case
when tx_hash is NULL then NULL
else concat('0x', encode(tx_hash, 'hex'))
end as tx_hash,
concat('0x', encode(solver, 'hex')) as solver,
execution_cost,
surplus,
fee,
uncapped_payment_eth,
capped_payment,
winning_score,
reference_score,
participating_solvers
FROM reward_per_auction
3 changes: 1 addition & 2 deletions src/sync/order_rewards.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,8 +87,7 @@ def sync_batch_rewards(
block_from=last_sync_block(
aws,
table=sync_table,
genesis_block=16691686, # First Recorded Batch Reward block
# TODO - use correct genesis block here. (note this is not actually determined yet)
genesis_block=16862919, # First Recorded Batch Reward block
),
block_to=fetcher.get_latest_block(),
)
Expand Down
85 changes: 85 additions & 0 deletions tests/integration/test_fetch_orderbook.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,91 @@ def test_get_order_rewards(self):

self.assertIsNone(pd.testing.assert_frame_equal(expected, rewards_df))

def test_get_batch_rewards(self):
block_number = 16846500
block_range = BlockRange(block_number, block_number + 25)
rewards_df = OrderbookFetcher.get_batch_rewards(block_range)
expected = pd.DataFrame(
{
"block_number": pd.Series([16846495, 16846502, pd.NA], dtype="Int64"),
"block_deadline": [16846509, 16846516, 16846524],
"tx_hash": [
"0x2189c2994dcffcd40cc92245e216b0fda42e0f30573ce4b131341e8ac776ed75",
"0x8328fa642f47adb61f751363cf718d707dafcdc258898fa953945afd42aa020f",
None,
],
"solver": [
"0xb20b86c4e6deeb432a22d773a221898bbbd03036",
"0x55a37a2e5e5973510ac9d9c723aec213fa161919",
"0x55a37a2e5e5973510ac9d9c723aec213fa161919",
],
"execution_cost": [
5417013431615490.0,
14681404168612460.0,
0.0,
],
"surplus": [
5867838023808109.0,
104011002982952096.0,
0.0,
],
"fee": [
7751978767036064.0,
10350680045815652.0,
0.0,
],
"uncapped_payment_eth": [
7232682540629268.0,
82825156151734416.0,
-3527106002507021.0,
],
"capped_payment": [
7232682540629268.0,
24681404168612460.0,
-3527106002507021.0,
],
"winning_score": [
6537976145828389.0,
95640781782532192.0,
3527282436747751.0,
],
"reference_score": [
6387134250214905.0,
31536526877033328.0,
3527106002507021.0,
],
"participating_solvers": [
[
"0x398890be7c4fac5d766e1aeffde44b2ee99f38ef",
"0xb20b86c4e6deeb432a22d773a221898bbbd03036",
],
[
"0x55a37a2e5e5973510ac9d9c723aec213fa161919",
"0x97ec0a17432d71a3234ef7173c6b48a2c0940896",
"0xa21740833858985e4d801533a808786d3647fb83",
"0xb20b86c4e6deeb432a22d773a221898bbbd03036",
"0xbff9a1b539516f9e20c7b621163e676949959a66",
"0xc9ec550bea1c64d779124b23a26292cc223327b6",
"0xda869be4adea17ad39e1dfece1bc92c02491504f",
],
[
"0x149d0f9282333681ee41d30589824b2798e9fb47",
"0x3cee8c7d9b5c8f225a8c36e7d3514e1860309651",
"0x55a37a2e5e5973510ac9d9c723aec213fa161919",
"0x7a0a8890d71a4834285efdc1d18bb3828e765c6a",
"0x97ec0a17432d71a3234ef7173c6b48a2c0940896",
"0xa21740833858985e4d801533a808786d3647fb83",
"0xb20b86c4e6deeb432a22d773a221898bbbd03036",
"0xbff9a1b539516f9e20c7b621163e676949959a66",
"0xc9ec550bea1c64d779124b23a26292cc223327b6",
"0xda869be4adea17ad39e1dfece1bc92c02491504f",
"0xe9ae2d792f981c53ea7f6493a17abf5b2a45a86b",
],
],
},
)
self.assertIsNone(pd.testing.assert_frame_equal(expected, rewards_df))


if __name__ == "__main__":
unittest.main()
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