[OrderbookDB] Different Reward Tables per Environment (#140)

* use different tables for each environment

* handle both possiblities

queries/orderbook/order_rewards.sql

@@ -31,6 +31,6 @@ select concat('0x', encode(trade_hashes.order_uid, 'hex')) as order_uid,
            when reward is null and fee_amount = 0 then False
            end                              as safe_liquidity
 from trade_hashes
-         left outer join order_rewards
-                         on trade_hashes.order_uid = order_rewards.order_uid
-                             and trade_hashes.auction_id = order_rewards.auction_id;
+         left outer join {{reward_table}} o
+                         on trade_hashes.order_uid = o.order_uid
+                             and trade_hashes.auction_id = o.auction_id;

src/pg_client.py

@@ -8,6 +8,7 @@
 import pandas as pd
 from dotenv import load_dotenv
 from pandas import DataFrame
+from sqlalchemy.exc import ProgrammingError
 from sqlalchemy import create_engine
 from sqlalchemy.engine import Engine
 
@@ -48,12 +49,29 @@ def _pg_engine(db_env: OrderbookEnv) -> Engine:
         db_string = f"postgresql+psycopg2://{user}:{password}@{host}:{port}/{database}"
         return create_engine(db_string)
 
+    @classmethod
+    def _exec_query(cls, query: str, engine: Engine) -> DataFrame:
+        # TODO - once both environments have been migrated, this will no longer be necessary.
+        try:
+            # New Query
+            return pd.read_sql(
+                sql=query.replace("{{reward_table}}", "order_executions"), con=engine
+            )
+        except ProgrammingError:
+            # Use Old Query
+            # Unfortunately it appears impossible to capture the Base Error:
+            # psycopg2.errors.UndefinedTable
+            # But we know what it is.
+            return pd.read_sql(
+                sql=query.replace("{{reward_table}}", "order_rewards"), con=engine
+            )
+
     @classmethod
     def from_query(cls, query: str) -> DualEnvDataframe:
         """Fetch results of DB query on both prod and barn and returns the results as a pair"""
         return cls(
-            barn=pd.read_sql(sql=query, con=cls._pg_engine(OrderbookEnv.PROD)),
-            prod=pd.read_sql(sql=query, con=cls._pg_engine(OrderbookEnv.BARN)),
+            barn=cls._exec_query(query, cls._pg_engine(OrderbookEnv.BARN)),
+            prod=cls._exec_query(query, cls._pg_engine(OrderbookEnv.PROD)),
         )
 
     def merge(self) -> DataFrame: