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Optimize OHLC queries: run Iceberg scans in threads and reuse DataFrames to avoid redundant scans

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This commit is contained in:
Tim Olson
2026-04-28 16:36:41 -04:00
parent 47471b7700
commit a0248540e0
2 changed files with 11 additions and 8 deletions
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5
sandbox/dexorder/iceberg_client.py
View File

@@ -169,7 +169,8 @@ class IcebergClient:
ticker: str,
period_seconds: int,
start_time: int,
end_time: int
end_time: int,
df: Optional[pd.DataFrame] = None,
) -> List[Tuple[int, int]]:
"""
Identify missing data ranges in the requested time period.
@@ -179,10 +180,12 @@ class IcebergClient:
period_seconds: OHLC period in seconds
start_time: Start timestamp in nanoseconds
end_time: End timestamp in nanoseconds
df: Optional pre-fetched DataFrame to avoid a redundant Iceberg scan
Returns:
List of (start_time, end_time) tuples for missing ranges (nanoseconds)
"""
if df is None:
df = self.query_ohlc(ticker, period_seconds, start_time, end_time)
if df.empty:

12
sandbox/dexorder/ohlc_client.py
View File

@@ -122,12 +122,12 @@ class OHLCClient:
start_time = ((start_time + period_nanos - 1) // period_nanos) * period_nanos
end_time = ((end_time + period_nanos - 1) // period_nanos) * period_nanos # exclusive
# Step 1: Check Iceberg for existing data
df = self.iceberg.query_ohlc(ticker, period_seconds, start_time, end_time)
# Step 1: Check Iceberg for existing data (run in thread — scan.to_pandas() blocks ~3-5s)
df = await asyncio.to_thread(self.iceberg.query_ohlc, ticker, period_seconds, start_time, end_time)
# Step 2: Identify missing ranges
# Step 2: Identify missing ranges — pass df to avoid a redundant Iceberg scan
missing_ranges = self.iceberg.find_missing_ranges(
ticker, period_seconds, start_time, end_time
ticker, period_seconds, start_time, end_time, df=df
)
if not missing_ranges:
@@ -148,8 +148,8 @@ class OHLCClient:
if result['status'] == 'ERROR':
raise ValueError(f"Historical data request failed: {result['error_message']}")
# Step 5: Query Iceberg again for complete dataset
df = self.iceberg.query_ohlc(ticker, period_seconds, start_time, end_time)
# Step 5: Query Iceberg again for complete dataset (run in thread)
df = await asyncio.to_thread(self.iceberg.query_ohlc, ticker, period_seconds, start_time, end_time)
return self._apply_decimal_correction(ticker, df)