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Batch processing utilities for scalable data lake analysis.
Provides patterns for aggregating, analyzing, and exporting data across
the entire data lake or subsets thereof.
"""
from typing import Callable, Dict, Any, Optional
import pandas as pd
from .query import DataLakeQuery
from .logger import setup_logger
logger = setup_logger(__name__)
class BatchProcessor:
"""
Batch processing utilities for scalable data lake analysis.
Provides high-level patterns for common analysis tasks across
multiple devices and messages.
"""
def __init__(self, query: DataLakeQuery):
"""
Initialize batch processor.
Args:
query: DataLakeQuery instance
"""
self.query = query
logger.info("Initialized BatchProcessor")
def aggregate_by_device_message(
self,
aggregation_func: Callable[[pd.DataFrame], Dict[str, Any]],
device_filter: Optional[str] = None,
message_filter: Optional[str] = None,
) -> Dict[str, Dict[str, Any]]:
"""
Apply aggregation function to each device/message combination.
Pattern for scalable analysis across entire data lake. Processes
each device/message combination separately to manage memory.
Args:
aggregation_func: Function (df) -> dict of metrics/statistics
device_filter: Device regex filter (applied via catalog)
message_filter: Message regex filter
Returns:
Nested dict: {device_id: {message: aggregation_result}}
Example:
>>> def compute_stats(df):
... return {
... 'count': len(df),
... 'rpm_mean': df['RPM'].mean() if 'RPM' in df else None
... }
>>> results = processor.aggregate_by_device_message(compute_stats)
"""
results: Dict[str, Dict[str, Any]] = {}
# Get filtered device list
devices = self.query.catalog.list_devices(device_filter)
for device in devices:
messages = self.query.catalog.list_messages(device, message_filter)
for message in messages:
try:
# Read data for this device/message
df = self.query.read_device_message(device, message)
if device not in results:
results[device] = {}
results[device][message] = aggregation_func(df)
except Exception as e:
logger.error(f"Aggregation failed for {device}/{message}: {e}")
if device not in results:
results[device] = {}
results[device][message] = {"error": str(e)}
logger.info(f"Aggregation completed for {len(results)} devices")
return results
def export_to_csv(
self,
device_id: str,
message: str,
output_path: str,
date_range: Optional[tuple[str, str]] = None,
limit: Optional[int] = None,
) -> None:
"""
Export device/message data to CSV.
Args:
device_id: Device identifier
message: Message name
output_path: Output CSV file path
date_range: Optional (start_date, end_date) tuple
limit: Optional row limit
Raises:
Exception: If export fails
"""
logger.info(f"Exporting {device_id}/{message} to {output_path}")
df = self.query.read_device_message(
device_id=device_id,
message=message,
date_range=date_range,
limit=limit,
)
if df.empty:
logger.warning(f"No data to export for {device_id}/{message}")
return
df.to_csv(output_path, index=False)
logger.info(f"Exported {len(df)} rows to {output_path}")
def export_to_parquet(
self,
device_id: str,
message: str,
output_path: str,
date_range: Optional[tuple[str, str]] = None,
) -> None:
"""
Export device/message data to Parquet file.
Args:
device_id: Device identifier
message: Message name
output_path: Output Parquet file path
date_range: Optional (start_date, end_date) tuple
Raises:
Exception: If export fails
"""
logger.info(f"Exporting {device_id}/{message} to {output_path}")
df = self.query.read_device_message(
device_id=device_id,
message=message,
date_range=date_range,
)
if df.empty:
logger.warning(f"No data to export for {device_id}/{message}")
return
df.to_parquet(output_path, index=False, compression='snappy')
logger.info(f"Exported {len(df)} rows to {output_path}")
def compute_statistics(self, df: pd.DataFrame) -> Dict[str, Any]:
"""
Compute basic statistics for aggregation.
Args:
df: Input DataFrame
Returns:
Dict with count, mean, min, max, std for numeric columns
Note:
Skips timestamp column 't' in statistics computation.
"""
stats: Dict[str, Any] = {"count": len(df)}
if df.empty:
return stats
# Compute statistics for numeric columns (excluding timestamp)
numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns
numeric_cols = [c for c in numeric_cols if c != 't']
for col in numeric_cols:
try:
stats[f"{col}_mean"] = float(df[col].mean())
stats[f"{col}_min"] = float(df[col].min())
stats[f"{col}_max"] = float(df[col].max())
stats[f"{col}_std"] = float(df[col].std())
stats[f"{col}_null_count"] = int(df[col].isna().sum())
except Exception as e:
logger.warning(f"Failed to compute stats for {col}: {e}")
return stats
def find_anomalies(
self,
device_id: str,
message: str,
signal_name: str,
threshold_std: float = 3.0,
) -> pd.DataFrame:
"""
Find anomalous values in a signal using z-score method.
Args:
device_id: Device identifier
message: Message name
signal_name: Signal column name
threshold_std: Number of standard deviations for anomaly threshold
Returns:
DataFrame with anomalous records
"""
df = self.query.read_device_message(
device_id=device_id,
message=message,
columns=['t', signal_name],
)
if df.empty or signal_name not in df.columns:
logger.warning(f"No data or signal not found: {signal_name}")
return pd.DataFrame()
# Compute z-scores
mean = df[signal_name].mean()
std = df[signal_name].std()
if std == 0:
logger.warning(f"Zero standard deviation for {signal_name}")
return pd.DataFrame()
z_scores = (df[signal_name] - mean) / std
anomalies = df[abs(z_scores) > threshold_std].copy()
logger.info(f"Found {len(anomalies)} anomalies in {signal_name} "
f"(threshold: {threshold_std} std)")
return anomalies
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