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"""
Schema Drift Detector for monitoring database schema changes
This module monitors customer databases for schema changes that might
affect query execution and mappings.
"""
from typing import List, Dict, Any, Optional, Set, Tuple
from datetime import datetime, timezone
from pathlib import Path
import sqlite3
import json
import logging
from collections import defaultdict
from schema_translator.knowledge_graph import SchemaKnowledgeGraph
from schema_translator.database_executor import DatabaseExecutor
logger = logging.getLogger(__name__)
class SchemaSnapshot:
"""Snapshot of a customer's database schema."""
def __init__(
self,
customer_id: str,
timestamp: datetime,
tables: Dict[str, List[str]],
row_counts: Dict[str, int]
):
"""Initialize schema snapshot.
Args:
customer_id: Customer identifier
timestamp: When snapshot was taken
tables: Dictionary of table_name -> [column_names]
row_counts: Dictionary of table_name -> row_count
"""
self.customer_id = customer_id
self.timestamp = timestamp
self.tables = tables
self.row_counts = row_counts
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary."""
return {
"customer_id": self.customer_id,
"timestamp": self.timestamp.isoformat(),
"tables": self.tables,
"row_counts": self.row_counts
}
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> 'SchemaSnapshot':
"""Create from dictionary."""
return cls(
customer_id=data["customer_id"],
timestamp=datetime.fromisoformat(data["timestamp"]),
tables=data["tables"],
row_counts=data["row_counts"]
)
class SchemaDrift:
"""Represents detected schema drift."""
def __init__(
self,
customer_id: str,
drift_type: str,
severity: str,
description: str,
details: Dict[str, Any]
):
"""Initialize schema drift.
Args:
customer_id: Customer identifier
drift_type: Type of drift (table_added, table_removed, column_added, etc.)
severity: Severity level (low, medium, high, critical)
description: Human-readable description
details: Additional details about the drift
"""
self.customer_id = customer_id
self.drift_type = drift_type
self.severity = severity
self.description = description
self.details = details
self.detected_at = datetime.now(timezone.utc)
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary."""
return {
"customer_id": self.customer_id,
"drift_type": self.drift_type,
"severity": self.severity,
"description": self.description,
"details": self.details,
"detected_at": self.detected_at.isoformat()
}
class SchemaDriftDetector:
"""Monitors database schemas for changes."""
def __init__(
self,
database_executor: DatabaseExecutor,
knowledge_graph: SchemaKnowledgeGraph,
snapshot_file: Optional[Path] = None
):
"""Initialize drift detector.
Args:
database_executor: Database executor for querying schemas
knowledge_graph: Knowledge graph with mappings
snapshot_file: File to store schema snapshots
"""
self.executor = database_executor
self.knowledge_graph = knowledge_graph
self.snapshot_file = snapshot_file or Path("data/schema_snapshots.json")
self.snapshot_file.parent.mkdir(parents=True, exist_ok=True)
# Load previous snapshots
self.snapshots: Dict[str, SchemaSnapshot] = {}
self._load_snapshots()
logger.info(f"SchemaDriftDetector initialized with {len(self.snapshots)} snapshots")
def capture_snapshot(self, customer_id: str) -> SchemaSnapshot:
"""Capture current schema snapshot for a customer.
Args:
customer_id: Customer identifier
Returns:
SchemaSnapshot object
"""
try:
# Get database path from executor's config
db_path = self.executor.config.get_database_path(customer_id)
if not db_path.exists():
raise FileNotFoundError(f"Database not found: {db_path}")
# Connect and query schema
conn = sqlite3.connect(str(db_path))
cursor = conn.cursor()
# Get all tables
cursor.execute(
"SELECT name FROM sqlite_master WHERE type='table' AND name NOT LIKE 'sqlite_%'"
)
table_names = [row[0] for row in cursor.fetchall()]
# Get columns for each table
tables = {}
row_counts = {}
for table_name in table_names:
# Get columns
cursor.execute(f"PRAGMA table_info({table_name})")
columns = [row[1] for row in cursor.fetchall()]
tables[table_name] = columns
# Get row count
cursor.execute(f"SELECT COUNT(*) FROM {table_name}")
row_counts[table_name] = cursor.fetchone()[0]
conn.close()
snapshot = SchemaSnapshot(
customer_id=customer_id,
timestamp=datetime.now(timezone.utc),
tables=tables,
row_counts=row_counts
)
logger.info(f"Captured schema snapshot for {customer_id}: "
f"{len(tables)} tables, {sum(row_counts.values())} total rows")
return snapshot
except Exception as e:
logger.error(f"Error capturing snapshot for {customer_id}: {e}", exc_info=True)
raise
def detect_drift(
self,
customer_id: str,
update_snapshot: bool = True
) -> List[SchemaDrift]:
"""Detect schema drift for a customer.
Args:
customer_id: Customer identifier
update_snapshot: Whether to update stored snapshot after detection
Returns:
List of detected drifts
"""
# Capture current snapshot
current_snapshot = self.capture_snapshot(customer_id)
# Get previous snapshot
previous_snapshot = self.snapshots.get(customer_id)
if not previous_snapshot:
logger.info(f"No previous snapshot for {customer_id}, storing baseline")
if update_snapshot:
self.snapshots[customer_id] = current_snapshot
self._save_snapshots()
return []
# Compare snapshots
drifts = self._compare_snapshots(previous_snapshot, current_snapshot)
# Update snapshot if requested
if update_snapshot and drifts:
self.snapshots[customer_id] = current_snapshot
self._save_snapshots()
logger.info(f"Detected {len(drifts)} drifts for {customer_id}, snapshot updated")
return drifts
def _compare_snapshots(
self,
old: SchemaSnapshot,
new: SchemaSnapshot
) -> List[SchemaDrift]:
"""Compare two snapshots and detect drifts.
Args:
old: Previous snapshot
new: Current snapshot
Returns:
List of detected drifts
"""
drifts = []
customer_id = new.customer_id
old_tables = set(old.tables.keys())
new_tables = set(new.tables.keys())
# Check for added tables
added_tables = new_tables - old_tables
for table in added_tables:
drifts.append(SchemaDrift(
customer_id=customer_id,
drift_type="table_added",
severity="medium",
description=f"New table '{table}' added with {len(new.tables[table])} columns",
details={
"table_name": table,
"columns": new.tables[table],
"row_count": new.row_counts.get(table, 0)
}
))
# Check for removed tables
removed_tables = old_tables - new_tables
for table in removed_tables:
# Check if this table was mapped
is_mapped = self._is_table_mapped(customer_id, table)
severity = "critical" if is_mapped else "high"
drifts.append(SchemaDrift(
customer_id=customer_id,
drift_type="table_removed",
severity=severity,
description=f"Table '{table}' removed (was mapped: {is_mapped})",
details={
"table_name": table,
"was_mapped": is_mapped,
"had_columns": old.tables[table]
}
))
# Check for column changes in existing tables
common_tables = old_tables & new_tables
for table in common_tables:
old_cols = set(old.tables[table])
new_cols = set(new.tables[table])
# Added columns
added_cols = new_cols - old_cols
if added_cols:
drifts.append(SchemaDrift(
customer_id=customer_id,
drift_type="columns_added",
severity="low",
description=f"Table '{table}': {len(added_cols)} columns added",
details={
"table_name": table,
"added_columns": list(added_cols)
}
))
# Removed columns
removed_cols = old_cols - new_cols
if removed_cols:
# Check if removed columns were mapped
mapped_cols = self._get_mapped_columns(customer_id, table)
affected_mappings = removed_cols & mapped_cols
severity = "critical" if affected_mappings else "high"
drifts.append(SchemaDrift(
customer_id=customer_id,
drift_type="columns_removed",
severity=severity,
description=f"Table '{table}': {len(removed_cols)} columns removed",
details={
"table_name": table,
"removed_columns": list(removed_cols),
"affected_mappings": list(affected_mappings)
}
))
# Check for significant row count changes
for table in common_tables:
old_count = old.row_counts.get(table, 0)
new_count = new.row_counts.get(table, 0)
if old_count > 0:
change_pct = abs(new_count - old_count) / old_count * 100
if change_pct > 50: # More than 50% change
drifts.append(SchemaDrift(
customer_id=customer_id,
drift_type="row_count_change",
severity="medium",
description=f"Table '{table}': significant row count change ({old_count} -> {new_count})",
details={
"table_name": table,
"old_count": old_count,
"new_count": new_count,
"change_percent": round(change_pct, 2)
}
))
return drifts
def _is_table_mapped(self, customer_id: str, table_name: str) -> bool:
"""Check if a table is used in any mappings.
Args:
customer_id: Customer identifier
table_name: Table name
Returns:
True if table is mapped
"""
for concept in self.knowledge_graph.concepts.values():
if customer_id in concept.customer_mappings:
mapping = concept.customer_mappings[customer_id]
if mapping.table == table_name:
return True
return False
def _get_mapped_columns(self, customer_id: str, table_name: str) -> Set[str]:
"""Get set of columns that are mapped for a table.
Args:
customer_id: Customer identifier
table_name: Table name
Returns:
Set of mapped column names
"""
mapped_cols = set()
for concept in self.knowledge_graph.concepts.values():
if customer_id in concept.customer_mappings:
mapping = concept.customer_mappings[customer_id]
if mapping.table == table_name:
mapped_cols.add(mapping.column)
return mapped_cols
def check_all_customers(self) -> Dict[str, List[SchemaDrift]]:
"""Check all customers for schema drift.
Returns:
Dictionary of customer_id -> list of drifts
"""
all_drifts = {}
# Get all customer databases from config
database_dir = self.executor.config.database_dir
if not database_dir.exists():
logger.warning(f"Database directory not found: {database_dir}")
return {}
for db_file in database_dir.glob("*.db"):
customer_id = db_file.stem
try:
drifts = self.detect_drift(customer_id, update_snapshot=True)
if drifts:
all_drifts[customer_id] = drifts
except Exception as e:
logger.error(f"Error checking {customer_id}: {e}")
return all_drifts
def get_drift_summary(self) -> Dict[str, Any]:
"""Get summary of recent drift detections.
Returns:
Summary statistics
"""
# Check all customers
all_drifts = self.check_all_customers()
if not all_drifts:
return {
"total_customers_checked": len(self.snapshots),
"customers_with_drift": 0,
"total_drifts": 0,
"drifts_by_severity": {},
"drifts_by_type": {},
"critical_drifts": []
}
total_drifts = sum(len(drifts) for drifts in all_drifts.values())
# Count by severity
severity_counts = defaultdict(int)
type_counts = defaultdict(int)
critical_drifts = []
for customer_id, drifts in all_drifts.items():
for drift in drifts:
severity_counts[drift.severity] += 1
type_counts[drift.drift_type] += 1
if drift.severity == "critical":
critical_drifts.append({
"customer_id": customer_id,
"type": drift.drift_type,
"description": drift.description
})
return {
"total_customers_checked": len(self.snapshots),
"customers_with_drift": len(all_drifts),
"total_drifts": total_drifts,
"drifts_by_severity": dict(severity_counts),
"drifts_by_type": dict(type_counts),
"critical_drifts": critical_drifts
}
def _load_snapshots(self):
"""Load snapshots from disk."""
if not self.snapshot_file.exists():
return
try:
with open(self.snapshot_file, 'r') as f:
data = json.load(f)
for customer_id, snapshot_data in data.items():
self.snapshots[customer_id] = SchemaSnapshot.from_dict(snapshot_data)
except Exception as e:
logger.error(f"Error loading snapshots: {e}", exc_info=True)
def _save_snapshots(self):
"""Save snapshots to disk."""
try:
data = {
customer_id: snapshot.to_dict()
for customer_id, snapshot in self.snapshots.items()
}
with open(self.snapshot_file, 'w') as f:
json.dump(data, f, indent=2)
except Exception as e:
logger.error(f"Error saving snapshots: {e}", exc_info=True)
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