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import json
import numpy as np
from typing import Dict, Any, List, Tuple, Optional
import os
import datetime
import statistics
import re
from collections import defaultdict
# DB-specific table-specific columns to skip
# Format: {'database_name': {'table_name': ['column1', 'column2', ...]}}
DB_TABLE_SPECIFIC_SKIP_COLUMNS = {
'Db-IMDB.sqlite': {
'Movie': ['index'],
'Genre': ['index'],
'Language': ['index'],
'Country': ['index'],
'Location': ['index'],
'M_Location': ['index', 'ID'],
'M_Country': ['index', 'ID'],
'M_Language': ['index', 'ID'],
'M_Genre': ['index', 'ID'],
'Person': ['index'],
'M_Producer': ['index', 'ID'],
'M_Director': ['index', 'ID'],
'M_Cast': ['index', 'ID']
}
# Add more database and table-specific skip rules as needed
}
def quote_column_name(col_name):
"""Properly quote column names that need it"""
if not col_name:
return '""'
# Special characters that require quoting
special_chars = ['%', '-', '+', '*', '/', '(', ')', '[', ']', ' ', '.', '&', '|', '!', '@', '#', '$', '^', '~', '`', '=', '<', '>', '?', ',', ';', ':', "'", '"']
# SQL reserved words (common ones)
reserved_words = [
'index', 'key', 'order', 'group', 'from', 'select', 'where', 'table', 'column',
'and', 'or', 'not', 'in', 'is', 'null', 'like', 'between', 'exists', 'case',
'when', 'then', 'else', 'end', 'union', 'all', 'distinct', 'having', 'limit',
'offset', 'join', 'inner', 'outer', 'left', 'right', 'full', 'cross', 'on',
'using', 'natural', 'as', 'desc', 'asc', 'primary', 'foreign', 'references',
'constraint', 'unique', 'check', 'default', 'create', 'drop', 'alter', 'insert',
'update', 'delete', 'into', 'values', 'set', 'truncate', 'commit', 'rollback',
'transaction', 'begin', 'savepoint', 'release', 'pragma', 'vacuum', 'analyze',
'explain', 'view', 'trigger', 'procedure', 'function', 'database', 'schema'
]
# Check if quoting is needed
needs_quoting = (
any(char in col_name for char in special_chars) or
col_name.lower() in reserved_words or
col_name.isdigit() or
(col_name and col_name[0].isdigit())
)
if needs_quoting:
# Escape any existing double quotes by doubling them
escaped_name = col_name.replace('"', '""')
return f'"{escaped_name}"'
return col_name
def quote_table_name(table_name):
"""Properly quote table names that need it"""
return quote_column_name(table_name) # Same logic applies
class SQLiteKeyFinder:
"""
A tool to identify potential primary and foreign keys in SQLite databases
where they are not explicitly defined.
"""
def __init__(self, db_path):
"""Initialize with the path to the SQLite database."""
if not os.path.exists(db_path):
raise FileNotFoundError(f"Database file not found: {db_path}")
self.db_path = db_path
self.db_name = os.path.basename(db_path)
self.conn = sqlite3.connect(db_path)
self.cursor = self.conn.cursor()
self.tables = []
self.table_columns = {}
self.primary_keys = {}
self.foreign_keys = defaultdict(list)
def should_skip_column(self, table_name, column_name):
"""
Determine if a column should be skipped based on database-specific table rules.
"""
if self.db_name in DB_TABLE_SPECIFIC_SKIP_COLUMNS and \
table_name in DB_TABLE_SPECIFIC_SKIP_COLUMNS[self.db_name] and \
column_name.lower() in [col.lower() for col in DB_TABLE_SPECIFIC_SKIP_COLUMNS[self.db_name][table_name]]:
return True
return False
def _get_tables(self):
"""Get all tables in the database."""
self.cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
self.tables = [row[0] for row in self.cursor.fetchall()
if not row[0].startswith('sqlite_')]
return self.tables
def _get_table_columns(self):
"""Get all columns for each table."""
for table in self.tables:
quoted_table = quote_table_name(table)
self.cursor.execute(f"PRAGMA table_info({quoted_table});")
columns = []
for col_info in self.cursor.fetchall():
col_name = col_info[1]
data_type = col_info[2]
columns.append((col_name, data_type))
self.table_columns[table] = columns
return self.table_columns
def _check_defined_keys(self):
"""Check for already defined primary and foreign keys."""
defined_pk = {}
defined_fk = defaultdict(list)
# Check primary keys
for table in self.tables:
quoted_table = quote_table_name(table)
self.cursor.execute(f"PRAGMA table_info({quoted_table});")
pk_columns = []
for col_info in self.cursor.fetchall():
if col_info[5] > 0: # pk column
pk_columns.append(col_info[1])
if pk_columns:
defined_pk[table] = {'columns': pk_columns, 'origin': 'db'}
# Check foreign keys
for table in self.tables:
quoted_table = quote_table_name(table)
self.cursor.execute(f"PRAGMA foreign_key_list({quoted_table});")
for fk_info in self.cursor.fetchall():
defined_fk[table].append({
'from': fk_info[3],
'to_table': fk_info[2],
'to_column': fk_info[4],
'origin': 'db'
})
return defined_pk, defined_fk
def find_potential_primary_keys(self):
"""Identify columns that are likely to be primary keys."""
# First check for explicitly defined keys
defined_pk, _ = self._check_defined_keys()
if defined_pk:
print("Found defined primary keys:", defined_pk)
self.primary_keys.update(defined_pk)
# For tables without defined primary keys, try to identify them
for table in self.tables:
if table in self.primary_keys:
continue
quoted_table = quote_table_name(table)
self.cursor.execute(f"SELECT COUNT(*) FROM {quoted_table};")
total_rows = self.cursor.fetchone()[0]
if total_rows == 0:
continue
pk_candidates = {}
for col_name, data_type in self.table_columns[table]:
if self.should_skip_column(table, col_name):
continue
try:
pk_score = 0
# Handle reserved keyword column names
quoted_col_name = quote_column_name(col_name)
# Check uniqueness
self.cursor.execute(f"SELECT COUNT(DISTINCT {quoted_col_name}) FROM {quoted_table};")
distinct_values = self.cursor.fetchone()[0]
uniqueness_ratio = distinct_values / max(1, total_rows)
if uniqueness_ratio < 0.9:
continue
# Check for NULL values
self.cursor.execute(f"SELECT COUNT(*) FROM {quoted_table} WHERE {quoted_col_name} IS NULL;")
null_count = self.cursor.fetchone()[0]
null_ratio = null_count / max(1, total_rows)
if null_ratio > 0.1:
continue
# Score uniqueness
if uniqueness_ratio == 1.0:
pk_score += 30
elif uniqueness_ratio > 0.98:
pk_score += 20
# No nulls is good for PKs
if null_count == 0:
pk_score += 20
# Data type scoring
if 'int' in data_type.lower():
pk_score += 15
elif data_type.lower() in ['text', 'varchar', 'char', 'string']:
pk_score += 5
# Check for naming patterns
name_patterns = [
(r'^id$', 15),
(r'^{}_id$'.format(table), 15),
(r'^{}_key$'.format(table), 15),
(r'^pk_', 15),
(r'^key$', 10),
(r'^code$', 8),
(r'^uuid$', 15),
(r'^guid$', 15),
(r'^serial$', 15),
(r'^seq', 10),
(r'id$', 5),
(r'uuid$', 10),
(r'code$', 5),
(r'num$', 5),
(r'no$', 5),
(r'^record', 8),
(r'^pid$', 15),
(r'^mid$', 15),
(r'^uid$', 15),
(r'^eid$', 15),
(r'^[a-z]+id$', 10),
(r'^[a-z]+_id$', 10),
]
for pattern, score in name_patterns:
if re.search(pattern, col_name, re.IGNORECASE):
pk_score += score
break
# Check for auto-increment indication
try:
self.cursor.execute(f"SELECT MIN({quoted_col_name}), MAX({quoted_col_name}) FROM {quoted_table};")
min_val, max_val = self.cursor.fetchone()
if (isinstance(min_val, int) and isinstance(max_val, int) and
min_val in [0, 1] and max_val == total_rows):
pk_score += 15
if total_rows <= 1000:
self.cursor.execute(f"""
WITH numbers AS (
SELECT ROW_NUMBER() OVER (ORDER BY {quoted_col_name}) + {min_val} - 1 as expected,
{quoted_col_name} as actual
FROM {quoted_table}
ORDER BY {quoted_col_name}
)
SELECT COUNT(*) FROM numbers
WHERE expected != actual;
""")
gaps = self.cursor.fetchone()[0]
if gaps == 0:
pk_score += 10
except (sqlite3.OperationalError, TypeError):
pass
# Check for index presence
self.cursor.execute(f"PRAGMA index_list({quoted_table});")
indexes = self.cursor.fetchall()
for idx_info in indexes:
idx_name = idx_info[1]
is_unique = idx_info[2]
self.cursor.execute(f"PRAGMA index_info({quote_column_name(idx_name)});")
index_columns = [info[2] for info in self.cursor.fetchall()]
if col_name in index_columns and len(index_columns) == 1:
pk_score += 10
if is_unique:
pk_score += 10
# Add to candidates if score is high enough
if pk_score >= 25:
pk_candidates[col_name] = {
'score': pk_score,
'data_type': data_type,
'uniqueness': uniqueness_ratio,
'null_ratio': null_ratio
}
except sqlite3.OperationalError as e:
print(f"Error analyzing column {table}.{col_name}: {e}")
continue
# Select the best primary key candidate(s)
if pk_candidates:
sorted_candidates = sorted(
pk_candidates.items(),
key=lambda x: x[1]['score'],
reverse=True
)
pk_columns = []
threshold_score = sorted_candidates[0][1]['score'] * 0.8
for col_name, info in sorted_candidates:
if info['score'] >= threshold_score:
pk_columns.append(col_name)
self.primary_keys[table] = {
'columns': pk_columns,
'origin': 'potential'
}
return self.primary_keys
def find_potential_foreign_keys(self):
"""Identify columns that are likely to be foreign keys."""
# First check for explicitly defined foreign keys
_, defined_fk = self._check_defined_keys()
if defined_fk:
print("Found defined foreign keys:", dict(defined_fk))
self.foreign_keys.update(defined_fk)
# Find potential foreign keys
for src_table in self.tables:
defined_fks_for_table = self.foreign_keys.get(src_table, [])
defined_fk_cols = set(fk['from'] for fk in defined_fks_for_table)
processed_relationships = set()
for fk in defined_fks_for_table:
processed_relationships.add((fk['from'], fk['to_table'], fk['to_column']))
quoted_src_table = quote_table_name(src_table)
for src_col_name, src_data_type in self.table_columns[src_table]:
if self.should_skip_column(src_table, src_col_name):
continue
if src_col_name in defined_fk_cols:
continue
quoted_src_col_name = quote_column_name(src_col_name)
for ref_table in self.tables:
if src_table == ref_table:
continue
if ref_table not in self.primary_keys:
continue
ref_col_list = self.primary_keys[ref_table].get('columns', [])
if not ref_col_list:
continue
quoted_ref_table = quote_table_name(ref_table)
for ref_col in ref_col_list:
if self.should_skip_column(ref_table, ref_col):
continue
quoted_ref_col = quote_column_name(ref_col)
if (src_col_name, ref_table, ref_col) in processed_relationships:
continue
# Define naming patterns for foreign keys
fk_patterns = [
r'^{}_{}$'.format(ref_table, ref_col),
r'^{}{}$'.format(ref_table, ref_col.capitalize()),
r'^{}_id$'.format(ref_table),
r'^{}Id$'.format(ref_table),
r'^{}_key$'.format(ref_table),
r'^fk_{}_'.format(ref_table),
r'^{}$'.format(ref_col)
]
name_pattern_match = False
for pattern in fk_patterns:
if re.match(pattern, src_col_name, re.IGNORECASE):
name_pattern_match = True
break
ref_col_type = next((dtype for col, dtype in self.table_columns[ref_table]
if col == ref_col), None)
if name_pattern_match:
if ref_col_type and src_data_type and ref_col_type.lower() != src_data_type.lower():
confidence = "low"
else:
confidence = "medium"
self.foreign_keys[src_table].append({
'from': src_col_name,
'to_table': ref_table,
'to_column': ref_col,
'origin': 'potential',
'confidence': confidence
})
processed_relationships.add((src_col_name, ref_table, ref_col))
# Check data value matching
else:
try:
if self.get_row_count(src_table) > 10000 or self.get_row_count(ref_table) > 10000:
continue
self.cursor.execute(f"""
SELECT COUNT(*) FROM {quoted_src_table}
WHERE {quoted_src_col_name} IS NOT NULL
AND {quoted_src_col_name} NOT IN (
SELECT {quoted_ref_col} FROM {quoted_ref_table}
)
""")
invalid_refs = self.cursor.fetchone()[0]
if invalid_refs == 0:
self.cursor.execute(f"""
SELECT COUNT(DISTINCT {quoted_src_col_name})
FROM {quoted_src_table}
WHERE {quoted_src_col_name} IS NOT NULL
""")
distinct_values = self.cursor.fetchone()[0]
self.cursor.execute(f"""
SELECT COUNT(DISTINCT {quoted_ref_col})
FROM {quoted_ref_table}
WHERE {quoted_ref_col} IS NOT NULL
""")
ref_distinct = self.cursor.fetchone()[0]
coverage_ratio = distinct_values / max(1, ref_distinct)
if (coverage_ratio > 0.01 and
(not ref_col_type or not src_data_type or
ref_col_type.lower() == src_data_type.lower())):
self.foreign_keys[src_table].append({
'from': src_col_name,
'to_table': ref_table,
'to_column': ref_col,
'origin': 'potential',
'confidence': 'high' if coverage_ratio > 0.3 else 'medium',
'evidence': 'data_match'
})
processed_relationships.add((src_col_name, ref_table, ref_col))
except sqlite3.OperationalError as e:
print(f"Error checking foreign key relationship {src_table}.{src_col_name} -> {ref_table}.{ref_col}: {e}")
continue
return dict(self.foreign_keys)
def get_row_count(self, table):
"""Get the number of rows in a table."""
quoted_table = quote_table_name(table)
self.cursor.execute(f"SELECT COUNT(*) FROM {quoted_table};")
return self.cursor.fetchone()[0]
def analyze(self):
"""Run the full analysis to find potential primary and foreign keys."""
print(f"Analyzing database: {self.db_path}")
self._get_tables()
print(f"Found {len(self.tables)} tables: {', '.join(self.tables)}")
self._get_table_columns()
print("\nFinding potential primary keys...")
pk_results = self.find_potential_primary_keys()
print("\nFinding potential foreign keys...")
fk_results = self.find_potential_foreign_keys()
return {
'tables': self.tables,
'columns': self.table_columns,
'primary_keys': pk_results,
'foreign_keys': fk_results
}
def close(self):
"""Close the database connection."""
if self.conn:
self.conn.close()
def extract_db_summary_for_schema(db_path: str,
sample_limit=5000,
include_samples=True,
include_column_names=True,
include_data_types=True,
detect_primary_keys=True,
detect_foreign_keys=True,
include_key_confidence=True,
include_row_count=True, # Enable: Critical for JOIN optimization
include_column_count=True,
include_distinct_count=True, # Enable: Cardinality for indexing decisions
include_null_count=True, # Enable: NULL handling in queries
include_cardinality=True, # Enable: Index selectivity
include_nullability=True, # Enable: NULL safety validation
include_min_max=True, # Enable: Range query optimization
include_average=False, # Optional: Less critical for SQL
include_median=False, # Optional: Less critical for SQL
include_stddev=False, # Optional: Less critical for SQL
include_avg_length=True, # Enable: String column sizing
include_common_values=True, # Enable: Pattern recognition
common_values_limit=5,
common_values_threshold=100,
include_date_range=True, # Enable: Time-based query optimization
include_date_range_days=True, # Enable: Date range insights
include_not_null_constraint=True, # Enable: Data quality validation
include_default_values=True, # Enable: INSERT statement help
include_indexes=True, # Enable: Performance optimization
max_rows_for_expensive_stats=10000,
max_string_display_length=100,
max_binary_display_bytes=50,
include_db_metadata=True, # Enable: Database-level info
include_table_metadata=True, # Enable: Table-level info
include_extraction_timestamp=True, # Enable: Freshness tracking
skip_empty_tables=True, # Enable: Focus on relevant tables
include_table_relationships=True,
include_schema_summary=True):
"""
Extract comprehensive database summary including schema, statistics, and sample data.
Args:
db_path: Path to SQLite database file
sample_limit: Number of sample rows per table
include_samples: Include sample data
include_column_names: Include column names
include_data_types: Include data types
detect_primary_keys: Detect primary keys
detect_foreign_keys: Detect foreign keys
include_key_confidence: Include key detection confidence
include_row_count: Include row counts
include_column_count: Include column counts
include_distinct_count: Include distinct value counts
include_null_count: Include NULL value counts
include_cardinality: Include uniqueness ratio
include_nullability: Include NULL ratio
include_min_max: Include min/max values
include_average: Include average values
include_median: Include median (expensive)
include_stddev: Include standard deviation (expensive)
include_avg_length: Include average text length
include_common_values: Include most frequent values
common_values_limit: How many common values to show
common_values_threshold: Only for columns with <= N distinct values
include_date_range: Include min/max dates
include_date_range_days: Include date range in days
include_not_null_constraint: Include NOT NULL constraints
include_default_values: Include default values
include_indexes: Include index information
max_rows_for_expensive_stats: Limit for median/stddev calculation
max_string_display_length: String truncation length
max_binary_display_bytes: Show binary data info up to N bytes
include_db_metadata: Include database metadata
include_table_metadata: Include table metadata
include_extraction_timestamp: Include timestamp
skip_empty_tables: Skip empty tables
include_table_relationships: Include relationship summary
include_schema_summary: Include overall schema statistics
Returns:
Dictionary containing database summary
"""
# Initialize key finder
key_finder = None
# Use SQLiteKeyFinder for enhanced PK/FK detection if requested
if detect_primary_keys or detect_foreign_keys:
key_finder = SQLiteKeyFinder(db_path)
key_analysis = key_finder.analyze()
else:
key_analysis = {'tables': [], 'columns': {}, 'primary_keys': {}, 'foreign_keys': {}}
# Still need to get basic table info
conn_temp = sqlite3.connect(db_path)
cursor_temp = conn_temp.cursor()
cursor_temp.execute("SELECT name FROM sqlite_master WHERE type='table';")
key_analysis['tables'] = [row[0] for row in cursor_temp.fetchall()
if not row[0].startswith('sqlite_')]
conn_temp.close()
# Connect to database for additional info extraction
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
# Get database size if metadata is requested
db_size = None
if include_db_metadata:
db_size = os.path.getsize(db_path) if os.path.exists(db_path) else None
tables = key_analysis['tables']
# Create summary structure
db_summary = {}
# Add metadata if requested
if include_db_metadata:
db_summary["metadata"] = {
"db_file": db_path,
"db_size_bytes": db_size,
"table_count": len(tables)
}
if include_extraction_timestamp:
db_summary["metadata"]["extracted_at"] = datetime.datetime.now().isoformat()
# Initialize schema summary if requested
schema_summary = {}
if include_schema_summary:
schema_summary = {
"total_tables": len(tables),
"total_columns": 0,
"tables_with_primary_keys": 0,
"tables_with_foreign_keys": 0,
"total_relationships": 0
}
db_summary["tables"] = {}
# Extract information for each table
for table in tables:
# Skip empty tables if requested
quoted_table = quote_table_name(table)
if skip_empty_tables:
cursor.execute(f"SELECT COUNT(*) FROM {quoted_table};")
if cursor.fetchone()[0] == 0:
continue
# Table schema
cursor.execute(f"PRAGMA table_info({quoted_table});")
columns_info = cursor.fetchall()
# Table structure
table_info = {"name": table}
# Add table metadata if requested
if include_table_metadata:
table_info["column_count"] = 0 # We'll count only non-skipped columns
# Get row count if requested
if include_row_count:
try:
cursor.execute(f"SELECT COUNT(*) FROM {quoted_table};")
table_info["row_count"] = cursor.fetchone()[0]
except sqlite3.Error as e:
print(f"Error getting row count for table {table}: {e}")
table_info["row_count"] = -1
# Initialize columns list
table_info["columns"] = []
# Initialize samples if requested
if include_samples:
table_info["samples"] = {}
# Get column information - with enhanced PK detection
if detect_primary_keys:
pk_info = key_analysis['primary_keys'].get(table, {})
primary_keys = pk_info.get('columns', [])
pk_origin = pk_info.get('origin', 'potential')
else:
primary_keys = []
pk_origin = 'unknown'
# Track foreign key columns
fk_columns = set()
# Process columns, skipping those that should be excluded
for col in columns_info:
column_name = col["name"]
# Skip columns that should be excluded based on DB_TABLE_SPECIFIC_SKIP_COLUMNS
if key_finder and key_finder.should_skip_column(table, column_name):
continue
# Count this column for metadata
if include_table_metadata:
table_info["column_count"] += 1
# Update schema summary
if include_schema_summary:
schema_summary["total_columns"] += 1
# Basic column information
column = {}
if include_column_names:
column["name"] = column_name
if include_data_types:
column["type"] = col["type"]
# Primary key information
if detect_primary_keys:
is_primary_key = column_name in primary_keys
column["is_primary_key"] = is_primary_key
if is_primary_key and include_key_confidence:
column["pk_origin"] = pk_origin
# NOT NULL constraint
if include_not_null_constraint:
column["not_null"] = bool(col["notnull"] == 1)
# Default values
if include_default_values:
column["default"] = col["dflt_value"]
# Extract column statistics
try:
# Handle reserved keyword column names
quoted_col_name = quote_column_name(column_name)
# Get distinct value count
if include_distinct_count:
cursor.execute(f"SELECT COUNT(DISTINCT {quoted_col_name}) FROM {quoted_table};")
column["distinct_count"] = cursor.fetchone()[0]
# Get null count
if include_null_count:
cursor.execute(f"SELECT COUNT(*) FROM {quoted_table} WHERE {quoted_col_name} IS NULL;")
column["null_count"] = cursor.fetchone()[0]
# Calculate derived statistics
if include_row_count and table_info.get("row_count", 0) > 0:
if include_nullability and include_null_count:
column["nullability"] = round(column["null_count"] / table_info["row_count"], 4)
if include_cardinality and include_distinct_count:
column["cardinality"] = round(column["distinct_count"] / table_info["row_count"], 4)
# For numeric columns, get min, max, avg
if any(num_type in col["type"].lower() for num_type in ["int", "real", "float", "double", "number", "decimal"]):
try:
if include_min_max or include_average:
cursor.execute(f"SELECT MIN({quoted_col_name}), MAX({quoted_col_name}), AVG({quoted_col_name}) FROM {quoted_table} WHERE {quoted_col_name} IS NOT NULL;")
min_val, max_val, avg_val = cursor.fetchone()
if include_min_max:
column["min"] = min_val
column["max"] = max_val
if include_average:
column["avg"] = round(avg_val, 4) if avg_val is not None else None
# For small to medium tables, get median and stddev
if (include_median or include_stddev) and table_info.get("row_count", 0) <= max_rows_for_expensive_stats:
cursor.execute(f"SELECT {quoted_col_name} FROM {quoted_table} WHERE {quoted_col_name} IS NOT NULL;")
values = [row[0] for row in cursor.fetchall() if row[0] is not None]
if values:
try:
if include_median:
column["median"] = round(statistics.median(values), 4)
if include_stddev and len(values) > 1:
column["stddev"] = round(statistics.stdev(values), 4)
except:
pass # Skip if not truly numeric
except:
pass # Skip if not truly numeric
# For text columns, get average length
if any(text_type in col["type"].lower() for text_type in ["text", "char", "varchar", "string"]):
try:
if include_avg_length:
cursor.execute(f"SELECT AVG(LENGTH({quoted_col_name})) FROM {quoted_table} WHERE {quoted_col_name} IS NOT NULL;")
avg_length = cursor.fetchone()[0]
column["avg_length"] = round(avg_length, 2) if avg_length is not None else None
# Get most common values for columns with reasonable cardinality
if (include_common_values and
include_distinct_count and
column.get("distinct_count", float('inf')) <= common_values_threshold):
cursor.execute(f"SELECT {quoted_col_name}, COUNT(*) as cnt FROM {quoted_table} WHERE {quoted_col_name} IS NOT NULL GROUP BY {quoted_col_name} ORDER BY cnt DESC LIMIT {common_values_limit};")
common_values = []
for row in cursor.fetchall():
value, count = row
if isinstance(value, str) and len(value) > max_string_display_length:
value = value[:max_string_display_length-3] + "..."
common_values.append({"value": value, "count": count})
column["common_values"] = common_values
except:
pass
# For date/time columns, get min and max dates
if any(date_type in col["type"].lower() for date_type in ["date", "time", "timestamp", "datetime"]):
try:
if include_date_range:
cursor.execute(f"SELECT MIN({quoted_col_name}), MAX({quoted_col_name}) FROM {quoted_table} WHERE {quoted_col_name} IS NOT NULL;")
min_date, max_date = cursor.fetchone()
column["min_date"] = min_date
column["max_date"] = max_date
# Try to calculate date range in days
if include_date_range_days:
try:
cursor.execute(f"SELECT julianday(MAX({quoted_col_name})) - julianday(MIN({quoted_col_name})) FROM {quoted_table} WHERE {quoted_col_name} IS NOT NULL;")
date_range = cursor.fetchone()[0]
if date_range is not None:
column["date_range_days"] = round(date_range, 2)
except:
pass
except:
pass
except sqlite3.Error as e:
print(f"Error getting statistics for column {table}.{column_name}: {e}")
# Add column to table
table_info["columns"].append(column)
# Prepare for sample data
if include_samples:
table_info["samples"][column_name] = []
# Get foreign keys using enhanced detection with categorization
if detect_foreign_keys:
table_info["foreign_keys"] = []
fk_list = key_analysis['foreign_keys'].get(table, [])
for fk in fk_list:
# Handle both regular and composite foreign keys
if 'type' in fk and fk['type'] == 'composite':
# Check if any columns in this composite key should be skipped
skip_columns = [col for col in fk['from_columns'] if key_finder and key_finder.should_skip_column(table, col)]
skip_ref_columns = [col for col in fk['to_columns'] if key_finder and key_finder.should_skip_column(fk['to_table'], col)]
# Skip this foreign key if it involves columns that should be excluded
if skip_columns or skip_ref_columns:
continue
# Composite foreign key
foreign_key = {
"columns": fk['from_columns'],
"references": {
"table": fk['to_table'],
"columns": fk['to_columns']
},
"type": "composite"
}
if include_key_confidence:
foreign_key["fk_origin"] = fk.get('origin', 'potential')
foreign_key["confidence"] = fk.get('confidence', 'low')
# Update the column records to mark them as foreign keys
for col_name in fk['from_columns']:
for column in table_info["columns"]:
if column.get("name") == col_name:
column["is_foreign_key"] = True
if include_key_confidence:
column["fk_origin"] = fk.get('origin', 'potential')
column["references_table"] = fk['to_table']
column["references_column"] = fk['to_columns'][fk['from_columns'].index(col_name)]
column["composite_fk"] = True
fk_columns.add(col_name)
else:
# Skip this foreign key if it involves columns that should be excluded
if key_finder and (key_finder.should_skip_column(table, fk['from']) or key_finder.should_skip_column(fk['to_table'], fk['to_column'])):
continue
# Regular foreign key
foreign_key = {
"column": fk['from'],
"references": {
"table": fk['to_table'],
"column": fk['to_column']
}
}
if include_key_confidence:
foreign_key["fk_origin"] = fk.get('origin', 'potential')
foreign_key["confidence"] = fk.get('confidence', 'medium')
# Update the column record to mark it as a foreign key
for column in table_info["columns"]:
if column.get("name") == fk['from']:
column["is_foreign_key"] = True
if include_key_confidence:
column["fk_origin"] = fk.get('origin', 'potential')
column["references_table"] = fk['to_table']
column["references_column"] = fk['to_column']
fk_columns.add(fk['from'])
# Add to table foreign keys
table_info["foreign_keys"].append(foreign_key)
# Get indexes if requested
if include_indexes:
table_info["indexes"] = []
cursor.execute(f"PRAGMA index_list({quoted_table});")
idx_list = cursor.fetchall()
for idx in idx_list:
# idx fields: (seq, name, unique, origin, partial)
index_name = idx["name"]
index_unique = (idx["unique"] == 1)
# Look up the columns used by this index
cursor.execute(f"PRAGMA index_info({quote_column_name(index_name)});")
idx_cols_info = cursor.fetchall()
col_names = [ic["name"] for ic in idx_cols_info]
# Filter out columns that should be excluded
if key_finder:
col_names = [col for col in col_names if not key_finder.should_skip_column(table, col)]
# Skip empty indexes (after filtering)
if not col_names:
continue
index = {
"name": index_name,
"unique": index_unique,
"columns": col_names
}
table_info["indexes"].append(index)
# Update column to indicate indexing
for col_name in col_names:
for column in table_info["columns"]:
if column.get("name") == col_name:
column["indexed"] = True
if "indexes" not in column:
column["indexes"] = []
column["indexes"].append(index_name)
# Get sample data if requested
if include_samples:
try:
# Debug info
cursor.execute(f"SELECT COUNT(*) FROM {quoted_table};")
row_count = cursor.fetchone()[0]
print(f"Table {table} has {row_count} rows")
# Get column names for SELECT, excluding those that should be skipped
valid_column_names = []
for col in columns_info:
col_name = col["name"]
if key_finder:
if not key_finder.should_skip_column(table, col_name):
valid_column_names.append(col_name)
else:
valid_column_names.append(col_name)
print(f"Valid columns for {table}: {valid_column_names}")
if valid_column_names and row_count > 0:
# Convert column names to quoted form for SQL
quoted_col_names = [quote_column_name(col) for col in valid_column_names]
select_cols = ', '.join(quoted_col_names)
# Try different ordering strategies
sample_rows = []
try:
# First try ordering by rowid
select_sql = f"SELECT {select_cols} FROM {quoted_table} ORDER BY rowid ASC LIMIT ?"
cursor.execute(select_sql, (sample_limit,))
sample_rows = cursor.fetchall()
print(f"Retrieved {len(sample_rows)} rows using rowid ordering")
except sqlite3.OperationalError as e1:
print(f"Rowid ordering failed: {e1}")
try:
# Fallback: order by first column
first_col_quoted = quote_column_name(valid_column_names[0])
select_sql = f"SELECT {select_cols} FROM {quoted_table} ORDER BY {first_col_quoted} ASC LIMIT ?"
cursor.execute(select_sql, (sample_limit,))
sample_rows = cursor.fetchall()
print(f"Retrieved {len(sample_rows)} rows using first column ordering")
except sqlite3.OperationalError as e2:
print(f"First column ordering failed: {e2}")
try:
# Last resort: no ordering
select_sql = f"SELECT {select_cols} FROM {quoted_table} LIMIT ?"
cursor.execute(select_sql, (sample_limit,))
sample_rows = cursor.fetchall()
print(f"Retrieved {len(sample_rows)} rows with no ordering")
except sqlite3.OperationalError as e3:
print(f"All query methods failed: {e3}")
sample_rows = []
# Process each sample row
if sample_rows:
print(f"Processing {len(sample_rows)} sample rows for table {table}")
if hasattr(sample_rows[0], 'keys'):
print(f"Sample row keys: {list(sample_rows[0].keys())}")
for row_idx, row in enumerate(sample_rows):
# Convert sqlite3.Row to dict for easier access
if hasattr(row, 'keys'):
row_dict = dict(row)
else:
# Fallback: assume it's a tuple and map to column names
row_dict = dict(zip(valid_column_names, row))
for col_name in valid_column_names:
# Skip columns not in our table_info (already filtered)
if col_name not in table_info["samples"]:
continue
# Get value - try different access methods
value = None
try:
# Method 1: Direct access by column name
value = row_dict.get(col_name)
if value is None:
# Method 2: Try case-insensitive lookup
for key in row_dict.keys():
if key.lower() == col_name.lower():
value = row_dict[key]
break
except (KeyError, IndexError, AttributeError) as e:
print(f"Error accessing column {col_name} in row {row_idx}: {e}")
print(f"Available keys: {list(row_dict.keys()) if isinstance(row_dict, dict) else 'Not a dict'}")
continue
# Handle special data types for JSON serialization
if isinstance(value, bytes):
if len(value) <= max_binary_display_bytes:
value = f"<binary data: {len(value)} bytes>"
else:
value = f"<binary data: {len(value)} bytes (truncated)>"
elif isinstance(value, str) and len(value) > max_string_display_length:
value = value[:max_string_display_length-3] + "..." # Truncate long strings
table_info["samples"][col_name].append(value)
else:
print(f"No sample rows retrieved for table {table}")
# Debug: Print final sample counts
for col_name, samples in table_info["samples"].items():
print(f"Column {col_name}: {len(samples)} samples")
except sqlite3.Error as e:
print(f"SQLite error getting sample data for table {table}: {e}")
print(f"Query attempted: {select_sql if 'select_sql' in locals() else 'No query built'}")
# Try to get actual column names from database for debugging
try:
cursor.execute(f"PRAGMA table_info({quoted_table});")
actual_columns = [col[1] for col in cursor.fetchall()]
print(f"Actual columns in {table}: {actual_columns}")
print(f"Valid columns we tried to use: {valid_column_names if 'valid_column_names' in locals() else 'None'}")
except Exception as debug_e:
print(f"Could not get debug info: {debug_e}")
except Exception as e:
print(f"Unexpected error getting sample data for table {table}: {e}")
import traceback
traceback.print_exc()
# Update schema summary
if include_schema_summary:
if detect_primary_keys and table in key_analysis['primary_keys']:
schema_summary["tables_with_primary_keys"] += 1
if detect_foreign_keys and table in key_analysis['foreign_keys']:
schema_summary["tables_with_foreign_keys"] += 1
schema_summary["total_relationships"] += len(key_analysis['foreign_keys'][table])
# Add table to db summary
db_summary["tables"][table] = table_info
# Add schema summary if requested
if include_schema_summary:
db_summary["schema_summary"] = schema_summary
# Add table relationships summary if requested
if include_table_relationships and detect_foreign_keys:
relationships = []
for table, fk_list in key_analysis['foreign_keys'].items():
for fk in fk_list:
if 'type' in fk and fk['type'] == 'composite':
relationships.append({
"from_table": table,
"from_columns": fk['from_columns'],
"to_table": fk['to_table'],
"to_columns": fk['to_columns'],
"type": "composite"
})
else:
relationships.append({
"from_table": table,
"from_column": fk['from'],
"to_table": fk['to_table'],
"to_column": fk['to_column'],
"type": "simple"
})
db_summary["relationships"] = relationships
# Close database connection
conn.close()
if key_finder:
key_finder.close()
return db_summary
def save_db_summary(db_summary, output_path, indent=2):
"""
Save database summary to JSON file
"""
def json_serialize(obj):
if isinstance(obj, np.integer):
return int(obj)
if isinstance(obj, np.floating):
return float(obj)
if isinstance(obj, np.ndarray):
return obj.tolist()
if isinstance(obj, datetime.datetime):
return obj.isoformat()
return str(obj)
# Create output directory if it doesn't exist
os.makedirs(os.path.dirname(output_path), exist_ok=True)
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(db_summary, f, default=json_serialize, indent=indent)
print(f"Database summary saved to {output_path}")
def main():
"""
Main function to run the schema analysis
"""
import argparse
from pathlib import Path
parser = argparse.ArgumentParser(description='Analyze a SQLite database and generate schema summary')
parser.add_argument('--db-path', required=True, help='Path to the SQLite database file (.sqlite)')
parser.add_argument('--output', help='Directory to save the JSON output',
default=str(Path(__file__).parent.parent.parent / 'data' / 'text2sql'))
args = parser.parse_args()
database_path = args.db_path
if not os.path.exists(database_path):
print(f"Error: Database file not found: {database_path}")
return None
print(f"Extracting schema summary from {database_path}...")
try:
db_summary = extract_db_summary_for_schema(database_path)
except Exception as e:
print(f"Error extracting database summary: {e}")
return None
# Save the summary
db_basename = os.path.splitext(os.path.basename(database_path))[0]
filename = f'{db_basename}_db_summary.json'
output_path = os.path.join(args.output, filename)
try:
save_db_summary(db_summary, output_path)
except Exception as e:
print(f"Error saving database summary: {e}")
return None
# Print summary statistics
print(f"\nSummary Statistics:")
if 'metadata' in db_summary:
metadata = db_summary['metadata']
db_size = metadata.get('db_size_bytes', 'unknown')
if isinstance(db_size, int):
db_size_mb = round(db_size / (1024 * 1024), 2)
print(f" Database size: {db_size:,} bytes ({db_size_mb} MB)")
else:
print(f" Database size: {db_size}")
print(f" Total tables: {metadata.get('table_count', 'unknown')}")
if 'schema_summary' in db_summary:
schema = db_summary['schema_summary']
print(f" Total columns: {schema.get('total_columns', 'unknown')}")
print(f" Tables with PKs: {schema.get('tables_with_primary_keys', 'unknown')}")
print(f" Tables with FKs: {schema.get('tables_with_foreign_keys', 'unknown')}")
print(f" Total relationships: {schema.get('total_relationships', 'unknown')}")
total_tables = len(db_summary.get('tables', {}))
print(f" Processed tables: {total_tables}")
# Calculate output file size
try:
output_size = os.path.getsize(output_path)
output_size_kb = round(output_size / 1024, 2)
print(f" Output file size: {output_size:,} bytes ({output_size_kb} KB)")
except:
pass
print(f"\nDatabase summary extraction completed and saved to {output_path}")
return db_summary
if __name__ == "__main__":
main() |