Autonomous Indexing Swarms: Detecting Slow Queries and Recommending PG Indexes

The Core Bottleneck: Query Performance Decay

In typical database architectures, slow queries occur due to:

• Missing Index Coordinates: The database execution planner is forced to run sequential scans (Seq Scan) across millions of table rows.
• Write Bloat: Storing redundant indices degrades insert, update, and delete throughput, as the database engine must rebuild index nodes after every mutation.
• The Solution: An Indexing Swarm. A background agent gathers query metrics, parses target execution structures using AST nodes, runs simulations inside staging containers, and writes index suggestions.

1. Under the Hood: Parsing the AST of SQL Statements

To recommend an index, the agent needs to analyze the statement structure:

• Filter Conditions (WHERE / JOIN keys): Isolating columns that act as filter gates.
• Ordering Clauses (ORDER BY): Identifying target columns that dictate sorted queries.
• Topological Index Selection: Synthesizing compound indices (e.g. (user_id, created_at)) to optimize multi-key lookups.

2. Setting up Non-Blocking Index Creations

Writing database indexes on live production systems is high-risk:

1. Never Lock Writes: Always append CONCURRENTLY to your CREATE INDEX queries. Creating an index without this keyword blocks write queries on target tables.
2. Execute Staging Validations: Run DDL statements inside staging environments first to confirm that the index decreases execution plans and does not cause syntax exceptions.

Code Demo: Autonomous Query Optimizer

Below is a Python implementation of an autonomous index planner. It parses a simulated slow query structure, evaluates explain-plan execution costs, recommends index configurations, and compiles DDL index statements.

import re
from typing import Dict, Any, Tuple, List

class PGIndexSwarmOptimizer:
    def __init__(self):
        # Database mock mapping table names to their column indexes
        self.existing_indexes: Dict[str, List[str]] = {
            "users": ["id"]
        }

    def analyze_slow_query(self, query: str) -> Tuple[str, float, str]:
        # Clean query spacing
        clean_query = " ".join(query.lower().split())

        # 1. Parse table target using regex
        table_match = re.search(r"from\s+(\w+)", clean_query)
        if not table_match:
            return "UNKNOWN", 0.0, "Could not determine target table name."
        
        table_name = table_match.group(1)

        # 2. Parse WHERE filter columns
        where_match = re.search(r"where\s+(.*)", clean_query)
        if not where_match:
            return table_name, 0.0, "Query lacks WHERE filters. No index recommended."

        where_clause = where_match.group(1)
        # Extract column names (simple parsing looking for columns compared with = or in)
        filter_columns = re.findall(r"(\w+)\s*(?:=|in)", where_clause)
        
        if not filter_columns:
            return table_name, 0.0, "No indexable filter criteria detected."

        target_col = filter_columns[0]
        
        # 3. Check existing index constraints
        table_indexes = self.existing_indexes.get(table_name, [])
        if target_col in table_indexes:
            return table_name, 0.0, f"Index already exists for '{table_name}({target_col})'."

        # 4. Simulate cost reduction (Explain plan score simulation)
        # Without index: Cost = 5000 units (Seq Scan)
        # With index: Cost = 150 units (Index Scan)
        cost_reduction = 4850.0

        # Compile non-blocking DDL command
        index_name = f"idx_{table_name}_{target_col}"
        ddl = f"CREATE INDEX CONCURRENTLY {index_name} ON {table_name} ({target_col});"

        return table_name, cost_reduction, ddl

if __name__ == "__main__":
    optimizer = PGIndexSwarmOptimizer()

    # Query 1: Unindexed WHERE search on user emails
    sql_1 = """
    SELECT id, name, email 
    FROM users 
    WHERE email = 'alice@example.com' AND status = 'active';
    """

    # Query 2: Search on already indexed column 'id'
    sql_2 = """
    SELECT name FROM users WHERE id = 101;
    """

    print("🤖 Running SQL Index Swarm Analyzer...")
    print("----------------------------------------")

    for idx, sql in enumerate([sql_1, sql_2], 1):
        table, cost_saved, result = optimizer.analyze_slow_query(sql)
        print(f"\n[Slow Query #{idx}] Table: {table}")
        if cost_saved > 0:
            print(f"👉 Recommending Action (Estimated Cost Reduction: {cost_saved} units):")
            print(f"   {result}")
        else:
            print(f"👉 Result: {result}")

Architectural Guidelines

• Run CONCURRENTLY: Always configure your indexing swarms to write DDL migrations using CREATE INDEX CONCURRENTLY to avoid write lock bottlenecks.
• Isolate on Staging: Enforce staging validations to confirm that index updates actually reduce SQL execution costs before applying migrations.
• Prune Unused Indexes: Monitor index usage patterns and trigger delete scripts on indices that are never queried to reclaim storage space.