import mysql.connector.pooling
import time
from contextlib import contextmanager

# Configure connection pool
pool_config = {
    "pool_name": "jusdb_pool",
    "pool_size": 10,
    "pool_reset_session": True,
    "host": "localhost",
    "user": "app_user",
    "password": "secure_password",
    "database": "production_db",
    "autocommit": True,
    "charset": "utf8mb4"
}

# Initialize connection pool
connection_pool = mysql.connector.pooling.MySQLConnectionPool(**pool_config)

@contextmanager
def get_db_connection():
    """Context manager for database connections"""
    connection = connection_pool.get_connection()
    try:
        yield connection
    finally:
        connection.close()  # Returns to pool

def get_user_orders(user_id):
    """Example query using connection pool"""
    start_time = time.time()
    
    with get_db_connection() as conn:
        cursor = conn.cursor(dictionary=True)
        query = """
            SELECT o.id, o.order_date, o.total_amount, 
                   COUNT(oi.id) as item_count
            FROM orders o
            JOIN order_items oi ON o.id = oi.order_id
            WHERE o.user_id = %s
            GROUP BY o.id, o.order_date, o.total_amount
            ORDER BY o.order_date DESC
        """
        cursor.execute(query, (user_id,))
        results = cursor.fetchall()
        cursor.close()
    
    execution_time = time.time() - start_time
    print(f"Query executed in {execution_time:.3f}ms")
    return results

EXPLAIN FORMAT=JSON
SELECT p.name, p.price, c.name as category_name, 
       AVG(r.rating) as avg_rating
FROM products p
JOIN categories c ON p.category_id = c.id
LEFT JOIN reviews r ON p.id = r.product_id
WHERE p.price BETWEEN 100 AND 500
AND c.status = 'active'
GROUP BY p.id, p.name, p.price, c.name
HAVING AVG(r.rating) > 4.0
ORDER BY avg_rating DESC, p.price ASC;

-- Key EXPLAIN metrics to understand:
-- type: ALL (table scan), range, ref, eq_ref, const
-- rows: Estimated rows examined
-- filtered: Percentage of rows that meet WHERE conditions
-- Extra: Additional execution information
-- cost: Query execution cost estimate

-- Session 1: Start transaction and update account balance
START TRANSACTION;
UPDATE accounts SET balance = balance - 1000 WHERE account_id = 12345;
-- Transaction ID: 15001 (not yet committed)

-- Session 2: Read the same account (concurrent with Session 1)
START TRANSACTION;
SELECT balance FROM accounts WHERE account_id = 12345;
-- Reads the OLD version (before Session 1's update)
-- Uses undo log to reconstruct previous version

-- Session 1: Commit the transaction
COMMIT;

-- Session 2: Read again after Session 1's commit
SELECT balance FROM accounts WHERE account_id = 12345;
-- Now reads the NEW version (after Session 1's update)
-- Different result within same transaction due to isolation level

-- Monitor current locks
SELECT 
    r.trx_id waiting_trx_id,
    r.trx_mysql_thread_id waiting_thread,
    r.trx_query waiting_query,
    b.trx_id blocking_trx_id,
    b.trx_mysql_thread_id blocking_thread,
    b.trx_query blocking_query
FROM information_schema.innodb_lock_waits w
INNER JOIN information_schema.innodb_trx b ON b.trx_id = w.blocking_trx_id
INNER JOIN information_schema.innodb_trx r ON r.trx_id = w.requesting_trx_id;

-- Check for deadlocks
SHOW ENGINE INNODB STATUS;

-- Monitor lock wait statistics
SELECT * FROM performance_schema.events_waits_summary_global_by_event_name 
WHERE event_name LIKE 'wait/synch/mutex/innodb%'
ORDER BY sum_timer_wait DESC;

/var/lib/mysql/
├── mysql/                          # System database
├── performance_schema/             # Performance monitoring
├── information_schema/             # Metadata access
├── sys/                           # Sys schema utilities
├── ecommerce_db/                  # Application database
│   ├── users.ibd                  # User table data/indexes
│   ├── products.ibd               # Product table data/indexes  
│   ├── orders.ibd                 # Orders table data/indexes
│   └── order_items.ibd            # Order items data/indexes
├── ib_logfile0                    # Redo log file 1
├── ib_logfile1                    # Redo log file 2  
├── ib_logfile2                    # Redo log file 3
├── ibdata1                        # System tablespace
├── ibtmp1                         # Temporary tablespace
├── undo_001                       # Undo tablespace 1
├── undo_002                       # Undo tablespace 2
├── binlog.000001                  # Binary log file
├── binlog.000002                  # Binary log file
├── binlog.index                   # Binary log index
├── relay-log.000001               # Relay log (replica)
├── mysql-bin.index                # Binary log index
├── auto.cnf                       # Server UUID
├── ca-key.pem                     # SSL certificate key
├── ca.pem                         # SSL certificate
└── mysql.sock                     # Unix socket file

-- Enable binary logging (requires restart)
# In my.cnf:
# log-bin = mysql-bin
# server-id = 1
# binlog_format = ROW

-- Check binary log status
SHOW MASTER STATUS;

-- List binary log files
SHOW BINARY LOGS;

-- View binary log events
SHOW BINLOG EVENTS IN 'mysql-bin.000001' LIMIT 10;

-- Purge old binary logs
PURGE BINARY LOGS TO 'mysql-bin.000010';
PURGE BINARY LOGS BEFORE '2024-01-01 00:00:00';

-- Set binary log retention
SET GLOBAL binlog_expire_logs_seconds = 604800; -- 7 days

-- Complex analytical query example
SELECT 
    DATE(o.order_date) as order_day,
    COUNT(*) as total_orders,
    SUM(o.total_amount) as daily_revenue,
    AVG(o.total_amount) as avg_order_value,
    COUNT(DISTINCT o.customer_id) as unique_customers
FROM orders o
JOIN order_items oi ON o.id = oi.order_id  
JOIN products p ON oi.product_id = p.id
WHERE o.order_date >= '2024-01-01' 
AND o.order_date < '2024-02-01'
AND p.category_id IN (1, 2, 3, 5, 8)
GROUP BY DATE(o.order_date)
ORDER BY order_day;

-- Performance on different storage:
-- HDD:      ~15 seconds (heavy disk I/O, sequential reads)
-- SATA SSD: ~2 seconds  (fast random access, reduced I/O wait)
-- NVMe SSD: ~0.5 seconds (ultra-fast random access, parallel I/O)

-- Table scan performance (1M rows, 100MB table):
-- HDD:      ~8 seconds  (sequential read advantage)
-- SATA SSD: ~1 second   (fast sequential throughput)
-- NVMe SSD: ~0.3 seconds (maximum throughput utilization)

# Monitor connection pool metrics
def monitor_connection_pool():
    pool_stats = connection_pool.get_stats()
    print(f"Active connections: {pool_stats.active_connections}")
    print(f"Total connections: {pool_stats.total_connections}")
    print(f"Connections in use: {pool_stats.connections_in_use}")
    print(f"Average wait time: {pool_stats.avg_wait_time}ms")
    
    # Alert if wait time exceeds threshold
    if pool_stats.avg_wait_time > 50:  # 50ms threshold
        alert_slow_connection_pool()

# Dynamic pool sizing based on load
def adjust_pool_size():
    current_load = get_current_application_load()
    if current_load > 0.8:  # High load
        connection_pool.resize(connection_pool.size * 1.2)
    elif current_load < 0.3:  # Low load
        connection_pool.resize(max(5, connection_pool.size * 0.8))

# Optimal buffer pool configuration in my.cnf
[mysqld]
# Buffer pool size (70-80% of RAM for dedicated DB server)
innodb_buffer_pool_size = 12G

# Multiple buffer pool instances for concurrency (1 per GB up to 64)
innodb_buffer_pool_instances = 12

# Buffer pool dump and load for faster restarts  
innodb_buffer_pool_dump_at_shutdown = 1
innodb_buffer_pool_load_at_startup = 1

# Advanced buffer pool tuning
innodb_old_blocks_pct = 37          # LRU list midpoint percentage
innodb_old_blocks_time = 1000       # Time before page moves to young list
innodb_read_ahead_threshold = 56    # Random read-ahead threshold

# Monitor buffer pool performance
SELECT 
    ROUND((A.VARIABLE_VALUE / B.VARIABLE_VALUE) * 100, 2) AS buffer_pool_hit_ratio
FROM 
    performance_schema.global_status A,
    performance_schema.global_status B
WHERE 
    A.VARIABLE_NAME = 'Innodb_buffer_pool_read_requests' AND
    B.VARIABLE_NAME = 'Innodb_buffer_pool_reads';

SELECT 
    o.id,
    o.order_date,
    o.total_amount,
    c.name as customer_name,
    c.email,
    GROUP_CONCAT(p.name SEPARATOR ', ') as products,
    COUNT(oi.id) as item_count
FROM orders o
JOIN customers c ON o.customer_id = c.id
JOIN order_items oi ON o.id = oi.order_id  
JOIN products p ON oi.product_id = p.id
WHERE o.order_date >= DATE_SUB(NOW(), INTERVAL 7 DAY)
GROUP BY o.id, o.order_date, o.total_amount, c.name, c.email
ORDER BY o.order_date DESC
LIMIT 50;

-- EXPLAIN showed:
-- Full table scan on orders (2.3M rows)
-- Nested loop joins with no index utilization
-- Filesort for ORDER BY
-- Using temporary for GROUP BY

-- Step 2a: Create optimized indexes
CREATE INDEX idx_orders_date_customer 
ON orders (order_date DESC, customer_id, id, total_amount);

CREATE INDEX idx_order_items_order_product 
ON order_items (order_id, product_id);

CREATE INDEX idx_customers_covering 
ON customers (id, name, email);

-- Step 2b: Rewrite query for better performance
SELECT 
    o.id,
    o.order_date,
    o.total_amount,
    c.name as customer_name,
    c.email,
    oi.item_count
FROM orders o
FORCE INDEX (idx_orders_date_customer)
JOIN customers c ON o.customer_id = c.id
JOIN (
    SELECT order_id, COUNT(*) as item_count
    FROM order_items
    GROUP BY order_id
) oi ON o.id = oi.order_id
WHERE o.order_date >= DATE_SUB(NOW(), INTERVAL 7 DAY)
ORDER BY o.order_date DESC
LIMIT 50;

-- Step 2c: Separate query for product details (loaded on demand)
SELECT 
    oi.order_id,
    GROUP_CONCAT(p.name SEPARATOR ', ') as products
FROM order_items oi
JOIN products p ON oi.product_id = p.id  
WHERE oi.order_id IN (/* selected order IDs */)
GROUP BY oi.order_id;

# Enhanced InnoDB configuration
[mysqld]
# Buffer pool optimizations
innodb_buffer_pool_size = 8G
innodb_buffer_pool_instances = 8
innodb_buffer_pool_dump_at_shutdown = 1
innodb_buffer_pool_load_at_startup = 1

# I/O optimizations  
innodb_io_capacity = 2000
innodb_io_capacity_max = 4000
innodb_read_io_threads = 8
innodb_write_io_threads = 8

# Log optimizations
innodb_log_file_size = 1G
innodb_log_buffer_size = 64M
innodb_flush_log_at_trx_commit = 2

# Advanced settings
innodb_adaptive_hash_index = 1
innodb_change_buffering = all
innodb_old_blocks_time = 1000

# Python example with detailed metrics collection
import time
import logging
from contextlib import contextmanager

class DatabaseMetrics:
    def __init__(self):
        self.query_times = []
        self.connection_times = []
        self.error_count = 0
    
    @contextmanager 
    def track_query(self, query_type):
        start_time = time.time()
        connection_start = time.time()
        
        try:
            connection = get_connection()
            connection_time = time.time() - connection_start
            self.connection_times.append(connection_time)
            
            yield connection
            
            execution_time = time.time() - start_time
            self.query_times.append(execution_time)
            
            # Log slow queries
            if execution_time > 1.0:  # 1 second threshold
                logging.warning(f"Slow query detected: {query_type} took {execution_time:.2f}s")
                
        except Exception as e:
            self.error_count += 1
            logging.error(f"Database error in {query_type}: {str(e)}")
            raise
        finally:
            if 'connection' in locals():
                connection.close()

    def get_stats(self):
        if not self.query_times:
            return {}
            
        return {
            'avg_query_time': sum(self.query_times) / len(self.query_times),
            'max_query_time': max(self.query_times),
            'total_queries': len(self.query_times),
            'avg_connection_time': sum(self.connection_times) / len(self.connection_times),
            'error_rate': self.error_count / len(self.query_times) if self.query_times else 0
        }

# Usage example
db_metrics = DatabaseMetrics()

def get_customer_orders(customer_id):
    with db_metrics.track_query('customer_orders') as conn:
        cursor = conn.cursor()
        cursor.execute("""
            SELECT o.id, o.order_date, o.total_amount
            FROM orders o 
            WHERE o.customer_id = %s 
            ORDER BY o.order_date DESC 
            LIMIT 20
        """, (customer_id,))
        return cursor.fetchall()

# I/O monitoring with iostat
iostat -x 1

# Key metrics to monitor:
# %util - Percentage of time device was busy
# avgqu-sz - Average queue length  
# await - Average wait time for requests
# r/s, w/s - Read/write operations per second
# rkB/s, wkB/s - Read/write kilobytes per second

# Memory monitoring
free -h
cat /proc/meminfo | grep -E "(MemTotal|MemAvailable|Cached|Buffers)"

# CPU monitoring  
top -p $(pgrep mysqld)
htop -p $(pgrep mysqld)

# Network monitoring
iftop -i eth0
netstat -i

# Disk space monitoring
df -h
du -sh /var/lib/mysql/*

# MySQL process monitoring
ps aux | grep mysql
pstree -p $(pgrep mysqld)

# Advanced I/O monitoring with iotop
iotop -p $(pgrep mysqld)