Matter AI | Code Reviewer Documentation

Database Performance Overview

Missing Indexes

-- Anti-pattern: Query without proper indexes
SELECT * FROM users 
WHERE last_name = 'Smith' AND status = 'active';

-- Better approach: Create appropriate indexes
CREATE INDEX idx_users_last_name_status ON users(last_name, status);

-- Now the query will use the index
SELECT * FROM users 
WHERE last_name = 'Smith' AND status = 'active';

// Anti-pattern: Not considering indexes in ORM entities
@Entity
public class Product {
    @Id
    private Long id;
    
    private String name;
    private String category;
    private boolean active;
    
    // Getters and setters...
}

// Repository usage without considering indexes
public List<Product> findActiveProductsByCategory(String category) {
    return productRepository.findByCategoryAndActiveTrue(category);
}

// Better approach: Adding indexes to frequently queried fields
@Entity
@Table(indexes = {
    @Index(name = "idx_product_category_active", columnList = "category,active")
})
public class Product {
    @Id
    private Long id;
    
    private String name;
    private String category;
    private boolean active;
    
    // Getters and setters...
}

Missing indexes on frequently queried columns can lead to full table scans, resulting in poor query performance, especially as the table grows.

To optimize indexing:

Identify and index columns used in WHERE, JOIN, and ORDER BY clauses
Create composite indexes for queries with multiple conditions
Consider the order of columns in composite indexes (most selective first)
Avoid over-indexing, as it impacts write performance
Regularly analyze query performance using EXPLAIN or similar tools
Consider partial indexes for large tables with specific query patterns
Monitor index usage and remove unused indexes
Rebuild indexes periodically to prevent fragmentation
Consider covering indexes for frequently used queries

SELECT * Anti-pattern

-- Anti-pattern: Using SELECT * unnecessarily
SELECT * FROM orders 
WHERE customer_id = 12345;

-- Better approach: Select only needed columns
SELECT order_id, order_date, total_amount 
FROM orders 
WHERE customer_id = 12345;

// Anti-pattern: Retrieving all fields in MongoDB
async function getCustomerOrders(customerId) {
  // Retrieves all fields for each order
  return await db.collection('orders').find({ customerId }).toArray();
}

// Better approach: Projecting only needed fields
async function getCustomerOrdersEfficiently(customerId) {
  return await db.collection('orders').find(
    { customerId },
    { projection: { orderId: 1, orderDate: 1, totalAmount: 1, _id: 1 }}
  ).toArray();
}

Using SELECT * or retrieving all fields from a database when only a subset is needed increases network traffic, memory usage, and processing time, especially for tables with many columns or large text/blob fields.

To optimize field selection:

Select only the columns needed for your application logic
Avoid retrieving large text or binary fields unless necessary
Use projections in NoSQL databases to limit returned fields
Consider the impact on covering indexes (queries that can be satisfied from the index alone)
Be mindful of ORM eager loading patterns
Use column selection to reduce network traffic between database and application
Consider creating views for frequently used column subsets
Be aware that SELECT * can break code if table schema changes

Inefficient Connection Management

// Anti-pattern: Opening and closing connections for each operation
public void processOrders() {
    for (Order order : getOrdersToProcess()) {
        // Opening a new connection for each order
        Connection conn = DriverManager.getConnection(DB_URL, USER, PASS);
        try {
            processOrder(conn, order);
        } finally {
            conn.close(); // Closing connection after each order
        }
    }
}

// Better approach: Using connection pooling
public void processOrdersEfficiently() {
    // Using a connection from the pool
    try (Connection conn = dataSource.getConnection()) {
        for (Order order : getOrdersToProcess()) {
            processOrder(conn, order);
        }
    } catch (SQLException e) {
        // Handle exception
    }
}

// Anti-pattern: Creating new database connections for each request
app.get('/api/users', async (req, res) => {
  // Creating a new connection for each request
  const client = new MongoClient(uri);
  try {
    await client.connect();
    const users = await client.db('myapp').collection('users').find({}).toArray();
    res.json(users);
  } finally {
    await client.close();
  }
});

// Better approach: Using connection pooling
// Initialize connection pool once during application startup
const mongoClient = new MongoClient(uri, { maxPoolSize: 20 });
await mongoClient.connect();

app.get('/api/users', async (req, res) => {
  try {
    // Reusing connection from the pool
    const users = await mongoClient.db('myapp').collection('users').find({}).toArray();
    res.json(users);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

Inefficient connection management, such as creating and closing database connections for each operation, can significantly impact performance due to the high overhead of connection establishment.

To optimize connection management:

Use connection pooling to reuse database connections
Configure appropriate pool sizes based on workload
Properly close connections when they’re no longer needed
Monitor connection usage and pool statistics
Consider using a single connection for multiple operations when appropriate
Implement proper error handling to prevent connection leaks
Use connection validation to detect stale connections
Consider using persistent connections for long-lived applications
Implement proper timeout settings to prevent hanging connections

Inefficient Pagination

-- Anti-pattern: Inefficient pagination with OFFSET
SELECT * FROM products
ORDER BY created_at DESC
LIMIT 20 OFFSET 10000;

-- Better approach: Using keyset pagination
SELECT * FROM products
WHERE created_at < :last_seen_created_at
ORDER BY created_at DESC
LIMIT 20;

// Anti-pattern: Inefficient pagination in MongoDB
async function getProductPage(pageNumber, pageSize) {
  return await db.collection('products')
    .find({})
    .sort({ createdAt: -1 })
    .skip((pageNumber - 1) * pageSize)
    .limit(pageSize)
    .toArray();
}

// Better approach: Using cursor-based pagination
async function getProductPageEfficiently(lastSeenId, lastSeenCreatedAt, pageSize) {
  return await db.collection('products')
    .find({
      $or: [
        { createdAt: { $lt: lastSeenCreatedAt } },
        { 
          createdAt: lastSeenCreatedAt,
          _id: { $lt: lastSeenId }
        }
      ]
    })
    .sort({ createdAt: -1, _id: -1 })
    .limit(pageSize)
    .toArray();
}

Inefficient pagination techniques, particularly using OFFSET/SKIP for deep pagination, can lead to poor performance as the database must still process all skipped rows.

To optimize pagination:

Use keyset pagination (cursor-based) instead of offset pagination
Implement filtering by the last seen value rather than using OFFSET
Include a unique identifier in pagination to handle ties
Consider using covering indexes for pagination queries
Implement proper caching for frequently accessed pages
Use appropriate indexes for sorting columns
Consider using materialized views for complex pagination scenarios
Limit maximum page sizes to prevent resource exhaustion
Use query hints when necessary to force index usage

Cartesian Product Joins

-- Anti-pattern: Cartesian product (missing JOIN condition)
SELECT o.order_id, c.customer_name
FROM orders o, customers c
-- Missing JOIN condition creates a cartesian product

-- Better approach: Proper JOIN with condition
SELECT o.order_id, c.customer_name
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id

// Anti-pattern: Cartesian product in JPA/JPQL
@Query("SELECT o, c FROM Order o, Customer c")
List<Object[]> findAllOrdersAndCustomers();

// Better approach: Proper JOIN with condition
@Query("SELECT o, c FROM Order o JOIN o.customer c")
List<Object[]> findAllOrdersWithCustomers();

Cartesian product joins (cross joins) occur when there’s no join condition between tables, resulting in each row from one table being combined with every row from another table. This can lead to exponential result set growth and severe performance issues.

To avoid cartesian product joins:

Always include proper JOIN conditions between tables
Use explicit JOIN syntax rather than comma-separated table lists
Verify that all table relationships have appropriate conditions
Use query analyzers to identify potential cartesian products
Implement proper foreign key constraints
Consider using EXISTS or IN clauses for existence checks instead of joins
Review and test queries with realistic data volumes
Be cautious with dynamically generated SQL
Use ORMs that enforce proper join conditions

N+1 Query Problem

// Anti-pattern: N+1 query problem in ORM
public void displayOrdersInefficiently() {
    // First query: Get all orders
    List<Order> orders = orderRepository.findAll();
    
    for (Order order : orders) {
        // N additional queries: Get customer for each order
        Customer customer = customerRepository.findById(order.getCustomerId());
        System.out.println("Order #" + order.getId() + " by " + customer.getName());
    }
}

// Better approach: Using join fetch or eager loading
public void displayOrdersEfficiently() {
    // Single query with join fetch
    List<Order> ordersWithCustomers = orderRepository.findAllWithCustomers();
    
    for (Order order : ordersWithCustomers) {
        // Customer is already loaded, no additional query needed
        Customer customer = order.getCustomer();
        System.out.println("Order #" + order.getId() + " by " + customer.getName());
    }
}

// Anti-pattern: N+1 query problem in Node.js/MongoDB
async function getUsersWithPostsInefficiently() {
  // First query: Get all users
  const users = await User.find({});
  
  // For each user, fetch their posts separately
  for (const user of users) {
    // N additional queries
    user.posts = await Post.find({ userId: user._id });
  }
  
  return users;
}

// Better approach: Using aggregation or population
async function getUsersWithPostsEfficiently() {
  // Option 1: Using MongoDB aggregation
  const usersWithPosts = await User.aggregate([
    {
      $lookup: {
        from: 'posts',
        localField: '_id',
        foreignField: 'userId',
        as: 'posts'
      }
    }
  ]);
  
  return usersWithPosts;
  
  // Option 2: Using Mongoose population
  const users = await User.find({}).populate('posts');
  return users;
}

The N+1 query problem occurs when an application makes one query to fetch a list of N items, then makes N additional queries to fetch related data for each item, resulting in N+1 total queries.

To avoid the N+1 query problem:

Use eager loading, join fetches, or includes in ORMs
Implement batch loading of related entities
Use GraphQL with DataLoader for efficient batching
Consider denormalization for frequently accessed data
Use database-specific features like MongoDB’s $lookup or SQL JOINs
Implement caching strategies for related entities
Monitor and analyze database query patterns
Use query optimization tools provided by your ORM or database
Consider using specialized libraries like Facebook’s DataLoader

Inefficient Transaction Management

// Anti-pattern: Transactions that are too long or too short
@Transactional
public void processOrderInefficiently(Long orderId) {
    Order order = orderRepository.findById(orderId).orElseThrow();
    
    // Potentially long-running operation inside transaction
    generateInvoicePDF(order); // This could take several seconds
    
    order.setStatus("PROCESSED");
    orderRepository.save(order);
    
    // External API call inside transaction
    notifyShippingService(order); // Network call that could fail or timeout
}

// Better approach: Appropriate transaction boundaries
public void processOrderEfficiently(Long orderId) {
    // Step 1: Get the order (read-only operation)
    Order order = orderRepository.findById(orderId).orElseThrow();
    
    // Step 2: Long-running operation outside transaction
    generateInvoicePDF(order);
    
    // Step 3: Database updates in a focused transaction
    updateOrderStatus(order.getId(), "PROCESSED");
    
    // Step 4: External calls after transaction
    notifyShippingService(order);
}

@Transactional
public void updateOrderStatus(Long orderId, String status) {
    Order order = orderRepository.findById(orderId).orElseThrow();
    order.setStatus(status);
    orderRepository.save(order);
}

// Anti-pattern: Poor transaction management in Node.js/MongoDB
async function transferFundsInefficiently(fromAccountId, toAccountId, amount) {
  // Two separate operations without transaction
  await Account.updateOne(
    { _id: fromAccountId },
    { $inc: { balance: -amount } }
  );
  
  // If an error occurs here, the money is lost
  
  await Account.updateOne(
    { _id: toAccountId },
    { $inc: { balance: amount } }
  );
}

// Better approach: Using transactions
async function transferFundsEfficiently(fromAccountId, toAccountId, amount) {
  const session = await mongoose.startSession();
  session.startTransaction();
  
  try {
    await Account.updateOne(
      { _id: fromAccountId },
      { $inc: { balance: -amount } },
      { session }
    );
    
    await Account.updateOne(
      { _id: toAccountId },
      { $inc: { balance: amount } },
      { session }
    );
    
    await session.commitTransaction();
  } catch (error) {
    await session.abortTransaction();
    throw error;
  } finally {
    session.endSession();
  }
}

Inefficient transaction management, such as transactions that are too long, too short, or missing entirely, can lead to performance issues, data inconsistency, and resource contention.

To optimize transaction management:

Keep transactions as short as possible
Avoid including long-running operations in transactions
Don’t include external API calls in transactions
Use read-only transactions for queries when appropriate
Consider transaction isolation levels based on requirements
Implement proper error handling and rollback mechanisms
Use optimistic locking for low-contention scenarios
Consider using compensating transactions for distributed systems
Monitor transaction duration and lock contention
Implement retry mechanisms for transient failures

Inefficient Batch Processing

// Anti-pattern: Processing records one at a time
public void updatePricesInefficiently(List<Product> products, double increase) {
    for (Product product : products) {
        // One database call per product
        product.setPrice(product.getPrice() * (1 + increase));
        productRepository.save(product);
    }
}

// Better approach: Using batch operations
public void updatePricesEfficiently(List<Product> products, double increase) {
    // Update all products in memory
    for (Product product : products) {
        product.setPrice(product.getPrice() * (1 + increase));
    }
    
    // Save all products in a single batch operation
    productRepository.saveAll(products);
}

// Anti-pattern: Individual MongoDB updates
async function updateUserStatusesInefficiently(userIds, newStatus) {
  for (const userId of userIds) {
    // One database call per user
    await User.updateOne(
      { _id: userId },
      { $set: { status: newStatus } }
    );
  }
}

// Better approach: Using bulk operations
async function updateUserStatusesEfficiently(userIds, newStatus) {
  // Single bulk operation
  await User.updateMany(
    { _id: { $in: userIds } },
    { $set: { status: newStatus } }
  );
}

Processing database operations one at a time instead of in batches can lead to excessive network round trips, higher latency, and poor throughput, especially when dealing with large datasets.

To optimize batch processing:

Use bulk operations provided by your database or ORM
Group similar operations into batches
Consider appropriate batch sizes (too large can cause memory issues)
Use multi-value inserts instead of individual inserts
Implement proper error handling for batch operations
Consider using JDBC batch operations in Java
Use prepared statements for batch operations
Monitor performance to find optimal batch sizes
Consider using asynchronous batch processing for non-critical operations
Implement proper transaction management for batches

Inefficient Query Patterns

-- Anti-pattern: Using functions on indexed columns
SELECT * FROM users
WHERE LOWER(email) = 'user@example.com';

-- Better approach: Store normalized data and use direct comparison
SELECT * FROM users
WHERE email = 'user@example.com';

-- Alternative: Create functional index if normalization is not possible
CREATE INDEX idx_users_email_lower ON users(LOWER(email));

// Anti-pattern: Inefficient MongoDB query patterns
async function findActiveUsersInefficiently() {
  // Using $ne which can't use indexes efficiently
  return await User.find({ status: { $ne: 'inactive' } });
}

// Better approach: Using equality operator
async function findActiveUsersEfficiently() {
  // Using equality which can use indexes efficiently
  return await User.find({ status: 'active' });
}

// Anti-pattern: Inefficient sorting without index
async function getRecentOrdersInefficiently(userId) {
  // Sorting without proper index
  return await Order.find({ userId })
    .sort({ createdAt: -1 })
    .limit(10);
}

// Better approach: Ensure proper index exists
// db.orders.createIndex({ userId: 1, createdAt: -1 });
async function getRecentOrdersEfficiently(userId) {
  // Now the same query will use the index for both filtering and sorting
  return await Order.find({ userId })
    .sort({ createdAt: -1 })
    .limit(10);
}

Inefficient query patterns, such as using functions on indexed columns, inefficient filtering, or poor sorting strategies, can prevent the database from using indexes effectively.

To optimize query patterns:

Avoid using functions on indexed columns in WHERE clauses
Use appropriate operators that can leverage indexes
Ensure proper indexes exist for sorting operations
Consider the selectivity of conditions in multi-column indexes
Use covering indexes for frequently executed queries
Avoid wildcard searches at the beginning of LIKE patterns
Consider denormalizing data for complex query patterns
Use query hints when necessary to force index usage
Analyze query plans regularly to identify inefficient patterns
Consider using materialized views for complex aggregations

Database Performance Checklist

Database Performance Optimization Checklist:

1. Indexing Strategy
   - Identify and index columns used in WHERE, JOIN, and ORDER BY clauses
   - Create composite indexes for multi-column conditions
   - Consider index column order (most selective first)
   - Avoid over-indexing (impacts write performance)
   - Implement covering indexes for frequent queries
   - Regularly analyze index usage and remove unused indexes

2. Query Optimization
   - Select only needed columns instead of using SELECT *
   - Avoid functions on indexed columns in WHERE clauses
   - Use appropriate JOIN types (INNER, LEFT, etc.)
   - Implement efficient pagination (keyset vs. offset)
   - Use query parameterization and prepared statements
   - Analyze query plans for inefficient operations
   - Consider denormalization for complex query patterns

3. Connection Management
   - Implement connection pooling
   - Configure appropriate pool sizes
   - Close connections properly to prevent leaks
   - Use connection validation to detect stale connections
   - Monitor connection usage and pool statistics

4. Transaction Management
   - Keep transactions as short as possible
   - Use appropriate isolation levels
   - Avoid including non-database operations in transactions
   - Implement proper error handling and rollback mechanisms
   - Consider optimistic locking for low-contention scenarios

5. Batch Processing
   - Use bulk operations for multiple similar operations
   - Determine appropriate batch sizes
   - Group similar operations together
   - Implement proper error handling for batches
   - Consider asynchronous processing for non-critical operations

Optimizing database performance requires a systematic approach that addresses multiple aspects of database usage, from schema design to query patterns and operational practices.

Key optimization strategies:

Implement proper indexing based on query patterns
Optimize queries to use indexes effectively
Use efficient connection and transaction management
Implement batch processing for multiple operations
Consider caching strategies for frequently accessed data
Monitor and analyze database performance regularly
Scale appropriately (vertical vs. horizontal)
Consider database-specific optimization techniques
Implement proper data modeling for your access patterns
Use appropriate tools for performance monitoring and tuning

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