Database Scaling: Strategies & Best Practices (Updated 2025)

What’s Database Scaling

Database Scaling: Strategies & Best Practices(Updated 2025)

1#Indexing

2#Denormalization

Pros of Denormalization

• Better Scalability: Denormalization can make database systems more scalable. It does this by reducing the number of tables and improving performance. Denormalization reduces the number of database transactions when reading data. This reduced number of transactions can adapt to different user loads, thereby improving the scalability of the application.
• Reduced Complexity: Denormalization can simplify the database schema. It does this by reducing join queries and combining related data into fewer tables. A simpler schema is easier to understand, query, and manage. In addition, this simplicity will help significantly reduce errors related to database operations.
• Improved Query Performance: Denormalization boosts query speed by reducing joins. Depending on the requirements, querying a normalized data store may require multiple joins of different tables.

Cons of Denormalization:

• Reduced Data Integrity: Denormalization introduces redundant data. This raises the risk of inconsistencies. Updates may not propagate correctly across all redundant fields.
• Higher Complexity: Denormalization can simplify some queries. But, it can also complicate the database by creating duplicate data. This can lead to discrepancies between datasets, especially in scenarios involving mirrored databases.
• Higher Storage Needs and Costs: Denormalization creates redundant data. Techniques like data duplication and table mirroring take up space. This drives up storage costs, which can be high for large datasets.
• More Updates and Reduced Flexibility: With redundant data, the frequency of updates increases, complicating database maintenance. This, in turn, limits the flexibility of the system, making it harder to adapt to changing requirements or modifications.

3#Database Caching

Pros of Database Caching

• Reduced Database Load: Caching offloads frequent queries from the database. This reduces strain on server resources. It allows the database to handle more requests efficiently.
• Performance Improvement: Caching stores frequently access data in memory. It dramatically reduces response times and speeds up data retrieval.
• Lower Latency: Cached data is in fast, in-memory structures. This minimizes latency and speeds up response times for users and apps.
• Fewer Disk I/O Operations: Caching cuts disk reads. It speeds up data access and is more efficient than disk-based storage.

Cons of Database Caching

• Cache Invalidation: It's hard to know when to refresh cached data. But, it's critical for data consistency.
• Higher Cost of External Cache: External caching often requires DRAM. It's more expensive than using SSDs or HDDs for storage.
• Decreased Availability: External caches usually have lower high availability (HA) than databases. This can cause failures and strain the database during cache outages.
• Interference with Database Cache: An external cache can disrupt a database's internal cache. This makes it less effective and increases disk access.

4#Replication

Pros of Replication

• Improved Disaster Recovery: Data replication creates database copies in multiple locations. This ensures high availability and access during outages from disasters.
• Reduced Server Load: Replication offloads data to a replicated environment. It reduces the load on the primary database. This optimizes performance and frees up resources.
• Enhanced Data Analytics: Replication creates isolated environments for running complex queries. This lets analysts explore data without affecting core systems.
• Real-Time Business Intelligence: Replication allows real-time data access across business units. This improves reporting accuracy and decision-making. It also integrates data from various sources for better business intelligence.
• Support for AI/ML Apps: Replicated databases provide consistent, up-to-date datasets for training AI/ML models. This improves predictive accuracy and enables real-time, data-driven apps.

Cons of Data Replication

• Threat of Data Compromise: Errors in replication can corrupt or lose data. This poses a significant risk to data integrity.
• Increased Costs: Data replication requires storing and transferring multiple data copies. It needs a lot of storage and bandwidth. This results in higher storage and operating costs, including the need for additional personnel to monitor and manage the process.
• Data Security Risks: Replicating data, especially to remote servers, introduces potential security vulnerabilities. It also complicates compliance with data protection and privacy laws. Unauthorized access and cyber threats are greater concerns now.

5#Sharding (Horizontal Scaling)

Pros of Sharding

• Improved Performance: Sharding spreads data across multiple servers. It reduces each server's load and speeds up query responses.
• Increased Capacity: Sharding allows for easy scalability. As data grows, we can add servers to boost the database's capacity without harming performance.
• Fault Isolation: If one shard fails, only some data is lost. The rest of the system stays operational. This improves resilience.

Cons of Sharding

• Sharding is Intricate: It requires careful planning. You must decide how to distribute data, how many shards to create, and how to route queries to the correct shard.
• Data Distribution Challenges: Ensuring data is evenly distributed across shards can be difficult. If data is unevenly spread, some shards may become overloaded, negating the performance benefits of sharding.
• Complex Data Joining: Joining data across multiple shards can be slow and tricky. It can hurt query performance.

6#Verticle Scaling

Pros of Vertical Scaling

• Reasonable Division: Vertical sharding divides a table into smaller, related subsets. They can be managed independently. This allows for better resource use and performance in parts of the database.
• Easy to Implement: Vertical scaling is simpler than horizontal scaling. It does not require changes to the app's architecture or managing distributed systems.
• Less Network Latency: All resources are on a single server. This minimizes network latency and improves response times.
• Efficient Resource Usage: Upgrading a single server maximizes its resources. For some workloads, this makes vertical scaling more efficient than using multiple servers.

Cons of Vertical Scaling

• Unbalanced Load: Some shards may get more traffic than others. This can reduce system efficiency.
• Management Complexity: Managing multiple shards makes tasks more complex. These include maintenance, backups, and synchronization. Each shard operates independently and requires more supervision.
• Limited Capacity: Vertical scaling has physical limits. Once a server reaches its maximum capacity, other scaling methods must be explored.
• Complex Queries: Querying or joining data across multiple shards can be inefficient and complex, requiring coordination between the shards.
• Single Point of Failure: Since all operations depend on a single server, any failure of that server could cause the entire application to go down.

7#Materialized Views

Pros of Materialized Views

• Improved Query Performance: Materialized views store precomputed query results. This cuts the time needed to retrieve complex data. This is especially beneficial for queries involving large datasets or complex calculations, such as aggregations.
• Reduced Resource Consumption: Materialized views cache the results of resource-intensive queries. This reduces the need for repeated query execution and lowers CPU, memory, and I/O usage.
• Faster Access to Aggregated Data: Materialized views are perfect for storing the results of often-run aggregation queries. They enable quicker access to summarized information.
• Offloading Workload: They can offload heavy computation from live data queries. This improves the database's performance by spreading the workload over time.
• Visual Data: Materialized views provide a snapshot of data. It is at their creation or last refresh. This is useful for historical analysis or reporting.

Cons of Materialized Views

• Restricted Functionality: After creating a materialized view, you can't change its SQL definition. You also can't replace it with another view of the same name. Materialized views also cannot query external, wildcard tables, or logical views. They support only a limited set of SQL functions. This makes them less flexible for complex queries. Additionally, they cannot be nested within other materialized views, limiting their use in advanced data modeling.
• Limited Direct Data Manipulation: You cannot update a materialized view's data using COPY, EXPORT, LOAD, or DML operations. This reduces flexibility in managing the view's data.
• Maintenance Overhead: To sync a materialized view with the base data, periodic refreshes are required. This refresh process can create high system overhead and consume resources. It depends on the dataset size and update frequency.
• Frequent Updates Raise Complexity: It's harder to maintain materialized views when the source data changes often. Updates must be carefully coordinated with the base tables to avoid inconsistencies. This adds complexity to the management process.

Why You Should Have a Scalable Database

• Improved Collaboration: A scalable database is a central, secure repository. It allows all team members to access project data. It improves decision-making and streamlines workflows. It does this by enabling better data sharing and collaboration.
• Supports Multiple Data Sources: Large organizations must integrate data from multiple channels. A scalable database consolidates these sources into a unified, centralized hub, making it easier to manage diverse information streams.
• Handles Growth Efficiently: As your business expands, so will your data and user requests. A scalable database can easily adjust to this growth. It will avoid frequent system overhauls and keep the business running.
• Manages Sudden Traffic Surges: High-traffic events, like holidays or promotions, may spike user activity in enterprise systems. A scalable database can quickly boost capacity. It will keep the system stable during peak traffic.
• Enhances Performance: Scalable databases optimize resource utilization, preventing performance slowdowns under heavy loads. They enable quick data querying, storage, and processing. This boosts response times and keeps the system reliable.
• Improves User Experience: A scalable database keeps your system stable. It prevents lags or downtime, no matter how many users your business has. This leads to a smoother, more satisfying user experience.

Conclusion