How do you reduce database storage and network overhead?

1 Use compression

One of the simplest ways to reduce database storage and network overhead is to use compression. Compression reduces the size of your data files and backups, which means less disk space and faster transfers. Most database management systems (DBMS) offer built-in compression features that you can enable and configure according to your needs. For example, in SQL Server, you can use row or page compression for tables and indexes, and backup compression for backups. Compression can also improve query performance by reducing I/O operations and fitting more data in memory.

2 Use normalization

Another way to reduce database storage and network overhead is to use normalization. Normalization is the process of organizing your data into tables and relationships that minimize redundancy and inconsistency. By eliminating duplicate and unnecessary data, you can reduce the amount of storage space and network traffic required for your database operations. Normalization also helps you maintain data integrity and avoid update anomalies. To normalize your database, you can follow a set of rules or normal forms that define the optimal structure for your data.

3 Use partitioning

Partitioning is a technique that divides your data into smaller and more manageable units based on some criteria, such as date, range, or value. Partitioning can help you reduce database storage and network overhead by allowing you to perform operations on specific subsets of data, rather than the entire data set. For example, you can use partitioning to archive old data, load and unload data in parallel, or improve query performance by reducing the scan scope. Partitioning can also enhance availability and scalability by distributing your data across multiple servers or disks.

4 Use indexing

Indexing is a technique that creates and maintains a structure that maps your data to its location in the database. Indexing can help you reduce database storage and network overhead by improving the speed and efficiency of your queries. By using indexes, you can avoid scanning the entire table or view for every query, and instead, locate the relevant data quickly and directly. Indexes can also reduce the amount of data that needs to be transferred over the network by filtering and sorting the results before sending them to the client. However, indexes also consume storage space and require maintenance, so you should use them wisely and selectively.

5 Use caching

Caching is a technique that stores frequently accessed or computed data in a fast and temporary storage location, such as memory or disk. Caching can help you reduce database storage and network overhead by reducing the number and frequency of requests to the database server. By using caching, you can avoid repeating expensive or redundant operations, and instead, retrieve the data from the cache. Caching can also improve the responsiveness and availability of your database applications by serving the data faster and more reliably. You can use various caching strategies and tools, such as application caching, query caching, or distributed caching.

6 Use batching

Batching is a technique that groups multiple database operations into a single unit of work, such as a transaction or a statement. Batching can help you reduce database storage and network overhead by minimizing the overhead of opening and closing connections, sending and receiving data, and committing and rolling back changes. By using batching, you can increase the throughput and performance of your database operations, and also reduce the risk of errors and inconsistencies. You can use different methods and tools to implement batching, such as bulk insert, bulk update, or batch execution.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?