How to design databases for maximum performance and optimal resource utilization? | Data Processing in practice

Database Optimization is one of the key elements influencing the performance of IT systems. Whether we’re dealing with processing large data sets, implementing CRM systems, or integrating external data processing services, a well-designed database is the foundation of success.

In this article, we will cover topics related to database design, normalization, data type selection, as well as discuss principles for creating indexes and foreign keys. All these elements will be presented with consideration of best practices that impact data processing in high-load environments.

Data Types – the foundation of efficient information processing

Choosing the right data types is one of the first steps in database design. Each data type has its specific properties that impact storage usage, processing performance, and compliance with business requirements. Here are a few examples:

1. Varchar – Ideal for storing text of a known maximum length. Always aim to limit its maximum length to the expected values. Using varchar for columns intended to be primary or foreign keys is not recommended, as text comparison operations are significantly slower than numeric comparisons.
2. Numeric (Decimal) – Used for storing values with known scale and precision, such as prices, exchange rates, or measurement values. Proper selection of scale can save storage space, which is crucial when processing large volumes of financial data.
3. Date, Time, Timestamp – These types allow for storing time-related information. Depending on the use case, you can choose versions with time zone information (timestamp with time zone) or without (timestamp without time zone).
4. JSON – Suitable for storing unstructured data. This is a convenient solution for flexible processing of variable data structures. Scalable indexing of scalar values using BTREE indexes and indexing maps and lists using GIN (General Inverted Index) improves the processing performance of complex data in JSON format.

Selecting the appropriate data type is one of the factors influencing the efficiency of CRUD (Create, Read, Update, Delete) operations and data aggregation in high-load systems.

Normalization – reducing redundancy and ensuring data integrity

The normalization process is used to eliminate data redundancy and ensure information consistency. Properly conducted normalization prevents anomalies during data insertion, updating, and deletion. The basic normalization principles include:

• 1NF (First Normal Form) – Eliminate repeating groups and columns.
• 2NF (Second Normal Form) – Every non-key column must be fully dependent on the primary key.
• 3NF (Third Normal Form) – Eliminate transitive dependencies.

For example, instead of storing all customer information in a single orders table, it is better to separate the data into customers, customer_addresses, and orders tables. This division reduces disk space usage, simplifies the process of making changes, and improves the efficiency of data processing operations in various business contexts.

Constraints – ensuring data integrity

Primary Keys and Foreign Keys are used to ensure referential integrity of data. A Primary Key uniquely identifies each record in a table, while a Foreign Key is used to establish relationships between tables. In practice, this means that every database operation must comply with integrity rules.

Additionally, other constraints include:

• Unique Key (UK) – Ensures the uniqueness of values in a specific column, which is essential, for example, for columns containing identification numbers.
• Not Null – Enforces that a column can never contain a null value.
• Check Constraint – Allows the introduction of additional rules, such as value range restrictions or letter case constraints, in a given field.

Indexes – accelerating data access

Indexes enable fast access to data without having to search the entire table. However, it’s important to remember that each additional index adds overhead to CRUD operations, so their number should be limited to the minimum necessary for efficient system performance. The primary type of index, present in all databases, is the BTREE, which facilitates fast searching, sorting, and range operations. Besides BTREE indexes, each database supports various additional index types. For instance, in PostgreSQL, we also have:

• Function indexes – based on a value calculated from one or more columns, useful for example for case-insensitive queries.
• Hash – Beneficial for indexing “large” text values, reduces index size, but only supports exact matches.
• GIN – Used for indexing complex data structures, such as JSON types.
• BRIN – Stores the minimum and maximum value of the indexed column in a data block, useful when the data in the indexed column is monotonically increasing or decreasing.
• Indexes for Geometric Data (GiST, SP-GiST) – Designed specifically for indexing spatial data structures.

In practice, it is recommended to regularly monitor which indexes are used and which create unnecessary performance overhead.

Query Optimization – Why aren’t indexes always used?

SQL query optimization is a key element of data processing. Despite having defined indexes, they may not always be utilized. Potential reasons for this include:

• An insufficient amount of test data, leading to a lack of optimization.
• Rare cases in the data that are not accounted for in statistics.
• Scenarios where the query returns the majority of data from a table – in such cases, a full table scan might be more efficient.

This is why understanding the nature of the data and designing databases in a way that supports efficient automatic data processing is crucial.

Summary | Data Processing in the context of database optimization

Database optimization is a comprehensive process that encompasses the selection of appropriate data types, table structures, and indexes. A well-designed database enables efficient data processing, minimizing system response times and enhancing overall performance. Properly structured databases support CRUD operations, aggregation, and complex queries, allowing for better use of hardware resources and faster access to information.

However, database management is not just about designing tables and indexes correctly; it also involves solutions that cater to the specific needs of applications and business processes. Integration with external systems, real-time data processing, and automation of analytical processes are just a few elements that can significantly impact project success.

At fireup.pro, we offer comprehensive data processing services and consulting to help your company leverage the full potential of its information resources. Would you like to learn more about how database optimization can impact efficient data processing? Contact our team and discover how our data processing services can help you achieve the highest system performance.