Internal optimization of DB operation

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When optimizing a database internally, it is important to remember that the effectiveness of any method depends on how well you have adapted it to your situation. For example, normalization, although it saves disk space by eliminating data redundancy, often leads to a deterioration in performance. This is especially noticeable when processing large amounts of data, where join operations can significantly slow down the work. A high degree of normalization will also make the database structure difficult for developers and users to understand, which can lead to errors at the design stage. The same applies to indexing: indexes speed up some processes, but they take up additional disk space and affect the performance of insert, update, and delete operations. Therefore, careful analysis is required when creating them.

Managing the size of the input/output buffer also relates to internal optimization of the DB operation. If the database uses read operations more often, the buffer can be increased, and if write operations, it is worth decreasing.

Setting up DBMS parameters and query optimization are equally important aspects in this matter. Let's talk about the second one separately.

Query optimization
The query optimization process involves three stages:

Building a structure that meets the needs of queries. It is necessary to ensure efficient storage of information and increase integrity by normalizing the data. Choose the right data types, place related data in close parts of the storage, and use indexes wisely to speed up search operations.
Optimize index structure. You need to identify columns that are frequently queried and create indexes on them. Cluster indexes, group data according to the physical order of their storage on disk.
Optimizing query texts. This includes choosing the right nepal whatsapp number database operators (e.g. JOIN instead of subqueries) to perform specific tasks, avoiding complex and slow conditions in WHERE clauses, using indexes to filter data, and limiting results. For example, if you only need to retrieve a subset of data, it is better to use LIMIT or TOP operators to limit the number of records returned.
Query optimization.Image by Freepik.
Best practices and tips
Often, database performance issues can be prevented. Let's look at a few ways.

Designing data schemas
Before development, it is worth paying attention to planning: choosing the types of data that you plan to store in the database, defining the relationships between tables, taking into account the features of information storage and the potential need for scaling.

Regular testing and monitoring
It is important to load test the system and monitor the database to identify bottlenecks (queries or operations) and potential problems that may arise under real load. Analyze the logs and monitor resource consumption.

Safety
Regular system updates, backups, data encryption and access control are integral parts of working with databases.

Training and skill development
Better optimization is facilitated by continuous training and development of skills in working with DBMS. Modern technologies and methods require constant updating of knowledge.

Recommendations for optimizing database performance