Wie bringen Sie Normalisierung und Leistung beim Datenbankdesign in Einklang?

There are 2 important points which i always keep in mind when dealing with databases. (i) Index is very good but un-necessary and incorrect use/creation of index is dangerous. Create index only if the column is frequently used. If table is huge then certainly consider partitioning. Also, avoid queries with complex aggregations if required frequently. Instead, try to store the required as pre-calculated values in the table(s). Monitoring - very important!! Monitor for long running, costly queries using reports (eg- AWR report in Oracle). Ideally, it is not preferred to have denormalization in databases unless you are creating a data warehouse. Important point to remember is performance of data fetch being done!!

Understanding normalization is crucial in database design. This process organizes data to minimize redundancy through a series of normal forms, each aimed at reducing duplication and dependency. Think of it as decluttering a closet, where items (data) are sorted into separate compartments (tables) for easy access and maintaining tidiness (data integrity). However, over-normalization can lead to a complex web of tables requiring intricate joins, which can slow down query response times. Striking a balance by knowing when to stop normalizing is essential to optimize both data integrity and performance.

Understanding normalization is crucial in improving database performance by organizing data efficiently. Normalization eliminates redundancy, ensuring that data is stored in only one place, which reduces the risk of data anomalies and inconsistencies. This streamlined data structure enhances query performance by minimizing the amount of data that needs to be processed. Additionally, normalized databases typically use less storage, which can improve access times. Proper normalization also facilitates easier maintenance and updates, as changes are made in a single location. Overall, normalization leads to a more efficient, reliable, and scalable database design.

Split large tables into smaller, more manageable pieces (partitions) to improve query performance and maintenance. Separate a table into smaller tables with fewer columns to reduce the size of the data that needs to be read. Rewrite SQL queries to be more efficient, avoiding unnecessary joins and subqueries.

Normalization is a foundational concept in database design that involves organizing data to minimize redundancy. The process is typically done through a series of normal forms, each with specific rules to reduce duplication and dependency. Think of it as decluttering a closet; you're organizing items (data) into separate compartments (tables) to find them easily and maintain tidiness (data integrity). However, over-normalization can lead to a maze of tables that require complex joins, slowing down your query response times. Therefore, understanding the levels of normalization and when to stop is key to maintaining a balance.

More than post execution querie analysis, the human experience is the best indicator. Dev teams, dont see with a good eye someone monitoring their work. But afterward, modifying a query involves tests+QA and then you face project manager.

Obviously run reindex automation based on table/index modification ratio. These jobs mut run at night time just after backups.

I often time saw that parcelling heavy queries help the optimizer to supply a good exec plan. Even if it includes use of temporary tables.