What is Denormalization

Denormalization is a tactic in database design that seeks to expedite data access by generating multiple copies of certain data sets at various locations. This approach is adopted to counter the shortcomings associated with normalization. Although normalization systematically segregates data into distinct tables, it often creates considerable interconnections between these tables, leading to slower data retrieval times, particularly affecting the performance of queries that join multiple tables.

To further optimize performance, denormalization incorporates redundant data into tables, an action achieved by either introducing additional columns that contain data duplicated from other tables or by creating duplicate rows. While this strategy can enhance the efficiency of queries that amalgamate data from multiple tables, it introduces a higher degree of complexity to the database, making it more challenging to maintain. Consequently, this might augment both the initial development time and the ongoing costs associated with database management.


Denormalization Techniques

A range of database denormalization techniques are available, each tailored to specific use cases, with their own respective advantages and downsides. We will delve into several of these methods, highlighting where they can be most effectively utilized.

  • Pre-joining Tables

    Pre-joining tables is a technique where two or more tables are merged in advance, often facilitating quicker data retrieval in the following scenarios:

    • When it is frequently necessary to query multiple tables simultaneously during data lookups.
    • To mitigate the considerable amount of resources that JOIN operations can consume.

    By utilizing pre-joined tables, one can notably reduce the time and database load typically associated with multiple JOIN operations.

  • Mirror Tables

    Mirror tables, categorized into "partial copy" and "full copy", are typically used to create backups. Additionally, they facilitate separate processing of large data volumes for analyses or modeling, preventing potential system performance impediments. This ensures the uninterrupted and smooth operation of the original service, devoid of slowdowns due to heavy computations.

  • Table Splitting

    Table splitting, a prevalent method in database management, can be executed in two primary ways:

    • Horizontal Splitting: This technique involves segregating different rows into separate tables, which can be done based on specific criteria or ID ranges.

    • Vertical Splitting: Here, different columns are partitioned into separate tables, with the primary key replicated across these tables to maintain relational integrity.

    Implementing table splitting can revamp data organization fundamentally, facilitating more streamlined data management and retrieval processes.

    Storing Derivable Values

    For frequently accessed values, it is prudent to calculate them beforehand to streamline operations. For instance, instead of calculating age from birthdays each time, a new column can be established to store age, thus eliminating repeated calculations during data access.

    Integrating these denormalization techniques into database design and management can markedly enhance data retrieval speeds and optimize resource utilization, fostering a more proficient and effective database system.


Advantages of Denormalization:

  • Speedier data retrieval: The redundancy introduced through denormalization means the system doesn't need to make as many joins, which can significantly speed up data retrieval.
  • Simpler queries: With less need for table joins, the queries become more straightforward and less prone to errors.
  • Less computational load: Denormalization requires less computing power for read operations, which can help improve the overall performance of the system.
  • Faster reporting: For large datasets, denormalization can help generate reports more quickly and efficiently.


What Challenges Does Denormalization Introduce?

  • Higher storage requirements: Denormalization means having more data, as it involves creating redundant copies. This, in turn, requires more storage space for your database.
  • More expensive updates and inserts: Due to data redundancy, denormalization can increase the time and effort required to update or insert data.
  • Risk of data inconsistencies: Denormalization means more data to manage and modify, which raises the risk of inconsistencies or data anomalies. To prevent this, you'll need to use additional tools such as triggers, stored procedures, and transactions.
  • Introduces inflexibility: Denormalization "petrifies" your data pipeline. A simple schema change requires reconfiguring the entire data pipeline and backfilling of the data.
  • Extra pipeline and slower operations: The inclusion of an extra data pipeline dedicated to building denormalized tables leads to slower database updates. This additional step introduces complexities that pose challenges in achieving optimal responsiveness and real-time performance.


The Ultimate Solution to Denormalization Dilemmas

With StarRocks, you can bid farewell to the hassle of denormalizing data in the upstream process/system. This revolutionary solution saves you time and effort in managing and utilizing your stream processing tool. The exceptional JOIN query performance offered by StarRocks enables the joining of multiple tables on the fly during query execution, without compromising on the overall query performance. Plus, it keeps your data in its original star/snowflake schema format, allowing for lightweight dimension and schema changes from upstream as per your business requirements.
But what about those more demanding scenarios, like highly concurrent user-facing analytics? Don't worry, StarRocks has you covered with its powerful feature: Partial Update. Even in situations where denormalization is still necessary, instead of dealing with complicated JOIN operations across multiple tables in your upstream stream processing job, you can simply update specific columns of a row. This streamlined approach simplifies denormalization without the need for a separate stream preprocessing application.