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4Баланс нормализации и денормализации данных в платформах управления спортивными соревнованиями: сравнительный анализ
Абызов Антон Николаевич
технический директор, Softgreat, anton.abyzov@gmail.com
В статье исследуется важность принятия обоснованных решений относительно нормализации и денормализации данных на платформах управления спортивными соревнованиями. Преимущества и ограничения нормализованных и денормализованных структур данных сравниваются в контексте платформ управления спортивными соревнованиями. Кроме того, в статье подробно рассматриваются преимущества и ограничения использования нормализованных и денормализо-ванных структур данных в контексте платформ управления спортивными соревнованиями. Также предоставляются рекомендации для разработчиков и архитекторов систем для принятия обоснованных решений о выборе между нормализацией данных и денормализацией в их платформах управления спортивными соревнованиями. Результаты и выводы обобщаются на основе анализа производительности, эффективности и простоты обслуживания нормализованных и денор-мализованных структур данных.
В статье представлен анализ производительности, эффективности и простоты обслуживания обоих подходов, а также рекомендации для разработчиков и системных архитекторов. Эти выводы могут быть полезны профессионалам, занимающимся разработкой и оптимизацией платформ управления спортивными соревнованиями, и послужат основой для дальнейших исследований в этой области. Ключевые слова: МопдойВ, нормализация и денормализация данных, платформы управления спортивными соревнованиями, производительность, эффективность, простота обслуживания.
Introduction
Data management in sports competition management platforms is a critical aspect that affects performance and accuracy of results. One of the main decisions faced by developers of such platforms is the choice between normalization and denormalization of data for the organization of the data structure [2].
Data normalization and denormalization are two processes related to the organization and presentation of data in sports competition management platforms.
Data normalization: This is the process of bringing data to a standard format or structure. Data normalization usually involves splitting data into smaller and more manageable components, such as tables or fields, and defining the relationships between them [1]. This can help in eliminating data redundancy, reducing duplication, and ensuring data integrity. Data normalization can improve system performance, facilitate data retrieval and analysis, and ensure data consistency.
Denormalization of data: This is the process of combining data from various sources or tables into one to facilitate data access and analysis [1]. Data denormalization may involve combining data from multiple tables, adding duplicate data, or duplicating data to improve performance and simplify the analysis process. Denormalization can be useful when fast data access and system performance optimization are required.
The choice between normalization and denormalization depends on the specific needs and goals of the sports competition management platform. Data normalization may be preferred when data integrity is important and duplication minimization is important, while denormalization may be preferred when fast data access and analysis is required [3]. This part of the article provides a comparative analysis of the advantages and trade-offs of data normalization and denormalization in the context of sports competition management platforms, using examples from the MongoDB server platform.
Methods:
MongoDB server platform were used to manage sports competitions consisting of stages, groups, matches and events with registration of players and teams. The performance, efficiency and maintainability of normalized and denormalized data structures in various scenarios were evaluated. For the denormalized scheme, team and player statistics were analyzed, including team position and top scorers, and the use of an aggregation pipeline for data extraction was discussed. A normalized scheme for registering players and equipment was also considered, and the use of a search operation to combine data from related collections was demonstrated.
Results:
In this topic, a comparative analysis is carried out between normalized and denormalized data structures in the context of sports competition management platforms. It was found that the denormalized data schema in MongoDB provides advantages in quick access to statistics of teams and players, since the data is combined in one collection. However, it can also have drawbacks,
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such as data duplication and increasing the size of the collection, which can complicate the processes of updating and maintaining data.
On the other hand, a normalized data schema using links between collections allows you to organize data more flexibly, minimize data duplication, and facilitate the processes of updating and maintaining data. However, it may also require additional data pooling operations when executing queries, which may affect performance.
For a more detailed analysis and evaluation of the advantages and trade-offs of using normalized and denormalized data structures in sports competition management platforms, three tables are presented:
Table 1
choose the optimal approach according to the requirements and expectations of the application.
Table 2
Advantages Compromises
Quick access to team and player statistics Data duplication
Reduced data merging operations in queries Increased collection sizes
Simplicity and speed in query execution Difficulty in updating and maintaining data
Additional data merging operations -
Potential performance reduction in query execution -
Advantages Compromises
Flexible data organization Additional data pooling operations
Minimizing data duplication Possible performance degradation when executing queries
Simplify data update and maintenance processes -
Table 1 presents a comparative analysis of the advantages of using a denormalized data schema in MongoDB. It shows that such a data schema has several benefits, such as:
1. Quick access to team and player statistics: Denormalized data schema allows for fast retrieval of information about team and player statistics without the need for complex data merging operations in queries.
2. Reduced data merging operations in queries: Denormalized data schema reduces the number of data merging operations in queries, which can improve system performance.
3. Simplicity and speed in query execution: Denormalization simplifies the process of executing queries as the data is already in one collection, and there is no need to perform complex data merging operations to extract information.
However, there are also compromises to consider with a denormalized data schema:
1. Data duplication: Denormalization can lead to data duplication as the same data may be reused in different documents, which can increase the volume of stored data.
2. Increased collection sizes: Denormalization can lead to increased collection sizes, especially in the case of large volumes of data, which can impact system performance.
3. Difficulty in updating and maintaining data: Updating and maintaining data in a denormalized data schema may be more challenging and require additional efforts as data may be distributed across different documents.
4. Potential performance reduction in query execution: In rare cases, denormalization may result in reduced performance when executing complex queries as the data may be scattered across different documents and require additional data merging operations to retrieve complete information.
In conclusion, the use of a denormalized data schema in MongoDB has its advantages, such as quick access to statistics, reduced data merging operations, and simplicity in query execution. However, it also comes with compromises, such as data duplication, increased collection sizes, difficulty in updating and maintaining data, and potential performance reduction in query execution. When designing a data schema in MongoDB, careful evaluation of the pros and cons of denormalization is necessary to
When choosing between denormalized and normalized data schemas in MongoDB or other databases, it is important to carefully assess the requirements of the application and choose an approach that best meets the needs of the project in terms of performance and satisfies the needs of developers and database administrators. Denormalized data schema may be preferred if quick access to team and player statistics, reduction of data merging operations, and simplicity of query execution are important. On the other hand, normalized data schema may be preferred if flexible data organization, minimization of data duplication, and simplification of data update and maintenance processes are important.
In addition, the specific features of the system and project requirements should be taken into account, such as data size, frequency of read and write operations, complexity of queries, and expected system performance. Each approach has its own advantages and trade-offs, and the choice between denormalized and normalized data schemas in MongoDB or other databases should be based on careful analysis of requirements and performance evaluation to meet the needs of the application.
Table 3
Aspect Comparison Denormalized Data Schema Normalized Data Schema
Fast access to team and player statistics + -
Reduction of data merging operations in queries - +
Simplicity and speed of query execution - +
Data duplication + -
Increase in collection size + -
Difficulty of data update and maintenance processes - +
Flexible data organization - +
Minimization of data duplication + -
Simplification of data update and maintenance processes + -
Additional data merging operations + -
Possible decrease in query performance - +
From Table 3, it can be seen that denormalized data schema in MongoDB has advantages in fast access to team and player statistics, data duplication, increase in collection size, and additional data merging operations, as well as simplification of data update and maintenance processes. However, it also has tradeoffs in reducing data merging operations in queries, simplicity and speed of query execution, and possible decrease in query performance.
On the other hand, normalized data schema using relationships has advantages in flexible data organization, minimization of data duplication, and simplification of data update and maintenance processes. However, it also has trade-offs in fast
access to team and player statistics, increase in data merging operations in queries, and difficulty in data update and maintenance processes.
Thus, the choice between denormalized and normalized data schemas in MongoDB or other databases should be carefully considered and based on the requirements of the specific project, taking into account the advantages and trade-offs of each approach, as well as the expected system performance.
These tables provide the advantages and trade-offs of using denormalized and normalized data schemas in MongoDB in the form of visual tools that can be used in an article for a more visual and accurate representation of information on the comparative analysis of these two approaches in sports competition management platforms.
It is also worth noting that one of the important aspects of using denormalized data schema in sports competition management platforms is the use of the aggregation pipeline in MongoDB. The aggregation pipeline allows performing data aggregation operations on the database server, which can significantly improve performance when retrieving complex data. For example, with the aggregation pipeline, it is easy to calculate team statistics or find the top scorers. However, the complexity of using the aggregation pipeline can be high and may require additional knowledge and experience in working with MongoDB.
On the other hand, using normalized data schema with relationships between collections allows for more flexible organization of data, especially in cases where there are complex relationships between entities such as players, teams, and competitions. For example, using MongoDB's search operation, we can easily find players registered for a specific competition and retrieve information about them from related collections. However, using relationships between collections can also result in complex data merging operations when executing queries, which can affect performance.
Optimization Strategies for Normalized Data Structures
Normalized data structures are an optimal approach for platforms that prioritize data consistency and maintainability, as they provide a better data structure [4]. However, they may require more complex join operations and could potentially impact query performance. The following strategies can be used to optimize normalized data structures:
1. Indexing:
• Implementing appropriate indexing for related collections and fields to optimize query performance.
• When choosing indexing strategies, trade-offs between query performance and storage requirements should be considered.
2. Caching:
• Implementing caching mechanisms to store frequently used data in memory and reduce the need for frequent database queries.
• Caching can significantly improve query performance for normalized data structures, especially in cases where data changes infrequently.
3. Materialized Views:
• Implementing materialized views, which are precomputed views storing aggregated data in separate collections, to reduce the need for complex join operations and improve query performance.
• Materialized views can be particularly useful in scenarios where data retrieval requires complex computations or aggregations.
Some recommendations for choosing between data normalization and denormalization in a sports competition management platform in MongoDB are as follows:
Requirements analysis: Carefully analyze the project requirements and determine which operations and data queries will be performed most frequently. If fast data access and server-side aggregation operations are a priority, denormalization of data may be a preferable option.
Performance evaluation: Study the performance of the system using different approaches to data organization. Perform performance testing and compare the results to determine which approach best meets the project requirements.
Complexity of data operations: Evaluating the complexity of data merge operations that will be performed in the project is also an important aspect. If data merge operations are complex and performed frequently, data normalization may be a preferable option, as it allows for more flexible handling of related data.
Experience of developers: Take into account the experience and knowledge of the development team. If the team is already familiar with one of the approaches (e.g., denormalization of data), choosing that approach may be more logical, as the team already has experience working with it.
Future changes: Anticipate future changes in the system and evaluate how the chosen approach to data organization can handle such changes. If it is expected that the data structure will change in the future, data normalization may be a more suitable option, as it allows for more flexible changes to the data structure without modifying the database schema.
In conclusion, the choice between data normalization and denormalization in sports competition management platforms depends on various factors, such as query performance, data consistency, scalability, and maintainability [6]. Denormalized data structures can provide improved query performance, especially for real-time updates, leaderboards, and frequently requested information. On the other hand, normalized data structures provide better data consistency and maintainability, making them an optimal choice in cases where data integrity and changeability are important. However, normalized data structures may require more complex data merge operations and may potentially impact query performance.
Denormalized Data Structure
In a denormalized data structure, data is stored redundantly, potentially improving query performance by reducing the need for complex joins. However, this approach may also result in increased storage and maintenance costs. In this section, code samples and metrics will be provided for standing calculations and player statistics in the context of denormalized data structures.
Standing Calculations
One approach to denormalized data structure is storing standings information directly within the team document in a MongoDB schema. This allows for retrieval of standings with a single query, eliminating the need for joins.
Consider the following example of a denormalized MongoDB schema for a team:
public class. Team {
public ObjectXd Id { get; set; } public string Nane { get; set; } public int Wins { get; set; } public int Losses { get; set; J" public int Draws { get; set; } public int Points X get; set; }
j
To retrieve the standings, the teams can be sorted by their points._
var teams = database.GetCollection<Team>('lteam51');
var standings = teams.Find(new BsonDacuient()).SortByDescending(t =>
t.Points);
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var fixtureRegistrations -
database. GetCollection< F ixtureRegist rat ion>("f ixture_registrations" ) ; var players = database.GetCollection<Player>("players")j
var fixtureld = Objectld.Parse("fixture_id_here");
var query = new BsonDocument {
"{lookup", new BsonDocument {
{ "from"j "players11 }, { "lacalField", "Player-Id" ], { "fareignField", "_id" }, { "as", "player" }
>
{
"irnatch"J new BsonDocuiaent {
{ "Fixtureld"j fixtureld }
>
{
"{unwind", "$player™
}
Metrics:
Query performance: High (reduced need for joins) Storage: High (redundant data) Maintainability: Moderate (potential for inconsistencies) In a normalized data structure, player registrations are stored separately from teams and players, enabling accurate maintenance of player information across multiple teams and sports.
Consider the following example of a normalized MongoDB schema for player registration:
public class PlayerRegistration {
public Objectld Id { get; set; } public Objectld Playerld { get; set; } public Objectld Teamld { get; set; } public Objectld 5portId { get; set; }
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To retrieve the players registered for a specific team and sport in a normalized data structure, a lookup operation can be utilized.
var playerRegistrations =
mongoDatabase.GetCollection<PlayerRegistration>("PlayerRegistrations" )
.Aggregate()
.Lookup("Players,,J "Playerld", " id", "Player") .Unwind("Player")
.Lookup("Teams", "Teamld", "_id", "Team") .Unwind("Team")
.Lookup("Competitions", "CompetitionId", "_id", "Competition") .Unwind("Competition")
.Project{new BsonDocument {
{"jd", i,
{ "PlayerRegistrationld", "i_id" }, { "Player", "JPlayer" }, { "Team", "iTeam" }, { "Competition", "{Competition" },
})
.ToListAsync()
Metrics:
• Query performance: Moderate (requires complex joins)
• Storage: Low (no redundant data)
• Maintainability: High (ensures data consistency)
In a normalized data structure, fixture registrations (lineups) are stored separately from fixtures and players, enabling the accurate maintenance of lineups for each fixture.
Consider the following example of a normalized MongoDB schema for fixture registration (lineups):
public class FixtureRegistration {
public Objectld Id { get; set; } public Objectld Playerld { get; set; } public Objectld Fixtureld { get; set; }
}
To retrieve the lineup for a specific fixture in a normalized data structure, a lookup operation can be employed.
Metrics:
• Query performance: Moderate (requires complex joins)
• Storage: Low (no redundant data)
• Maintainability: High (ensures data consistency)
In conclusion, the decision between using normalized or denormalized data structures for a sports competition management platform depends on the specific requirements of the platform. If query performance is prioritized, denormalization may be a more suitable approach as it can improve query performance by reducing the need for complex joins. However, if data consistency and maintainability are of higher importance, normalization should be considered as it allows for accurate maintenance of data across multiple entities. It is crucial to understand the trade-offs associated with each approach and carefully select the appropriate data structure based on the specific needs of the platform, as it can significantly impact performance, efficiency, and maintainability in the long run.
Conclusion
The development of a sports competition management platform requires careful consideration of the data structures used in the backend and their integration with front-end applications. Depending on the specific needs of the platform, a combination of normalized and denormalized data structures may be optimal. The balance of performance, cost-effectiveness and maintainability is crucial for making the right decision [5].
In addition, future developments and adaptations should be considered, such as hybrid data structures, increased scalability and performance, as well as the integration of advanced analytics and machine learning, so that the platform remains competitive and meets the changing needs of its users.
Seamless integration with front-end applications through well-designed APIs, data caching, adaptive UI design, testing, monitoring, regular updates and maintenance are also essential to ensure a consistent and attractive user interface.
By following these guidelines and best practices, the sports competition management platform can effectively manage its data structures and interface integration, ensuring optimal performance, data consistency and user satisfaction.
It is also worth emphasizing that the concepts of normalized and denormalized data structures discussed in this article are not limited to sports competition management platforms. These concepts are applicable in various fields and industries where data-driven applications are used. Understanding the trade-offs between normalized and denormalized data structures can help in the development and implementation of effective applications in various fields.
E-commerce Platforms: Product catalogs, customer profiles, and order histories on e-commerce platforms are examples of data that can benefit from careful consideration of normalized and denormalized data structures. The balance of productivity, cost-effectiveness and maintainability is crucial to ensure that customers make purchases smoothly and facilitate the efficient operation of retailers.
Social Media Platforms: Social media platforms handle a huge amount of user-generated content such as posts, comments, likes and shared posts. Optimizing the data structures that store and manage this content can have a significant impact on the performance and scalability of the platform, providing a smooth user interface even as the size and complexity of the platform grows.
Healthcare and Medical Applications: Healthcare and medical applications deal with sensitive and complex data such as electronic medical records, patient demographics and treatment histories. Ensuring data consistency, security, and maintainability is of paramount importance in this area, making the choice between normalized and denormalized data structures critical to data management.
Financial and Banking Applications: Financial and banking applications process huge amounts of transactional data, account information and customer profiles. The balance between performance and data consistency is crucial to provide customers with accurate and up-to-date information and ensure the efficient operation of financial institutions.
Balancing Data Normalization and Denormalization in Sports Competition
Management Platforms: A Comparative Analysis Abyzov A.N.
Softgreat
JEL classification: C01, C02, C1, C4, C5, C6, C8
This article explores the importance of making informed decisions regarding data normalization and denormalization in sports competition management platforms. It utilizes the MongoDB server program to manage competitions consisting of stages, groups, matches, and events, with player and team registration. The performance, effectiveness, and ease of maintenance of normalized and denormalized data structures are analyzed in various scenarios. The advantages and limitations of normalized and denormalized data structures are compared in the context of sports competition management platforms. It was found that denormalized data structures provide significant benefits in query performance, storage size, and data update efficiency in scenarios where multiple collections need to be joined or complex search and update operations are required. Denormalized data structures also simplify the system architecture, reduce the number of queries, and minimize data redundancy, resulting in improved performance and cost-effectiveness.
In conclusion, this article makes a significant contribution to the study of the application of data normalization and denormalization in sports competition management platforms. It provides analysis of performance, effectiveness, and ease of maintenance of both approaches, as well as recommendations for developers and system architects. These findings can be valuable to professionals involved in the development and optimization of sports competition management platforms and serve as a foundation for further research in this area.
Keywords: MongoDB, normalization and denormalization of data, sports competition management platforms, performance, efficiency, ease of maintenance.
References
1. Ehsan Maiquani. Database Normalization vs. Denormalization, 2020. [Electronic resource]
// Access mode: https://medium.com/analytics-vidhya/database-normalization-vs-denormalization-a42d211dd891
2. Ahmet Aytekin. Comparative Analysis of the Normalization Techniques in the Context of MCDM Problems, 2021. [Electronic resource] // Access mode: https://www.researchgate.net/publication/350243411_Comparative_Analysis_of _the_Normalization_Techniques_in_the_Context_of_MCDM_Problems
3. Patrick Divine. Normalization vs. Denormalization, 2019. [Electronic resource] // Access mode: https://betterprogramming.pub/normalization-vs-denormalization-7470bb20618d
4. Anuradha Kanade, Shantanu Kanade, Arpita Gopal. A study of normalization and
embedding in MongoDB, 2014. [Electronic resource] // Access mode: https://www.researchgate.net/publication/271547001_A_study_of_normalization _and_embedding_in_MongoDB
5. Jacob Wilson. Why is it best to Denormalize a data model in MongoDB? Technical-QA.com, 2021. [Electronic resource] // Access mode: https://technicqa.com/why-is-it-best-to-denormalize-a-data-model-in-mongodb/
6. Margaret Rouse. Database normalization. Techtarget. 2019. [Electronic resource] // Access mode: https://www.techtarget.com/searchdatamanagement/definition/normalization
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