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What are some common data partitioning strategies in distributed systems?

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Data partitioning is a technique to divide a large dataset into smaller and more manageable chunks, which can be stored and processed across multiple nodes in a distributed system. Data partitioning can improve the scalability, performance, availability, and fault tolerance of a distributed system, but it also introduces some challenges and trade-offs. In this article, we will explore some common data partitioning strategies in distributed systems and their advantages and disadvantages.

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Jonatha William

Analista de Manuten??o Sênior | Lean Seis Sigma Master Black Belt | PPCM | Desenvolvedor de Pessoas | Vale
Rodrigo Fernandes Garcia

Diretor Digital @ GOL | GOL Labs | ????????

1 Horizontal partitioning

Horizontal partitioning, also known as sharding, is a strategy that splits a dataset by rows, based on a partition key or a hash function. For example, a user table can be partitioned by user ID, so that each node stores a subset of users. Horizontal partitioning can reduce the load on each node, increase the parallelism of queries, and enable horizontal scaling. However, horizontal partitioning can also complicate the application logic, increase the network overhead, and create hotspots or imbalances if the partition key is not well chosen.

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Jonatha William

Analista de Manuten??o Sênior | Lean Seis Sigma Master Black Belt | PPCM | Desenvolvedor de Pessoas | Vale
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articionamento horizontal (Sharding): Descri??o: Os dados s?o divididos com base em linhas ou intervalos de valores em uma coluna. Aplica??es: útil quando as consultas frequentemente se aplicam a subconjuntos específicos de dados.

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Jeferson Luiz da Costa Júnior ????

Consultor SAP Abap | PI | BTP CI
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Sistemas operacionais s?o essenciais para o funcionamento de computadores, gerenciando recursos e permitindo a execu??o de programas.

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Tiago S.
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Horizontal Partitioning: Data split into rows across nodes - great for large sets! Vertical Partitioning: Divides data by columns for targeted queries. Functional Partitioning: Segments data by functionality or business logic. These strategies boost efficiency, performance, and scalability in distributed environments. #TechInsights #DistributedSystems

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Luciano Bessegato

Oracle P6 EPPM | Oracle Unifier | Oracle Aconex | Oracle Bi Publisher | AWS (1x) | AZURE (4x)| Oracle OCI (1x)
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Melhor Desempenho: Ao dividir grandes conjuntos de dados em partes menores, é possível melhorar o desempenho das consultas, especialmente quando apenas uma parte dos dados é necessária para uma opera??o. Facilidade de Manuten??o: Se houver necessidade de manter ou excluir apenas uma parte dos dados com base em critérios específicos, o particionamento horizontal pode simplificar essas opera??es. Distribui??o de Dados: Em sistemas distribuídos, o particionamento horizontal pode ser utilizado para distribuir dados em vários servidores, melhorando a escalabilidade.

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2 Vertical partitioning

Vertical partitioning, also known as normalization, is a strategy that splits a dataset by columns, based on the logical or functional grouping of attributes. For example, a user table can be partitioned by user profile and user preferences, so that each node stores a different aspect of the user information. Vertical partitioning can reduce the storage space, improve the cache efficiency, and isolate the failures of different nodes. However, vertical partitioning can also increase the number of joins, degrade the query performance, and create dependencies or inconsistencies between nodes.

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Jonatha William

Analista de Manuten??o Sênior | Lean Seis Sigma Master Black Belt | PPCM | Desenvolvedor de Pessoas | Vale
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Particionamento vertical: Descri??o: Os dados s?o divididos em colunas, com cada parti??o contendo um subconjunto diferente de colunas. Aplica??es: Adequado quando certas opera??es geralmente envolvem apenas um subconjunto específico de atributos.

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3 Functional partitioning

Functional partitioning, also known as service-oriented architecture, is a strategy that splits a dataset by functionality, based on the business or application domain of the data. For example, a social media platform can be partitioned by user service, post service, and message service, so that each node stores and processes a specific type of data. Functional partitioning can increase the modularity, maintainability, and testability of the system, and allow for different technologies and architectures for different services. However, functional partitioning can also increase the complexity, latency, and coupling of the system, and require more coordination and synchronization between services.

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4 Geographical partitioning

Geographical partitioning, also known as geo-replication, is a strategy that splits a dataset by location, based on the physical or logical proximity of the data consumers. For example, a global e-commerce platform can be partitioned by region, so that each node stores and serves the data of a specific geographical area. Geographical partitioning can improve the user experience, reduce the network bandwidth, and comply with the regulatory or legal requirements of different regions. However, geographical partitioning can also introduce the challenges of data consistency, replication, and conflict resolution across regions.

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5 Hybrid partitioning

Hybrid partitioning, also known as composite partitioning, is a strategy that combines two or more of the above strategies, based on the specific needs and characteristics of the dataset and the system. For example, a user table can be horizontally partitioned by user ID, and then vertically partitioned by user profile and user preferences. Hybrid partitioning can leverage the benefits of multiple strategies, and optimize the system for different scenarios and objectives. However, hybrid partitioning can also increase the difficulty of designing, implementing, and managing the system, and require more careful tuning and monitoring of the system.

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Rodrigo Fernandes Garcia

Diretor Digital @ GOL | GOL Labs | ????????
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A "melhor" estratégia de particionamento depende fortemente do contexto específico em que está sendo aplicada. O particionamento híbrido é uma escolha poderosa quando há necessidade de flexibilidade e otimiza??o personalizada, mas requer uma considera??o cuidadosa dos trade-offs envolvidos. Em cenários onde a simplicidade e a facilidade de manuten??o s?o prioritárias, estratégias de particionamento mais simples podem ser mais apropriadas.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Jonatha William

Analista de Manuten??o Sênior | Lean Seis Sigma Master Black Belt | PPCM | Desenvolvedor de Pessoas | Vale
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Particionamento por intervalo (Range-based): Descri??o: Os dados s?o divididos com base em intervalos de valores em uma coluna. Aplica??es: Semelhante ao particionamento horizontal, mas mais específico, permitindo controle sobre a faixa de valores. Particionamento round-robin: Descri??o: Os dados s?o distribuídos sequencialmente entre as parti??es, atribuindo cada registro à próxima parti??o na lista. Aplica??es: Simples e fácil de implementar, mas pode levar a desequilíbrios dependendo da distribui??o dos dados. A escolha da estratégia de particionamento depende da natureza dos dados, dos padr?es de acesso e das opera??es realizadas no sistema distribuído.

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What are some common data partitioning strategies in distributed systems?

1

2

3

4

5

6

1 Horizontal partitioning

2 Vertical partitioning

3 Functional partitioning

4 Geographical partitioning

5 Hybrid partitioning

6 Here’s what else to consider

Operating Systems

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What are some common data partitioning strategies in distributed systems?

1

2

3

4

5

6

1 Horizontal partitioning

2 Vertical partitioning

3 Functional partitioning

4 Geographical partitioning

5 Hybrid partitioning

6 Here’s what else to consider

Operating Systems

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