Horizontal Partitioning/Sharding
In this technique, the dataset is divided based on rows or records. Each partition contains a subset of rows, and the partitions are typically distributed across multiple servers or storage devices. Horizontal partitioning is often used in distributed databases or systems to improve parallelism and enable load balancing.
Advantages of Horizontal Partitioning/Sharding
- Greater scalability: By distributing data among several servers or storage devices, horizontal partitioning makes it possible to process large datasets in parallel.
- Load balancing: By partitioning data, the workload can be distributed equally among several nodes, avoiding bottlenecks and enhancing system performance.
- Data separation: Since each partition can be managed independently, data isolation and fault tolerance are improved. The other partitions can carry on operating even if one fails.
Disadvantages of Horizontal Partitioning/Sharding
- Join operations: Horizontal partitioning can make join operations across multiple partitions more complex and potentially slower, as data needs to be fetched from different nodes.
- Data skew: If the distribution of data is uneven or if some partitions receive more queries or updates than others, it can result in data skew, impacting performance and load balancing.
- Distributed transaction management: Ensuring transactional consistency across multiple partitions can be challenging, requiring additional coordination mechanisms.
Data Partitioning Techniques in System Design
Using data partitioning techniques, a huge dataset can be divided into smaller, simpler sections. A few applications for these techniques include parallel computing, distributed systems, and database administration. Data partitioning aims to improve data processing performance, scalability, and efficiency.
Important Topics for Data Partitioning Techniques in System Design
- Horizontal Partitioning/Sharding
- Vertical Partitioning
- Key-based Partitioning
- Range Partitioning
- Hash-based Partitioning
- Round-robin Partitioning