Which Type of Data Partitioning Is Used by IBM Db2?


Larry Thompson

IBM Db2 is a popular database management system that offers various partitioning techniques to enhance performance and optimize data storage. In this article, we will explore the different types of data partitioning used by IBM Db2 and understand their benefits.

What is Data Partitioning?
Data partitioning is a technique used to divide large datasets into smaller, more manageable parts called partitions. Each partition contains a subset of the data and is stored on separate storage devices or servers. By distributing data across multiple partitions, organizations can achieve improved performance, increased scalability, and better utilization of resources.

Types of Data Partitioning in IBM Db2:

1. Range Partitioning

Range partitioning divides data based on a specified range of values.

For example, you can partition customer data based on their registration dates or sales data based on transaction dates. Each partition contains records within a specific range.


  • Efficiently manages large datasets by distributing them across multiple partitions.
  • Enables faster query execution as Db2 can perform parallel processing on each partition.
  • Simplifies data archiving and maintenance by allowing easy addition or removal of partitions.

2. Hash Partitioning

Hash partitioning distributes data evenly across partitions using a hashing algorithm.

The algorithm calculates a hash value for each record and assigns it to a specific partition based on the result. This ensures an even distribution of data across all partitions.


  • Achieves load balancing by evenly distributing the workload across all available partitions.
  • Simplifies database management as each partition has an equal number of records.
  • Ensures fault tolerance as the loss of one partition does not affect the entire dataset.

3. List Partitioning

List partitioning allows you to define partitions based on specific values in a column.

For example, you can partition customer data based on their geographical location or product data based on categories. Each partition contains records that match the defined criteria.


  • Enables efficient data organization by grouping related records into separate partitions.
  • Simplifies data retrieval as Db2 only needs to search within relevant partitions.
  • Provides flexibility in managing and analyzing specific subsets of data.

4. Multi-Dimensional Clustering (MDC)

Multi-Dimensional Clustering (MDC) is a specialized form of partitioning that organizes data based on multiple columns simultaneously. It creates multidimensional clusters, allowing efficient retrieval of frequently accessed data.


  • Improves query performance by reducing disk I/O and optimizing data layout.
  • Enhances compression efficiency as similar data values are stored together.
  • Simplifies complex analytics by organizing related attributes into clusters.


In conclusion, IBM Db2 provides various types of data partitioning techniques to meet different performance and scalability requirements. Whether it’s range partitioning for managing large datasets, hash partitioning for load balancing, list partitioning for organizing related records, or multi-dimensional clustering for optimized retrieval, Db2 offers a comprehensive set of tools to enhance database performance and efficiency.

By leveraging these partitioning techniques effectively, organizations can optimize their database operations and achieve better utilization of resources while ensuring high availability and fault tolerance.

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