What Is Data Partitioning?

Data drives today’s world. How you handle and organize your data impacts your organizational productivity, especially as datasets grow. When building customer-centric apps or analyzing complex datasets, data partitioning allows for better data management, improved scalability, faster query resolution, and increased performance. 

By dividing data in a large-scale database into smaller, more manageable pieces called partitions, you can reduce bottlenecks and improve efficiency. In this guide, we’ll explore the basics of data partitioning, how to effectively implement it in your operations, and its benefits. 

What is data partitioning in databases?

Data partitioning splits data for improved efficiency and performance. Additionally, it allows scalability and increased fault tolerance. A large dataset is either split vertically by columns or horizontally by rows. Each partition contains a subset of the data.

When partitioning is implemented within a single database instance (such as PostgreSQL table partitioning or SQL Server partitioning), the partitions remain on the same server. However, in distributed system architectures, these partitions can be distributed across multiple nodes, systems, or servers to achieve horizontal scaling.

Splitting data across multiple servers allows engineers to leverage parallel processing. Parallel processing reduces the load on individual systems and optimizes how a database utilizes its computing resources. Data sharding is a specific type of horizontal partitioning in which a single logical database or table is distributed across multiple separate database instances or servers. Unlike basic partitioning, which divides data within one database, sharding takes one database and segments it across multiple physical database systems.

By partitioning a dataset into smaller chunks, your business can focus queries on smaller, relevant subsets. This reduces the amount of data your system needs to scan, allowing you to focus your time on analyzing relevant data. It also makes large volumes of data easier to manage while speeding up queries.

Why is database partitioning important?

Database management systems (DMBSes), distributed file systems, and big data processing frameworks all commonly use data partitioning. Data partitioning can help your business adapt to challenges that arise from processing enormous amounts of information. As data volumes grow, your infrastructure and query resolution processes will be pushed to their limits. 

Data partitioning helps overcome these new challenges in the following ways:

• Improved query performance: Data partitioning enables your queries to focus on a specific subset of data. When your system doesn’t have to scan an entire database, it can respond to and resolve queries faster.
• Scalability: The more information your database stores, the more difficult it is to maintain. Partitioning makes it easy to scale databases, because you can add new partitions when you need them without making significant changes to your existing architecture.
• Better resource utilization: Partitioning distributes processing across multiple servers. This distribution enables parallel processing, which reduces the load on any one resource and allows you to manage your workflow effectively.
• Data management and maintenance: Partitioning makes it easier to manage and maintain your database. Instead of needing to work with an entire dataset, you can target specific partitions for purging, archiving, and backups.
• Cost-effective storage: Data partitioning allows you to optimize your storage. You can keep frequently accessed data in faster storage tiers and move partitions that are less used to cheaper storage options. 

Types of database partitioning

There are a few main methods to partition databases: horizontal, vertical, hybrid, and functional. You can use these methods to fit different scenarios. The way you partition your database depends on several factors, such as data size, system architecture, access patterns, and processing requirements. 

Vertical partitioning

Vertical partitioning involves dividing a dataset or table into smaller segments by grouping related columns. Each partition contains similar attributes for each row or record. This method optimizes storage and performance by isolating frequently updated or queried columns in their own partitions. This division speeds up data retrieval by reducing the amount of data your system needs to scan during queries. 

Vertical partitioning is ideal when different sets of attributes are commonly searched together. It allows you to separate static data from dynamic data. For example, in a product database, you can vertically partition the data into two subsets. One can contain dynamic information, such as the price, while the other can house static information, such as name, detailed product descriptions, and images. 

Likewise, you can use the same process for a customer database. You can group basic customer information, such as names and contact details, into one partition and store their marketing preferences and social media accounts in another. By vertically partitioning information, you can find relevant information faster. 

Horizontal partitioning

Horizontal partitioning involves splitting database schema into multiple partitions based on records or rows. Each partition is made up of a subset of rows that share a common value or attribute. This method is ideal for large datasets that grow consistently, such as e-commerce websites or social media platforms. Within horizontal partitioning, there are distinct types, including: 

• Range: This method involves dividing data based on a specific range of values, such as dates. Each partition contains data within that discreet range, and queries targeting that range will scan only relevant partitions. This is ideal for time-series data, such as event tracking, financial transactions, and logs.
• List: List partitioning involves segmenting your data based on a predetermined list of values for a specific column. Each partition contains rows that match one or multiple values. This method is ideal for dividing categorical data, such as countries, departments, or product types.
• Hash: This method of horizontal partitioning applies a hash function to the partition key to determine where the data will be stored. This method is often used in distributed systems to evenly distribute data across all partitions, reducing overloads and bottlenecks.

Hybrid partitioning

Hybrid partitioning combines vertical and horizontal partitioning. This method is used to meet the demands of complex systems, allowing you to be more specific with your queries. For example, you can partition a set of social media posts by using horizontal partitioning to split the posts by data entries. You can then use vertical partitioning to split the posts by attributes.

This method allows you to address the limitations and trade-offs of each scheme while enjoying the benefits of both. However, it can lead to data complexity and increased overhead. It also may increase your risk of data duplication and inconsistency. 

Functional partitioning

Functional partitioning involves dividing a database schema into different partitions based on the application’s specific functions or modules. Each partition is assigned a different function, such as customer, inventory, or orders. You can also use separate partitions to isolate different services or workflows. 

This partitioning method is common in microservices architecture, such as an e-commerce platform in which each service operates independently. For example, Etsy may use functional partitioning to ensure that order service, customer service, and payment service are separated. 

How does data partitioning work?

Regardless of the specific partitioning method you use, at the foundation of the process is the partition key. A partition key is an attribute or criterion used to partition a dataset into subsets. This key is a part of data modeling and determines how data is distributed and organized within a database or system.

When data is partitioned, this key assigns each record or data item to a specific subset. For example, a partitioning key could be a geographic region, timestamp, or customer ID. Each record in the distributed database is then assigned to a partition based on the key’s value. 

Additionally, in a distributed database, replication is used in conjunction with partitioning. In the replication process, information is copied across nodes or servers. This means that each partition is assigned to more than one node, leading to better availability. The data is still accessible if one of the nodes fails. 

Best practices for data partitioning

To optimize your enterprise’s performance, manageability, and scalability, you need to implement effective data partitioning strategies, especially as your volume of data grows. The following best practices help you maximize these benefits: 

1. Understand your data: Look at how your data is used. Analyze its structure, relationships, size, growth patterns, and distribution. Understanding your data enables you to make informed decisions throughout the implementation process.
2. Select a partition key: Once you comprehend how your information is used, you can choose the right partition key. The right key ensures that data is evenly distributed, reduces data skew, and aligns with the most common queries.
3. Manage size: When it comes to partitions, size does matter. Partitions need to be large enough to reduce the overhead and small enough to improve query performance and maintenance tasks.
4. Combine partitioning and indexing: Partitioning narrows down the amount of data that needs to be scanned for queries. Indexing within the subset further speeds up the process.
5. Monitor and adjust: As data grows, you may need to adjust your partitioning strategy. Regular database maintenance and monitoring can ensure that your partitions continue to meet your needs. Depending on how you use your growing database, you may need to merge, split, or repartition data.
6. Leverage automation: Modern databases support automatic partitioning. Leveraging this feature reduces your administrative burden, as automatic partitioning can adjust to changing patterns and volumes on its own.
7. Test strategies: When dealing with critical or vast datasets, it is crucial to test your partitioning strategies. You can observe how your strategies impact performance in a staging environment without risking production.
8. Consider scalability: You must design your partitioning schema to anticipate growth. Your strategy should be able to accommodate new queries, requirements, and growing amounts of data without significant rework. 

Evaluating the impact of partitioning

As with any database process, there are positives and negatives to partitioning data. 

Benefits

For most large-scale databases, data partitioning is advantageous. These positives include the following: 

• Improved query performance by reducing the amount of data scanned
• Enhanced scalability by allowing data to be distributed across multiple storage systems
• Simplified maintenance tasks, such as archiving or purging old data
• Increased concurrency, which enables higher throughput and reduced processing delays
• Efficient resource utilization that prevents any single node from being overloaded
• Improved fault tolerance, which minimizes the impact of a system failure
• Increased data redundancy through replication, which reduces the risk of data loss
• Sensitive data protection, which is done by segregating it in a separate partition with stricter security controls
• Compliance with data privacy regulations through data localization

Challenges

Despite the benefits of data partitioning, it also raises some challenges that can harm performance and productivity, such as: 

• Increased complexity in database design and management
• Potential for uneven data distribution if not planned properly, which can negatively impact performance
• Need for careful planning to ensure data integrity and consistency

Leveraging EDB and OSS tools for efficient partitioning

In today’s data-driven world, data partitioning is essential to ensure that your growing databases remain functional and scalable. Now you can leverage EDB Postgres® AI and open source tools to modernize your existing systems and efficiently partition your data. 

This makes migrations easy and provides you with increased throughput efficiency. It is the only Postgres server solution that offers robust Oracle compatibility and can be deployed onsite or in the cloud to help optimize your workloads. Additionally, its support of horizontal, vertical, and hybrid partitioning allows you to customize it to fit your organizational needs. 

When combined with pgAdmin, EDB Postgres AI helps you visually manage partitions. This open source, multi-platform console has an intuitive graphical interface that makes it easy to merge, partition, and backup data.

To learn more about EDB Postgres AI’s capabilities, contact us today to request a demo. You can also view our technical resources and knowledge base for further guidance on how to implement partitioning effectively.