What Is Data Partitioning? A Complete Guide for Beginners

Whether you're building customer-centric apps or running complex analytics pipelines, one thing becomes clear: how you store and access data can make or break performance.

I’ve seen firsthand how much smoother systems run when data is structured thoughtfully, and that’s where data partitioning comes in. It’s one of those techniques that can drastically improve query speed, storage efficiency, and scalability.

In this post, I’ll explain data partitioning, the different types available, and the best practices I’ve learned along the way.

What is Data Partitioning?

Data partitioning is a technique for dividing large datasets into smaller, manageable chunks called partitions. Each partition contains a subset of data and is distributed across multiple nodes or servers. These partitions can be stored, queried, and managed as individual tables, though they logically belong to the same dataset. 

Data partitioning improves database performance and scalability. For instance, searching for a data point in the entire table takes longer and uses more resources than searching for it in a specific partition. That's why data is stored as partitions. 

Types of Data Partitioning

Data partitioning encompasses various types, each with a unique strategy and benefits. In this section, I discuss the most common types.

Horizontal partitioning

Instead of storing all the data in a single table, horizontal partitioning splits the data into rows, meaning different sets of rows are stored as partitions. 

All partitions of horizontal partitioning contain the same set of columns but different groups of rows. 

• Example: Say you have a weather dataset for multiple states in three different countries. You can partition this into three tables by splitting the rows by each country. 

Horizontal partitioning by country:

Vertical partitioning

Vertical partitioning divides data by columns, so each partition contains the same number of rows but fewer columns. 

The partition key or the primary column will be present in every partition, maintaining the logical relationship. 

Vertical partitioning is popular when sensitive information is to be stored separately from regular data. It allows sensitive columns to be saved in one partition and standard data in another.

Another common use case is grouping columns that are frequently updated into one partition and the remaining in a separate partition.

• Example: If the employee phone number and salary columns in the table below are confidential, let’s partition the table accordingly. 

Emp_id is the unique identifier in the above table, so all partitions have it.

Partition 1:

Partition 2:

Range partitioning

Range partitioning splits data based on a range of values for a particular column. 

Typically, each partition has a lower and upper bound for the key column, and a record that falls within this range is assigned to that partition. 

• Example: Let’s say sales data is available from 2021 to 2023. We can partition this by year so that sales from 2021 to 2022 fall in one partition and from 2022 to 2023 in another.

Partition 1:

Partition 2:

Hash partitioning

A hash function is applied to the partition key, and the output determines which record should be stored in which partition. In this technique, the number of partitions is decided beforehand. 

The remainder obtained by dividing the hash value by the number of partitions determines which partition that specific record should be stored in. Let’s see this with an example:

• Example: Say, for the product with ID z0yu9h, hash(‘z0yu9h’) = 101 and number of partitions = 4, then 101%4 = 1. So, this record is stored in the first partition. 

This way, hash values are calculated for the partition key column divided by the number of partitions, and the result determines the final partition.

List partitioning

The procedure is similar to range partitioning, but instead of dividing data by a range of values, list partitioning divides the data based on a predefined set of values. 

Take the temperature table again as an example. 

In the above data, the column ‘Season’ contains a list of values [‘winter’, ‘summer’]. So, the list partitioning on season looks like the following:

Partition 1:

Partition 2:

Composite partitioning

A composite partition is a combination of two partitioning techniques. A table is first divided using one strategy, and each partition is further divided using another technique.

Partitioning strategies are frequently discussed in technical interviews—see the top DBA interview questions for 2025.

Use Cases for Data Partitioning

Now that we have evaluated the different types of partitioning strategies, let’s examine when data partitioning is useful.

Distributed databases 

Distributed systems are collections of nodes physically located in separate servers but linked together using a network. Examples include Apache Cassandra, Amazon DynamoDB, and Google Bigtable. 

These systems internally use data partitioning to organize and distribute data across nodes. 

OLAP operations

OLAP represents multidimensional data as cubes, allowing users to explore data from different angles. Though these cubes represent pre-aggregated data, the underlying data is still vast, so the queries need further optimization using data partitioning. 

For example, applying cube partitioning (a data partitioning technique) divides the data into smaller cubes based on specific criteria. When you query the data, the search is performed on a particular cube by skipping irrelevant ones, reducing I/O operations.

Similarly, dimension partitioning can be applied to split the data by level, hierarchy, or column.

Log management

Logs contain information about the events, transactions, and operations occurring in a system. They are used to trace failures, bugs, edge cases, and even success messages. 

Log partitioning speeds up the debugging process and gives you the necessary information faster. For instance, if you have partitioned logs by time period, you can easily retrieve them by date, week, month, or year. 

Machine learning pipelines

In machine learning use cases, data is partitioned into training, validation, and testing sets. This approach ensures that insights from the test set are not revealed to the model during training to avoid overfitting. 

Moreover, if the training data is huge, a single machine cannot process and feed the entire data into a model. So, the data is divided into chunks and inputted into the model through data partitioning. 

Tools for Data Partitioning

Data partitioning can be implemented in both SQL and NoSQL databases. Let’s discuss them in this section.

SQL databases

PostgreSQL

PostgreSQL offers built-in support for range, list, and hash partitioning. For example, the following query creates a partition for each year to efficiently access sales profits and total sales for each financial year.

CREATE TABLE sales(
    city_id         int not null,
    sales_year      date not null,
    total_sales     int,
    sales_profits   int
) PARTITION BY RANGE (sales_year);

MySQL

MySQL supports different types of horizontal partitioning, such as range, list, hash, and key partitioning. As of now (version 8.4), it doesn't support vertical partitioning.

The MySQL query below partitions employee data into three groups, p1, p2, and p3, by their joining dates. All employees who joined before 2000 fall into the p1 partition. Employees who joined between 2000 and 2010 fall into the p2 partition, and employees who joined between 2010 and 2020 fall under the p3 partition.

CREATE TABLE employee(
    firstname VARCHAR(25) NOT NULL,
    username VARCHAR(16) NOT NULL,
    email VARCHAR(35),
    salary INT,
    joined DATE NOT NULL
)
PARTITION BY RANGE( YEAR(joined) ) (
    PARTITION p1 VALUES LESS THAN (2000),
    PARTITION p2 VALUES LESS THAN (2010),
    PARTITION p3 VALUES LESS THAN (2020)
);

Microsoft SQL Server

SQL Server offers partitioning features through partition functions and partition schemes. A partition function specifies how the data is split, while the partition scheme determines the filegroup where the partition will be stored. 

In the following queries:

• Function name: sales_by_year
• Scheme name: sales_filegroup
• Filegroup names: fg01, fg02, fg03, fg04

Partition function:

create partition function sales_by_year (date)
as range right for values 
('2024-01-01', '2024-02-01', '2024-03-01', ‘2024-04-01’);

Partition scheme:

CREATE PARTITION SCHEME sales_filegroup
AS PARTITION sales_by_year
TO (fg01, fg02, fg03, fg04);

Here,  the files store data as follows:

• fg01 - dates before ‘2024-01-01’,
• fg02 - ‘2024-01-01’ to ‘2024-01-31’
• fg03 - ‘2024-02-01’ to ‘2024-02-29’
• fg04 - ‘2024-03-01’ to ‘2024-03-31’

Because we mentioned range right in the query, each boundary date is included in the next partition.

If you're using SQL Server, this SQL Server tutorial walks through key concepts and tools for managing partitioned data. For a structured learning path, the SQL Server Fundamentals track introduces the core features needed to build performant SQL databases.

NoSQL databases

Amazon DynamoDB

DynamoDB automatically handles partitioning internally. When you write an item to a DynamoDB table, it passes through a request router. The router parses the partition key and applies a hash function. The output of the hash function determines the specific partition where the record will be stored. 

This entire process is automated within DynamoDB, so the user doesn't need to worry about the hash function or how the partitions are managed.

Mongodb

MongoDB distributes data across servers through the concept of sharding. The data is partitioned based on a shard key, which should be chosen carefully to avoid uneven distributions. 

Typically, the shard key should have high cardinality and should be the frequently used column in queries. These MongoDB interview questions cover almost all important topics, along with sharding. 

Apache Cassandra

Cassandra partitioner is a hashing algorithm that determines which node in a cluster should store a particular partition. The partition key is parsed through the algorithm to produce a token. This token is then compared against the token values assigned to the nodes. Finally, the data is stored in the node that matches the token. 

Common partition algorithms include Murmur3Partitioner, RandomPartitioner, and ByteOrderedPartitioner. 

How to Implement Data Partitioning?

Now, let’s discuss the necessary steps to correctly implement data partitioning.

Step 1: Understand data and access patterns

Thoroughly assess how your application queries the database, what data is often accessed, the size of the data, and performance bottlenecks. These insights will help you define clear goals for partitioning and identify key columns frequently involved in data operations.

Step 2: Choose a partitioning technique

We covered common techniques before, and here’s how to decide between them:

• Horizontal partitioning: When you have millions and billions of rows that would be stored in a single table and lead to memory issues, you should choose horizontal partitioning. It stores a different set of rows in different tables. 
• Vertical partitioning: If a group of columns is often accessed and others are occasionally queried, you can implement vertical partitioning to store columns separately based on their access patterns.
• Hybrid approach: When you have larger datasets that require you to reduce the number of rows while also optimizing the queries for a subset of columns, you should choose a hybrid approach. It combines the advantages of both horizontal partitioning and vertical partitioning.

Step 3: Create partitions

The following is an example SQL query to create partitions using hash partitioning.

CREATE TABLE employees (
    emp_id INT NOT NULL,
    name VARCHAR(30),
    hired DATE NOT NULL DEFAULT '2024-01-01',
    job_id INT,
    location_id INT
)
PARTITION BY HASH(location_id)
PARTITIONS 4;

In the above code, the ‘location_id’ column is passed to the hash function, and the output determines the partition to which the record should be assigned. The code line partitions 4 specifies the total number of partitions that should be created.  

Bookmark this handy SQL Basics Cheat Sheet to reference core SQL syntax as you implement partitions.

Step 4: Monitor and optimize partitions 

• Continuously monitor your data operations and ensure the partitions are up-to-date and valid. 
• As the application scales, you may need to add more partitions or choose a different approach, like a hybrid partition, to accommodate changing demands. 
• Ensure the partition size and strategy benefit the frequent operations performed on the database. 
• Ensure the data is evenly distributed among partitions; otherwise, you may need to rebalance it. 

Challenges and Considerations for Data Partitioning

Optimizations come with tradeoffs and challenges, as with everything in the software world. Here are the ones you should consider: 

• Partition skew: Partition skew means a few partitions get more data and processing work while others often remain idle. To avoid this, choose the right partition key and strategy that evenly distributes data across all partitions. 
• Maintenance overhead: As the data grows, you need to re-evaluate the existing partitioning strategies and ensure they are relevant to the current data. If not, you may need to repartition or merge partitions. 
• Data migrations: In the case of migrating your data to a new architecture, extra care is needed to ensure that the partitions are compatible with the new environment. Additionally, moving data between partitions involves complexities like managing data consistency across shards.
• Steep learning curve: Data partitioning adds complexity to a system or cloud architecture. They aren’t straightforward to query, migrate, or analyze, resulting in a steep learning curve for beginners.

Best Practices for Data Partitioning

As you can see, data partitioning becomes complex as the data volume grows. However, implementing these practices will ensure that partitioning improves efficiency while not affecting performance. 

• Align partitions with query patterns: Choosing the right partition strategy and partition key can significantly improve database performance. Understand the most common data operations and query patterns in your database, and select a partition key that is frequently involved in queries while also ensuring even data distribution.
• Monitor regularly: Today, applications are dynamic and constantly evolving with changing trends and demands. Therefore, it's important to regularly monitor your partitions to align with current access patterns and re-partition when necessary.
• Minimize overhead: Manually managing partitions at a large scale is complex. However, NoSQL databases like Apache Cassandra and Amazon DynamoDB automate the process without requiring a lot of extra work. Moreover, some automation tools adjust the partitions as the data scales, enabling repartitioning or merge partitioning according to your needs. 

Conclusion

Effective data partitioning is critical for maintaining performance and scalability as your data grows. With the right strategy in place, you can reduce query latency, optimize storage, and simplify maintenance. It’s a practical step with a measurable impact.

For a deeper dive into how partitioning enables distributed systems to scale, check out this guide to Kafka partitions. To understand how relational structures influence partitioning decisions, start with this relational databases course.