Data partitioning

What is data partitioning?

Data partitioning is a database management process about splitting large datasets into manageable portions called partitions. Each partition has separate access and management to improve query performance and overall system efficiency.

Data partitioning in DBMS is a key concept in data warehouses, databases, and distributed systems for optimal storage and retrieval.

Some other use cases of data partitioning include multi-tenant applications, applications handling time-series data, and enterprise databases.

Data partitioning in SQL and PostgreSQL

Data partitioning in SQL and PostgreSQL is an in-built feature used to divide data into small portions. The SQL user will require permissions like CREATE, ALTER, CONTROL, and ALTER DATABASE to create data partitions in SQL.  

In PostgreSQL, data partitioning works by splitting the parent table into child tables. Parent table doesn’t hold data, and the child table refers to the partitions, with the schema inherited from the parent table.

Types of data partitioning

Data partitioning is of 6 types - vertical, horizontal, range, list, hash, and hybrid partitioning.

1. Vertical partitioning splits data tables vertically into two or multiple tables, each partition with a few subsets of columns. Example: Splitting customer records into three partitions, one with names and user id, another with login attempts, and the last one with contact information. This type of partitioning is effective when only certain aspects of the data are accessed. 

2. Horizontal partitioning splits data horizontally, where each partition is left with different rows of information. Example: Customer records split into two partitions, one carrying users from North America and the other with the European region. Horizontal database is also called sharding and is suitable for distributed data systems.

3. Range partitioning in DBMS divides data tables based on the range of values within the columns. For example, a large table is divided, where each partition carries rows following under a specific time period. For this reason, time series data.

4. List partitioning is also like range partitioning, but it divides databases based on a predefined list of values of the respective column. For example, every partition must hold datasets in columns related to a certain region like ‘Asia’, ‘Europe’. Etc.

5. Hash partitioning is when you divide rows into partitions and use a partition key to evenly distribute the load across all partitions. 

6. Hybrid partitioning is when you use two or more partitioning techniques for optimized performance and equal distribution. Like using hash and range partitioning together, where you partition first based on the range, and then sub-partition using hash for balancing.

All these data partitioning techniques are available in SQL and PostgreSQL and other dbms platforms.

Benefits of data partitioning

Data partitioning in DBMS has the following advantages. 

Better data management: database managers and data teams find it easier to perform operations like storage, archiving, retrieval, or movement without having to touch the entire table.

Query performance: easier to execute queries with smaller partitions, as you don’t have to search large and complex volumes of data.

Fault tolerance: your teams can still access other partitions when one partition becomes down or unavailable. Data partitioning also helps with data recovery. If one partition becomes corrupted or lost, one can still have access to other parts of the datasets.

Parallel processing capabilities: you could access two or more partitions concurrently without one query affecting or overlapping others.

Scales with your organization: scaling horizontally becomes achievable for distributed data systems, as their partitions are divided across different nodes. Partition key ensures load balancing, ensuring even and smooth scaling.

Steps involved in data partitioning

You can turn large datasets into data partitions without losing data using DBMS platforms like PostgreSQL. 

1. Get the data ready for partitioning. Perform any backups required before you begin. This will prevent any data loss possibly happening during data partitioning.

2. Depending on the data table and its columns, decide the right partitioning technique from range, list, hash, etc.

3. Create a parent table with the existing schema structure and mention your partition type. Now, create partitions based on the selected technique.

4. Move data from the parent table to partitions using comments like ‘INSERT’ and ‘SELECT INTO’.

5. Make sure that data is moved to partitions correctly, by checking the number of rows and columns. That’s all. You could drop the original table if you want to free up space.

Growing organizations have increased data requirements, along with growing volumes of data. For them, data partitioning is an effective way to improve data management and query speed, if planned and performed correctly. It’s becoming inevitable for database admins and managers who must constantly see to the right functioning of existing databases.