Partitioning: A Data Engineer’s Secret Weapon

Adding Data Partitioning to your tool belt

10 min readJul 29, 2024

What’s the big deal about partitioning

Odds are if you are a data engineer or working with the data in your job, you have probably heard of the term ‘Partitioning’ or how Partitioning could make your queries run faster or make your other tasks run smoothly in general. We will be using analogies to delve into the meaning of partitioning, the scenarios it might be needed, the right strategies for implementing it, and how exactly it fits into your workflows.

In the ever-expanding world of workflows involving data, efficiency is a precious commodity. Data engineers are constantly bombarded with datasets and the requirement of designing ETL pipelines to transform raw data into a format suitable for analysis while also managing their storage correctly, and this is where the technique of Data Partitioning can be beneficial.

Partitioning Explained using the Library Analogy

Imagine you have a private library with all the books piled haphazardly in one giant room, and you need to find a specific novel imagine how stress and effort it would take you to get to get that particular book.

Partitioning is the same as organizing those books into sections — history, fiction, science. Suddenly, locating a specific book you want now becomes way more easier.

Data partitioning applies the same logic to your datasets. It involves dividing your data into smaller, more manageable segments based on specific criteria, like date, region, or customer ID. This seemingly simple concept unlocks a treasure trove of benefits:

Blazing Fast Queries: Need to analyze sales data for the past quarter? Partitioned tables allow your database to zoom straight to the relevant section, significantly reducing query times. It’s like having a librarian who knows exactly where each book is shelved!
Scalability for the Future: As your data grows, partitioned tables can seamlessly expand to accommodate it. It's like getting new bookshelves to store new books when scaling up for the future. There is no more worrying about monolithic tables grinding your systems to a halt.
Optimized Storage Utilization: Not all data is created equal. Partitioning allows you to store frequently accessed data on faster storage tiers, while less-used data can reside on more economical options. It’s like having a premium shelf for your favourite reads and a secondary storage unit for the rest.
Efficient Data Management: Partitioning simplifies data maintenance tasks like backups, deletions, and archiving. Think of it as decluttering your library — you only need to remove the history section, not the entire collection, when it’s time for an upgrade.

To understand how this works in practice, let's look at a scenario where which you have a sales fact table namedFactSales partitioned by the Date column (sales_date). Let's assume for this table, we only need to store only 3 months’ worth of data and the FactSales table currently contains the data for 20220101, 20220201, and 20220301. For this scenario, we will be archiving the first month’s data (20220101) before deleting it and adding the latest month’s data, 20220401 to the table.

First, Let's have a look at the schema of the sales fact table, which will contain the three months’ data :

CREATE TABLE FactSales (
  id INT PRIMARY KEY,
  product_id INT,
  customer_id INT,
  sales_amount DECIMAL(10,2),
  sales_date INT
)
WITH (
  DISTRIBUTION = HASH(id),
  CLUSTERED COLUMNSTORE INDEX);

To archive the first-month data we will be archiving, we need to create a dummy table that has the same structure as the original table and then swap out the partition for the dummy table that will contain a single partition value just for 20220101(January).

CREATE TABLE FactSales_20220101(
  id INT PRIMARY KEY,
  product_id INT,
  customer_id INT,
  sales_amount DECIMAL(10,2),
  sales_date INT
)
WITH (
  DISTRIBUTION = HASH(id),
  CLUSTERED COLUMNSTORE INDEX);

As seen above, we have created the archive table for FactSales_20220101 with the exact same schema as the original fact table

In this example, we have created two tables with the same schema: FactSales and FactSales_20220101. FactSaleshas a partition column on the sales_date, while FactSales_20220101 does not yet have one.

2. Let’s populate the originalFactSales table:

INSERT INTO FactSales VALUES
  (1, 37, 125, 89.23, 20220101),
  (2, 34, 95, 29.23, 20220201),
  (3, 44, 25, 39.63, 20220301);

We have added three records to the FactSales table containing the three records for the month the fact sales table needs.

3. Now we add a partition for the empty dummy FactSales_20220101 table:

ALTER TABLE factSales_20220101 ADD
  PARTITION (sales_Date = 20220101);

This creates a partition in the empty FactSales_20220101 table with the same partition column values as in the Factsales table

4. Finally, we switch the partition from the original table to the dummy table:

ALTER TABLE factsales SWITCH PARTITION 20220101
TO FactSales_20220101 PARTITION 20220101 WITH (TRUNCATE_TARGET = ON);

This switches the partition on salesDate partition column containing 20220101 (January) from the FactSales table to the FactSales_20220101 table. The FactSales_20220101 table now contains the data from the switched partition on the FactSalestable, but the data no longer exists on the Factsales table as it was deleted after the switch with the Truncate_Target command.

5. Lastly, we add the data for 20220401 by inserting it into the FactSales table

INSERT INTO FactSales VALUES
  (4, 57, 123, 49.23, 20220401)

Therefore from this example above, we have archived the 20220101 data and have added 20220401 data by using partition switching and partition deletion

By using these partitioning techniques, you can see how we quickly and efficiently moved data between these two table tables. This can be especially useful for scenarios where you need to move large amounts of data or perform frequent data transfers like archival or deletion.

Next, let’s debunk a particular pervasive rumour about data partitioning

Myth Debunked : Partitioning is just used for Large Datasets.

There is a misconception that partitioning only creates benefits for large datasets, but that’s not true. Even moderately sized tables, i.e., staging tables, can benefit from partitioning. The performance gains can be substantial, especially when dealing with frequent queries that target specific subsets of the data.

Data Partitioning Strategies: using a Zoo analogy

Data partitioning, like organizing a zoo, keeps things categorized and easy to find. Imagine a zoo with all the animals crammed into one giant enclosure; there would be chaos

Partitioning separates animals into specific habitats based on their needs. This makes it easier for zookeepers (data analysts) to care for them (analyze data) and for visitors (applications) to find specific animals (data points).

Here’s a breakdown of common data partitioning strategies with analogies:

1. Range Partitioning (Like Separating Animals by Age):

Analogy: At the zoo, the elephants might have separate enclosures for baby elephants, young adults, and adults.
Concept: Data is divided into partitions based on a range of values for the chosen partition key.
Example: A table storing sales data could be partitioned by “year,” with partitions for 2023, 2022, etc. Queries targeting a specific year only need to search the relevant partition, speeding things up.

2. Hash Partitioning (Like Distributing Animals by Species Across Zones):

Analogy: The zoo might distribute different species of monkeys (e.g., spider monkeys, howler monkeys) across various zones within the primate area.
Concept: Data is distributed across partitions based on a hash function applied to the partition key value. This ensures even distribution across partitions.
Example: A table storing customer data could be partitioned by a hash function of the “customer ID.” This spreads data evenly across partitions, improving performance for queries that don’t target specific customer IDs.

3. List Partitioning (Like Grouping Animals by Specific Traits):

Analogy: The zoo might have a separate enclosure for animals with unique features, like white tigers or albino lions.
Concept: Data is partitioned based on specific values or a list of values in the partition key.
Example: A product table could be partitioned by “product category,” with partitions for “electronics,” “clothing,” etc. This allows for faster queries targeting specific product categories.

4. Composite Partitioning (Mixing and Matching Enclosures):

Analogy: The zoo might have a special enclosure for both pregnant animals and large herbivores, combining two partitioning criteria.
Concept: Combines multiple criteria for partitioning.
Example: A web traffic log table could be partitioned by “year” and “country,” allowing for efficient retrieval of data for specific timeframes and geographic locations.

Choosing the Right Strategy:

The best strategy depends on your data and query patterns. Consider these factors:

Access Patterns: How do you typically query the data? Choose a partitioning strategy that aligns with your most frequent queries.
Data Distribution: How is your data distributed across the partition key values? Ensure even distribution for hash partitioning.
Scalability: Consider future data growth and choose a strategy that can easily accommodate additional partitions.

By understanding partitioning strategies and their analogies, you can transform your data from a disorganized jungle into a well-structured zoo, where finding the information you need is swift and efficient.

Does partitioning work for all Tables?

No, data partitioning doesn’t necessarily work on all table types. While it offers significant benefits for specific tables, it might not be suitable for others. Here’s a breakdown of when partitioning is most effective and some table types where it might not be the best approach:

Scenarios Suitable for data partitioning

Tables Well-Suited for Partitioning:

Data partitioning shines brightest in a few key scenarios where it can significantly improve data management and query performance. Here’s a breakdown of the situations where partitioning is most suitable:

1. Large Datasets:

When you’re dealing with massive tables containing millions or billions of rows, partitioning becomes a game-changer. Traditional queries might have to scan the entire table, leading to slow response times. Partitioning breaks down the data into manageable chunks, allowing queries to target specific partitions instead of the entire dataset. This significantly reduces query execution time and improves overall efficiency.

2. Targeted Queries:

If your data access patterns involve frequent queries that focus on specific subsets of the data, partitioning becomes highly valuable. By aligning the partition key with your typical query criteria (e.g., date range, customer segment), you can direct queries to the relevant partitions. This eliminates the need to search through irrelevant data, leading to faster results.

3. Time-Series Data:

For data that changes over time, such as sensor readings, website traffic logs, or financial transactions, partitioning based on timestamps is a perfect fit. You can create partitions for specific timeframes (e.g., daily, monthly, yearly). This allows you to efficiently retrieve historical data for analysis without sifting through the entire dataset.

4. Scalability and Manageability:

As your data volume grows, partitioning facilitates easier manageability. You can simply add new partitions to accommodate the additional data without impacting the performance of existing queries. This promotes horizontal scaling of your database, allowing it to handle ever-increasing data loads.

5. Optimized Storage Management:

Partitioning allows you to potentially optimize storage costs by placing frequently accessed partitions on faster storage tiers while storing less-used partitions on more economical options. This approach leverages the varying access patterns within your data to ensure optimal storage utilization.

Here are some additional tips for identifying suitable partitioning scenarios:

Analyze your most frequent queries and identify the key criteria used for filtering or joining data.
Consider the data’s growth pattern and how partitioning can facilitate future data volume increases.
Evaluate the trade-offs between managing partitions and the potential performance gains for your specific use case.

By understanding these scenarios and carefully evaluating your data characteristics, you can determine if data partitioning is the right approach to optimize your data management and unlock faster insights from your data.

Scenarios not suitable for data partitioning

Data partitioning offers significant advantages for managing large datasets, but it’s not a universally applicable technique. Here are some scenarios where data partitioning might not be the most suitable approach:

1. Small Tables:

Overhead vs. Benefit: For very small tables (in terms of data volume and row count), the overhead of creating and managing partitions might outweigh the potential performance gains from partitioning. The complexity of managing partitions might not justify the benefit for small datasets.

2. Uniform Data Access:

Limited Performance Improvement: If your application queries tend to scan the entire table frequently, regardless of specific values in the partition key, partitioning might not offer a significant performance boost. In such cases, the benefits of faster targeted queries might be negated by the overhead of managing partitions.

3. Highly Volatile Data:

Management Challenges: If your data undergoes constant updates, insertions, or deletions across the entire dataset, partitioning can introduce complexity. Keeping partitions synchronized with the main table and managing partition boundaries can become a challenge with highly volatile data.

4. Complex Queries:

Limited Benefits: Partitioning excels at optimizing queries that leverage the partitioning key for filtering or targeting specific data subsets. However, for complex queries involving joins or aggregations across multiple partitions, the performance benefits might be diminished. Partitioning might even add complexity to managing these queries.

Here are some additional considerations:

Data Update Frequency: If your table is constantly updated, the performance benefits of partitioning might be reduced as partitions need to be updated frequently as well. Evaluate the update patterns and weigh them against the access patterns for your data.
Schema Changes: Adding or removing columns that were used for partitioning can necessitate complex data migrations and repartitioning strategies. Consider the stability of your table schema before implementing partitioning.

Alternative Approaches:

If data partitioning isn’t ideal for your scenario, consider these alternatives:

Clustering: This technique organizes data physically based on frequently used columns to improve query performance for specific access patterns. It groups related data together on storage devices, potentially leading to faster retrieval times for queries that target those specific data clusters.
Materialized Views: These are pre-computed summaries of frequently used queries stored as separate database objects. They can improve query performance by reducing the need to re-execute complex queries on the main table every time.

Choosing the Right Approach:

In conclusion, the best approach depends on your specific data characteristics, access patterns, and update frequency. Carefully evaluate these factors to determine if data partitioning aligns with your needs. For small tables, uniformly accessed data, or highly volatile data, alternative strategies like clustering or materialized views might be more suitable.

Remember, data partitioning is a powerful tool, but it’s crucial to understand its limitations and choose the most appropriate technique to optimize your data storage and retrieval strategies.