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Partition Evolution

Only Iceberg makes this possible
Partitioning is one of the most critical aspects of optimizing query performance in big data systems. Traditionally, partitioning strategies are set when a table is created, and altering them later is nearly impossible without costly data migration. Apache Iceberg, however, introduces partition evolution, enabling seamless changes to partitioning strategies without rewriting existing data. This blog explores how partition evolution in Apache Iceberg revolutionizes data partitioning.

The Traditional Approach to Partitioning

When creating a table, it is essential to plan partitioning based on how the data will most frequently be queried. This helps minimize query scan times and enhances performance. For instance, if most queries filter data by year, the table should be partitioned by the year column:

CREATE TABLE sales (
id BIGINT,
amount DECIMAL(10,2),
sale_date DATE
) PARTITIONED BY (year(sale_date));

Querying a Partitioned Table Efficiently
To maximize query performance, SQL queries should include the partition column in the WHERE clause:

SELECT * FROM sales WHERE year(sale_date) = 2025;

This approach ensures that the query scans only relevant partitions instead of performing a full table scan, thereby significantly improving efficiency.

The Problem with Traditional Partitioning

As data evolves, the way it is queried can also change. Suppose the sales data initially needed only yearly partitions, but as the dataset grew, analysts began querying data on a daily basis. This requires switching from year-based partitions to day-based partitions.

With traditional data lakes (e.g., Hive, or Delta Lake), changing partitioning requires:

• Creating a new table with the desired partition strategy.
• Extracting and transforming data from the old table.
• Loading all existing and future data into the new table.

This process is cumbersome, error-prone, and resource-intensive. A more flexible solution is needed.

Apache Iceberg: Enabling Partition Evolution

Apache Iceberg eliminates the constraints of traditional partitioning by allowing partition evolution without requiring data migration. This means you can modify partitioning strategies dynamically as data and query patterns change. It’s as easy as simply ALTERing the existing table with the new partition strategy.

How Partition Evolution Works in Iceberg
With Iceberg, when a partitioning strategy changes, new data follows the updated partitioning scheme, while older data remains under the previous partitioning method. Queries against the table automatically apply the correct partitioning strategy based on the data being accessed.

Example: Changing from Yearly to Daily Partitioning
Initially, the table is created with yearly partitioning:

CREATE TABLE sales (
id BIGINT,
amount DECIMAL(10,2),
sale_date DATE
) USING ICEBERG PARTITIONED BY (year(sale_date));

Over time, as query patterns shift to daily filtering, we can evolve the partitioning without recreating the table:

ALTER TABLE sales SET PARTITION SPEC (day(sale_date));

Now, new data follows daily partitioning, while old data remains in yearly partitions. Iceberg’s query engine intelligently applies the appropriate partitioning strategy:

• Queries for older data use the original yearly partitioning.
• Queries for newer data leverage the new daily partitioning.

Querying an Iceberg Table with Evolved Partitions
Since Iceberg automatically handles partition pruning, queries remain efficient without requiring any special handling:

SELECT * FROM sales WHERE sale_date = '2025-03-01';

The engine applies daily partitioning for newer data and yearly partitioning for older data, ensuring optimal query performance.

Even though the SELECT query above used “sale_date” and not Year(sale_date) or Day(sale_date) in the WHERE clause which is what was the partition scheme, iceberg intelligently is able to use the partitions correctly. This is possible owing to its Hidden Partitioning feature.

Iceberg’s Hidden Partitioning
Traditionally to partition the table based on Year or Day of the sales_date, the table must have a column explicitly defined and the query must explicitly include the partition columns (year) in queries (in the WHERE clause).

CREATE TABLE sales (
id BIGINT,
amount DECIMAL(10,2),
sale_date DATE,
Year INT
) USING PARTITIONED BY Year;
SELECT * FROM sales WHERE Year = 2025;

But Iceberg has hidden partitioning, which eliminates the need for users to be aware of how data is partitioned, including eliminating the need to explicitly add columns like Year which could be inferred from sale_date. With Iceberg, partitioning is handled automatically, allowing users to simply write:

SELECT * FROM sales WHERE sale_date BETWEEN '2024-01-01' AND '2025-03-28';

Iceberg produces partition values by taking a column value and optionally transforming it. It supports transforms like Year, Month, Day, Hour, Truncate, Bucket, Identity and is responsible for converting the column (sales_date) into the specified transform (year) and keeps track of the relationship internally. There is no need for an explicit Year column.

Thus, Iceberg automatically applies partition pruning, making queries more intuitive and less error-prone. This also ensures that queries remain valid even when partitioning evolves from yearly to daily or any other strategy.

Conclusion

Apache Iceberg’s partition evolution removes the constraints of static partitioning, allowing data engineers to adapt partitioning strategies as data grows and query patterns evolve. Unlike traditional approaches that require expensive table recreation and data migration, Iceberg enables seamless partition changes while preserving historical data structures. If your workloads demand scalability and flexibility, Iceberg is the ideal solution for efficient and future-proof data management.

Article source: https://community.qlik.com.