Paimon Logical Delete

5 min read Oct 03, 2024

Understanding and Implementing Logical Delete in Paimon

Paimon, a powerful data lakehouse engine, offers robust features for data management. One such crucial aspect is the concept of logical delete, often preferred over physical deletion for various reasons. This article delves into the intricacies of logical delete in Paimon, explaining its importance and guiding you through its implementation.

Why Choose Logical Delete Over Physical Deletion?

Imagine you have a dataset containing sensitive customer information. Deleting records directly, a physical deletion, might seem like a straightforward solution. However, physical deletion poses several challenges:

Data Loss: Permanent deletion could lead to irreversible data loss, especially if you need to retrieve information for compliance reasons or audit trails.
Data Integrity: Removing records without proper tracking can disrupt data integrity and historical analysis.
Performance Impact: Physical deletion often involves complex data manipulation, impacting database performance.

Logical delete offers a more cautious and controlled approach. Instead of physically removing data, logical delete marks records as inactive or deleted, preserving the original data for future reference. This ensures:

Data Retention: Historical information remains accessible for analysis, auditing, or potential recovery.
Data Integrity: The database maintains consistency, avoiding data inconsistencies caused by direct removal.
Performance Improvement: Logical delete often involves simple flag modifications, leading to faster processing and improved performance.

Implementing Logical Delete in Paimon

Paimon provides flexible options to implement logical delete based on your specific requirements.

1. Using a "Deleted" Flag:

This is the simplest approach. A dedicated column, typically named "deleted," is added to your Paimon table.

Example:
```
ALTER TABLE your_table ADD COLUMN deleted BOOLEAN DEFAULT FALSE;
```
When a record needs to be logically deleted, the "deleted" flag is set to TRUE. You can then use filtering conditions like WHERE deleted = FALSE to exclude logically deleted records during queries.

2. Utilizing Time-based Techniques:

If you need to track data deletions over time, you can employ a time-based approach.

Example:
```
ALTER TABLE your_table ADD COLUMN deleted_at TIMESTAMP; 
```
Instead of a boolean flag, you use a timestamp column (deleted_at) to record the time when a record is logically deleted.

3. Implementing Soft Delete with a "Deleted" Partition:

For tables partitioned by time, you can utilize a specific partition for logically deleted records.

Example:

CREATE TABLE your_table (
   ...
) 
PARTITIONED BY (dt STRING);

-- Logically delete records for a specific date
ALTER TABLE your_table 
ADD PARTITION (dt='2023-09-01') 
LOCATION 'your_deleted_partition_path';

This approach allows you to group logically deleted records in a separate partition, making it easier to manage them.

Retrieving and Restoring Data

Retrieving Data:

To retrieve all records, including logically deleted ones, simply omit the deleted flag condition in your query.

Restoring Data:

Restoring logically deleted records is straightforward:

Using a Flag: Set the deleted flag back to FALSE.
Using Time: Update the deleted_at column to NULL.
Using Partitions: Move the data back to the original partitions.

Example (using a flag):

UPDATE your_table 
SET deleted = FALSE 
WHERE deleted = TRUE;

Conclusion

Logical delete in Paimon is a powerful technique for managing data efficiently. By utilizing logical delete, you can retain historical information, maintain data integrity, and improve performance. Choose the method that best suits your specific needs and coding style.