When you delete a row in PostgreSQL, the operation may seem simple — but under the hood, it can cascade across dozens of related tables. If the row is referenced by other rows, PostgreSQL enforces referential integrity using foreign key constraints.
That enforcement comes at a cost: every delete must check whether dependent rows exist, and if ON DELETE CASCADE is set, it must recursively delete them as well.
This raises an intriguing question: Why doesn’t PostgreSQL have a “fast delete” option — something that can instantly wipe a row and all of its dependencies without expensive constraint checks? Oracle, SQL Server, and some other systems provide shortcuts or optimizations, but PostgreSQL does not. Let’s unpack why.
1. How Deletion Works in PostgreSQL
PostgreSQL doesn’t actually delete rows immediately — it uses MVCC (Multi-Version Concurrency Control).
- A deleted row is simply marked as dead by creating a new version of the row that’s invisible to future transactions.
- Foreign key constraints trigger checks to ensure that deleting the row won’t leave orphaned references.
- If ON DELETE CASCADE is used, PostgreSQL executes child deletes as separate operations, walking through each referencing table.
This design ensures data integrity but makes cascading deletes potentially very expensive.
2. Why Fast Deletion is Hard in PostgreSQL
Unlike Oracle’s partition pruning or some NoSQL engines that can simply “drop” chunks of related data, PostgreSQL is cautious by design. Here’s why:
a) Referential Integrity Enforcement
Postgres guarantees correctness. Skipping constraint checks would risk leaving inconsistent data in related tables. If you had 20 foreign key references, a naive fast delete would risk orphaned rows.
b) MVCC Overhead
Deleting in PostgreSQL isn’t just a flag flip. Old versions must remain visible until vacuuming reclaims them. A “fast delete” would need to bypass MVCC rules — which would break transaction isolation.
c) Arbitrary Relationships
In relational databases, dependencies aren’t always obvious. One row might be referenced by foreign keys, triggers, materialized views, or even user-defined functions. Tracking all dependencies quickly is non-trivial.
d) WAL and Replication Safety
Every delete must be WAL-logged for durability and replication. A fast bulk-delete that skips per-row logging would break crash recovery and replication unless new mechanisms are added.
3. Can We Bring Fast Deletion to PostgreSQL?
Yes—but it would require trade-offs. Let’s look at possible approaches:
a) Partition Drop Trick
If your table is partitioned, dropping an entire partition is effectively an instant delete — metadata-only, very fast.
- Works well for time-series or archival workloads.
- Only practical if your deletion criteria align with partitions (e.g., DROP PARTITION WHERE year = 2022).
b) TRUNCATE (Fast Table-Wide Delete)
Postgres already has TRUNCATE, which drops all rows in a table instantly by resetting storage.
- Extremely fast.
- Not row-level; wipes the entire table.
c) Fast Cascade Extensions (Hypothetical)
We could imagine an extension that provides:
FAST DELETE FROM orders WHERE id = 123 CASCADE;
This would:
- Skip foreign key lookups.
- Skip per-row logging (maybe WAL-bulk logging).
- Mark pages as free in bulk.
The cost? Potential data corruption if not carefully designed.
d) Deferred Integrity Checking
Another middle-ground: allow bulk deletes that temporarily disable constraint checks, then validate later (like Oracle’s ENABLE NOVALIDATE).
- Faster deletes.
- Risky — inconsistent state if validation is skipped or fails.
4. Alternatives You Can Use Today
Until PostgreSQL adds true fast deletion, you can achieve performance gains using:
- Partitioning: Align deletions with partitions and drop them instantly.
- Logical Partitioning: Move “deleted” rows to an archive table instead of deleting.
- Batch Deletes: Delete in smaller chunks to avoid massive locks and WAL bloat.
- Asynchronous Cleanup: Use background jobs (cron, pg_cron, or custom workers) to lazily clean up dependent rows.
PostgreSQL doesn’t have a true fast delete like Oracle or specialized databases, and that’s intentional. Its MVCC model and strict referential integrity make such a feature non-trivial.
But depending on your workload, you can mimic fast deletion using partition drops, truncates, or deferred cleanup strategies.
Could PostgreSQL one day introduce a FAST DELETE option? Possibly—but it would require loosening guarantees around referential integrity, WAL safety, and MVCC rules. Until then, clever schema design (partitioning, cascading strategies, and cleanup jobs) is the way to go.