We need to talk about your PostgreSQL Insert Strategy. For some reason, the standard advice in modern development has become “just use an ORM and call it a day,” and it is killing performance on high-volume workloads. I’ve spent years refactoring backend services where the “clean code” approach created a massive bottleneck, turning what should be a 10-second ingestion into a 30-minute ordeal.
Lately, I’ve seen too many devs chasing micro-benchmarks without understanding the abstraction layers involved. If you are building a standard CRUD app, the ORM is your best friend. But when you’re backfilling analytics or syncing external APIs, you need to understand the spectrum between safety and raw throughput.
The Performance Spectrum: ORM, Core, and Driver
When you choose a PostgreSQL Insert Strategy, you’re really choosing where you want to spend your CPU cycles. Do you want them spent on Python object mapping, or on moving raw bytes into the database?
- SQLAlchemy ORM: The Ferrari. It’s expensive, high-maintenance, and beautiful. It handles state tracking and relationships, but that bookkeeping makes bulk operations crawl.
- SQLAlchemy Core: The Jeep. It’s rugged and versatile. It abstracts the SQL dialect so you don’t have to worry about syntax quirks, providing a solid balance of safety and speed.
- Psycopg3 (The Driver): The Firehose. This is low-level power. If you need to insert 2 million records per second, you drop the abstractions and use
COPY.
I recently worked on a project where we had to optimize API performance boosts for a data-heavy integration. We started with standard ORM adds, which worked fine for 100 rows. At 100,000 rows? The session management triggered a memory leak. We had to refactor the entire ingestion pipeline.
Naive Approach vs. Bulk Optimization
The biggest mistake is iterating over a list and calling session.add() in a loop. This creates a race condition for your database’s WAL (Write-Ahead Log) and wastes time on network round-trips.
# The "Bad" Way - Row by Row
for record in large_dataset:
new_row = User(name=record['name'], email=record['email'])
session.add(new_row)
session.commit()
# The "Refactored" Way - Using Core for throughput
from sqlalchemy import insert
engine.execute(
insert(User.__table__),
large_dataset # List of dicts
)Dropping to Core avoids the overhead of creating Python objects for every single row. If you’re pushing the limits, however, even Core might be too slow because it still has to compile the SQL statement.
When to use Psycopg3 COPY
For true “firehose” workloads, your PostgreSQL Insert Strategy should bypass the INSERT syntax entirely. The COPY command is the fastest way to get data into Postgres. According to the official Psycopg3 documentation, using write_row() on a copy object can handle millions of records with minimal overhead.
# Using Psycopg3 COPY for maximum power
with cursor.copy("COPY users (name, email) FROM STDIN") as copy:
for record in dataset_tuples:
copy.write_row(record)This approach minimizes the conversion work between Python and the database engine. Furthermore, it allows you to handle REST API performance bottlenecks when the bottleneck is actually the database write-latency rather than the network itself.
Look, if this PostgreSQL Insert Strategy stuff is eating up your dev hours, let me handle it. I’ve been wrestling with WordPress and high-scale backend architecture since the 4.x days.
The Senior Takeaway
Faster is not always better if it makes your code unmaintainable. Specifically, use the ORM for business logic where correctness is king. Specifically, move to SQLAlchemy Core when you’re doing ETL or batch transformations. Finally, only drop to the raw Driver level when you are pushing hardware limits and every millisecond counts. Don’t over-engineer a Ferrari to drive through a forest.
