Batching at Scale: High-Velocity Bulk Operations with insert_many in WSQLite

Batching at scale: High-velocity bulk operations with insert_many.

Inserting records in a for-loop is the number one performance mistake in SQLite development. wsqlite's insert_many groups thousands of records into a single atomic transaction for a 10x speedup.

Here is how you use Bulk Insert & Batch Operations in production with wsqlite:

from pydantic import BaseModel
from wsqlite import WSQLite

class Metric(BaseModel):
    id: int
    sensor_id: str
    reading: float

db = WSQLite(Metric, "telemetry.db")

# Prepare a large batch of 5,000 metrics
batch = [
    Metric(id=i, sensor_id=f"sensor_{i % 10}", reading=20.5 + (i * 0.1))
    for i in range(1, 5001)
]

# Insert all 5,000 in a single atomic transaction
db.insert_many(batch)
print(f"Total metrics inserted: {db.count()}")

Why developers love wsqlite:

• Define database tables using standard Pydantic v2 models.
• Auto-syncs columns on startup without writing manual migrations.
• Thread-safe connection pooling with WAL mode enabled by default (5,000+ inserts/sec).
• Full sync and async/await support.

Check out the repo on GitHub!