> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dbdock.xyz/llms.txt
> Use this file to discover all available pages before exploring further.

# Dry runs & validation

> Validate a migration plan against a temporary schema before committing.

`--dry-run` runs the full migration into a temporary schema (or collection prefix) so you can inspect the result before touching production. Nothing is written to your real target.

## Basic usage

```bash theme={null}
npx dbdock migrate "$SRC" "$DST" --dry-run
```

## What changes in dry-run mode

### MongoDB → PostgreSQL

A dry run creates a schema named `dbdock_dryrun_<timestamp>` and migrates into tables there:

```
source: mongodb://.../myapp
target: postgresql://.../myapp.dbdock_dryrun_20260416_080000.users
                                  dbdock_dryrun_20260416_080000.orders
                                  ...
```

Query those tables to verify the mapping. When you're satisfied, drop the schema:

```sql theme={null}
DROP SCHEMA dbdock_dryrun_20260416_080000 CASCADE;
```

### PostgreSQL → MongoDB

A dry run creates collections prefixed with `dbdock_dryrun_<timestamp>_`:

```
dbdock_dryrun_20260416_080000_users
dbdock_dryrun_20260416_080000_orders
```

Drop them when done:

```javascript theme={null}
db.getCollectionNames()
  .filter(n => n.startsWith('dbdock_dryrun_'))
  .forEach(n => db[n].drop())
```

## What to check in a dry run

<CardGroup cols={2}>
  <Card title="Row counts" icon="list-ol">
    Verify every document/row made it: `SELECT count(*) FROM dryrun.users;` vs. the source count.
  </Card>

  <Card title="Type fidelity" icon="check">
    Spot-check sample rows. Dates, nulls, and numbers are the most common sources of trouble.
  </Card>

  <Card title="Error table" icon="triangle-exclamation">
    Look at `_migration_errors` — any skipped rows?
  </Card>

  <Card title="Query performance" icon="bolt">
    Run your most important queries against the dry-run schema to confirm the indexes are right.
  </Card>
</CardGroup>

## Size of the dry run

Dry runs migrate the full dataset by default, which is ideal for validation but slow on huge databases. To speed things up, use `--batch-size` and run against a reduced source if possible.

DBDock doesn't have a `--sample` flag yet — open an issue on [GitHub](https://github.com/dbdock/dbdock/issues) if you need one.

## Production checklist after a successful dry run

Before running the real migration:

1. ✅ Row counts match
2. ✅ Indexes created match expectations
3. ✅ Sample queries return correct results
4. ✅ Error table is empty (or errors are acceptable)
5. ✅ Target database has enough disk space
6. ✅ Team is informed (migrations can lock tables briefly)
7. ✅ You have a rollback plan (usually: drop the target schema)

## Cleaning up dry-run artifacts

DBDock doesn't auto-drop dry-run schemas so you can inspect them. Drop them manually once done to reclaim space.

## See also

<CardGroup cols={2}>
  <Card title="dbdock migrate" icon="arrows-left-right" href="/migration/migrate">
    Run the real migration.
  </Card>

  <Card title="Incremental" icon="arrow-rotate-right" href="/migration/incremental">
    Pull only new/changed rows.
  </Card>
</CardGroup>