Move an application from SQLite to PostgreSQL in two controlled stages: create the PostgreSQL schema using either the application’s framework migrations or a database migration tool, then transfer and validate the existing rows. Before cutover, inspect SQLite’s actual stored values, rehearse the load against a disposable PostgreSQL database, fix conversion or constraint errors, and test the application on the new backend.
Why an SQLite-to-PostgreSQL move needs more than a data copy
SQLite’s flexible typing means a column’s declared type does not guarantee that every value in it has the same storage class. As the SQLite documentation’s “Datatypes In SQLite” section puts it, “The datatype of a value is associated with the value itself, not with its container.” That flexibility can make old data valid in SQLite but incompatible with a PostgreSQL column or constraint.
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Schema history and existing data are separate concerns. Framework migrations establish the target schema; a loader or another transfer method copies existing rows into it. Django describes migrations as a version-control system for database schema and applies them with its migrate command. Check the documentation for the framework version your application actually uses.
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SQLite has no dedicated Boolean or date/time storage class. Boolean values are integers, while date/time values may be stored as text, real Julian-day numbers, or integer Unix timestamps. PostgreSQL has explicit data types, so choose the intended representation and verify that existing values convert correctly.
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Choose who owns the PostgreSQL schema
Pick one clear schema owner before transferring data. A framework-managed schema is usually the natural choice when the application’s version-controlled migrations are authoritative. A loader-created schema can suit a database-level migration, but its discovered types and constraints need review.
| Approach | Useful when | Trade-offs |
|---|---|---|
| Apply framework migrations, then load data | The ORM’s migration history defines the intended application schema. | Keeps schema definitions close to the code. Source columns and values still need to align with PostgreSQL types and constraints. |
| Let pgloader discover and create schema while transferring data | A direct database-level migration is appropriate. | Combines schema discovery and transfer, but inferred types and constraints still need review; special casts or other rules may be needed. |
pgloader also supports loading data into a schema that already exists, so these choices need not be treated as all-or-nothing features. Avoid letting both the framework and loader create competing versions of the schema.
Inventory the application and SQLite database
Before choosing mappings, record the application and database adapter versions, current schema, and framework migration state. Inventory tables, indexes, constraints, triggers, and views, then inspect actual values in columns where type conversion matters.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems- Check Boolean-like values, dates and times, numeric precision, identifiers, nulls, blobs, and text-encoding assumptions.
- Look for values the application may have relied on SQLite to coerce, including mixed storage classes in one column.
- Remember that SQLite columns other than an
INTEGER PRIMARY KEYcan hold values from any storage class unless the table uses stricter typing. SQLite STRICT tables were introduced in version 3.37.0; do not assume an existing application uses them.
Use the inventory to decide how each field should be represented in PostgreSQL. Do not assume a loader can infer application intent from ambiguous values.
Rehearse the transfer with pgloader
Set up a migration environment that can connect to a PostgreSQL test database, with the application’s PostgreSQL driver and connection settings configured. Work against a disposable target while learning the loader options: pgloader’s documented SQLite defaults include dropping matching target tables, so understand destructive behavior before using a valuable database.
The pgloader tutorial gives this basic form: pgloader <SQLite-source> pgsql:///<target>. Treat it as a starting point, not a complete production plan. Credentials, network access, source consistency, schema ownership, and loader version depend on the deployment. For more control, a command file can specify options such as create tables, create indexes, and reset sequences. pgloader supports repeatable runs, partial loads, schema-only or data-only modes, and configurable casts.
Set or review casts wherever source values do not match the PostgreSQL type expected by the application. Automation can transfer and transform data, but it cannot decide what an ambiguous date, Boolean, or numeric value was intended to mean.
Resolve load errors before accepting the result
Check how the specific command handles errors. pgloader distinguishes stopping on errors from continuing while saving rejected rows; behavior can differ by command and input type. Do not treat a completed process as a successful migration if rows were rejected or constraints skipped.
- Inspect every rejected row and the associated error.
- Determine whether the cause is dirty source data, an incorrect cast, or a schema mismatch.
- Repair the source or mapping rules, then rerun the rehearsal until the outcome is understood.
Legacy schemas may need structural changes, not just type conversions. The pgloader tutorial documents an SQLite schema with multiple primary-key definitions that PostgreSQL rejects, illustrating why discovered objects must be checked before production use.
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Once the load finishes, compare source and target table counts and important aggregate values. Check primary-key uniqueness, foreign-key relationships, null versus empty-string handling, date/time conversions, numeric values, and representative application queries. Then run the application’s test suite against PostgreSQL and exercise its main read and write flows.
If you use CSV as an intermediate transfer instead, PostgreSQL’s COPY supports text, CSV, and binary formats as well as client input. Its documented default for input conversion errors is to stop. Configure CSV null and empty-string handling deliberately so those values retain the intended meaning.
Plan cutover and recovery
Rehearse the final procedure using a recent, consistent copy of the source. For the production move, decide how to prevent or capture writes made after that copy, who authorizes the switch, and how long to retain SQLite. The right write-freeze, dual-write, or change-capture approach depends on the application; there is no universal live-replication plan in the cited tool documentation.
- Make the final source copy consistent with the cutover plan.
- Run the rehearsed schema and data steps against the production PostgreSQL target.
- Perform the agreed validation checks and switch the application’s database connection only after they pass.
- Monitor application errors and database behavior, and retain a documented recovery path.
Do not delete the SQLite source just because the initial load command exited successfully.
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