Migrating Legacy Code to SQLiteWrapper: Step-by-Step StrategyMigrating legacy code that uses raw SQLite calls to a modern wrapper like SQLiteWrapper can improve maintainability, reduce bugs, and streamline cross-platform compatibility. This article outlines a pragmatic, step-by-step strategy to plan, prepare, and execute a migration with minimal downtime and risk. It includes preparatory audits, test strategies, migration patterns, performance considerations, and rollback plans.
Why migrate to SQLiteWrapper?
- Cleaner API: SQLiteWrapper usually provides higher-level abstractions (connections, query builders, typed bindings) that make code easier to read and maintain.
- Safety: Wrappers often handle parameter binding, transaction scoping, and resource cleanup to reduce SQL injection and memory leaks.
- Compatibility: Many wrappers abstract platform differences (Android/iOS/desktop), easing cross-platform development.
- Feature set: Convenience functions (migrations, connection pooling, async APIs) speed development and reduce boilerplate.
Pre-migration audit
- Inventory database usage
- List all modules that open connections, execute queries, or manage transactions.
- Identify places that build SQL strings dynamically or interpolate values.
- Catalog schema and migrations
- Collect current schema definitions, PRAGMA settings, indices, triggers, and foreign keys.
- Locate any migration scripts or schema evolution logic in code.
- Tests and coverage
- Generate test coverage reports focused on data-layer code. Priority: areas with complex SQL or business rules enforced in SQL.
- Performance-critical paths
- Identify slow queries and hotspots using profiling, EXPLAIN QUERY PLAN, and logs.
- Non-SQL invariants
- Document assumptions made by app code about row ordering, NULL handling, or side effects from triggers.
Prepare the project
- Choose the right SQLiteWrapper
- Evaluate features: synchronous vs asynchronous APIs, migrations support, typed models/ORM features, thread-safety, and ecosystem maturity.
- Confirm license compatibility and support for target platforms.
- Add wrapper incrementally
- Integrate SQLiteWrapper into the project as a new dependency without removing existing SQLite code yet.
- Establish coding patterns
- Decide on patterns to adopt: repository/DAO layer, data mappers, or thin wrapper around SQL for complex queries.
- Create a migration branch and CI
- Work in a feature branch and ensure CI runs tests for each change. Add a test database configuration for automated tests.
Design migration approach
Choose one of these strategies based on project size and risk tolerance:
- Strangler pattern (recommended for large codebases)
- Incrementally replace features by routing new/updated functionality to SQLiteWrapper while leaving legacy code untouched until fully replaced.
- Big bang (risky)
- Replace the entire data layer at once. Only recommended if the codebase is small and well-covered with tests.
- Hybrid approach
- Combine: replace low-risk modules first, then tackle complex pieces once patterns and tests stabilize.
Implement a compatibility layer
To minimize changes across the codebase, implement an adapter that replicates the legacy interface while using SQLiteWrapper under the hood. Benefits:
- Reduce immediate refactors across many modules.
- Allow incremental replacement of internal implementations.
- Provide a single point to handle differences in null semantics, row ordering, or types.
Example adapter responsibilities:
- Expose the same connection/execute/query functions signatures.
- Translate legacy parameter formats into safe bound parameters.
- Convert wrapper result objects into legacy data structures (e.g., arrays, maps).
Migrate schema & migrations
- Export current schema
- Use “sqlite_master” to extract CREATE statements for tables, indices, triggers, and views.
- Reconcile with wrapper migrations
- If the wrapper provides migration tooling, translate existing SQL into migration files the wrapper can run.
- Preserve PRAGMAs and settings
- Ensure journal_mode, synchronous, and other PRAGMAs are set consistently.
- Test migrations
- Run migrations forward and backward against copies of production data. Verify schema integrity and data preservation.
- Handle data transformations
- For schema changes requiring data reshaping (split/combine columns), write idempotent migration steps and include verification checks.
Rewrite data access logic incrementally
- Start with read-only access
- Replace select queries first to validate result mapping and types.
- Move to write paths
- Migrate inserts, updates, and deletes carefully; add transactional tests.
- Replace complex queries last
- Keep raw SQL in wrapper-supported forms; where necessary, use the wrapper’s raw-sql escape hatch.
- Maintain behavior parity
- Ensure NULL handling, default values, row ordering, and collations behave the same as before.
- Use typed models or DAOs
- Map rows to typed domain models; centralize SQL in DAOs or repository classes to ease future changes.
Testing strategy
- Unit tests
- Mock the wrapper where appropriate for pure logic tests.
- Integration tests
- Use an in-memory or ephemeral file DB to run integration tests against the real wrapper and schema.
- Migration tests
- Simulate upgrading from older schema versions to the current one using snapshot data.
- Property-based tests
- For operations with many edge cases (NULLs, Unicode, binary blobs), use randomized inputs to catch hidden bugs.
- Performance regression tests
- Benchmark key queries before and after migration using representative datasets.
Performance considerations
- Prepared statements and parameter binding often improve speed and safety.
- Batch writes inside transactions to reduce I/O overhead.
- For large datasets, ensure indices match query patterns; use EXPLAIN to verify plans.
- Monitor connection pooling or concurrency if using async APIs to avoid contention.
- Profiling helps detect regressions introduced by mapping layers or extra allocations.
Data integrity and safety
- Back up production data before any migration; test restores regularly.
- Use checksums or row counts to verify that migrated data matches originals.
- Add migration verification steps that run as part of deployment (e.g., simple queries that validate row counts, key constraints).
- Keep transactions short and well-scoped to avoid long-running locks on busy databases.
Rollout and deployment
- Staged rollout
- Deploy to staging with a production-sized dataset, then to a small subset of users (canary) before global rollout.
- Feature flags
- Use flags to switch between legacy and new data-layer behavior to quickly rollback if issues arise.
- Live migrations
- If changing schema on a live DB, prefer online-compatible changes (adding columns with defaults, creating indices concurrently if supported).
- Monitoring
- Track errors, latency, and key invariants after deployment. Add alerts for any regressions.
Rollback plan
- Keep backward-compatible schema changes during initial releases.
- Retain the legacy data-access code until the new implementation is verified stable.
- If issues occur, flip feature flags to route traffic back to legacy paths and iterate on fixes.
- Restore from backups only as a last resort; prefer application-level rollbacks to avoid data loss.
Post-migration cleanup
- Remove the legacy SQLite code and adapters once confidence is high and usage is zero.
- Prune unused migration scripts, but keep a versioned archive.
- Update documentation and onboard team members to the new patterns.
- Run a final performance and correctness audit.
Checklist (quick)
- [ ] Inventory all DB usage points
- [ ] Add SQLiteWrapper to project
- [ ] Implement compatibility adapter
- [ ] Translate schema and create migrations
- [ ] Migrate read paths, then write paths
- [ ] Add/expand tests (unit, integration, migration)
- [ ] Performance and integrity verification
- [ ] Staged rollout with monitoring and feature flags
- [ ] Remove legacy code once stable
Migrating legacy SQLite code to SQLiteWrapper is best done incrementally with thorough testing, clear adapters, and careful attention to schema and performance. Following the steps above reduces risk and makes the new codebase easier to maintain and extend.
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