Automating MysqlToSqlite: Scripts and Workflows for Reliable Migrations

MysqlToSqlite: Tools and Best Practices for Seamless ConversionMigrating a database from MySQL to SQLite can be a practical choice for many projects: SQLite is lightweight, zero-configuration, and ideal for embedded applications, local development, desktop apps, and mobile projects. However, the conversion isn’t always straightforward due to differences in SQL dialects, data types, indexing behaviors, and features (like stored procedures or certain constraints) that MySQL supports but SQLite handles differently or does not support at all. This article walks through tools, step-by-step best practices, common pitfalls, and optimization tips to help you convert MySQL databases to SQLite reliably and efficiently.

Why migrate from MySQL to SQLite?

• Simplicity and portability: SQLite stores the whole database in a single file, making distribution and backups trivial.
• Zero administration: No server process, no configuration — ideal for desktop apps, IoT devices, or local development.
• Low resource usage: Smaller footprint, faster startup, and reduced maintenance overhead.
• Great for testing: Quick local testing environment when you don’t need a full MySQL server.

That said, SQLite is not a drop-in replacement for production-grade MySQL servers for high-concurrency or very large datasets. Choose based on your app’s concurrency, scaling, and feature needs.

Preparation: assess compatibility and plan

Before converting, analyze the MySQL schema and usage patterns.

1. Inventory features used:

   • Stored procedures, functions, triggers.
   • Views and materialized views.
   • Foreign key constraints and cascading behaviors.
   • Data types (ENUM, SET, unsigned integers, BLOB/TEXT usage).
   • Character sets and collations.
   • AUTO_INCREMENT and sequence behaviors.
   • Full-text search, JSON columns, spatial extensions.

2. Decide what must be preserved and what can be adapted:

   • If your app relies on stored procedures or complex triggers, plan to rewrite logic at the application layer or emulate via triggers supported by SQLite.
   • If you need concurrency and heavy write load, reconsider switching to SQLite.

3. Back up your MySQL database:

   • Create logical dumps (mysqldump) and, if needed, physical backups.
   • Test restores to ensure dump integrity.

Tools for conversion

Below are commonly used tools and approaches, with strengths and caveats.

• mysqldump + sqlite3 (manual/SQL-based)

  • Export MySQL schema and data as SQL, adapt syntax, then import into SQLite using the sqlite3 CLI.
  • Strengths: fully controllable, no third-party dependency.
  • Caveats: Requires careful SQL translation (data types, AUTO_INCREMENT, backticks, engines, CHARSET).

• mysql2sqlite shell scripts

  • Small shell/Perl scripts that transform mysqldump output to SQLite-compatible SQL.
  • Strengths: quick and simple for many schemas.
  • Caveats: May fail on complex schema or nonstandard types.

• sqlite3dump or dump tools with translators

  • Some projects offer scripts to parse MySQL dumps and emit SQLite SQL.

• Tools and libraries:

  • python scripts (using mysql-connector-python + sqlite3) — custom ETL gives fine control.
  • SQLAlchemy-based migration scripts — useful if you already use SQLAlchemy models.
  • DB Browser for SQLite — GUI for inspecting and importing.
  • Full-featured tools like “sqlite-utils” (Python) can help load CSV or JSON exports into SQLite.

• ETL approach (export as CSV per table then import)

  • Use SELECT … INTO OUTFILE or client exports, then import with sqlite3 .import or sqlite-utils.
  • Strengths: robust for data-only migrations, avoids SQL dialect translation.
  • Caveats: must handle quoting, NULLs, datetimes, binary blobs carefully.

Step-by-step conversion workflow

Follow a structured workflow to reduce risk.

1. Export schema and data

   • Schema-first approach: export schema only to adjust definitions before data load.
   • Data-first approach: export data to CSV/TSV if you prefer to avoid SQL translation.
   • Example mysqldump commands:
     • Schema only: mysqldump –no-data –skip-comments –skip-set-charset dbname > schema.sql
     • Data only: mysqldump –no-create-info –skip-comments –compact dbname > data.sql

2. Convert schema to SQLite-friendly SQL

   • Remove or convert MySQL-specific syntax:
     • Replace INT AUTO_INCREMENT with INTEGER PRIMARY KEY (for a single-column PK).
     • Remove ENGINE=…, DEFAULT CHARSET=…, COLLATE=…, and backticks (`).
     • Convert UNSIGNED types to signed equivalents; enforce range checks in app if needed.
     • Convert ENUM and SET to TEXT with CHECK constraints or separate lookup tables.
     • Convert TINYINT(1) often used as boolean — store as INTEGER 0/1.
     • Convert DATETIME/TIMESTAMP handling: SQLite stores dates as TEXT, REAL, or INTEGER — choose a representation and be consistent.
     • Translate foreign key constraints syntax; enable PRAGMA foreign_keys = ON after import if you want enforcement.
   • Remove or rewrite stored procedures/functions — SQLite doesn’t support them.
   • Adjust indexes and full-text search: use FTS5 for full-text, but schema differs.

3. Create the SQLite database and apply the schema

   • Use sqlite3 to create the DB and run the adapted schema.sql.
   • Enable PRAGMA settings early if needed:
     • PRAGMA journal_mode = WAL;
     • PRAGMA synchronous = NORMAL; (or OFF for faster but riskier imports)
     • PRAGMA foreign_keys = ON;

4. Import data

   • Option A — SQL import:
     • Adapt INSERT statements in mysqldump (remove backticks, disable LOCK TABLES statements).
     • Use transactions: wrap multiple inserts in BEGIN; …; COMMIT; for performance.
     • Use prepared inserts via scripts for better control and binary handling.
   • Option B — CSV import:
     • Export table data to CSV with proper quoting and NULL markers.
     • Use sqlite3’s .mode csv and .import commands or sqlite-utils for robust import.
     • For BLOBs, use hex or base64 encoding and decode into SQLite.

5. Post-import validation and fixes

   • Row counts: compare counts per table between source and target.
   • Checksums: sample checksums on critical columns.
   • Referential integrity: if you deferred foreign keys, run checks and enable enforcement.
   • Query validation: run application test suites or a set of representative queries to spot semantic differences.

6. Performance tuning

   • Create indexes after bulk import for faster data loading (or drop and recreate).
   • Use PRAGMA settings appropriate for your workload (journal_mode, synchronous).
   • Consider VACUUM and ANALYZE to optimize file layout and query planner stats.

Handling specific MySQL features

• AUTO_INCREMENT

  • SQLite: define the primary key as INTEGER PRIMARY KEY; this column becomes an alias for the rowid and auto-increments.
  • If your MySQL table used an AUTO_INCREMENT column that was not a single-column INTEGER PRIMARY KEY, emulate logic in application or use triggers.

• Foreign keys

  • SQLite requires PRAGMA foreign_keys = ON; to enforce them.
  • SQLite’s FK support is sufficient for cascades but ensure your schema uses supported syntax.

• ENUM / SET

  • Convert ENUM to TEXT with CHECK(…) or create separate reference tables.
  • Example: CREATE TABLE t (status TEXT CHECK(status IN (‘a’,‘b’,‘c’)));

• Full-text search

  • MySQL’s FULLTEXT is different. Use SQLite FTS5 virtual tables and adjust queries accordingly.

• JSON columns

  • MySQL has JSON type with functions. SQLite can store JSON as TEXT and use the JSON1 extension for querying.

• Stored procedures / functions / triggers

  • SQLite supports triggers but not stored routines. Move complex logic into application code or rewrite using triggers where possible.

• Character sets & collations

  • MySQL may use UTF8MB4 and custom collations. SQLite stores TEXT as UTF-8 or UTF-16 and has limited built-in collations; you may need to normalize strings in the application.

Example: simple conversion using Python (mysql-connector + sqlite3)

Below is a concise conceptual example showing how to stream data from MySQL into SQLite while converting types and handling NULLs. Use this as a starting point — adapt for batch size, error handling, and binary blobs.

# example.py import mysql.connector import sqlite3 mysql_cfg = {"host":"localhost","user":"user","password":"pw","database":"db"} src = mysql.connector.connect(**mysql_cfg) cur_src = src.cursor(dictionary=True) dst = sqlite3.connect("out.db") cur_dst = dst.cursor() # Example: create table in SQLite cur_dst.execute(""" CREATE TABLE IF NOT EXISTS users (   id INTEGER PRIMARY KEY,   name TEXT,   email TEXT,   created_at TEXT ) """) cur_src.execute("SELECT id, name, email, DATE_FORMAT(created_at, '%Y-%m-%d %H:%i:%s') AS created_at FROM users") rows = cur_src.fetchall() dst.execute("BEGIN") for r in rows:     cur_dst.execute("INSERT INTO users (id, name, email, created_at) VALUES (?, ?, ?, ?)",                     (r['id'], r['name'], r['email'], r['created_at'])) dst.commit() cur_src.close() cur_dst.close() src.close() dst.close() 

Common pitfalls and how to avoid them

• Ignoring data type mismatches — compare schemas column-by-column and convert appropriately.
• Losing precision for numeric or datetime types — decide on representation (TEXT ISO8601, INTEGER Unix epoch, or REAL) and convert consistently.
• Skipping transaction batching — inserting row-by-row without a transaction is painfully slow in SQLite.
• Forgetting to enable foreign keys — constraints silently won’t be enforced unless PRAGMA foreign_keys = ON.
• Overlooking case sensitivity differences — SQLite’s default collations differ from MySQL; string comparisons may change behavior.
• Importing large blobs incorrectly — ensure proper encoding and use parameterized inserts.

Performance tips

• Wrap bulk inserts inside a transaction (BEGIN; …; COMMIT;).
• Use PRAGMA synchronous = OFF or NORMAL during import for faster loads (restore safer setting after).
• Use WAL mode (PRAGMA journal_mode = WAL) for better concurrency.
• Create indexes after bulk load when possible.
• Use prepared statements or executemany for batch inserts in client libraries.
• Run ANALYZE after import to update SQLite’s query planner statistics.

Validation checklist

• [ ] Row counts match for every table.
• [ ] Key constraints validated (FKs, uniqueness).
• [ ] Sample data spot-checked (dates, integers, text encoding).
• [ ] Critical queries tested for correct results and performance.
• [ ] Application test-suite passes against SQLite DB.
• [ ] Backups and migration scripts saved for repeatability.

When not to migrate

• High-concurrency write-heavy production workloads — SQLite uses database-level locks for some operations and may not scale.
• Applications relying heavily on MySQL-specific features (stored procedures, advanced replication, complex collation rules).
• Very large datasets where a single-file database becomes unwieldy (though SQLite handles many GBs well, operational constraints apply).

Summary

Migrating MySQL to SQLite can simplify deployment and reduce operational overhead when your application requirements fit SQLite’s strengths. Successful migrations depend on careful schema analysis, thoughtful handling of data type and feature differences, and a tested, repeatable import process. Use available tools (mysqldump + translators, scripts, or custom ETL code) and follow best practices: export schema and data separately, adapt SQL, use transactions and PRAGMA tuning during import, validate thoroughly, and test your application against the new database.

If you want, I can:

• produce a ready-to-run conversion script for a specific schema,
• convert a small sample dump you provide,
• or outline commands tailored to your environment (Linux/Windows, MySQL version, expected data size).