Migrating from MS SQL to PostgreSQL with DBConvert: Best Practices

Migrating from MS SQL to PostgreSQL with DBConvert: Best PracticesMigrating a production database from Microsoft SQL Server (MS SQL) to PostgreSQL is a significant technical undertaking that can deliver long-term benefits: lower licensing costs, open-source flexibility, and powerful SQL/Postgres features. DBConvert for MS SQL & PostgreSQL is a specialized migration tool designed to simplify and accelerate that process. This article walks through best practices for planning, executing, and validating a migration using DBConvert, with practical tips to avoid common pitfalls.

Why migrate from MS SQL to PostgreSQL?

• Cost savings: PostgreSQL is open-source and eliminates per-core or per-instance licensing expenses.
• Portability and standards: PostgreSQL adheres closely to SQL standards and supports cross-platform deployments.
• Advanced features: Rich extension ecosystem (PostGIS, TimescaleDB), robust concurrency, and strong JSON/JSONB support.
• Community & vendor neutrality: Large community, no vendor lock-in.

Overview of DBConvert for MS SQL & PostgreSQL

DBConvert is a commercial tool tailored for converting and synchronizing databases between Microsoft SQL Server and PostgreSQL. Key capabilities include:

• Schema conversion with mapping options for data types and constraints.
• Data migration with batch/transaction control and progress reporting.
• Synchronization (one-way and two-way) to keep source and target aligned during cutover.
• Support for scheduling, command-line automation, and logging.
• Options to convert stored procedures, views, and triggers (with caveats — see limitations).

Best practice: Use DBConvert for the heavy lifting of schema and data transfer, but plan manual review for business logic (stored procedures, functions, and triggers), which often require reimplementation in PostgreSQL.

Pre-migration planning

1. Inventory and scop e

   • List all databases, tables, views, stored procedures, functions, triggers, jobs, and linked services.
   • Identify components that must move (core OLTP data) vs. those that can stay (legacy archives).

2. Assess compatibility

   • Compare MS SQL data types, collation/encoding, and built-in functions against PostgreSQL equivalents.
   • Identify unsupported SQL Server features (e.g., T-SQL-specific constructs, SQL Server Agent jobs) that will require redesign.

3. Define migration goals

   • Zero-downtime vs. scheduled downtime?
   • One-time migration vs. phased migration with synchronization?
   • Required data freshness and acceptable RTO/RPO.

4. Prepare team & environment

   • Assign roles: DBA (source), DBA (target), application owner, QA/testers.
   • Set up test and staging PostgreSQL instances that mirror production sizing and configuration.

5. Backup and rollback strategy

   • Take full backups of MS SQL before any migration test.
   • Plan a rollback path (restore from backups, fall back to MS SQL endpoints, or keep both systems in sync until cutover).

Schema conversion best practices

1. Automatic conversion settings

   • Use DBConvert’s schema conversion but review mappings. Some MS SQL types need explicit mapping (e.g., NVARCHAR(max) → TEXT or VARCHAR with encoding; UNIQUEIDENTIFIER → UUID).
   • Map identity/auto-increment columns: MS SQL IDENTITY → PostgreSQL SERIAL/IDENTITY or explicit sequences.

2. Collation and encoding

   • Ensure PostgreSQL database encoding is set to UTF8 if MS SQL uses Unicode (NVARCHAR). Collation differences can affect sorting and comparisons—decide whether to use PostgreSQL collations or handle sorting in the application.

3. Constraints and indexes

   • Recreate primary keys, unique constraints, foreign keys, and indexes. DBConvert can migrate these, but verify index types (e.g., MS SQL INCLUDE columns vs. PostgreSQL expression/index types).
   • Consider removing nonessential indexes during initial bulk load and recreating them after data import for faster copy speeds.

4. Views, procedures, functions, triggers

   • DBConvert can export view definitions; however, T-SQL procedural code (stored procedures, triggers, functions) often contains T-SQL-specific syntax and must be rewritten in PL/pgSQL or another PostgreSQL language.
   • Treat these as application code: extract, document, and rewrite with tests.

5. Data model adjustments

   • Use native PostgreSQL types where beneficial (JSONB instead of NVARCHAR storing JSON; arrays; hstore; ranges).
   • Re-examine denormalized patterns or SQL Server-specific features and refactor when appropriate.

Data migration best practices

1. Dry runs & sample migrations

   • Perform test migrations with representative data volumes. Validate data integrity, types, and query behavior.
   • Use DBConvert’s logging and preview options to catch mapping issues early.

2. Batch size and transactions

   • Tune DBConvert batch and commit sizes. Large single transactions risk long locks and high rollback costs; small batches can increase overhead. Start with moderate batches and adjust based on throughput and locking behavior.

3. Disable constraints during load

   • Temporarily disable foreign key checks and triggers on the target during bulk load for speed, then re-enable and validate. Ensure uniqueness constraints still hold or use staged checks.

4. Handle large objects and binary data

   • Map varbinary, FILESTREAM, and large object handling to PostgreSQL bytea or large object (lo) types. Test file sizes and performance.

5. Nullability and default values

   • Ensure column nullability and default values are preserved. DBConvert typically maps defaults, but verify server-side defaults and application expectations.

6. Timezones and datetime handling

   • Decide on timestamptz vs. timestamp without time zone. Normalize timestamps in a consistent timezone (UTC recommended) during migration.

Synchronization and cutover strategies

1. One-time cutover (small datasets / downtime acceptable)

   • Stop writes to MS SQL (maintenance window), run final DBConvert sync, validate, switch application to PostgreSQL, monitor, and rollback if needed.

2. Phased or near-zero-downtime (large datasets / minimal downtime)

   • Use DBConvert in synchronization mode to perform initial bulk copy, then apply incremental syncs to capture changes.
   • During final cutover, briefly pause writes, run a final sync, switch application connections, and monitor.
   • Consider logical replication or CDC (Change Data Capture) solutions if extremely low RPO is required; DBConvert plus other tools may be combined.

3. Dual-write or coexistence

   • Some organizations adopt a dual-write approach where the application writes to both MS SQL and PostgreSQL during transition. This requires careful conflict resolution and is more complex.

4. Validation before switch

   • Run application smoke tests, compare row counts, checksums, and sample queries between systems.
   • Validate critical reports and business transactions end-to-end.

Testing and validation

1. Data integrity checks

   • Use checksums/hashes on large tables (for example, MD5 of concatenated column values or PostgreSQL’s built-in functions) to compare source and target.
   • Validate foreign key relationships and unique constraints.

2. Functional testing

   • Run application test suites against the PostgreSQL instance. Validate business logic, transactions, and stored-proc rewrites.

3. Performance testing

   • Benchmark critical queries and optimize: examine execution plans, add or adjust indexes, and consider PostgreSQL-specific optimizations (VACUUM, ANALYZE, configuration like shared_buffers, work_mem).
   • Test concurrency and peak-load behavior.

4. Regression testing

   • Ensure reports, scheduled jobs, and ETL processes (external to the database) continue to function or are adapted.

Post-migration adjustments

1. Tune PostgreSQL configuration

   • Adjust memory, checkpoint, WAL, and planner-related settings relative to workload and hardware. Common parameters: shared_buffers, work_mem, maintenance_work_mem, effective_cache_size, wal_buffers, checkpoint_completion_target.

2. Index and query tuning

   • Rebuild indexes if they were deferred. Use EXPLAIN/ANALYZE to find slow queries and add appropriate indexes or rewrite queries for PostgreSQL’s planner.

3. Implement maintenance routines

   • Schedule VACUUM (or autovacuum tuning), ANALYZE, and backups. PostgreSQL’s vacuuming is different from MS SQL’s maintenance and requires attention.

4. Monitor and alert

   • Deploy monitoring for replication lag (if used), long-running queries, disk usage, and autovacuum behavior. Tools include pg_stat_statements and third-party monitors.

5. Knowledge transfer and documentation

   • Document schema differences, rewritten procedures, new operational runbooks, and rollback steps. Train operations and development teams on PostgreSQL-specific practices.

Common migration pitfalls and how to avoid them

• Underestimating application-level differences: Audit T-SQL usage in the application and plan for rewrites.
• Ignoring collation/encoding issues: Test sorting and string comparisons early.
• Migrating without performance testing: Indexing and query plans differ — benchmark important workloads.
• Skipping incremental sync strategy: For large active databases, not having incremental sync will lead to long downtime.
• Overlooking administrative features: SQL Server Agent jobs, linked servers, and certain security features need alternatives in PostgreSQL (cron jobs, foreign data wrappers, roles).

Example DBConvert workflow (concise)

1. Install DBConvert on a server with network access to both DBs.
2. Configure source (MS SQL) and target (PostgreSQL) connections; test connectivity.
3. Use DBConvert schema conversion wizard; review and adjust data-type mappings.
4. Perform a test data migration for a subset or the full schema in a staging environment.
5. Recreate or rewrite stored procedures and triggers in PL/pgSQL as needed.
6. Run full data migration (initial load). Optionally, disable target indexes/constraints for speed.
7. Run incremental synchronizations until cutover window.
8. Final sync, application cutover, validate, enable constraints and indexes, and monitor.

When to seek outside help

• Very large datasets (>TB) with tight RTO/RPO requirements.
• Complex stored-proc logic entangled with application code.
• Multiple interdependent systems or external integrations that must remain functional during migration.
• Lack of in-house PostgreSQL expertise.

Conclusion

Migrating from MS SQL to PostgreSQL with DBConvert can be efficient and reliable if approached methodically: plan thoroughly, test repeatedly, rewrite procedural logic deliberately, use DBConvert for schema and data transfer, and validate performance and integrity before final cutover. With careful execution, organizations can realize the cost, flexibility, and technical benefits PostgreSQL offers while minimizing downtime and risk.

If you want, I can: outline a step-by-step migration checklist tailored to your environment, estimate time for a sample-sized database, or draft a rewrite example of a T-SQL stored procedure into PL/pgSQL.