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Troubleshooting Common Issues with DBConvert for MS SQL & PostgreSQL

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How to Use DBConvert for MS SQL & PostgreSQL: Step-by-Step TutorialDBConvert for MS SQL & PostgreSQL is a powerful tool for migrating and synchronizing data between Microsoft SQL Server and PostgreSQL. This tutorial walks you through planning a migration, installing and configuring DBConvert, performing one-way and two-way conversions, mapping schemas and data types, handling large datasets, testing, and troubleshooting common issues.

Why choose DBConvert?

DBConvert offers:

  • Graphical interface for easy setup and monitoring.
  • High performance with batch processing and parallel jobs.
  • Flexible conversion modes (one-way migrate, two-way sync, scheduled jobs).
  • Data type and schema mapping tools to resolve compatibility differences.
  • Support for transaction-safe transfers and logging for recoverability.

Before you begin: prerequisites and planning

  1. System requirements:

    • A Windows machine (DBConvert GUI runs on Windows).
    • Network access to both SQL Server and PostgreSQL servers.
    • Sufficient disk space for temporary files and backups.

  2. Database credentials:

    • MS SQL: server hostname/IP, instance name (if any), port (default 1433), login (SQL Auth or Windows Auth), password, database name.
    • PostgreSQL: host, port (default 5432), database name, username, password.

  3. Permissions:

    • Source DB: read/select permission, and if you want to convert schema, permission to view schema metadata.
    • Target DB: create table, insert, update, alter, and optionally drop permissions for full migration.

  4. Backup and testing:

    • Always create full backups of both source and target before running large operations.
    • Test on a staging environment before production.

Step 1 — Install DBConvert

  1. Download DBConvert for MS SQL & PostgreSQL from the official vendor site.
  2. Run the installer and follow prompts. Choose the GUI version unless you need command-line automation.
  3. If you plan scheduled or automated tasks, ensure you have a valid license and configure the command-line utility (DBConvert CLI) if desired.

Step 2 — Create connections to source and target

  1. Launch DBConvert and choose the conversion direction: MS SQL to PostgreSQL or PostgreSQL to MS SQL.
  2. Configure the MS SQL source connection:
    • Enter Server/Instance, Authentication type (SQL Server or Windows), Username, Password, and Database.
    • Test the connection — fix firewall/port or authentication issues if necessary.
  3. Configure the PostgreSQL target connection:
    • Enter Host, Port, Database, Username, Password.
    • Test the connection.

Step 3 — Select databases, schemas, and objects to convert

  1. After connections are validated, DBConvert shows schemas and objects (tables, views, stored procedures depending on features).
  2. Select tables you want to convert. For initial migrations, convert core tables first (lookup/reference tables, then transactional tables).
  3. If DBConvert supports views or procedures in your license, choose whether to convert them. Note: stored procedure conversion between MS SQL and PostgreSQL often requires manual rewriting because of T-SQL vs PL/pgSQL differences.

Step 4 — Configure schema and data type mapping

  1. Open the mapping or options panel in DBConvert.
  2. Review automatic type mappings. Common differences:
    • MS SQL INT → PostgreSQL integer
    • MS SQL DATETIME → PostgreSQL timestamp without time zone or timestamp with time zone depending on needs
    • MS SQL NVARCHAR → PostgreSQL text or varchar with appropriate encoding
  3. Adjust mappings where DBConvert’s defaults don’t suit your schema (e.g., precision for decimal/numeric, handling of identity/serial columns).
  4. Handle primary keys, unique constraints, indexes, and foreign keys: decide whether to create constraints during data load or after to speed up bulk load.

Step 5 — Configure conversion options for data transfer

Key options to set:

  • Batch size / Commit frequency: choose a commit interval (rows per transaction). For large tables, smaller batches reduce transaction log growth; larger batches can be faster.
  • Preserve identity/auto-increment: map MS SQL identity to PostgreSQL sequences or use serial/bigserial.
  • Null handling and default values: ensure defaults and nullability are preserved.
  • Character encoding: ensure text encoding (UTF-8 recommended for PostgreSQL).

Example recommendation: For large tables, set batch size to 5,000–50,000 rows depending on your server memory and transaction log capacity.

Step 6 — Preview and run a test conversion

  1. Use the “Preview” feature to generate DDL statements DBConvert will run on PostgreSQL. Review these carefully.
  2. Perform a test run on a subset of data (select only a few tables or use “LIMIT”/sample rows). Verify:
    • Data types match expectations.
    • Primary and foreign keys are correct.
    • Row counts match.
    • No truncation or encoding issues.

Step 7 — Run full conversion

  1. Start the conversion. Monitor progress in DBConvert’s progress pane.

  2. Watch for errors and warnings. Common issues:

    • Constraint violations: may require disabling constraints during load and re-enabling after.
    • Data truncation: increase target column sizes or convert problematic columns to text.
    • Timeouts/network interruptions: use retry or resume features if available.

  3. After data transfer, apply indexes and constraints if you deferred them for speed.

Step 8 — Sync mode (optional, for incremental updates)

  1. If you need ongoing synchronization (one-way incremental or two-way bi-directional), configure DBConvert’s sync mode.
  2. Choose sync keys (unique identifiers, primary keys) and conflict resolution strategy (source-wins, target-wins, timestamp-based).
  3. Schedule periodic sync jobs using the built-in scheduler or OS scheduler invoking DBConvert CLI.

Step 9 — Validate and reconcile

  1. Run row counts on all tables and compare source vs target.
  2. Use checksums or sample queries to validate data integrity (for example, compare SUMs of numeric columns).
  3. Check application behavior against the new PostgreSQL database in a staging environment.
  4. Run performance tests and tune indexes or queries as PostgreSQL optimizer differs from SQL Server.

Troubleshooting common issues

  • Connection failures: verify host, port, firewall rules, and credentials. For MS SQL, ensure TCP/IP is enabled and SQL Server Browser is running if using named instances.
  • Encoding problems: ensure source data encoding matches target DB encoding; convert to UTF-8 if needed.
  • Identity columns: if IDs clash, consider mapping to sequences and remapping foreign keys or using UUIDs.
  • Stored procedures/functions: convert manually; DBConvert may export procedure code but it often requires rewriting.
  • Large object (LOB) handling: for BLOBs/CLOBs, use DBConvert options for streaming LOBs rather than loading whole values into memory.

Performance tips

  • Disable indexes and foreign keys during bulk load, then recreate them after.
  • Increase PostgreSQL maintenance_work_mem temporarily when building indexes.
  • Tune work_mem and shared_buffers for faster sorting and joins during conversion.
  • Use parallel workers (DBConvert parallel job settings) if CPU and I/O can support it.
  • For very large databases, migrate in logical chunks: historical/archival partitions first, then active partitions.

After migration: cutover checklist

  • Point application connection strings to the new PostgreSQL host in a controlled maintenance window.
  • Monitor application logs for SQL errors or performance regressions.
  • Keep MS SQL read-only for a fallback period until you’re confident.
  • Establish backup routines and monitoring for PostgreSQL.

Example: simple conversion walkthrough (summary)

  1. Install DBConvert.
  2. Connect to MS SQL source and PostgreSQL target.
  3. Select tables and preview DDL.
  4. Map data types and identity columns.
  5. Test with a subset.
  6. Run full conversion, monitor, and resolve errors.
  7. Recreate indexes/constraints, validate data, cut over.

Final notes

DBConvert simplifies many tasks in cross-database migration, but careful planning, testing, and validation are essential. For complex database logic (triggers, stored procedures, advanced T-SQL), plan manual rewrites and thorough functional testing.

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