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Ora2Pg vs. Other Migration Tools: When to Choose It

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Automating Schema and Data Conversion Using Ora2Pg

What Ora2Pg does

Ora2Pg is an open-source Perl tool that analyzes Oracle databases and generates PostgreSQL-compatible SQL. It can export schema (tables, indexes, constraints), data, views, sequences, functions, and migrate PL/SQL to PL/pgSQL where possible.

When to automate

Automate when you have repetitive migrations (multiple schemas/environments), routine syncs between Oracle and PostgreSQL for testing, or large migrations where manual steps are error-prone.

High-level automated workflow

  1. Inventory & Assessment
    • Export list of schemas, tables, and dependencies.
  2. Configure Ora2Pg
    • Create an ora2pg.conf per environment (connection, export type, mapping rules, chunk size).
  3. Schema export
    • Run ora2pg to generate PostgreSQL DDL; save to version-controlled files.
  4. Type/function mapping & transformation
    • Apply custom mapping rules (data types, sequences, PL/SQL adjustments).
  5. Data export
    • Use COPY or INSERT exports; split large tables into chunks.
  6. Apply migrations
    • Load DDL into PostgreSQL in a controlled order; load data; fix constraint/order issues.
  7. Validation
    • Row counts, checksums, spot-check queries, and application-level smoke tests.
  8. Iterate & Repeat
    • Tune configuration and mappings; automate via CI/CD pipelines.

Key Ora2Pg configuration options to automate

  • ORACLE_DSN / ORACLE_USER / ORACLE_PWD — connection details.
  • EXPORT — set to TABLE, VIEW, TRIGGER, FUNCTION, or ALL.
  • SCHEMA — specific schema(s) to export.
  • TYPE — export format (e.g., pgsql).
  • PG_DUMP — use for data export method; set to COPY for faster loads.
  • FETCH_SIZE — rows per fetch to control memory.
  • COMMIT — commit frequency when inserting.
  • CHUNKSIZE — split data exports.
  • MAP — custom type mappings.
  • IGNORE/INCLUDE — filter objects.
  • OUTPUT — directory for generated files.

Automation tips

  • Use configuration templates and environment-specific overrides.
  • Store generated DDL/data in Git; run reviews before applying.
  • Integrate with CI/CD (Jenkins/GitLab/GitHub Actions) to run ora2pg, lint DDL, and apply to staging.
  • Parallelize table data exports for large datasets.
  • Use pgrestore/psql with COPY for speed.
  • Maintain a mapping document for data types and PL/SQL gaps.

Common pitfalls and fixes

  • Unsupported PL/SQL constructs: keep manual conversion or rewrite as functions in PostgreSQL.
  • Data type mismatches: map NUMBER precision/scale explicitly; handle DATE/TIMESTAMP differences.
  • Sequences vs. Oracle NEXTVAL usage: convert triggers to sequences properly.
  • Large LOBs: export with specific settings and increase fetch sizes.
  • Foreign key order issues: load data without FKs, then add constraints after data load.

Validation checklist

  • Row counts per table match.
  • Checksums on sampled columns.
  • Application queries return same results for key workflows.
  • Indexes and constraints present and performing.
  • Performance benchmarks for critical queries.

Minimal example commands

  • Generate schema:
Code
ora2pg -c ora2pg.conf -t TABLE -o schema.sql
  • Export data with COPY:
Code
ora2pg -c ora2pg.conf -t COPY -o data.sql

Closing recommendation

Automate ora2pg runs in stages: generate artifacts, store them in version control, run automated validation, and promote through environments. Iterate on mappings and test thoroughly for PL/SQL and performance differences.

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