How Organizations Can Reduce Costs and Boost Agility by Migrating from Oracle to PostgreSQL RDS

As organizations strive to optimize their IT budgets without compromising performance, the high costs associated with legacy database systems like Oracle have become a significant concern. Transitioning from Oracle to open-source databases like PostgreSQL offers a cost-effective solution while maintaining robust functionality, flexibility, and performance.

Migrating to PostgreSQL helps organizations break free from expensive licensing fees and vendor lock-in while enabling greater control over their data infrastructure. The benefits extend beyond cost savings—PostgreSQL offers superior scalability, advanced indexing, and support for modern technologies like containerization and microservices.

Using tools like AWS Database Migration Service (DMS) and Schema Conversion Tool (SCT), the migration process is streamlined to ensure data accuracy, schema compatibility, and minimal business disruption.

Why Migrate from Oracle to PostgreSQL?

Cost Efficiency: PostgreSQL eliminates hefty licensing costs associated with Oracle, offering a cost-effective alternative.

Open-Source Flexibility: PostgreSQL’s open-source nature provides flexibility, agility, and a vibrant community for continuous development.

Feature Parity: PostgreSQL’s evolving feature set narrows the gap with Oracle, offering required capabilities without the associated costs.

Key Difference between Oracle and PostgreSQL

Feature/Difference	Oracle	PostgreSQL
SQL Syntax	Utilizes PL/SQL with proprietary syntax	Relies on PL/pgSQL with open-source syntax
Data Types	Proprietary data types	Rich set of built-in and extensible data types
Concurrency Control	Implements MVCC for concurrency control	Also uses MVCC with potential implementation differences
Sequences	Uses SEQUENCE for generating unique identifiers	Features its version of sequences for the same purpose
Constraints	Supports constraints like primary keys, foreign keys, etc.	Implements similar constraints with nuanced syntax
Isolation Levels	Default isolation level is read committed	Defaults to read committed with configurable isolation levels
Transaction Control	Uses COMMIT and ROLLBACK for transaction control	Employs the same commands with nuanced syntax
Savepoints	Allows defining save points within a transaction	Offers a similar mechanism for save points
Index Types	Supports B-tree, bitmap, and various domain indexes	Also supports B-tree, bitmap, and additional index types
Full-Text Search	Full-text search capabilities may require additional components	Built-in support for full-text search with robust functionality
Stored Procedures	Uses PL/SQL for stored procedures and triggers	Leverages PL/pgSQL, a procedural language extension of SQL
Procedural Language Support	Extensive support for PL/SQL, a proprietary language	Encourages the use of PL/pgSQL and other supported procedural languages

Planning and Assessment Stage:

Database Size and Complexity: Evaluate the size of the current Oracle database, the number of schemas, tables, stored procedures, and the complexity of the schema design.

Feature Usage: Identify Oracle-specific features such as PL/SQL, sequences, packages, and proprietary data types that may require special handling in PostgreSQL.

Application Dependencies: Assess how applications depend on Oracle database features, stored procedures, and data types.

Workload Analysis: Understand the types of workloads (OLTP, OLAP) and peak traffic times to ensure PostgreSQL can handle similar or improved performance levels.

Tools and Techniques for Migration:

Ora2PG: Leverage ora2pg for comprehensive assessment and migration planning. It provides insights into schema structures, dependencies, and potential challenges.

AWS SCT: AWS Schema Conversion Tool (AWS SCT) to convert your existing database schema from one database engine to another.

AWS DMS: The AWS Database Migration Service (DMS) helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database.

Migration Phase:

Schema Conversion:

AWS Schema Conversion Tool (SCT) is a powerful utility that helps migrate database schemas from commercial databases such as Oracle to open-source databases like PostgreSQL. The main goal of the schema conversion phase is to convert the Oracle database schema (tables, indexes, views, stored procedures, etc.) into a PostgreSQL-compatible format, ensuring that the new schema retains the functionality and structure of the original Oracle schema.

Data Migration:

With AWS DMS, the migration begins with an initial load of existing data from the Oracle database to the PostgreSQL instance. This full load captures all relevant data, setting the foundation for the new PostgreSQL environment. Once the initial load is complete, AWS DMS employs CDC to continuously monitor the Oracle database for any changes, including inserts, updates, and deletes. This real-time replication ensures that the target PostgreSQL database remains synchronized with the source Oracle database, capturing any modifications made during the migration process.

The use of CDC is particularly beneficial for larger databases, as it allows for ongoing updates without requiring a complete downtime of the Oracle system.

Data Validation:

Conduct validation checks to ensure data integrity and accuracy after migration. Perform data quality audits to verify that all data has been migrated correctly and completely.

Code Conversion:

Ora2Pg scans the Oracle database, identifying SQL queries, PL/SQL functions, and stored procedures. It generates a detailed report of the required changes, allowing developers to analyse Oracle-specific syntax and replace it with PostgreSQL-compatible alternatives. This includes modifying SQL queries to align with PostgreSQL’s syntax and refactoring any PL/SQL logic present in the application layer to fit PostgreSQL’s PL/pgSQL.

Validation Phase:

Validate (Compare row count of every table) the data in the PostgreSQL database against the source Oracle database to ensure consistency.

Cut-over phase:

Plan a maintenance window to perform the final data migration and switch over to PostgreSQL as the production database.

Update application connection strings and configurations to point to the new PostgreSQL.

Optimization Strategies for Oracle to PostgreSQL Migration

Data Type Optimization: Review and adapt data types to PostgreSQL’s offerings for better performance and storage efficiency.

Indexing Strategies: Analyze and create appropriate indexes in PostgreSQL, leveraging its various index types to enhance query performance.

Query Optimization: Refactor SQL queries to utilize PostgreSQL’s features, such as CTEs and window functions, and analyze performance with the EXPLAIN command.

Partitioning: Implement table partitioning in PostgreSQL to improve performance and manageability for large datasets.

Batch Processing: Use batch inserts and PostgreSQL’s COPY command for efficient data loading during migration.

Use of Stored Procedures: Rewrite and optimize Oracle PL/SQL procedures in PostgreSQL’s PL/pgSQL for better performance.

Configuration Tuning: Adjust PostgreSQL settings like work_mem and shared_buffers based on workload requirements.

Regular Maintenance and Monitoring: Establish a maintenance plan and use monitoring tools to track performance and address issues proactively.

Performance Testing: Conduct thorough testing to evaluate the effectiveness of optimizations and ensure improved performance post-migration.

Challenges Faced During Data Migration to Redshift:

Foreign Key Dependencies: Since the source tables used foreign keys, AWS DMS did not load tables in any specific order. This led to issues when child tables were loaded before their parent tables, causing errors in the migration process due to foreign key dependencies.

Latency Issues: During the migration, we faced significant latency issues, causing the data to load very slowly. This impacted the overall migration timeline and performance of the DMS tasks.

Issue Fixes for Migration Challenges:

Foreign Key Dependencies: To address this, we configured specific endpoint attributes in AWS DMS to ignore foreign key dependencies during the migration. This ensured that DMS did not enforce foreign key constraints while loading the data, allowing the process to continue without errors. The foreign keys remained intact in the target Redshift database, ensuring referential integrity after the migration.

Latency Issues: To resolve the slowness, we identified specific queries that were causing delays. By adding indexes to the required columns in the target database, we optimized query performance and significantly improved data loading speed in DMS

Conclusion:

Migrating from Oracle to PostgreSQL offers numerous advantages, including cost savings, flexibility, and access to advanced features. By leveraging tools like AWS SCT and DMS, alongside optimization strategies such as data type adjustments, indexing, and query refactoring, organizations can ensure a smooth transition to PostgreSQL. Proper planning, thorough testing, and ongoing performance tuning are essential to maximizing the benefits of this migration.

Ultimately, a well-executed migration not only enhances database performance but also positions businesses for future growth and innovation in their data management practices.

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