oracle2postgres_schema_validator 0.1.1

Tool to compare Oracle objects with Postgres objects

Oracle to Postgres Database Comparison Utility

This utility streamlines the process of comparing the schemas and code objects of Oracle and Postgres databases. This utility can be useful for heterogenous migrations from Oracle to Postgres or vice-versa to check whether all the objects i.e: PLSQL, tablec, indexes, views, columns, sequences are migrated. It leverages Google BigQuery for efficient data analysis and reporting.

Key Features

• Metadata Collection: Extracts comprehensive schema metadata (tables, views, functions, procedures, etc.) from Oracle and Postgres databases.
• BigQuery Integration: Seamlessly imports collected metadata into Google BigQuery for centralized analysis.
• Detailed Comparison Reports: Generates clear, text and html based reports highlighting differences in:
  • Object counts (tables, views, etc.)
  • Missing objects in either database
  • Discrepancies in PLSQL and plpgsql (procedures, functions)
  • Other customizable comparison metrics

Prerequisites

• Oracle Database Access: Credentials for Oracle database.
• Postgres Database Access: Credentials for Postgres database.
• Staging Database BigQuery or Postgres database is needed to stage extracted metadata
  • BigQuery as Staging:
    • Google Cloud Project: A Google Cloud project with BigQuery enabled.
    • Service Account: A Google Cloud service account with BigQuery Data Editor and Job User roles.
  • Postgres as Staging:
    • Postgres Database A Postgres database (maybe CloudSQL), database and username that create tables in the database.

Installation

1. Clone the Repository:

   git clone https://github.com/samkaradag/oracle2postgres-schema-validator
   

2. Set Up Environment: Install required Python packages: pip install -r requirements.txt

Set environment variables (refer to config.py.example):

• ORACLE_CONN_STRING: Connection string for the Oracle database(s).
• GOOGLE_APPLICATION_CREDENTIALS: Use gcloud auth application-default login (This is required if the staging area is BigQuery)
• PROJECT_ID: Your Google Cloud project ID (This is required if the staging area is BigQuery).
• DATASET_ID: The BigQuery dataset to use (Tool will create if it doesn't exist) (This is required if the staging area is BigQuery).

Client requirements:

• python3.9
• Network connectivity to Oracle and Postgres instances and BigQuery APIs.

Usage

• Collect Metadata:

  • Oracle Collect: Grant required access to metadata views

    Please replace oracle_db_user with your database user.

        Option 1:
        Grant select any dictionary to oracle_db_user;
    
        Option 2:
        GRANT SELECT ON all_objects TO <db_user>;
        GRANT SELECT ON all_synonyms TO <db_user>;
        GRANT SELECT ON all_source TO <db_user>;
        GRANT SELECT ON all_indexes TO <db_user>;
        GRANT SELECT ON all_users TO <db_user>;
        GRANT SELECT ON all_role_privs TO <db_user>;
        GRANT SELECT ON all_roles TO <db_user>;
        GRANT SELECT ON all_triggers TO <db_user>;
        GRANT SELECT ON all_tab_columns TO <db_user>;
        GRANT SELECT ON all_tables TO <db_user>;
        GRANT SELECT ON all_constraints TO <db_user>;
        GRANT SELECT ON all_tab_privs TO <db_user>;
        GRANT SELECT ON all_sys_privs TO <db_user>;
    
    ```bash 
    oracollector --host oracle_ip_address --user db_user --password db_passwd --service oracle_service_name
    

  • Postgres Collect:

    pgcollector --host pg_ip_address --database db_name --user user_name --password db_pwd
    

  Collector files will output 2 zip files. These needs to be moved under extracts directory before running importer.

• Import

  • Import to BigQuery:

    mkdir extracts
    cp *.zip extracts/
    importer --project_id your_project_id --dataset_id your_dataset_name 
    
    You can specify an empty dataset otherwise dataset will be created if not exists.This command will unzip all the zip files underthe extracts folder.
    

  • Import to Postgres: Create database schema and tables using create_pg_user.sql in postgres

        CREATE SCHEMA IF NOT EXISTS schema_compare;
    
        CREATE TABLE schema_compare.instances
        (
        pkey VARCHAR(1000),
        con_id INT
        );
    
        CREATE TABLE schema_compare.dbobjectnames
        (
        pkey VARCHAR(1000),
        con_id INT,
        owner VARCHAR(1000),
        object_name VARCHAR(1000),
        object_type VARCHAR(1000),
        dma_source_id VARCHAR(1000),
        dma_manual_id VARCHAR(1000)
        );
    
        CREATE TABLE schema_compare.sourcecodedetailed
        (
        pkey VARCHAR(1000),
        con_id INT,
        owner VARCHAR(1000),
        name VARCHAR(1000),
        type VARCHAR(1000),
        nr_lines INT,
        dma_source_id VARCHAR(1000),
        dma_manual_id VARCHAR(1000)
        );
    
        CREATE TABLE schema_compare.views
        (
        pkey VARCHAR(1000),
        con_id INT,
        owner VARCHAR(1000),
        view_name VARCHAR(1000),
        dma_source_id VARCHAR(1000),
        dma_manual_id VARCHAR(1000)
        );
    
        CREATE TABLE schema_compare.triggers
        (
        pkey VARCHAR(1000),
        con_id INT,
        owner VARCHAR(1000),
        trigger_name VARCHAR(1000),
        base_table VARCHAR(1000),
        base_object_type VARCHAR(1000),
        status VARCHAR(1000),
        dma_source_id VARCHAR(1000),
        dma_manual_id VARCHAR(1000)
        );
    
        CREATE TABLE schema_compare.indexes
        (
        pkey VARCHAR(1000),
        con_id INT,
        owner VARCHAR(1000),
        index_name VARCHAR(1000),
        index_type VARCHAR(1000),
        table_owner VARCHAR(1000),
        table_name VARCHAR(1000),
        uniqueness VARCHAR(1000),
        status VARCHAR(1000),
        num_rows INT,
        dma_source_id VARCHAR(1000),
        dma_manual_id VARCHAR(1000)
        );
    
        CREATE TABLE schema_compare.eoj
        (
        pkey VARCHAR(1000)
        );
    
        CREATE TABLE schema_compare.defines
        (
        pkey VARCHAR(1000)
        );
    
        CREATE TABLE schema_compare.tableconstraints
        (
        pkey VARCHAR(1000),
        con_id INT,
        owner VARCHAR(1000),
        table_name VARCHAR(1000),
        constraint_type VARCHAR(1000),
        constraint_name VARCHAR(1000),
        status VARCHAR(1000)
        );
    
        CREATE TABLE schema_compare.columns
        (
        pkey VARCHAR(1000),
        con_id INT,
        owner VARCHAR(1000),
        table_name VARCHAR(1000),
        column_name VARCHAR(1000),
        data_type VARCHAR(1000),
        data_length INT,
        data_precision INT,
        data_scale INT,
        nullable BOOL,
        dma_source_id VARCHAR(1000),
        dma_manual_id VARCHAR(1000)
        );
    
    ```bash 
        importer --postgres_connection_string "postgresql://db-user:db-pwd@db_ip/db_name"
    

• Generate Reports:

  • BigQuery as Staging Area:

    reporter --project_id your_project_id --dataset_name your_dataset_name --table_name instances --format html
    

  • Postgres as Staging Area:

    reporter --db_type postgres --postgres_host your_postgres_host --postgres_port your_postgres_port --postgres_user your_postgres_user --postgres_password your_postgres_password --postgres_database your_postgres_database 
    

  • Filter Schemas:

    reporter --db_type postgres --postgres_host your_postgres_host --postgres_port your_postgres_port --postgres_user your_postgres_user --postgres_password your_postgres_password --postgres_database your_postgres_database --schemas_to_compare 'SCHEMA1','SCHEMA2','SCHEMA3'
    

Report Output

The generated reports will be saved in the reports directory.

Contributing

Contributions are welcome! Please feel free to open issues or submit pull requests.

License

This project is licensed under the Google License.