Skillshub comparing-database-schemas

install
source · Clone the upstream repo
git clone https://github.com/ComeOnOliver/skillshub
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/jeremylongshore/claude-code-plugins-plus-skills/comparing-database-schemas" ~/.claude/skills/comeonoliver-skillshub-comparing-database-schemas && rm -rf "$T"
manifest: skills/jeremylongshore/claude-code-plugins-plus-skills/comparing-database-schemas/SKILL.md
source content

Database Diff Tool

Overview

Compare database schemas between two environments (development vs. staging, staging vs.

Prerequisites

  • Connection credentials to both source and target databases
  • psql
    or
    mysql
    CLI configured to connect to both environments
  • Read access to
    information_schema
    and
    pg_catalog
    (PostgreSQL) or
    information_schema
    (MySQL)
  • Permission to run
    pg_dump --schema-only
    for full schema extraction
  • Understanding of which environment is the "source of truth" (typically the migration-managed environment)

Instructions

  1. Extract the full schema from both databases for comparison:

    • PostgreSQL:
      pg_dump --schema-only --no-owner --no-privileges -f schema_source.sql source_db
      and repeat for target_db
    • MySQL:
      mysqldump --no-data --routines --triggers source_db > schema_source.sql
    • Alternatively, query
      information_schema
      directly for programmatic comparison
  2. Compare tables present in each database:

    • SELECT table_name FROM information_schema.tables WHERE table_schema = 'public' AND table_catalog = 'source_db' EXCEPT SELECT table_name FROM information_schema.tables WHERE table_schema = 'public' AND table_catalog = 'target_db'
    • This reveals tables that exist in source but not in target (and vice versa)
  3. Compare columns for each shared table:

    • Query
      information_schema.columns
      from both databases for:
      column_name
      ,
      data_type
      ,
      character_maximum_length
      ,
      is_nullable
      ,
      column_default
      ,
      ordinal_position
    • Flag differences in data type, nullability, default values, and column ordering
    • Detect added columns (in source, not target) and dropped columns (in target, not source)
  4. Compare indexes:

    • PostgreSQL: Query
      pg_indexes
      for
      indexname
      ,
      indexdef
      on each database
    • MySQL: Query
      information_schema.STATISTICS
      for
      INDEX_NAME
      ,
      COLUMN_NAME
      ,
      NON_UNIQUE
    • Flag missing, extra, or differently-defined indexes
  5. Compare constraints (primary keys, foreign keys, unique, check):

    • Query
      information_schema.table_constraints
      and
      information_schema.key_column_usage
    • Detect missing foreign keys, changed constraint names, and altered check constraint expressions
  6. Compare functions, stored procedures, and triggers:

    • PostgreSQL: Query
      pg_proc
      for function signatures and
      pg_trigger
      for trigger definitions
    • MySQL: Query
      information_schema.ROUTINES
      and
      information_schema.TRIGGERS
    • Compare function bodies for logical differences
  7. Compare enum types and custom types (PostgreSQL):

    • Query
      pg_type
      and
      pg_enum
      for enum label differences
    • Detect added or removed enum values (note: PostgreSQL only supports adding enum values, not removing)
  8. Generate a structured diff report categorizing differences as:

    • Added: Objects in source not present in target (require CREATE statements)
    • Removed: Objects in target not present in source (require DROP statements, confirm intentional)
    • Modified: Objects differing between source and target (require ALTER statements)
  9. Generate migration SQL to synchronize the target database to match the source:

    • CREATE TABLE for new tables, ALTER TABLE ADD COLUMN for new columns
    • ALTER TABLE ALTER COLUMN for type changes, ALTER TABLE DROP COLUMN for removed columns
    • CREATE INDEX / DROP INDEX for index differences
    • Include transaction wrapping and rollback-safe operations
  10. Validate the generated migration by applying it to a copy of the target database and re-running the diff. The second diff should report zero differences, confirming the migration produces the expected state.

Output

  • Schema diff report listing all differences categorized by type (added, removed, modified)
  • Migration SQL script to synchronize target schema to match source
  • Rollback SQL script to reverse the migration if needed
  • Side-by-side comparison of differing object definitions
  • Drift detection summary highlighting changes not tracked in migration files

Error Handling

ErrorCauseSolution
Connection refused to one databaseNetwork or credential issue on source or targetVerify connection strings; check firewall rules; confirm credentials work with direct
psql
or
mysql
connection
Permission denied on
pg_catalog
queries
User lacks read access to system catalogsGrant
pg_read_all_settings
role; or use
pg_dump --schema-only
which requires fewer privileges
False positive differences from default value formattingPostgreSQL normalizes default expressions differently in different versionsNormalize default value strings before comparison; ignore whitespace differences; compare semantic equivalence
Enum type modification blockedPostgreSQL does not support removing enum values or reorderingCreate a new enum type, migrate the column, drop the old type; document this as a multi-step migration
Generated migration fails on targetTarget has data that violates new constraintsAdd data validation queries before constraint creation; backfill default values; handle edge cases in migration

Examples

Detecting schema drift between staging and production: After 3 months without auditing, the diff reveals: 2 columns added to production manually (not in migrations), 1 index missing from staging, and 3 functions with different implementations. A migration script is generated to bring staging in sync, and the manual production changes are backported into migration files.

Pre-deployment schema validation: Before deploying a release with 5 migration files, run the diff between the post-migration staging schema and the expected schema. The diff catches a migration that accidentally dropped a constraint that a later migration depends on. The migration ordering is fixed before production deployment.

Comparing PostgreSQL schemas across major version upgrade: Schema extracted from PostgreSQL 14 and compared against PostgreSQL 16 after migration. Diff reveals function signature changes for built-in function calls, updated default values for new parameters, and deprecated syntax in stored procedures. Migration script updates function definitions for the new version.

Resources