🗄️ Database Blueprint¶

📘 What Is a Database Blueprint?¶

A Database Blueprint is a structured, agent-generated artifact that defines the database schema design, migration strategies, query optimization plans, and multi-tenant data isolation for a ConnectSoft-generated component — whether it's a microservice, module, bounded context, or shared data platform.

It represents the data persistence contract of record, created during the generation pipeline and continuously evaluated by downstream data engineering agents, DevOps pipelines, and runtime observability systems.

In the AI Software Factory, the Database Blueprint is not just a schema dump — it's a machine-readable, agent-enforceable contract for how data is structured, accessed, protected, migrated, and scaled.

🧠 Blueprint Roles in the Factory¶

The Database Blueprint plays a pivotal role in making data persistence composable, observable, and resilient:

🗄️ Defines table structures, relationships, constraints, and index strategies
🔄 Manages version-controlled migrations with rollback plans and zero-downtime strategies
🧠 Enables AI agents to enforce schema standards, naming conventions, and performance budgets
🏢 Encodes multi-tenant data isolation patterns — RLS, schema-per-tenant, or database-per-tenant
📊 Drives query performance baselines, index optimization, and execution plan monitoring
💾 Specifies backup schedules, RPO/RTO targets, and disaster recovery procedures

It ensures that data architecture is not an afterthought, but a first-class agent responsibility in the generation pipeline.

🎯 Purpose and Motivation¶

The Database Blueprint exists to solve one of the most persistent and costly problems in modern software delivery:

"Database schemas are inconsistently designed, migrations are fragile, queries degrade silently, and tenant data boundaries are poorly enforced."

In the ConnectSoft AI Software Factory, data persistence is integrated at the blueprint level, making it:

✅ Deterministic (agent-generated, based on traceable domain models)
✅ Repeatable (diffable migrations validated through CI/CD)
✅ Observable (integrated with query metrics, drift detection, and performance budgets)
✅ Composable (aligned with service, security, and infrastructure blueprints)

🚨 Problems It Solves¶

Problem Area	How the Database Blueprint Helps
🧩 Inconsistent Schema Designs	Enforces naming conventions, type standards, and constraint patterns
🔄 Unmanaged Migrations	Generates version-controlled, rollback-safe migrations with zero-downtime
📉 Poor Query Performance	Defines performance budgets, index strategies, and execution plan baselines
🏢 Tenant Data Leakage	Enforces RLS policies, schema isolation, or database-per-tenant partitioning
🔍 No Index Optimization Strategy	Specifies covering indexes, composite indexes, filtered indexes, and maintenance
💾 Backup/Recovery Gaps	Defines RPO/RTO targets, backup schedules, and geo-replication strategies
🧬 Schema Drift Across Environments	Tracks blueprint mutations and configuration drift over time
📊 Opaque Data Contracts	Makes data expectations visible to other agents and orchestrators

🧠 Why Blueprints, Not Just Migrations?¶

While traditional environments rely on ad hoc migration scripts or ORM-generated schemas, the Factory approach uses blueprints because:

Blueprints are memory-linked to every module, entity, and trace ID
They are machine-generated and human-readable
They support forward/backward analysis across schema versions and data model changes
They coordinate multiple agents across Data Architecture, Backend Dev, Infra, Security, and DevOps
They encode intent — not just the final DDL — enabling intelligent regeneration and optimization

This allows database design to be treated as code — but also as a living architectural asset.

🧑‍🤝‍🧑 Blueprint Consumers and Usage¶

The Database Blueprint is not an isolated artifact. It is actively consumed across the ConnectSoft AI Software Factory by agents, humans, and CI systems to design, implement, validate, and operate data persistence layers.

Each consumer interprets the blueprint differently based on its context, but all share a common source of truth.

🤖 Agent Consumers¶

Agent	Role in Consumption
`🗄️ Database Engineer Agent`	Designs schemas, generates migrations, optimizes indexes, tunes query performance
`🧠 Data Architect Agent`	High-level data modeling, entity relationships, bounded context alignment
`💻 Backend Developer Agent`	Implements data access layers, ORM configuration, repository patterns
`📦 Infrastructure Engineer Agent`	Provisions database infrastructure, configures replicas and failover
`🛡️ Security Architect Agent`	Defines row-level security, encryption at rest, data masking policies
`🚀 DevOps Engineer Agent`	Manages database CI/CD pipelines, migration deployment automation
`🧪 Test Automation Engineer Agent`	Database integration tests, data seeding, test data generation

👤 Human Stakeholders¶

Role	Value Derived from Database Blueprint
Data Architect	Validates data models, reviews entity relationships and bounded context boundaries
Backend Developer	Understands table structures, relationships, and ORM mapping expectations
DBA	Reviews index strategies, query plans, replication, and backup configurations
DevOps Engineer	Applies migration pipelines, ensures database provisioning aligns across environments
QA Lead	Confirms test data seeding strategies and integration test coverage
Security Officer	Verifies encryption, RLS policies, and data masking for compliance

🧠 Machine Consumers¶

GitOps systems: verify drift in schema definitions and migration state
CI/CD pipelines: run migration validation gates and performance regression tests
ORM frameworks: consume schema metadata for code generation and mapping
Monitoring systems: compare query performance against declared baselines
Memory Indexing Layer: links blueprint data assertions to downstream agents and alerts

The Database Blueprint becomes a persistence contract, providing guarantees that must be respected by services, infrastructure, CI/CD, and security processes alike.

🧠 Agent-Created, Trace-Ready Artifact¶

In the ConnectSoft AI Software Factory, the Database Blueprint is not written manually — it is generated, enriched, and validated by multiple agents, then stored as part of the system's memory graph.

This ensures every data persistence contract is:

📌 Traceable to its origin domain model or product feature
🔁 Regenerable with context-aware mutation
📊 Auditable through observability-first design
🧠 Embedded into the long-term agentic memory system

🤖 Agents Involved in Creation¶

Agent	Responsibility
`🧠 Data Architect Agent`	Designs high-level data models, defines aggregate boundaries and relationships
`🗄️ Database Engineer Agent`	Translates models into schemas, indexes, constraints, and migration scripts
`💻 Backend Developer Agent`	Aligns ORM mappings and repository patterns with schema definitions
`🛡️ Security Architect Agent`	Injects RLS policies, encryption specs, and data masking rules
`📦 Infrastructure Engineer Agent`	Provisions database servers, replicas, and backup infrastructure
`🚀 DevOps Engineer Agent`	Integrates migration pipelines and schema validation into CI/CD

Each agent contributes signals, decisions, and enriched metadata to create a complete, executable artifact.

📈 Memory Traceability¶

Database Blueprints are:

🔗 Linked to the project-wide trace ID
📂 Associated with the microservice, module, or bounded context
🧠 Indexed in vector memory for AI reasoning and enforcement
📜 Versioned and tagged (v1, approved, migrated, drifted, etc.)

This makes the blueprint machine-auditable, AI-searchable, and human-explainable.

📁 Example Storage and Trace Metadata¶

traceId: trc_82db_OrderService_v3
agentId: db-engineer-001
serviceName: OrderService
dataProfile: relational-multi-tenant
tags:
  - postgresql
  - rls
  - migrations
  - indexes
  - multi-tenant
version: v3
state: approved
createdAt: "2025-07-15T09:30:00Z"
diffFrom: v2
changedFields:
  - schemas.orders.columns
  - indexes.orders_tenant_date_idx
  - migrations.v3_add_order_status

📦 What It Captures¶

The Database Blueprint encodes a comprehensive set of data persistence dimensions that affect a service or module throughout its lifecycle — from initial design to production runtime.

It defines what data structures exist, how they relate, how they evolve, and how they perform — making it a living contract between the generated component and its data layer.

🗄️ Core Database Elements Captured¶

Category	Captured Details
Schema Definitions	Tables, columns, data types, constraints, defaults, computed columns
Entity-Relationship Models	ER diagrams, aggregate boundaries, reference data, foreign key graphs
Migration Strategies	Version-controlled migrations, rollback plans, zero-downtime DDL changes
Index Optimization Plans	Covering indexes, composite indexes, filtered indexes, index maintenance windows
Multi-Tenant Data Isolation	Schema-per-tenant, RLS, shared-schema with tenant ID, database-per-tenant
Query Performance Baselines	Expected query patterns, execution plan targets, performance budgets
Backup and Recovery Specs	RPO/RTO targets, backup schedules, point-in-time recovery, geo-replication
Replication and Sharding	Read replicas, horizontal sharding strategies, data partitioning schemes
Data Seeding	Seed data definitions, test data generation rules, data masking for non-prod
Temporal Data	Temporal tables, audit trails, soft deletes, history tracking

📎 Blueprint Snippet (Example)¶

schema:
  name: OrderService
  engine: postgresql
  version: "16"

tables:
  - name: orders
    schema: public
    columns:
      - name: id
        type: uuid
        primaryKey: true
        default: gen_random_uuid()
      - name: tenant_id
        type: uuid
        nullable: false
        index: true
      - name: customer_id
        type: uuid
        nullable: false
        foreignKey:
          table: customers
          column: id
          onDelete: RESTRICT
      - name: order_number
        type: varchar(50)
        nullable: false
        unique: true
      - name: status
        type: varchar(20)
        nullable: false
        default: "'pending'"
        check: "status IN ('pending', 'confirmed', 'shipped', 'delivered', 'cancelled')"
      - name: total_amount
        type: decimal(18,2)
        nullable: false
      - name: currency_code
        type: char(3)
        nullable: false
        default: "'USD'"
      - name: created_at
        type: timestamptz
        nullable: false
        default: now()
      - name: updated_at
        type: timestamptz
        nullable: false
        default: now()
      - name: is_deleted
        type: boolean
        nullable: false
        default: "false"
    indexes:
      - name: ix_orders_tenant_id
        columns: [tenant_id]
      - name: ix_orders_customer_id
        columns: [tenant_id, customer_id]
      - name: ix_orders_status_created
        columns: [tenant_id, status, created_at]
        where: "is_deleted = false"
    rowLevelSecurity:
      enabled: true
      policy: "tenant_id = current_setting('app.current_tenant')::uuid"

multiTenant:
  strategy: shared-schema-with-rls
  tenantColumn: tenant_id
  enforcement: row-level-security

migrations:
  tool: flyway
  baselineVersion: "1.0.0"
  currentVersion: "3.2.0"

backupStrategy:
  schedule: "0 2 * * *"
  retention: 30d
  pointInTimeRecovery: true
  rpoMinutes: 15
  rtoMinutes: 60

🧠 Cross-Blueprint Intersections (Overview)¶

Security Blueprint → defines encryption at rest, RLS policies, data masking
Infrastructure Blueprint → defines database provisioning, replicas, networking
Service Blueprint → defines ORM mappings, connection management, data access patterns
Test Blueprint → defines integration tests, seed data, and performance test baselines
Observability Blueprint → defines query metrics, slow query alerts, connection pool monitoring

The Database Blueprint aggregates, links, and applies data persistence rules across all of these — ensuring coherence and alignment.

🗂️ Output Formats¶

The Database Blueprint is generated and consumed across multiple layers of the AI Software Factory — from human-readable design reviews to machine-enforced CI/CD migration gates.

To support both automation and collaboration, it is produced in multiple coordinated formats, each aligned with a different set of use cases.

📄 Human-Readable Markdown (`.md`)¶

Used in Studio, code reviews, data architecture reviews, and documentation layers.

Sectioned by category: schemas, migrations, indexes, multi-tenancy, backup
Rich formatting with ER diagrams and Mermaid visuals
Includes YAML/SQL code samples and policy excerpts
Links to upstream and downstream blueprints

📜 Machine-Readable JSON (`.json`)¶

Used by agents, pipelines, and enforcement scripts.

Flattened and typed schema definitions
Includes metadata and trace headers
Validated against a shared schema specification
Compatible with ORM code generators and migration tools

Example excerpt:

{
  "traceId": "trc_82db_OrderService_v3",
  "engine": "postgresql",
  "version": "16",
  "schemas": {
    "public": {
      "tables": {
        "orders": {
          "columns": {
            "id": { "type": "uuid", "primaryKey": true },
            "tenant_id": { "type": "uuid", "nullable": false },
            "customer_id": { "type": "uuid", "nullable": false },
            "order_number": { "type": "varchar(50)", "unique": true },
            "status": { "type": "varchar(20)", "default": "pending" },
            "total_amount": { "type": "decimal(18,2)" },
            "created_at": { "type": "timestamptz", "default": "now()" }
          },
          "indexes": [
            { "name": "ix_orders_tenant_id", "columns": ["tenant_id"] },
            { "name": "ix_orders_status_created", "columns": ["tenant_id", "status", "created_at"], "filtered": true }
          ],
          "rls": {
            "enabled": true,
            "tenantColumn": "tenant_id"
          }
        }
      }
    }
  },
  "migrations": {
    "tool": "flyway",
    "currentVersion": "3.2.0"
  }
}

📂 SQL Migration Files (`.sql`)¶

Direct migration scripts generated from blueprint changes.

Version-numbered and idempotent
Include both up and down scripts
Validated against zero-downtime migration rules

🔁 CI/CD Compatible YAML (`.yaml`)¶

Used to configure database pipelines, backup schedules, and monitoring hooks.

Migration deployment steps
Schema validation gate configurations
Performance regression test definitions

🧠 Embedded Memory Shape (Vectorized)¶

Captured in agent long-term memory
Indexed by concept (e.g., rls, migration, index-strategy, backup)
Linked to all agent discussions, generations, and validations
Enables trace-based enforcement and reuse

📁 Storage Convention¶

Each Database Blueprint is stored in a consistent, discoverable location within the Factory workspace:

blueprints/database/{component-name}/database-blueprint.md
blueprints/database/{component-name}/database-blueprint.json
blueprints/database/{component-name}/migrations/
blueprints/database/{component-name}/schemas/
blueprints/database/{component-name}/indexes/

📂 Directory Structure Example¶

blueprints/database/order-service/
├── database-blueprint.md           # Human-readable specification
├── database-blueprint.json         # Machine-readable schema definition
├── migrations/
│   ├── V1.0.0__initial_schema.sql
│   ├── V1.1.0__add_order_status.sql
│   ├── V2.0.0__add_rls_policies.sql
│   ├── V3.0.0__add_audit_columns.sql
│   ├── V3.1.0__add_composite_index.sql
│   └── V3.2.0__add_currency_support.sql
├── schemas/
│   ├── orders.yaml
│   ├── customers.yaml
│   ├── order_items.yaml
│   └── reference_data.yaml
├── indexes/
│   ├── orders_indexes.yaml
│   ├── customers_indexes.yaml
│   └── index_maintenance.yaml
└── seed/
    ├── reference_data.sql
    ├── test_tenants.sql
    └── test_orders.sql

Markdown is human-readable and Studio-rendered.
JSON is parsed by orchestrators and enforcement agents.
SQL migration files are executed by Flyway, Liquibase, or EF Core Migrations.
YAML schema/index files are consumed by agents for validation and regeneration.

🏗️ Schema Design Principles¶

The Database Blueprint enforces a rigorous set of schema design principles that ensure consistency, maintainability, and performance across all generated components.

These principles are not guidelines — they are agent-enforced constraints embedded in every schema definition.

📐 Naming Conventions¶

Element	Convention	Example
Tables	`snake_case`, plural	`orders`, `order_items`
Columns	`snake_case`, descriptive	`created_at`, `total_amount`
Primary Keys	`id` (UUID preferred)	`id uuid PRIMARY KEY`
Foreign Keys	`{referenced_table}_id`	`customer_id`, `tenant_id`
Indexes	`ix_{table}_{columns}`	`ix_orders_tenant_id`
Unique Constraints	`uq_{table}_{columns}`	`uq_orders_order_number`
Check Constraints	`ck_{table}_{description}`	`ck_orders_status_valid`
Sequences	`seq_{table}_{column}`	`seq_orders_order_number`

🔄 Normalization Strategy¶

The blueprint prescribes Third Normal Form (3NF) as the default, with explicit, documented exceptions for denormalization:

schemaDesign:
  defaultNormalization: 3NF
  denormalizationPolicy:
    allowed: true
    requiresJustification: true
    commonPatterns:
      - pattern: materialized-view
        useCase: "Read-heavy dashboards and reporting queries"
      - pattern: json-column
        useCase: "Flexible metadata or configuration storage"
      - pattern: computed-column
        useCase: "Derived values that avoid expensive JOINs"
      - pattern: denormalized-aggregate
        useCase: "Pre-computed totals for high-frequency reads"

🕐 Temporal Tables and Audit Trails¶

Every table that requires audit history is configured with temporal tracking:

temporalTables:
  enabled: true
  strategy: system-versioned
  tables:
    - name: orders
      historyTable: orders_history
      periodColumns:
        start: valid_from
        end: valid_to
    - name: customers
      historyTable: customers_history
      periodColumns:
        start: valid_from
        end: valid_to

Equivalent SQL (PostgreSQL with temporal extension):

CREATE TABLE orders (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    tenant_id UUID NOT NULL,
    customer_id UUID NOT NULL REFERENCES customers(id),
    order_number VARCHAR(50) NOT NULL,
    status VARCHAR(20) NOT NULL DEFAULT 'pending',
    total_amount DECIMAL(18,2) NOT NULL,
    created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
    updated_at TIMESTAMPTZ NOT NULL DEFAULT now(),
    is_deleted BOOLEAN NOT NULL DEFAULT false,
    valid_from TIMESTAMPTZ NOT NULL DEFAULT now(),
    valid_to TIMESTAMPTZ NOT NULL DEFAULT 'infinity'
);

CREATE TABLE orders_history (LIKE orders INCLUDING ALL);

🗑️ Soft Delete Strategy¶

All entities use soft deletes by default, with hard delete reserved for compliance-driven data purging:

softDelete:
  enabled: true
  column: is_deleted
  type: boolean
  default: false
  globalFilter: true
  hardDeletePolicy:
    allowed: true
    requiresApproval: true
    useCases:
      - gdpr-right-to-erasure
      - data-retention-expiry

📘 Blueprint Schema Validation Rules¶

schemaValidation:
  rules:
    - rule: all-tables-must-have-primary-key
      severity: error
    - rule: all-foreign-keys-must-have-index
      severity: error
    - rule: no-nullable-foreign-keys
      severity: warning
    - rule: all-tables-must-have-created-at
      severity: error
    - rule: all-tables-must-have-updated-at
      severity: error
    - rule: varchar-must-have-max-length
      severity: error
    - rule: decimal-must-have-precision
      severity: error
    - rule: tenant-column-required-for-business-tables
      severity: error
    - rule: soft-delete-column-required
      severity: warning

🤖 Agent Collaboration¶

Agent	Role
`Data Architect Agent`	Defines naming conventions, normalization rules, and domain models
`Database Engineer Agent`	Validates schemas against rules, generates DDL, applies constraints
`Backend Developer Agent`	Ensures ORM entities align with schema definitions
`Security Architect Agent`	Verifies sensitive columns are encrypted or masked

🔄 Migration Lifecycle¶

Migrations are the mechanism by which schema changes are applied to database environments. The Database Blueprint treats migrations as first-class, version-controlled, agent-generated artifacts with full traceability and rollback capabilities.

📈 Migration Lifecycle Flow¶

flowchart TD
    A[Schema Change Request] --> B[Data Architect Agent]
    B --> C[Database Engineer Agent]
    C --> D[Generate Migration Script]
    D --> E[Validate Against Rules]
    E --> F{Passes Validation?}
    F -->|Yes| G[Generate Rollback Script]
    F -->|No| H[Return to Engineer Agent]
    H --> C
    G --> I[Test in Staging]
    I --> J{Performance OK?}
    J -->|Yes| K[Add to Migration Pipeline]
    J -->|No| L[Optimize & Retry]
    L --> D
    K --> M[Deploy to Production]
    M --> N[Verify & Update Blueprint]
    N --> O[Update Memory Graph]

Hold "Alt" / "Option" to enable pan & zoom

🧩 Migration Types¶

Migration Type	Description	Example
Schema Migration	DDL changes: CREATE, ALTER, DROP	Add new table, modify column type
Data Migration	DML changes: INSERT, UPDATE, DELETE	Backfill computed column values
Index Migration	CREATE INDEX, DROP INDEX, REINDEX	Add covering index for new query
Security Migration	RLS policies, GRANT/REVOKE, encryption	Enable RLS on new table
Reference Data	Seed or update lookup/reference tables	Add new order status values
Rollback Migration	Reverse a previously applied migration	Undo column rename

📜 Migration Naming Convention¶

V{version}__{description}.sql

Examples:

V1.0.0__initial_schema.sql
V1.1.0__add_order_items_table.sql
V1.2.0__add_rls_policies.sql
V2.0.0__add_audit_columns.sql
V2.0.0__rollback.sql

📎 Migration Script Example (Up)¶

-- V3.2.0__add_currency_support.sql
-- Migration: Add multi-currency support to orders table
-- Agent: db-engineer-001
-- TraceId: trc_82db_OrderService_v3
-- Zero-Downtime: Yes (additive only)

BEGIN;

ALTER TABLE orders
    ADD COLUMN currency_code CHAR(3) NOT NULL DEFAULT 'USD';

ALTER TABLE orders
    ADD COLUMN exchange_rate DECIMAL(12,6) DEFAULT 1.000000;

ALTER TABLE orders
    ADD CONSTRAINT ck_orders_currency_valid
    CHECK (currency_code ~ '^[A-Z]{3}$');

CREATE INDEX CONCURRENTLY ix_orders_currency
    ON orders (tenant_id, currency_code)
    WHERE is_deleted = false;

COMMENT ON COLUMN orders.currency_code IS 'ISO 4217 currency code';
COMMENT ON COLUMN orders.exchange_rate IS 'Exchange rate to base currency at time of order';

COMMIT;

📎 Migration Script Example (Down / Rollback)¶

-- V3.2.0__rollback.sql
-- Rollback: Remove multi-currency support from orders table

BEGIN;

DROP INDEX CONCURRENTLY IF EXISTS ix_orders_currency;

ALTER TABLE orders
    DROP CONSTRAINT IF EXISTS ck_orders_currency_valid;

ALTER TABLE orders
    DROP COLUMN IF EXISTS exchange_rate;

ALTER TABLE orders
    DROP COLUMN IF EXISTS currency_code;

COMMIT;

🚀 Zero-Downtime Migration Patterns¶

The blueprint enforces zero-downtime migration strategies for production deployments:

Pattern	Description	When to Use
Expand-Contract	Add new → migrate data → remove old	Column renames, type changes
Additive Only	Only add columns/tables, never remove in same release	New features
Concurrent Index	`CREATE INDEX CONCURRENTLY` to avoid table locks	All index additions
Background Data Migration	Async backfill with batched updates	Large data transformations
Feature Flag Guard	Code reads old and new columns, flag controls which is authoritative	Breaking schema changes

📘 Zero-Downtime YAML Configuration¶

migrationPolicy:
  zeroDowtime: true
  patterns:
    allowedOperations:
      - ADD_COLUMN
      - ADD_INDEX_CONCURRENTLY
      - ADD_CONSTRAINT_NOT_VALID
      - CREATE_TABLE
      - ADD_RLS_POLICY
    prohibitedOperations:
      - DROP_COLUMN_IMMEDIATE
      - ALTER_COLUMN_TYPE_DIRECT
      - DROP_TABLE_WITH_DATA
      - ADD_NOT_NULL_WITHOUT_DEFAULT
    expandContract:
      enabled: true
      maxPhases: 3
      phaseDurationDays: 7
  validation:
    requireRollbackScript: true
    requirePerformanceTest: true
    requireZeroDowntimeProof: true

🤖 Agent Collaboration¶

Agent	Role
`Database Engineer Agent`	Generates migration scripts, validates zero-downtime compliance
`Data Architect Agent`	Approves schema-level changes that affect domain model boundaries
`DevOps Engineer Agent`	Integrates migrations into deployment pipelines
`Test Automation Engineer Agent`	Runs migration tests against staging databases
`Backend Developer Agent`	Updates ORM mappings to align with migrated schemas

📊 Index Strategy and Optimization¶

Indexes are the primary mechanism for query acceleration in relational databases. The Database Blueprint defines a comprehensive index strategy that balances read performance, write overhead, and storage costs.

🧩 Index Types and Selection¶

Index Type	Description	Use Case
B-Tree	Default balanced tree index	Equality and range queries
Hash	Hash-based lookup	Exact equality only (rare in PostgreSQL)
GIN	Generalized Inverted Index	Full-text search, JSONB containment
GiST	Generalized Search Tree	Geometric, range, and proximity queries
BRIN	Block Range Index	Large tables with naturally ordered data
Partial/Filtered	Index with WHERE clause	Active records, non-deleted rows
Covering	Includes non-key columns via INCLUDE	Index-only scans for common query patterns
Composite	Multi-column index	Queries filtering on multiple columns

📎 Index Blueprint Definition¶

indexes:
  orders:
    - name: ix_orders_tenant_id
      type: btree
      columns: [tenant_id]
      purpose: "Tenant isolation filter"

    - name: ix_orders_tenant_customer
      type: btree
      columns: [tenant_id, customer_id]
      purpose: "Customer order lookups within tenant"

    - name: ix_orders_tenant_status_date
      type: btree
      columns: [tenant_id, status, created_at]
      where: "is_deleted = false"
      purpose: "Active order filtering by status and date"

    - name: ix_orders_order_number
      type: btree
      columns: [order_number]
      unique: true
      purpose: "Unique order number lookups"

    - name: ix_orders_total_amount_covering
      type: btree
      columns: [tenant_id, created_at]
      include: [total_amount, currency_code, status]
      purpose: "Index-only scan for order summaries"

    - name: ix_orders_jsonb_metadata
      type: gin
      columns: [metadata]
      purpose: "JSONB containment queries on order metadata"

📜 Generated SQL Examples¶

-- Partial (filtered) index for active orders
CREATE INDEX CONCURRENTLY ix_orders_tenant_status_date
    ON orders (tenant_id, status, created_at DESC)
    WHERE is_deleted = false;

-- Covering index for index-only scans
CREATE INDEX CONCURRENTLY ix_orders_total_amount_covering
    ON orders (tenant_id, created_at DESC)
    INCLUDE (total_amount, currency_code, status);

-- GIN index for JSONB queries
CREATE INDEX CONCURRENTLY ix_orders_jsonb_metadata
    ON orders USING gin (metadata jsonb_path_ops);

-- BRIN index for time-series data
CREATE INDEX ix_audit_log_created_brin
    ON audit_log USING brin (created_at)
    WITH (pages_per_range = 128);

🔧 Index Maintenance Strategy¶

indexMaintenance:
  schedule:
    reindex: "weekly-sunday-02:00"
    analyzeFrequency: "daily-03:00"
    bloatThreshold: 30%
  monitoring:
    unusedIndexThreshold: 30d
    duplicateDetection: true
    bloatTracking: true
  automation:
    autoDropUnused: false
    alertOnUnused: true
    alertOnBloat: true
    alertOnDuplicates: true

📊 Index Monitoring Queries¶

-- Find unused indexes
SELECT
    schemaname || '.' || relname AS table,
    indexrelname AS index,
    idx_scan AS times_used,
    pg_size_pretty(pg_relation_size(indexrelid)) AS size
FROM pg_stat_user_indexes
WHERE idx_scan = 0
    AND indexrelname NOT LIKE 'uq_%'
    AND indexrelname NOT LIKE 'pk_%'
ORDER BY pg_relation_size(indexrelid) DESC;

-- Find duplicate indexes
SELECT
    indrelid::regclass AS table_name,
    array_agg(indexrelid::regclass) AS duplicate_indexes
FROM pg_index
GROUP BY indrelid, indkey
HAVING COUNT(*) > 1;

-- Index bloat estimation
SELECT
    schemaname || '.' || tablename AS table,
    indexname AS index,
    pg_size_pretty(pg_relation_size(indexname::regclass)) AS index_size,
    ROUND(100 * (1 - (pg_relation_size(indexname::regclass)::float / 
        NULLIF(pg_total_relation_size(tablename::regclass), 0))), 2) AS bloat_pct
FROM pg_indexes
WHERE schemaname = 'public'
ORDER BY pg_relation_size(indexname::regclass) DESC;

🤖 Agent Collaboration¶

Agent	Role
`Database Engineer Agent`	Designs index strategy, generates DDL, monitors performance impact
`Data Architect Agent`	Reviews index alignment with query patterns and data access paths
`DevOps Engineer Agent`	Schedules maintenance windows, automates REINDEX operations
`Test Automation Engineer Agent`	Runs query performance tests to validate index effectiveness

🏢 Multi-Tenant Data Architecture¶

ConnectSoft's AI Software Factory supports multi-tenant SaaS systems by default. The Database Blueprint encodes strict controls for data partitioning, tenant-aware access, and scoped operations at the database layer.

This section defines the three primary isolation patterns, their trade-offs, and when each should be used.

🧩 Isolation Pattern Comparison¶

Pattern	Isolation Level	Complexity	Cost	Compliance	Scale
Shared Schema with RLS	Logical	Low	Low	Moderate	High
Schema-Per-Tenant	Logical+	Medium	Medium	High	Medium
Database-Per-Tenant	Physical	High	High	Very High	Per-tenant

🔐 Pattern 1: Shared Schema with Row-Level Security (RLS)¶

The default and recommended pattern for most ConnectSoft services. All tenants share the same tables, with PostgreSQL RLS policies enforcing data isolation.

flowchart TD
    subgraph SharedDatabase["Shared Database"]
        subgraph Tables["Shared Tables"]
            T1["orders (all tenants)"]
            T2["customers (all tenants)"]
            T3["products (all tenants)"]
        end
        RLS["Row-Level Security Policies"]
    end

    App1["Tenant A Request"] -->|"SET app.current_tenant = 'A'"| RLS
    App2["Tenant B Request"] -->|"SET app.current_tenant = 'B'"| RLS
    RLS --> Tables

Hold "Alt" / "Option" to enable pan & zoom

SQL Implementation¶

-- Enable RLS on table
ALTER TABLE orders ENABLE ROW LEVEL SECURITY;
ALTER TABLE orders FORCE ROW LEVEL SECURITY;

-- Create tenant isolation policy
CREATE POLICY tenant_isolation_orders ON orders
    USING (tenant_id = current_setting('app.current_tenant')::uuid);

-- Create tenant-scoped insert policy
CREATE POLICY tenant_insert_orders ON orders
    FOR INSERT
    WITH CHECK (tenant_id = current_setting('app.current_tenant')::uuid);

-- Application sets tenant context per connection
SET app.current_tenant = '550e8400-e29b-41d4-a716-446655440000';

YAML Blueprint Declaration¶

multiTenant:
  strategy: shared-schema-with-rls
  tenantColumn: tenant_id
  tenantColumnType: uuid
  enforcement: row-level-security
  connectionManagement:
    pooling: shared
    tenantContextSetting: "app.current_tenant"
    setOnConnectionOpen: true
  tables:
    - orders
    - customers
    - order_items
    - products
    - invoices
  excludedTables:
    - reference_currencies
    - reference_countries
    - system_configurations

Pros and Cons¶

Pros	Cons
✅ Lowest operational complexity	⚠️ Noisy neighbor risk on shared resources
✅ Shared connection pool	⚠️ Must ensure RLS policies on every table
✅ Easy schema migrations (one database)	⚠️ Harder compliance for strict data sovereignty
✅ Cost-effective at scale	⚠️ Cross-tenant queries require elevated access

🔐 Pattern 2: Schema-Per-Tenant¶

Each tenant gets a dedicated PostgreSQL schema within a shared database. Provides stronger isolation without separate database instances.

flowchart TD
    subgraph SharedDatabase["Shared Database"]
        subgraph SchemaA["Schema: tenant_alpha"]
            TA1["orders"]
            TA2["customers"]
        end
        subgraph SchemaB["Schema: tenant_beta"]
            TB1["orders"]
            TB2["customers"]
        end
        subgraph SchemaC["Schema: tenant_gamma"]
            TC1["orders"]
            TC2["customers"]
        end
    end

    App1["Tenant Alpha"] -->|"SET search_path = 'tenant_alpha'"| SchemaA
    App2["Tenant Beta"] -->|"SET search_path = 'tenant_beta'"| SchemaB
    App3["Tenant Gamma"] -->|"SET search_path = 'tenant_gamma'"| SchemaC

Hold "Alt" / "Option" to enable pan & zoom

SQL Implementation¶

-- Create tenant schema
CREATE SCHEMA tenant_alpha AUTHORIZATION app_user;

-- Create tables within tenant schema
CREATE TABLE tenant_alpha.orders (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    customer_id UUID NOT NULL REFERENCES tenant_alpha.customers(id),
    order_number VARCHAR(50) NOT NULL,
    status VARCHAR(20) NOT NULL DEFAULT 'pending',
    total_amount DECIMAL(18,2) NOT NULL,
    created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);

-- Tenant onboarding: clone template schema
CREATE SCHEMA tenant_new_tenant;

DO $$
DECLARE
    rec RECORD;
BEGIN
    FOR rec IN
        SELECT tablename FROM pg_tables WHERE schemaname = 'template_schema'
    LOOP
        EXECUTE format(
            'CREATE TABLE tenant_new_tenant.%I (LIKE template_schema.%I INCLUDING ALL)',
            rec.tablename, rec.tablename
        );
    END LOOP;
END $$;

YAML Blueprint Declaration¶

multiTenant:
  strategy: schema-per-tenant
  schemaPrefix: "tenant_"
  templateSchema: "template_schema"
  connectionManagement:
    pooling: per-schema
    searchPathSetting: true
  provisioning:
    onTenantCreated: clone-template-schema
    onTenantDeleted: archive-and-drop-schema
  migrations:
    strategy: iterate-all-schemas
    parallelDegree: 4
    failurePolicy: stop-and-alert

Pros and Cons¶

Pros	Cons
✅ Strong logical isolation	⚠️ Migrations must iterate across all schemas
✅ Per-tenant backup/restore possible	⚠️ Higher connection pool overhead
✅ Schema-level GRANT/REVOKE controls	⚠️ Schema proliferation at scale
✅ Easier compliance than shared-schema	⚠️ Cross-tenant analytics requires schema merging

🔐 Pattern 3: Database-Per-Tenant¶

Each tenant gets a completely separate database instance. Maximum isolation, typically used for enterprise or regulated tenants.

flowchart TD
    Router["Connection Router"] --> DB_A["Database: tenant_alpha_db"]
    Router --> DB_B["Database: tenant_beta_db"]
    Router --> DB_C["Database: tenant_gamma_db"]

    DB_A --> RA["Full Schema + Data"]
    DB_B --> RB["Full Schema + Data"]
    DB_C --> RC["Full Schema + Data"]

Hold "Alt" / "Option" to enable pan & zoom

YAML Blueprint Declaration¶

multiTenant:
  strategy: database-per-tenant
  namingPattern: "{tenant_slug}_db"
  connectionManagement:
    routing: connection-string-per-tenant
    connectionStringSource: azure-key-vault
    pooling: per-database
  provisioning:
    provider: azure-database-for-postgresql
    skuPerTier:
      starter: "B_Standard_B1ms"
      professional: "GP_Standard_D2ds_v5"
      enterprise: "MO_Standard_E4ds_v5"
    onTenantCreated: provision-new-database
    onTenantDeleted: backup-and-decommission
  migrations:
    strategy: rolling-per-database
    parallelDegree: 10
    failurePolicy: continue-and-report
  backup:
    perTenantSchedule: true
    retentionDays: 90

Pros and Cons¶

Pros	Cons
✅ Maximum data isolation	⚠️ Highest operational cost
✅ Per-tenant performance tuning	⚠️ Complex migration orchestration
✅ Strongest compliance posture	⚠️ Connection routing complexity
✅ Independent backup/restore/scaling	⚠️ Cross-tenant analytics extremely difficult
✅ No noisy neighbor risk	⚠️ Resource waste for low-activity tenants

📊 Decision Matrix¶

tenantIsolationDecision:
  defaultStrategy: shared-schema-with-rls
  overrideRules:
    - condition: "tenant.complianceLevel == 'SOC2_TYPE_II' OR tenant.dataResidency == true"
      strategy: schema-per-tenant
    - condition: "tenant.tier == 'enterprise' OR tenant.regulatoryRequirement == 'HIPAA'"
      strategy: database-per-tenant
    - condition: "tenant.expectedDataVolume > '100GB'"
      strategy: database-per-tenant
  hybridSupport:
    enabled: true
    description: "Mix strategies within the same platform based on tenant tier"

🤖 Agent Collaboration¶

Agent	Role
`Data Architect Agent`	Selects isolation pattern based on requirements and constraints
`Database Engineer Agent`	Implements RLS policies, schema templates, or database provisioning
`Security Architect Agent`	Validates isolation enforcement and compliance alignment
`Infrastructure Engineer Agent`	Provisions database infrastructure per selected pattern
`DevOps Engineer Agent`	Automates tenant onboarding/offboarding and migration rollouts

📈 Query Performance Management¶

The Database Blueprint defines query performance expectations as first-class contract elements. Every service's data access patterns are documented, baselined, and monitored for regression.

🎯 Performance Budget Model¶

queryPerformance:
  budgets:
    - query: "GET /api/orders (list by tenant)"
      maxDurationMs: 50
      expectedRows: 100
      scanType: index-only
      index: ix_orders_tenant_status_date

    - query: "GET /api/orders/:id (single lookup)"
      maxDurationMs: 5
      expectedRows: 1
      scanType: index-scan
      index: ix_orders_order_number

    - query: "GET /api/orders/summary (aggregation)"
      maxDurationMs: 200
      expectedRows: 1
      scanType: index-only
      index: ix_orders_total_amount_covering

    - query: "POST /api/orders (insert)"
      maxDurationMs: 20
      expectedIndexUpdates: 4

    - query: "Dashboard: daily order totals"
      maxDurationMs: 500
      expectedRows: 30
      scanType: bitmap-index
      acceptsMatView: true

📊 Execution Plan Baselines¶

The blueprint captures expected execution plan shapes for critical queries:

executionPlanBaselines:
  - queryId: "orders_by_tenant_status"
    expectedPlan:
      type: "Index Only Scan"
      index: ix_orders_tenant_status_date
      estimatedCost: 0.43
      actualRows: 100
    regressionThreshold:
      costIncrease: 200%
      scanTypeChange: true
      seqScanDetection: true

🔍 Query Performance Monitoring¶

queryMonitoring:
  enabled: true
  tool: pg_stat_statements
  configuration:
    trackUtility: true
    trackPlanning: true
    maxStatements: 10000
  alerts:
    - condition: "mean_exec_time > 2x baseline"
      action: alert-data-team
    - condition: "seq_scan_count > 100 per hour on indexed table"
      action: alert-db-engineer
    - condition: "lock_wait_time > 5s"
      action: alert-devops
  dashboards:
    - name: "Query Performance Overview"
      metrics:
        - mean_exec_time_ms
        - calls_per_minute
        - rows_returned_avg
        - shared_blks_hit_ratio

📜 Performance Monitoring SQL¶

-- Top 10 slowest queries
SELECT
    queryid,
    ROUND(mean_exec_time::numeric, 2) AS avg_ms,
    calls,
    ROUND(total_exec_time::numeric, 2) AS total_ms,
    ROUND((shared_blks_hit::float / NULLIF(shared_blks_hit + shared_blks_read, 0) * 100)::numeric, 2) AS cache_hit_pct,
    LEFT(query, 100) AS query_preview
FROM pg_stat_statements
WHERE dbid = (SELECT oid FROM pg_database WHERE datname = current_database())
ORDER BY mean_exec_time DESC
LIMIT 10;

-- Detect sequential scans on indexed tables
SELECT
    schemaname,
    relname AS table_name,
    seq_scan,
    seq_tup_read,
    idx_scan,
    idx_tup_fetch,
    ROUND(100.0 * seq_scan / NULLIF(seq_scan + idx_scan, 0), 2) AS seq_scan_pct
FROM pg_stat_user_tables
WHERE seq_scan > 100
    AND idx_scan > 0
    AND seq_scan > idx_scan
ORDER BY seq_scan DESC;

🔁 Regression Detection Pipeline¶

flowchart LR
    A[Query Runs in CI] --> B[Capture EXPLAIN ANALYZE]
    B --> C[Compare to Baseline]
    C --> D{Regression Detected?}
    D -->|Yes| E[Block Deployment]
    D -->|No| F[Pass Gate]
    E --> G[Alert Database Engineer Agent]
    G --> H[Optimize Query / Index]
    H --> A
    F --> I[Deploy to Production]

Hold "Alt" / "Option" to enable pan & zoom

🤖 Agent Collaboration¶

Agent	Role
`Database Engineer Agent`	Defines performance budgets, creates baseline plans
`Backend Developer Agent`	Aligns data access code with expected query patterns
`Test Automation Engineer Agent`	Runs performance regression tests in CI pipeline
`DevOps Engineer Agent`	Configures monitoring dashboards and alert routing

💾 Backup, Recovery, and Disaster Recovery¶

The Database Blueprint defines comprehensive backup and disaster recovery specifications for every database component. These are not optional — they are contractual SLA elements that must be provisioned, tested, and monitored.

🎯 RPO/RTO Definitions¶

Metric	Definition	Blueprint Field
RPO	Recovery Point Objective — max acceptable data loss	`rpoMinutes`
RTO	Recovery Time Objective — max acceptable downtime	`rtoMinutes`

📘 Backup Strategy Blueprint¶

backupAndRecovery:
  engine: postgresql
  provider: azure-database-for-postgresql-flexible

  rpo:
    minutes: 15
    justification: "Business-critical order data with financial implications"

  rto:
    minutes: 60
    justification: "SLA requires 99.95% availability"

  backup:
    automated:
      type: continuous
      walArchiving: true
      fullBackup:
        schedule: "0 2 * * *"
        retention: 35d
      differentialBackup:
        schedule: "0 */6 * * *"
        retention: 7d
      transactionLogBackup:
        continuous: true
        retention: 7d

    pointInTimeRecovery:
      enabled: true
      granularity: "any point within retention window"
      maxRecoveryWindow: 35d

    storage:
      type: geo-redundant
      primaryRegion: eastus2
      secondaryRegion: westus2
      encryption: AES-256

  geoReplication:
    enabled: true
    mode: asynchronous
    readReplicas:
      - region: westus2
        purpose: read-offload
        lagThresholdSeconds: 30
      - region: northeurope
        purpose: disaster-recovery
        lagThresholdSeconds: 60

  disasterRecovery:
    strategy: geo-failover
    automaticFailover:
      enabled: true
      detectionThreshold: 60s
      gracePeridSeconds: 300
    manualFailover:
      runbookLocation: "runbooks/database/failover-procedure.md"
      notifyChannels:
        - opsgenie
        - teams
    drTesting:
      schedule: quarterly
      type: full-failover-drill
      lastTested: "2025-06-15T14:00:00Z"
      results: "runbooks/database/dr-test-results-2025-q2.md"

🔄 Backup Architecture Flow¶

flowchart TD
    subgraph Primary["Primary Region (East US 2)"]
        DB_Primary["PostgreSQL Primary"]
        WAL["WAL Archiving"]
        FullBackup["Full Backup (Daily 2AM)"]
        DiffBackup["Differential (Every 6h)"]
    end

    subgraph Secondary["Secondary Region (West US 2)"]
        DB_ReadReplica["Read Replica"]
        DB_DR["DR Standby"]
    end

    subgraph Storage["Geo-Redundant Storage"]
        BackupStore["Backup Storage (AES-256)"]
        PITR["Point-in-Time Recovery Store"]
    end

    DB_Primary --> WAL
    WAL --> BackupStore
    WAL --> PITR
    DB_Primary --> FullBackup --> BackupStore
    DB_Primary --> DiffBackup --> BackupStore
    DB_Primary -->|"Async Replication"| DB_ReadReplica
    DB_Primary -->|"Async Replication"| DB_DR

    DB_DR -->|"Failover"| DB_Primary

Hold "Alt" / "Option" to enable pan & zoom

📜 Recovery Procedures¶

Scenario	Procedure	RTO Target
Accidental Data Deletion	Point-in-time recovery to pre-deletion state	< 30 minutes
Database Corruption	Restore from latest full + WAL replay	< 60 minutes
Region Outage	Promote DR standby in secondary region	< 15 minutes
Full Infrastructure Failure	Restore from geo-redundant backup	< 4 hours
Ransomware/Security Breach	Restore from immutable backup + audit trail	< 2 hours

🧪 DR Testing Configuration¶

drTesting:
  schedule:
    frequency: quarterly
    nextScheduled: "2025-09-15T14:00:00Z"
  tests:
    - type: failover-drill
      description: "Promote read replica to primary, validate application connectivity"
      acceptanceCriteria:
        - rtoMet: true
        - dataIntegrity: verified
        - applicationHealthy: true
    - type: point-in-time-recovery
      description: "Recover to specific timestamp, validate data consistency"
      acceptanceCriteria:
        - rpoMet: true
        - rowCountMatch: true
    - type: full-restore
      description: "Restore from geo-redundant backup to new instance"
      acceptanceCriteria:
        - schemaMatch: true
        - dataMatch: true
        - indexesIntact: true

🤖 Agent Collaboration¶

Agent	Role
`Infrastructure Engineer Agent`	Provisions backup infrastructure, configures replication
`Database Engineer Agent`	Defines RPO/RTO targets, validates backup coverage
`DevOps Engineer Agent`	Automates backup scheduling, monitors backup health
`Security Architect Agent`	Ensures backup encryption and access controls
`Test Automation Engineer Agent`	Schedules and validates DR test exercises

🌱 Data Seeding and Test Data¶

The Database Blueprint defines how seed data, reference data, and test data are managed across environments. This ensures consistent, reliable, and privacy-safe data for development, testing, and staging.

🧩 Seed Data Categories¶

Category	Description	Environments
Reference Data	Lookup tables: currencies, countries, status codes	All
System Configuration	Default settings, feature flags, role definitions	All
Sample Tenants	Pre-configured tenant accounts for development/testing	Dev, Test, Staging
Test Entities	Orders, customers, products for integration tests	Dev, Test
Performance Data	Large-volume generated data for load testing	Staging, Perf

📘 Seed Data Blueprint¶

dataSeeding:
  referenceData:
    source: "seed/reference_data.sql"
    idempotent: true
    appliesTo: [dev, test, staging, production]
    tables:
      - reference_currencies
      - reference_countries
      - reference_order_statuses
      - reference_payment_methods

  testData:
    source: "seed/test_data.sql"
    appliesTo: [dev, test]
    generator:
      tool: faker
      language: csharp
      config:
        tenants: 5
        customersPerTenant: 100
        ordersPerCustomer: 20
        locale: "en_US"
    cleanup:
      strategy: truncate-and-reseed
      beforeEachTestRun: true

  performanceData:
    source: generated
    appliesTo: [staging, performance]
    generator:
      tenants: 50
      customersPerTenant: 10000
      ordersPerCustomer: 500
      totalExpectedRows: 250000000
      parallelInsertDegree: 8

🔒 Data Masking for Non-Production¶

dataMasking:
  enabled: true
  appliesTo: [dev, test, staging]
  rules:
    - column: "customers.email"
      strategy: hash-and-domain
      output: "{hash}@example.com"
    - column: "customers.phone_number"
      strategy: randomize
      pattern: "+1-555-XXX-XXXX"
    - column: "customers.first_name"
      strategy: faker
      fakerMethod: "Name.FirstName"
    - column: "customers.last_name"
      strategy: faker
      fakerMethod: "Name.LastName"
    - column: "orders.total_amount"
      strategy: noise
      variance: 15%
    - column: "payment_methods.card_number"
      strategy: redact
      output: "XXXX-XXXX-XXXX-XXXX"

📜 Reference Data SQL Example¶

-- Reference data: idempotent seed script
-- Applied to all environments including production

INSERT INTO reference_currencies (code, name, symbol, decimal_places)
VALUES
    ('USD', 'US Dollar', '$', 2),
    ('EUR', 'Euro', '€', 2),
    ('GBP', 'British Pound', '£', 2),
    ('JPY', 'Japanese Yen', '¥', 0),
    ('CAD', 'Canadian Dollar', 'C$', 2)
ON CONFLICT (code) DO UPDATE SET
    name = EXCLUDED.name,
    symbol = EXCLUDED.symbol,
    decimal_places = EXCLUDED.decimal_places;

INSERT INTO reference_order_statuses (code, name, display_order, is_terminal)
VALUES
    ('pending', 'Pending', 1, false),
    ('confirmed', 'Confirmed', 2, false),
    ('processing', 'Processing', 3, false),
    ('shipped', 'Shipped', 4, false),
    ('delivered', 'Delivered', 5, true),
    ('cancelled', 'Cancelled', 6, true),
    ('refunded', 'Refunded', 7, true)
ON CONFLICT (code) DO UPDATE SET
    name = EXCLUDED.name,
    display_order = EXCLUDED.display_order,
    is_terminal = EXCLUDED.is_terminal;

🤖 Agent Collaboration¶

Agent	Role
`Database Engineer Agent`	Generates seed scripts, configures idempotent reference data
`Test Automation Engineer Agent`	Defines test data requirements, generates faker-based data scripts
`Security Architect Agent`	Specifies data masking rules and ensures PII protection in non-prod
`Backend Developer Agent`	Integrates seed data with application bootstrap and test fixtures

🔗 Cross-Blueprint Intersections¶

The Database Blueprint does not exist in isolation. It is deeply interconnected with other blueprints in the Factory ecosystem, creating a coherent, multi-dimensional specification for every generated component.

🛡️ Security Blueprint Intersection¶

Intersection Area	Security Blueprint Contribution	Database Blueprint Contribution
Encryption at Rest	Defines encryption requirements and key management	Implements TDE, column-level encryption
Row-Level Security	Defines tenant isolation requirements	Implements RLS policies and tenant context propagation
Data Masking	Specifies PII/PHI classification	Implements masking rules and non-prod data generation
Audit Logging	Requires audit trails for compliance	Implements temporal tables and change tracking
Secrets Management	Defines connection string storage in Key Vault	Consumes connection secrets at runtime

📦 Infrastructure Blueprint Intersection¶

Intersection Area	Infrastructure Blueprint Contribution	Database Blueprint Contribution
Database Provisioning	Provisions Azure Database for PostgreSQL, networking	Defines required SKU, configuration, extensions
Replication Setup	Configures read replicas, geo-replication infrastructure	Defines replication topology and lag thresholds
Backup Infrastructure	Provisions backup storage, configures retention policies	Defines RPO/RTO targets and recovery procedures
Networking	Configures VNet integration, private endpoints	Specifies connection requirements and firewall rules
Monitoring	Deploys monitoring agents and dashboards	Defines query metrics and performance alert thresholds

🧪 Test Blueprint Intersection¶

Intersection Area	Test Blueprint Contribution	Database Blueprint Contribution
Integration Tests	Defines test scenarios requiring database interaction	Provides schema definitions and seed data
Performance Tests	Defines load test scenarios and SLA targets	Provides performance budgets and baseline query plans
Migration Tests	Validates migration scripts execute correctly	Provides migration files and rollback scripts
Data Integrity Tests	Validates foreign key consistency and constraint enforcement	Defines constraints and relationship specifications
Test Data Management	Requires repeatable test data setup and teardown	Provides seeding scripts and data generation rules

📊 Observability Blueprint Intersection¶

Intersection Area	Observability Blueprint Contribution	Database Blueprint Contribution
Query Metrics	Collects and dashboards query performance telemetry	Defines expected performance baselines and budgets
Connection Pool Monitoring	Monitors connection usage, pool exhaustion	Defines pool sizing and connection management strategy
Replication Lag	Alerts on replication lag exceeding thresholds	Defines acceptable lag thresholds per replica
Backup Monitoring	Monitors backup success/failure, storage usage	Defines backup schedules and retention requirements
Schema Drift Detection	Detects when deployed schema differs from blueprint	Provides canonical schema definitions for comparison

🔁 Cross-Blueprint Flow¶

flowchart TD
    DB[Database Blueprint] --> SEC[Security Blueprint]
    DB --> INFRA[Infrastructure Blueprint]
    DB --> TEST[Test Blueprint]
    DB --> OBS[Observability Blueprint]
    DB --> SVC[Service Blueprint]

    SEC -->|"Encryption, RLS, Masking"| DB
    INFRA -->|"Provisioning, Replicas, Networking"| DB
    TEST -->|"Integration Tests, Seed Data"| DB
    OBS -->|"Query Metrics, Alerts"| DB
    SVC -->|"ORM Mappings, Repository Patterns"| DB

Hold "Alt" / "Option" to enable pan & zoom

🧭 Blueprint Location, Traceability, and Versioning¶

A Database Blueprint is not just content — it's a traceable artifact, part of a multi-agent lineage graph, and lives at a predictable location in the Factory's file and memory hierarchy.

This enables cross-agent validation, rollback, comparison, and regeneration.

📁 File System Location¶

Each blueprint is stored in a consistent location within the Factory workspace:

blueprints/database/{component-name}/database-blueprint.md
blueprints/database/{component-name}/database-blueprint.json
blueprints/database/{component-name}/migrations/
blueprints/database/{component-name}/schemas/
blueprints/database/{component-name}/indexes/

Markdown is human-readable and Studio-rendered.
JSON is parsed by orchestrators and enforcement agents.
Migrations folder contains version-controlled SQL scripts.
Schemas folder contains per-table YAML definitions.
Indexes folder contains index strategy YAML files.

🧠 Traceability Fields¶

Each blueprint includes a set of required metadata fields for trace alignment:

Field	Purpose
`traceId`	Links blueprint to full generation pipeline
`agentId`	Records which agent(s) emitted the artifact
`originPrompt`	Captures human-initiated signal or intent
`createdAt`	ISO timestamp for audit
`dataProfile`	Type of data architecture (relational, document)
`engine`	Database engine (postgresql, sqlserver, etc.)
`tenantStrategy`	Multi-tenant isolation pattern
`migrationVersion`	Current migration version

These fields ensure full trace and observability for regeneration, validation, and compliance review.

🔁 Versioning and Mutation Tracking¶

Mechanism	Purpose
`v1`, `v2`, ...	Manual or automatic version bumping by agents
`diff-link:` metadata	References upstream and downstream changes
GitOps snapshot tags	Bind blueprint versions to commit hashes or releases
Migration versioning	Each schema change is a numbered, traceable migration
Drift monitors	Alert if deployed schema deviates from blueprint

📜 Mutation History Example¶

metadata:
  traceId: "trc_82db_OrderService_v3"
  agentId: "db-engineer-001"
  originPrompt: "Add multi-currency support to order processing"
  createdAt: "2025-07-15T09:30:00Z"
  version: "v3"
  diffFrom: "v2"
  changedFields:
    - "tables.orders.columns.currency_code"
    - "tables.orders.columns.exchange_rate"
    - "indexes.ix_orders_currency"
    - "migrations.V3.2.0__add_currency_support"
  migrationVersion: "3.2.0"
  engine: "postgresql"
  tenantStrategy: "shared-schema-with-rls"

These mechanisms ensure that database design is not an afterthought, but a tracked, versioned, observable system artifact.

🚀 Database CI/CD Integration¶

The Database Blueprint defines how database changes flow through the CI/CD pipeline — from schema validation to migration deployment to performance regression testing.

🔄 Pipeline Architecture¶

flowchart LR
    subgraph CI["Continuous Integration"]
        A[PR Created] --> B[Schema Lint]
        B --> C[Migration Validation]
        C --> D[Seed Data Test]
        D --> E[Integration Tests]
        E --> F[Performance Regression]
        F --> G{All Gates Pass?}
    end

    subgraph CD["Continuous Deployment"]
        G -->|Yes| H[Deploy to Staging]
        H --> I[Run Migrations]
        I --> J[Smoke Tests]
        J --> K[Performance Validation]
        K --> L{Staging OK?}
        L -->|Yes| M[Deploy to Production]
        M --> N[Run Migrations]
        N --> O[Health Check]
        O --> P[Monitor & Alert]
    end

    G -->|No| Q[Block & Notify]
    L -->|No| R[Rollback Staging]

Hold "Alt" / "Option" to enable pan & zoom

📘 Pipeline Stage Definitions¶

databasePipeline:
  ci:
    stages:
      - name: schema-lint
        description: "Validate schema YAML against naming conventions and rules"
        tool: custom-schema-linter
        failOn: error
        config:
          rules: "blueprints/database/validation-rules.yaml"

      - name: migration-validation
        description: "Validate migration scripts are syntactically correct and idempotent"
        tool: flyway-validate
        failOn: error
        config:
          locations: "blueprints/database/{component}/migrations/"

      - name: migration-dry-run
        description: "Execute migrations against ephemeral test database"
        tool: flyway-migrate
        failOn: error
        config:
          target: ephemeral-postgres
          cleanOnStart: true
          validateOnMigrate: true

      - name: seed-data-test
        description: "Apply seed data and validate reference data integrity"
        tool: custom-seed-runner
        failOn: error

      - name: integration-tests
        description: "Run database integration tests"
        tool: dotnet-test
        failOn: error
        config:
          filter: "Category=DatabaseIntegration"
          database: ephemeral-postgres

      - name: performance-regression
        description: "Compare query execution plans against baselines"
        tool: custom-perf-gate
        failOn: warning
        config:
          baselines: "blueprints/database/{component}/performance-baselines.yaml"
          regressionThreshold: 200%

  cd:
    stages:
      - name: deploy-migrations
        description: "Apply pending migrations to target environment"
        tool: flyway-migrate
        config:
          outOfOrder: false
          validateOnMigrate: true
          baselineOnMigrate: false

      - name: verify-schema
        description: "Compare deployed schema against blueprint definition"
        tool: custom-schema-diff
        failOn: error

      - name: health-check
        description: "Validate database connectivity and basic query functionality"
        tool: custom-health-check
        config:
          timeout: 30s
          queries:
            - "SELECT 1"
            - "SELECT count(*) FROM reference_currencies"

🔁 Rollback Strategy¶

rollback:
  strategy: migration-rollback
  config:
    automaticRollback:
      onMigrationFailure: true
      onHealthCheckFailure: true
      onPerformanceRegression: false
    manualRollback:
      approvalRequired: true
      approvers: ["data-architect", "devops-lead"]
    rollbackScripts:
      required: true
      location: "blueprints/database/{component}/migrations/"
      namingPattern: "V{version}__rollback.sql"

🧪 Schema Validation Gate Example¶

schemaValidationGate:
  name: "Database Schema Validation"
  trigger: pull-request
  checks:
    - name: naming-conventions
      description: "All tables, columns, and indexes follow naming standards"
      severity: error
    - name: required-columns
      description: "All business tables have tenant_id, created_at, updated_at, is_deleted"
      severity: error
    - name: foreign-key-indexes
      description: "All foreign key columns have supporting indexes"
      severity: error
    - name: migration-reversibility
      description: "Every migration has a corresponding rollback script"
      severity: warning
    - name: zero-downtime-compliance
      description: "No prohibited DDL operations in migration"
      severity: error
    - name: performance-budget
      description: "New queries have documented performance budgets"
      severity: warning

🤖 Agent Collaboration¶

Agent	Role
`DevOps Engineer Agent`	Designs pipeline stages, configures deployment automation
`Database Engineer Agent`	Creates validation rules, defines performance baselines
`Test Automation Engineer Agent`	Implements integration tests and performance regression tests
`Backend Developer Agent`	Ensures application code is compatible with migration changes
`Infrastructure Engineer Agent`	Provisions ephemeral databases for CI and manages environment configurations

🧠 Memory Graph Representation¶

The Database Blueprint is embedded in the ConnectSoft Memory Graph — a semantic, vector-indexed knowledge graph that enables AI agents to reason about data architecture across the entire platform.

🔗 Memory Node Structure¶

memoryNode:
  type: database-blueprint
  id: "blueprint:database:order-service:v3"
  traceId: "trc_82db_OrderService_v3"
  attributes:
    engine: postgresql
    tenantStrategy: shared-schema-with-rls
    migrationVersion: "3.2.0"
    tables: ["orders", "customers", "order_items", "products"]
    indexCount: 12
    rlsEnabled: true
    backupRpo: 15
    backupRto: 60
  links:
    - type: belongs-to
      target: "service:order-service"
    - type: secured-by
      target: "blueprint:security:order-service:v2"
    - type: provisioned-by
      target: "blueprint:infrastructure:order-service:v4"
    - type: tested-by
      target: "blueprint:test:order-service:v5"
    - type: observed-by
      target: "blueprint:observability:order-service:v3"
    - type: depends-on
      target: "blueprint:database:customer-service:v2"
  embedding:
    model: text-embedding-ada-002
    dimensions: 1536
    indexed: true
    concepts:
      - postgresql
      - multi-tenant
      - row-level-security
      - migration
      - index-optimization
      - backup-recovery

🧩 Graph Relationships¶

graph TD
    DB_BP["Database Blueprint\n(order-service v3)"] --> SVC["Service Blueprint\n(order-service)"]
    DB_BP --> SEC_BP["Security Blueprint\n(order-service v2)"]
    DB_BP --> INFRA_BP["Infrastructure Blueprint\n(order-service v4)"]
    DB_BP --> TEST_BP["Test Blueprint\n(order-service v5)"]
    DB_BP --> OBS_BP["Observability Blueprint\n(order-service v3)"]
    DB_BP --> CUST_DB["Database Blueprint\n(customer-service v2)"]

    DB_BP --> MIG["Migration: V3.2.0"]
    DB_BP --> IDX["Index Strategy"]
    DB_BP --> RLS["RLS Policies"]
    DB_BP --> BACKUP["Backup Config"]

    MIG --> TRACE["Trace: trc_82db"]
    IDX --> TRACE
    RLS --> TRACE

Hold "Alt" / "Option" to enable pan & zoom

🧠 AI Agent Reasoning¶

The memory graph enables agents to:

🔍 Find related blueprints — "What security policies apply to the orders database?"
🔄 Detect drift — "Has the deployed schema diverged from the blueprint?"
🧪 Generate tests — "What integration tests are needed for the new migration?"
📊 Optimize performance — "Which indexes are unused across all services?"
🔗 Trace lineage — "What prompt created this table? Which agent last modified it?"

📊 Memory Query Examples¶

memoryQueries:
  - query: "Find all database blueprints using RLS"
    filter:
      type: database-blueprint
      attributes.rlsEnabled: true
    returns: [id, traceId, attributes.tables]

  - query: "Find blueprints with RPO > 30 minutes"
    filter:
      type: database-blueprint
      attributes.backupRpo: { $gt: 30 }
    returns: [id, attributes.engine, attributes.backupRpo]

  - query: "Find cross-service database dependencies"
    filter:
      type: database-blueprint
      links.type: depends-on
    returns: [id, links.target]

🏁 Final Summary¶

The Database Blueprint is a comprehensive, agent-generated artifact that serves as the data persistence contract of record for every component in the ConnectSoft AI Software Factory.

📊 Blueprint Coverage Summary¶

Dimension	Coverage
Schema Design	Tables, columns, types, constraints, naming conventions, normalization
Entity Relationships	Foreign keys, aggregate boundaries, reference data, ER diagrams
Migrations	Version-controlled, zero-downtime, rollback-safe, CI/CD integrated
Index Strategy	B-Tree, GIN, GiST, BRIN, partial, covering, composite, maintenance
Multi-Tenant Isolation	Shared-schema + RLS, schema-per-tenant, database-per-tenant, hybrid
Query Performance	Budgets, baselines, execution plans, regression detection
Backup & Recovery	RPO/RTO targets, continuous backup, PITR, geo-replication, DR testing
Data Seeding	Reference data, test data generation, data masking, faker integration
CI/CD Integration	Schema linting, migration validation, performance gates, rollback
Cross-Blueprint Links	Security, Infrastructure, Test, Observability, Service blueprints
Memory Graph	Vectorized, concept-indexed, trace-linked, agent-queryable

🤖 Agent Participation Summary¶

Agent	Primary Responsibilities
`Database Engineer Agent`	Schema design, migration generation, index optimization, query tuning
`Data Architect Agent`	Data modeling, entity relationships, bounded contexts, normalization
`Backend Developer Agent`	ORM configuration, data access layers, repository patterns
`Infrastructure Engineer Agent`	Database provisioning, replication, networking, backup infrastructure
`Security Architect Agent`	RLS policies, encryption, data masking, audit trails
`DevOps Engineer Agent`	Migration pipelines, CI/CD gates, monitoring, backup automation
`Test Automation Engineer Agent`	Integration tests, performance tests, seed data, DR drills

📁 Artifact Output Summary¶

Format	Purpose	Location
`database-blueprint.md`	Human-readable specification	`blueprints/database/{component}/`
`database-blueprint.json`	Machine-readable schema definition	`blueprints/database/{component}/`
`*.sql`	Migration scripts (up and rollback)	`blueprints/database/{component}/migrations/`
`*.yaml`	Schema and index definitions	`blueprints/database/{component}/schemas/`
Embedding	Vector-indexed memory node	Memory graph

🧠 Key Design Principles¶

Agent-First: Every schema, migration, and index is generated and validated by specialized agents
Trace-Linked: Every artifact carries a traceId linking it to the full generation pipeline
Zero-Downtime: All production migrations follow expand-contract or additive-only patterns
Multi-Tenant Native: Tenant isolation is not a feature — it's a systemic guarantee
Performance-Budgeted: Every query has an expected execution plan and regression threshold
Disaster-Ready: Backup and recovery are contractual SLA elements, not optional configurations
Memory-Integrated: The blueprint lives in the semantic memory graph, enabling AI reasoning

🗄️ The Database Blueprint transforms database design from a manual, error-prone activity into a deterministic, traceable, agent-orchestrated discipline — ensuring every service has a reliable, performant, and secure data foundation.

🗄️ Database Blueprint¶

📘 What Is a Database Blueprint?¶

🧠 Blueprint Roles in the Factory¶

🎯 Purpose and Motivation¶

🚨 Problems It Solves¶

🧠 Why Blueprints, Not Just Migrations?¶

🧑‍🤝‍🧑 Blueprint Consumers and Usage¶

🤖 Agent Consumers¶

👤 Human Stakeholders¶

🧠 Machine Consumers¶

🧠 Agent-Created, Trace-Ready Artifact¶

🤖 Agents Involved in Creation¶

📈 Memory Traceability¶

📁 Example Storage and Trace Metadata¶

📦 What It Captures¶

🗄️ Core Database Elements Captured¶

📎 Blueprint Snippet (Example)¶

🧠 Cross-Blueprint Intersections (Overview)¶

🗂️ Output Formats¶

📄 Human-Readable Markdown (.md)¶

📜 Machine-Readable JSON (.json)¶

📂 SQL Migration Files (.sql)¶

🔁 CI/CD Compatible YAML (.yaml)¶

🧠 Embedded Memory Shape (Vectorized)¶

📁 Storage Convention¶

📂 Directory Structure Example¶

🏗️ Schema Design Principles¶

📐 Naming Conventions¶

🔄 Normalization Strategy¶

🕐 Temporal Tables and Audit Trails¶

🗑️ Soft Delete Strategy¶

📘 Blueprint Schema Validation Rules¶

🤖 Agent Collaboration¶

🔄 Migration Lifecycle¶

📈 Migration Lifecycle Flow¶

🧩 Migration Types¶

📜 Migration Naming Convention¶

📎 Migration Script Example (Up)¶

📎 Migration Script Example (Down / Rollback)¶

🚀 Zero-Downtime Migration Patterns¶

📘 Zero-Downtime YAML Configuration¶

🤖 Agent Collaboration¶

📊 Index Strategy and Optimization¶

🧩 Index Types and Selection¶

📎 Index Blueprint Definition¶

📜 Generated SQL Examples¶

🔧 Index Maintenance Strategy¶

📊 Index Monitoring Queries¶

🤖 Agent Collaboration¶

🏢 Multi-Tenant Data Architecture¶

🧩 Isolation Pattern Comparison¶

🔐 Pattern 1: Shared Schema with Row-Level Security (RLS)¶

SQL Implementation¶

YAML Blueprint Declaration¶

Pros and Cons¶

🔐 Pattern 2: Schema-Per-Tenant¶

SQL Implementation¶

YAML Blueprint Declaration¶

Pros and Cons¶

🔐 Pattern 3: Database-Per-Tenant¶

YAML Blueprint Declaration¶

Pros and Cons¶

📊 Decision Matrix¶

🤖 Agent Collaboration¶

📈 Query Performance Management¶

🎯 Performance Budget Model¶

📊 Execution Plan Baselines¶

🔍 Query Performance Monitoring¶

📜 Performance Monitoring SQL¶

🔁 Regression Detection Pipeline¶

🤖 Agent Collaboration¶

💾 Backup, Recovery, and Disaster Recovery¶

🎯 RPO/RTO Definitions¶

📘 Backup Strategy Blueprint¶

🔄 Backup Architecture Flow¶

📜 Recovery Procedures¶

🧪 DR Testing Configuration¶

🤖 Agent Collaboration¶

🌱 Data Seeding and Test Data¶

🧩 Seed Data Categories¶

📄 Human-Readable Markdown (`.md`)¶

📜 Machine-Readable JSON (`.json`)¶

📂 SQL Migration Files (`.sql`)¶

🔁 CI/CD Compatible YAML (`.yaml`)¶