kailash-dataflow 2.3.2

Workflow-native database framework for Kailash SDK

Kailash DataFlow

Multi-Database Data Operations Framework — Django simplicity meets enterprise-grade production quality with PostgreSQL, MySQL, SQLite, and MongoDB support, plus external data source integration via the Data Fabric Engine.

✅ Database Support: DataFlow supports PostgreSQL (full features), MySQL (100% feature parity since v0.5.6), SQLite (near-complete parity), and MongoDB (document database with flexible schema).

🚨 2.0.0 Breaking Changes — Read Before Upgrading

DataFlow 2.0.0 is the result of a full Phase 5-9 wiring sweep that closed 9 CRITICAL security findings and ~11,800 lines of non-functional code. The fabric subsystem in particular has material breaking changes. Full details in CHANGELOG.md; the operational highlights:

• Fabric cache is now pluggable — PipelineExecutor delegates storage to FabricCacheBackend (InMemoryFabricCacheBackend or RedisFabricCacheBackend). Dev-mode deployments keep working unchanged; production Redis deployments MUST pass redis_url= when constructing the runtime.
• Fabric cache keys include tenant_id — every product with multi_tenant=True requires an explicit tenant extractor. Reads without a tenant now raise FabricTenantRequiredError instead of silently defaulting to a global cache slot. See docs/fabric/ for migration guidance.
• FabricRuntime.product_info / invalidate / invalidate_all are now async — wrap existing call sites in await or asyncio.run(...). Sync wrappers have been removed so the Redis backend can participate without deadlocking.
• Express cache keys are tenant-scoped — db.express.list("User", filter={...}) against a multi_tenant=True model now requires tenant_id context; missing tenant raises TenantRequiredError.
• @classify("field", PII, REDACT) actually redacts on read — the decorator was a no-op before 2.0.0. Every existing read of a classified field will now return [REDACTED] for callers whose clearance level doesn't include the field. Set per-request clearance via set_current_clearance(CONFIDENTIAL).
• Trust executor runs on every query — TrustAwareQueryExecutor, DataFlowAuditStore, and TenantTrustManager were exposed on db.* but unused until 2.0.0. Queries now emit audit events and enforce tenant boundaries. Disable per-model by setting enable_trust=False on DataFlow.__init__.
• rules/security.md §No secrets in logs — Redis/Postgres/Mongo URLs are now masked via dataflow.utils.masking.mask_url; downstream consumers importing fabric.cache._mask_url still work via a backward-compatible re-export.
• Prometheus metrics at /fabric/metrics — 13 metric families exposed via the FabricMetrics singleton. Requires the fabric optional extra for prometheus-client; without the extra the endpoint returns a plain-text explanation and counters become loud no-ops.

Migration checklist before upgrading:

• Audit every db.express read of a multi_tenant=True model for an explicit tenant_id context.
• Audit every FabricRuntime.invalidate(...) / .product_info(...) call site and wrap in await.
• Install the fabric extra if you scrape /fabric/metrics: pip install 'kailash-dataflow[fabric]'.
• Set a per-request clearance via set_current_clearance(...) before any query that should see PII fields.
• If you rely on the pre-2.0 silent-default behavior for missing tenants, revisit — that path is now a hard error.

See CHANGELOG.md § 2.0.0 for the full list, commit SHAs, and test coverage.

⚠️ Common Mistakes (Read This First!)

Mistake	Impact	Correct Approach
Using user_id or model_id instead of id	10-20 min debugging	CRITICAL: Primary key MUST be named id (not user_id, agent_id, etc.)
Applying CreateNode pattern to UpdateNode	1-2 hours debugging	CreateNode uses flat fields, UpdateNode uses {"filter": {...}, "fields": {...}}
Including created_at/updated_at in updates	Validation errors	DataFlow auto-manages these fields - NEVER include them manually
Wrong node naming	Node not found errors	Use ModelOperationNode pattern (e.g., UserCreateNode, not User_Create)
Missing db_instance parameter	Generic validation errors	ALL DataFlow nodes require db_instance and model_name parameters

Critical Rules

# ✅ CORRECT: Primary key MUST be named 'id'
@db.model
class User:
    id: str  # ✅ MUST use 'id' - not 'user_id', 'model_id', etc.
    name: str

# ❌ WRONG: Custom primary key names cause errors
@db.model
class User:
    user_id: str  # ❌ FAILS - DataFlow requires 'id'
    name: str

# ✅ CORRECT: Different patterns for Create vs Update
# CreateNode: Flat individual fields
workflow.add_node("UserCreateNode", "create", {
    "db_instance": "my_db",
    "model_name": "User",
    "id": "user_001",  # Individual fields at top level
    "name": "Alice",
    "email": "[e-mail]"
})

# UpdateNode: Nested filter + fields
workflow.add_node("UserUpdateNode", "update", {
    "db_instance": "my_db",
    "model_name": "User",
    "filter": {"id": "user_001"},  # Which records to update
    "fields": {"name": "Alice Updated"}  # What to change
    # ⚠️ Do NOT include created_at or updated_at - auto-managed!
})

🚀 Quick Start

Prerequisites

• PostgreSQL 12+ OR MySQL 5.7+ (recommended for production), SQLite 3.x (development/testing), OR MongoDB 4.0+ (document database)
• Python 3.12+

Installation

pip install kailash-dataflow

Optional Extras (Data Fabric Engine)

pip install kailash-dataflow[fabric]        # REST, file, core fabric support + Prometheus metrics (httpx, watchdog, msgpack, prometheus-client)
pip install kailash-dataflow[cloud]          # Cloud storage adapters (S3, GCS, Azure)
pip install kailash-dataflow[streaming]      # Streaming adapters (Kafka, WebSocket)
pip install kailash-dataflow[fabric-all]     # All fabric dependencies (fabric + cloud + excel + streaming)

The fabric extra is required if you want to scrape /fabric/metrics for Prometheus observability. Without it, the endpoint returns a plain-text explanation and fabric counters silently no-op.

Basic Usage

from dataflow import DataFlow

# PostgreSQL (production) or SQLite (development)
db = DataFlow("postgresql://user:pass@localhost/dbname")
# db = DataFlow("sqlite:///app.db")  # SQLite alternative

# Define your model
@db.model
class User:
    id: str  # String IDs now preserved! (v0.4.7+)
    name: str
    email: str

# DataFlow automatically creates:
# ✅ Database schema with migrations (PostgreSQL)
# ✅ 9 workflow nodes per model (CRUD + bulk ops)
# ✅ Real SQL operations with injection protection
# ✅ Connection pooling and transaction management
# ✅ MongoDB-style query builder
# ✅ Concurrent access protection with locking
# ✅ Schema state management with rollback

Connection Pool Configuration

DataFlow auto-detects safe pool sizes from your database's max_connections. No configuration needed for most deployments.

# Auto-scaling (recommended) — pool size computed from max_connections
db = DataFlow("postgresql://user:pass@localhost/mydb")

# Explicit override (PgBouncer, shared databases)
db = DataFlow("postgresql://...", pool_size=3)

# Check pool health at runtime
stats = db.pool_stats()
# {"active": 5, "idle": 12, "max": 17, "utilization": 0.19}

Environment Variables

Variable	Purpose	Default
DATAFLOW_POOL_SIZE	Override auto-scaled pool size	Auto-detect
DATAFLOW_WORKER_COUNT	Worker count for pool division	Auto-detect
DATAFLOW_STARTUP_VALIDATION	Validate pool config at startup	true

🎯 What Makes DataFlow Different?

Multi-Database Support

# Production PostgreSQL
db = DataFlow("postgresql://user:pass@localhost/dbname")

# Production MySQL (100% feature parity since v0.5.6)
db = DataFlow("mysql://user:pass@localhost/dbname")

# Development SQLite
db = DataFlow("sqlite:///app.db")

# Document Database MongoDB
db = DataFlow("mongodb://localhost:27017/dbname")

# Environment-based configuration
# DATABASE_URL=postgresql://... or mysql://... or sqlite:///... or mongodb://...
db = DataFlow()  # Reads from DATABASE_URL

# Advanced features (all SQL databases)
# Pool auto-scales from max_connections — no pool_size needed
db = DataFlow(
    "postgresql://...",  # or "mysql://..." or "sqlite:///..."
    auto_migrate=True,
    monitoring=True
)

Real Database Operations (Currently Available)

# Traditional ORMs: Imperative code
User.objects.create(name="Alice")  # Django
user = User(name="Alice"); session.add(user)  # SQLAlchemy

# DataFlow: Workflow-native database operations
workflow = WorkflowBuilder()
workflow.add_node("UserCreateNode", "create_user", {
    "name": "Alice",
    "email": "[e-mail]"
})
workflow.add_node("UserListNode", "find_users", {
    "limit": 10,
    "offset": 0
})

# Real SQL is executed: INSERT INTO users (name, email) VALUES ($1, $2)

MongoDB-Style Query Builder (NEW!)

# Get QueryBuilder from any model
builder = User.query_builder()

# MongoDB-style operators
builder.where("age", "$gte", 18)
builder.where("status", "$in", ["active", "premium"])
builder.where("email", "$regex", "^[a-z]+@company\.com$")
builder.order_by("created_at", "DESC")
builder.limit(10)

# Generates optimized SQL for your database
sql, params = builder.build_select()
# PostgreSQL: SELECT * FROM "users" WHERE "age" >= $1 AND "status" IN ($2, $3) AND "email" ~ $4 ORDER BY "created_at" DESC LIMIT 10

# Works seamlessly with ListNode
workflow.add_node("UserListNode", "search", {
    "filter": {
        "age": {"$gte": 18},
        "status": {"$in": ["active", "premium"]},
        "email": {"$regex": "^admin"}
    }
})

Database Support Status

# PostgreSQL: Full feature support
db = DataFlow(database_url="postgresql://user:pass@localhost/db")

# SQLite: Near-complete parity (missing only schema discovery)
db = DataFlow(database_url="sqlite:///app.db")

# Both support full workflow execution
runtime = LocalRuntime()
results, run_id = runtime.execute(workflow.build())  # ✅ Works with both databases

# Only limitation: Real schema discovery (PostgreSQL only)
schema = db.discover_schema(use_real_inspection=True)  # PostgreSQL only

Database Operations as Workflow Nodes

# Traditional ORMs: Imperative code
user = User.objects.create(name="Alice")  # Django
user = User(name="Alice"); session.add(user)  # SQLAlchemy

# DataFlow: Workflow-native (11 nodes per model!)
workflow = WorkflowBuilder()
workflow.add_node("UserCreateNode", "create_user", {
    "name": "Alice",
    "email": "[e-mail]"
})
workflow.add_node("UserListNode", "find_users", {
    "filter": {"name": {"$like": "A%"}}
})

Enterprise Configuration

# Multi-tenancy configuration (query modification planned)
db = DataFlow(multi_tenant=True)

# Real SQL generation with security
# Pool auto-scales from max_connections; override only for PgBouncer/shared DBs
db = DataFlow(
    database_url="postgresql://user:pass@localhost/db",
    monitoring=True,
    echo=False  # No SQL logging in production
)

# All generated nodes use parameterized queries for security
# INSERT INTO users (name, email) VALUES ($1, $2)  -- Safe from SQL injection

🔧 Context-Aware Improvements (v0.4.7+)

String ID Preservation

# String IDs are now preserved without forced integer conversion
@db.model
class Session:
    id: str  # Explicitly string - no more PostgreSQL type errors!
    user_id: str
    token: str

# String IDs work correctly in all operations
workflow.add_node("SessionCreateNode", "create", {
    "id": "sess-uuid-12345",  # Preserved as string
    "user_id": "user-uuid-67890",
    "token": "token-abc-def"
})

Multi-Instance Isolation

# Each DataFlow instance maintains separate context
dev_db = DataFlow("sqlite:///dev.db")
prod_db = DataFlow("postgresql://prod...")

# Models registered to specific instances
@dev_db.model
class User:
    name: str

@prod_db.model
class User:  # Same name, different instance - works!
    name: str
    email: str

# Nodes bound to correct instance automatically

Deferred Schema Operations

• Synchronous registration: Models register immediately with @db.model
• Async table creation: Tables created on first use, not registration
• Better performance: No blocking during model definition phase

🚦 Implementation Status

✅ Currently Available (Production-Ready)

• Database Schema Generation: Complete CREATE TABLE for PostgreSQL, MySQL, SQLite
• Auto-Migration System: PostgreSQL-only, production-ready automatic schema synchronization
• Real Database Operations: All 11 CRUD + bulk nodes execute actual SQL
• SQL Security: Parameterized queries prevent SQL injection
• Connection Management: Connection pooling, DDL execution, error handling
• Workflow Integration: Full compatibility with WorkflowBuilder/LocalRuntime
• Configuration System: Zero-config to enterprise patterns
• MongoDB-Style Query Builder: Complete with all operators ($eq, $gt, $in, $regex, etc.)
• Concurrent Access Protection: Migration locking and atomic operations
• Schema State Management: Change detection, caching, and rollback capabilities
• String ID Preservation: String/UUID IDs preserved without forced conversion (v0.4.7+)
• Multi-Instance Isolation: Separate contexts for different DataFlow instances (v0.4.7+)
• Deferred Schema Operations: Better performance with lazy table creation (v0.4.7+)
• Vector Similarity Search: PostgreSQL pgvector support for semantic search, RAG, and AI applications (v0.6.0+)
• MongoDB Document Database: Complete NoSQL support with flexible schema, aggregation pipelines, and 8 specialized workflow nodes (v0.6.0+)

⚠️ Current Limitations

• Schema Discovery: Real database introspection (discover_schema(use_real_inspection=True)) is currently supported for PostgreSQL and SQLite only
• Complex Migrations: Some SQLite migration operations limited by ALTER TABLE syntax
• Production Use: Thorough testing recommended for production deployments

🔄 Planned Features (Roadmap)

• Redis Query Caching: User.cached_query() with automatic invalidation
• Multi-Database Runtime: SQLite/MySQL execution support
• Advanced Multi-Tenancy: Automatic query modification for tenant isolation

Data Fabric Engine

DataFlow now supports external data sources and derived data products. The fabric engine extends DataFlow from database operations to unified data operations — connect any data source, define declarative products, and serve them with auto-generated endpoints.

Core API

Three new methods on the DataFlow instance:

from dataflow import DataFlow
from dataflow.fabric import RestSourceConfig, BearerAuth, StalenessPolicy

db = DataFlow("postgresql://user:pass@localhost/mydb")

# 1. Register an external data source
db.source("crm", RestSourceConfig(
    url="https://api.example.com",
    auth=BearerAuth(token_env="CRM_API_TOKEN"),
    poll_interval=60,
))

# 2. Define a data product (decorator)
@db.product("dashboard", depends_on=["User", "crm"])
async def dashboard(ctx):
    users = await ctx.express.list("User")
    deals = await ctx.source("crm").fetch("deals")
    return {"users": len(users), "deals": len(deals)}

# 3. Start the fabric runtime
await db.start(host="127.0.0.1", port=8000)

Product Modes

Mode	Behavior
materialized	Pre-computed and cached. Auto-refreshes on source changes. Best for dashboards and reports.
parameterized	Computed per-request with parameters. Cached by parameter combination with configurable TTL.
virtual	Computed on every request, never cached. Best for real-time or user-specific data.

@db.product("stats", mode="materialized", depends_on=["Order"])
async def stats(ctx):
    return await ctx.express.count("Order")

@db.product("search", mode="parameterized", depends_on=["Product"])
async def search(ctx, q: str = "", limit: int = 10):
    return await ctx.express.list("Product", {"name": {"$like": f"%{q}%"}}, limit=limit)

@db.product("live", mode="virtual", depends_on=["Sensor"])
async def live(ctx):
    return await ctx.express.list("Sensor", {"active": True})

Source Types

Source Type	Config Class	Description
REST	RestSourceConfig	HTTP APIs with ETag caching, auth, webhook support, SSRF protection
File	FileSourceConfig	Local files with filesystem watching (watchdog)
Cloud	CloudSourceConfig	S3, GCS, Azure Blob storage with prefix filtering
Database	DatabaseSourceConfig	External databases (read-only by default)
Stream	StreamSourceConfig	Kafka topics and WebSocket streams

db.start() Parameters

await db.start(
    fail_fast=True,            # Raise on source health check failure
    dev_mode=False,            # Skip pre-warming, use in-memory cache
    nexus=None,                # Attach to existing Nexus instance for auth
    coordination=None,         # "redis" or "postgresql" (auto-detects)
    host="127.0.0.1",         # Bind address for fabric endpoints
    port=8000,                 # Port for fabric endpoints
    enable_writes=False,       # Enable write pass-through endpoints
    tenant_extractor=None,     # Multi-tenant request handler
)

Observability

The fabric runtime exposes built-in observability:

• Health endpoints: Source health status with circuit breaker state
• Pipeline traces: Execution traces for each product refresh
• Prometheus metrics: Request counts, latencies, cache hit rates
• SSE (Server-Sent Events): Real-time product update notifications

Key Features

• Pipeline executor with change detection and configurable debounce
• Leader election for multi-worker coordination (Redis or in-memory)
• Circuit breaker per source with configurable staleness policies
• Webhook receiver with HMAC validation and nonce deduplication (Redis or in-memory)
• Auto-generated REST endpoints for all registered products
• Write pass-through with event-driven product refresh
• SSRF protection with DNS rebinding defense on REST sources

Migration Guide

All existing DataFlow code continues to work unchanged. Fabric features are opt-in — you only need to install the extras and use the new API methods (db.source(), @db.product(), await db.start()) when you want external data source integration. Existing models, workflows, and express operations are unaffected.

📚 Documentation

Getting Started

• 5-Minute Tutorial - Build your first app
• Core Concepts - Understand DataFlow
• Examples - Complete applications

Development

• Models - Define your schema
• CRUD Operations - Basic operations
• Relationships - Model associations

Production

• Deployment - Go to production
• Performance - Optimization guide
• Monitoring - Observability

💡 Real-World Examples

E-Commerce Platform

# Define your models
@db.model
class Product:
    id: int
    name: str
    price: float
    stock: int

@db.model
class Order:
    id: int
    user_id: int
    total: float
    status: str

# Use in workflows
workflow = WorkflowBuilder()

# Check inventory
workflow.add_node("ProductGetNode", "check_stock", {
    "id": "{product_id}"
})

# Create order with transaction
workflow.add_node("TransactionContextNode", "tx_start")
workflow.add_node("OrderCreateNode", "create_order", {
    "user_id": "{user_id}",
    "total": "{total}"
})
workflow.add_node("ProductUpdateNode", "update_stock", {
    "id": "{product_id}",
    "stock": "{new_stock}"
})

Multi-Tenant SaaS (Current Implementation)

# Enable multi-tenancy configuration
db = DataFlow(
    database_url="postgresql://user:pass@localhost/db",
    multi_tenant=True
)

# Multi-tenant models get tenant_id field automatically
@db.model
class User:
    name: str
    email: str
    # tenant_id: str automatically added

# Use in workflows with real database operations
workflow.add_node("UserCreateNode", "create_user", {
    "name": "Alice",
    "email": "[e-mail]"
})
workflow.add_node("UserListNode", "list_users", {
    "limit": 10,
    "filter": {}
})

High-Performance ETL (Current Implementation)

# Bulk operations with real database execution
workflow.add_node("UserBulkCreateNode", "import_users", {
    "data": users_data,  # List of user records
    "batch_size": 1000,
    "conflict_resolution": "skip"
})

# Real bulk INSERT operations executed
# Uses parameterized queries for security
# Processes data in configurable batches

# List operations with filters
workflow.add_node("UserListNode", "active_users", {
    "limit": 1000,
    "offset": 0,
    "order_by": ["created_at"],
    "filter": {"active": True}
})

RAG Application with Vector Search (v0.6.0+)

from dataflow import DataFlow
from dataflow.adapters import PostgreSQLVectorAdapter
from dataflow.nodes.vector_nodes import VectorSearchNode

# Initialize with pgvector support
adapter = PostgreSQLVectorAdapter(
    "postgresql://localhost/vectordb",
    vector_dimensions=1536,  # OpenAI embeddings
    default_distance="cosine"
)
db = DataFlow(adapter=adapter)

# Define knowledge base with vector embeddings
@db.model
class KnowledgeBase:
    id: str
    topic: str
    content: str
    embedding: list[float]  # Vector column

await db.initialize()

# Semantic search for RAG
query_embedding = await embedding_model.embed("How do I authenticate users?")

workflow = WorkflowBuilder()
workflow.add_node("VectorSearchNode", "search", {
    "table_name": "knowledge_base",
    "query_vector": query_embedding,
    "k": 5,  # Top 5 relevant documents
    "distance": "cosine"
})

results = await runtime.execute_workflow_async(workflow.build())
relevant_docs = results["search"]["results"]

# Use retrieved context for LLM generation
# See examples/pgvector_rag_example.py for complete RAG pipeline

MongoDB Document Database (v0.6.0+)

from dataflow import DataFlow
from dataflow.adapters import MongoDBAdapter
from dataflow.nodes.mongodb_nodes import DocumentInsertNode, AggregateNode

# Initialize MongoDB adapter (flexible schema, no models needed!)
adapter = MongoDBAdapter("mongodb://localhost:27017/ecommerce")
db = DataFlow(adapter=adapter)
await db.initialize()

# Direct document operations - no schema constraints
user_id = await adapter.insert_one("users", {
    "name": "Alice",
    "email": "[e-mail]",
    "profile": {
        "age": 30,
        "city": "NYC"
    },
    "tags": ["developer", "python"],
    # Any fields! Flexible schema
})

# MongoDB query language
users = await adapter.find(
    "users",
    filter={"age": {"$gte": 25}, "tags": {"$in": ["python"]}},
    sort=[("name", 1)],
    limit=10
)

# Aggregation pipelines for analytics
workflow = WorkflowBuilder()
workflow.add_node("AggregateNode", "sales_by_category", {
    "collection": "orders",
    "pipeline": [
        {"$match": {"status": "completed"}},
        {"$group": {
            "_id": "$category",
            "total_sales": {"$sum": "$amount"},
            "order_count": {"$sum": 1}
        }},
        {"$sort": {"total_sales": -1}},
        {"$limit": 10}
    ]
})

results = await runtime.execute_workflow_async(workflow.build())
# See examples/mongodb_crud_example.py for complete CRUD workflow

🏗️ Architecture

DataFlow seamlessly integrates with Kailash's workflow architecture:

┌─────────────────────────────────────────────────────┐
│                 Your Application                     │
├─────────────────────────────────────────────────────┤
│                    DataFlow                          │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐         │
│  │  Models  │  │   Nodes  │  │ Migrations│         │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘         │
│       └──────────────┴──────────────┘               │
│                Core Features                         │
│  QueryBuilder │ QueryCache │ Monitoring │ Multi-tenant │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐         │
│  │MongoDB-  │  │Redis     │  │Pattern   │         │
│  │style     │  │Caching   │  │Invalidate│         │
│  └──────────┘  └──────────┘  └──────────┘         │
├─────────────────────────────────────────────────────┤
│               Kailash SDK                           │
│         Workflows │ Nodes │ Runtime                 │
└─────────────────────────────────────────────────────┘

🧪 Testing

DataFlow includes comprehensive testing support:

# Test with in-memory database
def test_user_creation():
    db = DataFlow(testing=True)

    @db.model
    class User:
        id: int
        name: str

    # Automatic test isolation
    user = db.test_create(User, name="Test User")
    assert user.name == "Test User"

🤝 Contributing

We welcome contributions! DataFlow follows Kailash SDK patterns:

1. Use SDK components and patterns
2. Maintain zero-config philosophy
3. Write comprehensive tests
4. Update documentation

See CONTRIBUTING.md for details.

📊 Performance & Testing Status

Current Performance

• Real SQL execution with parameterized queries (PostgreSQL, SQLite)
• Real NoSQL execution with MongoDB query language
• Connection pooling with configurable pool sizes
• Bulk operations with batching for large datasets
• 11 nodes auto-generated per model (7 CRUD + 4 Bulk)
• 95% unit test pass rate (615/648 tests passing)

Recent Test Improvements

• 100% NO MOCKING compliance in Tier 2-3 tests
• Real infrastructure testing with PostgreSQL
• 167 test files covering all scenarios
• 3-tier testing strategy (Unit/Integration/E2E)
• Fixed critical bugs: checksum tracking, field type serialization

Testing Requirements

• PostgreSQL 12+ required for SQL integration testing
• MongoDB 4.0+ required for NoSQL integration testing
• Performance benchmarks available for PostgreSQL and MongoDB
• Advanced caching and query optimization features in development

⚡ Why DataFlow?

• Real Database Operations: Actual SQL execution, not mocks
• Workflow-Native: Database ops as first-class nodes
• Production-Ready: PostgreSQL support with connection pooling
• Progressive: Simple to start, enterprise features available
• 100% Kailash: Built on proven SDK components

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