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Why Cascade?Platform Capabilities

Platform Capabilities: Infrastructure You Don’t Build

Cascade provides a complete platform stack out-of-the-box. This page shows exactly what infrastructure you avoid building by using CDL instead of imperative code.

Key insight: When you define workflows in CDL, the platform provides durable execution, state management, retry logic, database access, and observability automatically. You write business logic. Platform handles infrastructure.


Overview: What Cascade Eliminates

When you write orchestration in CDL, you get these systems automatically:

Core Services:

  • Durable execution (Temporal orchestration engine)
  • State persistence (PostgreSQL with tenant isolation)
  • Event streaming (NATS JetStream)
  • Human task management (pause workflows for days or months)
  • Error handling (automatic retry with exponential backoff)
  • Policy engines (OPA and DMN decision support)

Infrastructure Services:

  • Multi-tenant isolation (automatic tenant_id injection)
  • Secret management (Vault integration)
  • Observability (OpenTelemetry traces and metrics)
  • Schema migrations (Atlas declarative migrations)
  • API gateway (unified REST and gRPC)

Result: 96-98% code reduction compared to equivalent imperative implementation.


Capability Comparison Matrix

CapabilityManual ImplementationCascade Platform
State machine1,000+ linesAutomatic (Temporal)
Retry logic40 lines per scenarioConfig (1 line)
Database access800-1,200 linesPlatform SDK
Schema management5,000-30,000 LOC/year50-100 lines HCL
Event system500+ lines (Kafka)NATS built-in
Webhook handling150 linesPlatform handles
Conditional logic50 lines8 lines (Choice)
Human tasks200 lines10 lines config
Tenant isolationManual (error-prone)Automatic (secure)
API gateway100+ endpoints10-20 domain-driven

Overall reduction: 11,340+ lines → 165-500 lines (96-98% less code)


Deep Dive: Core Capabilities

1. Durable State Management

What you avoid building: State machine infrastructure with crash recovery, distributed locking, event sourcing, and transaction management.

Imperative Approach: Build Your Own State Machine (~1,000 lines)

// Traditional Node.js orchestration class WorkflowStateMachine { constructor() { this.db = new PostgreSQL(); this.redis = new Redis(); this.eventStore = new EventStore(); this.kafka = new KafkaClient(); } async execute(orderId) { // Step 1: Persist state before any operation const workflowState = { orderId, workflowId: uuidv4(), currentStep: "waiting_for_approval", data: {}, version: 1, timestamp: Date.now(), status: "RUNNING" }; await this.db.saveWorkflowState(workflowState); await this.redis.set(`workflow:${workflowState.workflowId}`, JSON.stringify(workflowState)); // Step 2: Create approval task const taskId = await this.db.createTask({ orderId, assignedTo: "manager", timeout: Date.now() + (24 * 60 * 60 * 1000), // 24 hours type: "APPROVAL_REQUIRED" }); // ⚠️ PROBLEM: How do you wait for hours/days/weeks? // Option A: Polling (BAD - wastes resources) while (true) { const task = await this.db.getTask(taskId); if (task.status === "completed") { break; } await sleep(60000); // Check every minute // ❌ Server must stay running continuously // ❌ Database hit every minute // ❌ Doesn't scale } // Option B: Event queue (COMPLEX - 200+ lines) await this.kafka.publish("task.created", { orderId, taskId, workflowId: workflowState.workflowId }); // ⚠️ Separate consumer process needed (different codebase!) // ⚠️ Must reconstruct exact execution context // ⚠️ Need distributed locking to prevent concurrent updates // ⚠️ Complex error scenarios (message lost, duplicate delivery) // Option C: Database triggers (LIMITED - vendor lock-in) // ❌ Can't handle complex business logic // ❌ Hard to test and debug // ❌ PostgreSQL vs MySQL differences // You need to manually implement: // 1. State serialization/deserialization (100+ lines) // - JSON encoding, versioning, schema evolution // 2. Event sourcing (200+ lines) // - Event log, event replay, snapshots // 3. Crash recovery (150+ lines) // - Detect crashed workflows, resume from last checkpoint // 4. Distributed locking (80+ lines) // - Redis locks, deadlock detection, lease renewal // 5. Transaction management (120+ lines) // - ACID guarantees, rollback, compensation // 6. Timeout handling (60+ lines) // - TTL tracking, timeout callbacks, escalation // 7. Retry mechanisms (80+ lines) // - Exponential backoff, error classification // 8. Webhook correlation (200+ lines) // - UUID mapping, signature validation // // = ~990 lines of infrastructure code } // Separate webhook handler (DIFFERENT PROCESS/CODEBASE!) async handleTaskComplete(taskId, decision) { // ⚠️ How do we resume from the EXACT point? // 1. Load workflow state from database const state = await this.db.getWorkflowState({ taskId }); if (!state) { throw new Error("Workflow state not found - data loss!"); } // 2. Reconstruct execution context // ⚠️ Need to rebuild variables, local state, call stack const context = { orderId: state.orderId, currentStep: state.currentStep, data: state.data }; // 3. Handle concurrent updates // ⚠️ Need optimistic locking (version checking) const lock = await this.redis.acquireLock(`workflow:${state.workflowId}`, 30000); if (!lock) { throw new Error("Failed to acquire lock"); } try { // 4. Continue execution if (state.currentStep === "waiting_for_approval") { if (decision === "APPROVED") { await this.processOrder(context); } else { await this.rejectOrder(context); } } // 5. Update state await this.db.updateWorkflowState(state.workflowId, { currentStep: "completed", status: "SUCCESS", completedAt: Date.now() }); } finally { await this.redis.releaseLock(lock); } // Total for webhook handler: ~200 lines } } // Total infrastructure code: ~1,190 lines // And this doesn't even handle: // - Process crashes during execution // - Deployment rollouts (loses in-memory state) // - Database connection failures // - Redis unavailability // - Kafka rebalancing

Production Reality: Most teams give up on durable execution and just use polling loops or database triggers, losing the ability to pause workflows for days/weeks.


Cascade Approach: Zero Infrastructure Code

workflows: - name: process-order start: WaitForApproval states: - name: WaitForApproval type: Task resource: "urn:cascade:waitFor:human" parameters: schema: type: object properties: decision: {type: string, enum: ["APPROVED", "REJECTED"]} notes: {type: string} assignment: role: "Manager" # Automatically routes to managers in tenant timeout: "7d" # ← Workflow pauses for 7 DAYS! result_path: "$.approval" next: CheckDecision - name: CheckDecision type: Choice choices: - variable: "$.approval.decision" string_equals: "APPROVED" next: ProcessPayment default: NotifyRejection - name: ProcessPayment type: Task resource: "urn:cascade:action:process-payment" next: Complete

What happens automatically:

  1. State Persistence (Temporal + PostgreSQL):

    • Every state transition persisted atomically
    • Event sourcing with full audit trail
    • Workflow survives process crashes
    • Workflow survives Kubernetes pod restarts
    • Workflow survives deployments
  2. Pause & Resume:

    • Workflow pauses at WaitForApproval
    • State saved to database (0 memory consumption)
    • Manager can submit approval hours/days/weeks later
    • Workflow resumes from exact point
    • No polling, no wasted resources
  3. Distributed Locking:

    • Temporal ensures only one execution per workflow instance
    • No concurrent updates possible
    • No deadlocks
  4. Timeout Handling:

    • After 7 days, timeout event fires automatically
    • Can route to escalation flow
    • Declarative configuration
  5. Error Recovery:

    • Platform retries transient failures
    • Permanent failures trigger compensation
    • Full observability via OpenTelemetry

Platform provides automatically:

  • Durable execution (Temporal engine)
  • Event sourcing (PostgreSQL with event log)
  • Automatic recovery after crashes
  • Resume from exact point when event arrives
  • No polling (event-driven)
  • Distributed locking (Temporal handles)
  • Transaction management (ACID guarantees)
  • Timeout handling (declarative)
  • OpenTelemetry tracing (automatic)
  • Audit trail (compliance-ready)

Infrastructure code required: 0 lines

Performance: Sub-1ms orchestration overhead


Visual Comparison


2. Retry & Resilience

What you avoid building: Exponential backoff logic, error classification, retry attempt tracking, circuit breakers, and fallback strategies.

Imperative approach (40+ lines per scenario):

async function callPaymentAPI(payload) { let attempt = 0; const maxAttempts = 3; let delay = 1000; while (attempt < maxAttempts) { try { return await stripe.charge(payload); } catch (error) { attempt++; // Classify error manually const isTransient = error.code === "NetworkError" || error.code === "ServiceUnavailable"; if (!isTransient || attempt >= maxAttempts) { throw error; } // Exponential backoff with jitter await sleep(delay); delay = Math.min(delay * 2, 10000); delay = delay * (1 + Math.random() * 0.2); } } }

Cascade approach (12 lines):

- name: ChargePayment type: Task resource: "urn:cascade:action:stripe.charge" parameters: amount.$: "$.order.total" customer.$: "$.customer.id" retry: - error_equals: ["NetworkError", "ServiceUnavailable", "TimeoutError"] max_attempts: 3 interval_seconds: 1 backoff_rate: 2.0 max_interval_seconds: 10 jitter_strategy: "FULL" catch: - error_equals: ["CardDeclined", "ValidationError"] result_path: "$.payment_error" next: NotifyPaymentFailed result_path: "$.payment" next: ReserveInventory

Platform handles automatically:

  • Exponential backoff (with jitter)
  • Error classification (permanent vs transient)
  • Retry attempt tracking
  • Error logging and tracing
  • Metrics (retry count, success rate)
  • Circuit breaker (optional)

Code reduction: 70% fewer lines


3. Database Operations & Tenant Isolation

What you avoid building: Manual query construction, connection pooling, tenant isolation checks, error handling, and query logging.

Imperative Approach: Manual Everything (800-1,200 lines per app)

// Traditional Node.js with PostgreSQL class InventoryService { constructor() { // Manual connection pool setup (~300 lines) this.pool = new PgPool({ host: process.env.DB_HOST, database: process.env.DB_NAME, max: 20, idleTimeoutMillis: 30000, connectionTimeoutMillis: 2000, }); } async checkStock(productIds, warehouseId, tenantId) { // ⚠️ SECURITY RISK: Easy to forget tenant_id const query = ` SELECT product_id, available_quantity, reserved_quantity FROM inventory WHERE product_id = ANY($1) AND warehouse_id = $2 AND tenant_id = $3 -- ← MUST NEVER FORGET THIS! `; let client; try { // Manual connection management client = await this.pool.connect(); const result = await client.query(query, [ productIds, warehouseId, tenantId // ← Must pass everywhere, error-prone ]); return result.rows; } catch (error) { // Manual error handling, retry logic, logging... if (error.code === 'CONNECTION_LOST') { await sleep(1000); return this.checkStock(productIds, warehouseId, tenantId); } if (error.code === 'DEADLOCK') { throw new RetryableError("Database deadlock"); } // Log error manually logger.error('Query failed', { error, query, params }); throw error; } finally { // Manual connection cleanup if (client) client.release(); } } async reserveInventory(productId, quantity, warehouseId, tenantId) { // Another 80+ lines for transaction management... const client = await this.pool.connect(); try { await client.query('BEGIN'); // Lock row await client.query(` SELECT available_quantity FROM inventory WHERE product_id = $1 AND warehouse_id = $2 AND tenant_id = $3 FOR UPDATE `, [productId, warehouseId, tenantId]); // Update quantity await client.query(` UPDATE inventory SET reserved_quantity = reserved_quantity + $1 WHERE product_id = $2 AND warehouse_id = $3 AND tenant_id = $4 `, [quantity, productId, warehouseId, tenantId]); await client.query('COMMIT'); } catch (error) { await client.query('ROLLBACK'); throw error; } finally { client.release(); } } } // Every service needs: // - Connection pooling (~300 lines) // - Error classification (~200 lines) // - Retry logic (~150 lines) // - Logging integration (~100 lines) // - Transaction management (~200 lines) // - Deadlock recovery (~80 lines) // = 1,030+ lines BEFORE business logic

Security vulnerability: Forgetting tenant_id in ONE query exposes all customer data. This has caused major data breaches in production systems.


Cascade Approach 1: Pure CDL (Zero Custom Code) ⭐

For simple queries, use declarative query definitions:

# service.yaml - Define queries declaratively spec: components: queries: # Read-only query (type-safe) - name: check-inventory type: sql operation: select source: | SELECT product_id, available_quantity, reserved_quantity FROM inventory WHERE product_id = ANY(:productIds) AND warehouse_id = :warehouseId -- tenant_id is AUTOMATIC (platform injects) parameters: productIds: type: array items: { type: uuid } required: true warehouseId: type: uuid required: true returns: type: array items: type: object properties: product_id: { type: uuid } available_quantity: { type: integer } reserved_quantity: { type: integer }

Use in workflow (no custom code needed):

workflows: - name: process-order states: - name: CheckInventory type: Task resource: "urn:cascade:query:check-inventory" parameters: productIds.$: "$.order.items[*].product_id" warehouseId.$: "$.order.warehouse_id" result_path: "$.inventory" next: ValidateStock - name: ValidateStock type: Choice choices: - variable: "$.inventory[?(@.available_quantity < @.reserved_quantity)]" is_present: true next: OutOfStock default: ReserveInventory

What happens automatically:

  • ✓ Tenant isolation (platform injects tenant_id)
  • ✓ Connection pooling (20-100 connections)
  • ✓ Query validation (compile-time checks)
  • ✓ Type safety (parameters validated)
  • ✓ Error categorization (retryable vs permanent)
  • ✓ OpenTelemetry tracing (distributed tracing)
  • ✓ Query logging (structured logs)
  • ✓ Performance metrics (automatic)

Code required: 0 lines of custom code


Cascade Approach 2: SDK with Custom Logic (Escape Hatch)

When you need custom logic, use the Platform SDK:

# Workflow calls custom action - name: CheckInventory type: Task resource: "urn:cascade:action:check-inventory-with-logic" parameters: product_ids.$: "$.order.items[*].product_id" warehouse.$: "$.order.warehouse" result_path: "$.inventory" next: ValidateStock

Custom action (Go with Platform SDK):

package actions import ( "context" cascade "github.com/cascade-platform/sdk-go" ) func CheckInventoryWithLogic(ctx context.Context, input Input) (*Output, error) { sdk := cascade.FromContext(ctx) // ✅ Tenant isolation is AUTOMATIC rows, err := sdk.DatabaseQuery(ctx, ` SELECT product_id, available_quantity, reserved_quantity, warehouse_id FROM inventory WHERE product_id = ANY($1) AND warehouse_id = $2 -- tenant_id is AUTOMATIC (platform injects) `, map[string]interface{}{ "product_ids": input.ProductIDs, "warehouse": input.Warehouse, // NO need to pass tenant_id - SDK adds it automatically }) if err != nil { return nil, err // Platform handles retry/logging } // Custom business logic available := make([]Item, 0) for _, row := range rows { if row["available_quantity"].(int) > row["reserved_quantity"].(int) { available = append(available, Item{ ProductID: row["product_id"].(string), Available: row["available_quantity"].(int), Reserved: row["reserved_quantity"].(int), }) } } return &Output{ Items: available, AllAvailable: len(available) == len(input.ProductIDs), }, nil }

Platform SDK provides automatically:

  • Automatic tenant_id injection (impossible to forget)
  • Row-level security (RLS) enforcement
  • Connection pooling (20-100 connections)
  • Query logging (structured logs)
  • OpenTelemetry tracing (automatic)
  • Error categorization (retryable vs permanent)
  • Query performance metrics
  • Connection lifecycle management

Code reduction: 81% fewer lines (1,030 → 35 lines) plus security by default


Cascade Approach 3: WASM Runtime (Maximum Performance) 🚀

For ultra-fast execution, compile to WASM:

# Workflow uses WASM action - name: CheckInventory type: Task resource: "urn:cascade:action:check-inventory-wasm" runtime: wasm # ← Compiled to WebAssembly parameters: product_ids.$: "$.order.items[*].product_id" warehouse.$: "$.order.warehouse" result_path: "$.inventory"

WASM action (Rust compiled to WASM):

// actions/check_inventory.rs use cascade_wasm_sdk::*; #[cascade_action] pub fn check_inventory(input: Input) -> Result<Output, Error> { // Access platform capabilities via WASM host functions let rows = database_query( "SELECT product_id, available_quantity, reserved_quantity FROM inventory WHERE product_id = ANY($1) AND warehouse_id = $2", &[&input.product_ids, &input.warehouse] )?; // Custom logic (runs in WASM sandbox) let available: Vec<Item> = rows.iter() .filter(|r| r.available > r.reserved) .map(|r| Item { product_id: r.product_id.clone(), available: r.available, reserved: r.reserved, }) .collect(); Ok(Output { items: available, all_available: available.len() == input.product_ids.len(), }) }

Build and deploy:

# Compile Rust to WASM cargo build --target wasm32-wasi --release # Platform automatically loads and executes # Cold start: <1ms # Hot path: <0.1ms

WASM Benefits:

  • Sub-1ms cold start (vs 200ms for containers)
  • Near-native performance (no JIT warmup)
  • Memory isolation (sandboxed execution)
  • 1000+ concurrent instances per node
  • No Docker overhead (runs in-process)

Performance Comparison

ApproachCold StartHot PathMemorySecurityUse Case
Pure CDL0ms (config)Sub-0.1ms0 MBMaximumSimple queries, 80% of cases
SDK (Go)100-200ms1-5ms10-50 MBHighCustom logic needed
WASM (Rust)Sub-1msSub-0.1ms1-5 MBMaximumPerformance-critical paths
Container3-10s5-50ms100-500 MBMediumLegacy code, complex deps

Visual Comparison

See comprehensive database operations guide →


4. Conditional Logic & Decision Making

Decision priority for conditional logic:

  1. CDL Choice States (1-3 conditions) - Default choice, sub-0.1ms
  2. OPA Policies (5-20 rules) - Complex logic, versioned
  3. DMN Tables (10-100+ rules) - Business analyst authoring

CDL Choice States (Simple Conditions)

For 1-3 simple conditions, use CDL Choice states (fastest):

- name: RouteByAmount type: Choice choices: - variable: "$.expense.amount" numeric_less_than: 500 next: AutoApprove - variable: "$.expense.amount" numeric_less_than: 5000 next: ManagerApproval - variable: "$.expense.amount" numeric_less_than: 20000 next: DirectorApproval default: CFOApproval

Performance: Sub-0.1ms (in-process evaluation)

Supported operators:

  • Numeric comparisons (Equals, LessThan, GreaterThan, etc.)
  • String comparisons
  • Boolean equals
  • Timestamp comparisons
  • Logical operators (And, Or, Not)
  • Type checks (IsPresent, IsNull, IsNumeric, etc.)

OPA Policy (Complex Rules)

For 5-20 rules with complex logic:

package cascade.expense default approver = "manager" default requires_approval = true # Executive privilege approver = "auto" { input.employee.level == "executive" } # Amount-based routing approver = "auto" { input.amount < 500 } approver = "manager" { input.amount >= 500; input.amount < 5000 } approver = "director" { input.amount >= 5000; input.amount < 20000 } approver = "cfo" { input.amount >= 20000 } # Category overrides approver = "cfo" { input.category == "travel" input.amount > 10000 }

Performance: 1-5ms (Redis cached)

When to use:

  • Complex nested conditions
  • Business rules requiring versioning
  • Rules that change frequently
  • Cross-cutting concerns

DMN Table (Business Analyst Authoring)

For 10-100+ rules in decision table format:

- name: EvaluateExpenseRules type: Task resource: "urn:cascade:dmn:expense-routing-rules" parameters: expense_amount.$: "$.expense.amount" employee_level.$: "$.employee.level" category.$: "$.expense.category" result_path: "$.routing_decision"

Decision table (visual editor):

AmountEmployee LevelCategory→ ApproverRequires Approval
< 500ANYANYautofalse
< 5000executiveANYautofalse
< 5000ANYANYmanagertrue
< 20000ANYtravelcfotrue
< 20000ANYANYdirectortrue
>= 20000ANYANYcfotrue

When to use:

  • 10-100+ rules
  • Business analysts need to edit
  • Visual decision table format
  • Regulatory compliance requirements

5. Event System (NATS Built-in)

What you avoid building: Kafka cluster management, consumer groups, schema registry, DLQ handling, and monitoring infrastructure.

Imperative approach (500+ lines):

  • Kafka cluster setup and configuration
  • Topic management
  • Consumer group coordination
  • Schema registry
  • Error handling and dead letter queues
  • Monitoring (lag, throughput)

Cascade approach (20 lines):

Publish event:

- name: PublishOrderCreated type: Task resource: "urn:cascade:event:publish" parameters: type: "com.acme.order.created" source: "urn:cascade:workflow:order-processing" data: order_id.$: "$.order.id" customer_id.$: "$.order.customer_id" total.$: "$.order.total" next: Complete

Declarative routing (no consumer code):

apiVersion: cascade.io/v1 kind: EventRouter metadata: name: order-events spec: routing_rules: - name: route-high-value pattern: "com.acme.order.created" filters: - "$.data.total > 1000" actions: - start_process: "fraud-detection" parameters: order_id: "$.data.order_id" - name: route-inventory pattern: "com.acme.order.*" actions: - signal_process: "inventory-management" signal_name: "order_placed"

NATS provides automatically:

  • Pub/sub messaging (no Kafka setup)
  • CloudEvents v1.0 compliance
  • JSONPath filtering (declarative)
  • Dead letter queue (automatic)
  • At-least-once delivery
  • Distributed tracing (OpenTelemetry)
  • Hot-reload routing rules (no restart)

Code reduction: 95% fewer lines, no Kafka cluster


6. Webhook Integration

What you avoid building: Webhook URL generation, correlation tracking, state persistence, timeout handling, and signature validation.

Imperative approach challenges:

  • Separate code paths (initial call and webhook handler)
  • Manual state management and correlation
  • Timeout handling missing
  • Orchestration logic duplicated

Cascade approach:

- name: InitiatePayment type: Task resource: "urn:cascade:action:stripe.charge" parameters: amount.$: "$.order.total" result_path: "$.payment" next: WaitForCallback - name: WaitForCallback type: Task resource: "urn:cascade:waitFor:webhook" parameters: schema: type: object properties: transaction_id: {type: string} status: {type: string, enum: ["success", "failure"]} timeout: "30m" result_path: "$.callback" catch: - error_equals: ["TimeoutError"] next: RetryPayment next: CheckStatus - name: CheckStatus type: Choice choices: - variable: "$.callback.status" string_equals: "success" next: ReserveInventory default: RefundPayment

Platform provides:

  • Webhook URL generation (automatic)
  • Correlation (workflow instance mapping)
  • State persistence (Temporal)
  • Resume from exact point
  • Timeout handling (declarative)
  • Signature validation (configurable)
  • Retry on timeout

Code reduction: 90% fewer lines


7. Schema Management (Atlas Declarative Migrations)

What you avoid building: Manual SQL migrations with rollback procedures, schema drift detection, and breaking change management.

Imperative approach (5,000-30,000 LOC annually):

-- Migration 001_create_customers.sql (100 lines) CREATE TABLE app.customers ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), email VARCHAR(255) NOT NULL UNIQUE, ... ); -- Migration 002_create_orders.sql (80 lines) -- Migration 003_add_status_column.sql (50 lines) -- ... 47 more migrations for a 50-table system

Challenges:

  • 50-100 migrations per year for medium company
  • 100-300 LOC per migration
  • Manual testing required
  • Rollback procedures complex
  • Schema drift detection manual

Cascade approach (50-100 lines total):

schema "app" {} table "customers" { schema = schema.app column "id" { type = "uuid" default = sql("gen_random_uuid()") } column "email" { type = "varchar(255)" null = false } column "status" { type = "varchar(50)" default = "active" } primary_key { columns = [column.id] } unique "unique_email" { columns = [column.email] } } table "orders" { schema = schema.app column "id" { type = "uuid" default = sql("gen_random_uuid()") } column "customer_id" { type = "uuid" null = false } foreign_key "fk_customer" { columns = [column.customer_id] ref_columns = [table.customers.column.id] on_delete = "RESTRICT" } }

Platform provides automatically:

  • Automatic migration generation (SQL created for you)
  • Breaking change detection (prevents data loss)
  • Schema drift detection (continuous validation)
  • Zero-downtime migrations
  • Query validation at build time
  • Type generation for all queries
  • Version tracking and rollback
  • CI/CD integration

Code reduction: 99% (5,000-30,000 LOC annually → 50-100 lines HCL)

See comprehensive schema management guide →


8. REST API & gRPC: Unified Gateway Architecture

Problem with traditional approach: REST frameworks auto-generate every table as an endpoint, resulting in 100+ endpoints that are hard to document, secure, and evolve.

Cascade approach: Unified API Gateway with flexible endpoint options—auto-generated CRUD for simple cases, domain-driven custom endpoints for complex workflows.

Gateway handles automatically:

  • JWT validation (via Ory Hydra)
  • Rate limiting (Redis plus Token Bucket)
  • Authorization enforcement
  • Tenant extraction from JWT claims
  • Idempotency checks
  • Structured error responses
  • Request/response logging
  • OpenTelemetry tracing

Option 1: Auto-Generated CRUD & Search (Simple Resources)

For standard resource operations, declare in dspec:

# resources/orders.dspec.yaml spec: resources: - name: Order description: Customer order type: aggregate entity: orders fields: - name: id type: uuid description: Order ID - name: customer_id type: uuid required: true - name: status type: string enum: ["PENDING", "PROCESSING", "COMPLETED", "CANCELLED"] default: "PENDING" - name: total type: decimal minimum: 0 - name: created_at type: timestamp readonly: true # Auto-generate CRUD endpoints endpoints: create: true read: true update: true delete: true # Auto-generate search/filtering search: - field: status type: exact - field: customer_id type: exact - field: created_at type: range - field: total type: range # Auto-generate pagination pagination: default_limit: 50 max_limit: 500

Cascade auto-generates these endpoints:

GET /orders # List with search & pagination POST /orders # Create GET /orders/{id} # Read PUT /orders/{id} # Update DELETE /orders/{id} # Delete

Automatically handled:

  • Input validation (from dspec schema)
  • Row-level security (tenant isolation)
  • Optimistic concurrency (versioning)
  • Audit logging (all changes tracked)
  • OpenTelemetry tracing

Result: 0 lines of API code for standard CRUD resources


Option 2: Refine & Customize Auto-Generated Endpoints

When you need custom logic, use the refine mechanism:

# resources/orders.dspec.yaml spec: resources: - name: Order entity: orders endpoints: create: enabled: true refine: "urn:cascade:action:validate-order-creation" # ← Custom logic read: true # Standard CRUD update: enabled: true refine: "urn:cascade:action:validate-order-update" allowed_fields: ["status", "notes"] # Only these can be updated delete: enabled: false # Not allowed # Custom endpoints beyond CRUD custom_endpoints: - name: "cancel-order" method: POST path: "/orders/{id}/cancel" action: "urn:cascade:action:cancel-order" description: "Cancel an order (only if status is PENDING or PROCESSING)" - name: "ship-order" method: POST path: "/orders/{id}/ship" action: "urn:cascade:action:ship-order" description: "Mark order as shipped"

Custom refine action (Go with Platform SDK):

package actions func ValidateOrderCreation(ctx context.Context, order Order) (*Order, error) { sdk := cascade.FromContext(ctx) // Custom validation: check inventory inventory, err := sdk.DatabaseQuery(ctx, ` SELECT available_quantity FROM inventory WHERE product_id = ANY($1) `, map[string]interface{}{"product_ids": order.Items}) if err != nil { return nil, err } // Custom logic: verify sufficient inventory for _, item := range order.Items { if item.Quantity > inventory[item.ProductID] { return nil, &ValidationError{ Field: "items", Message: "Insufficient inventory", } } } return &order, nil }

Result: Auto-generated CRUD + custom business logic where needed


Option 3: MVP Domain-Driven Endpoints (Complex Workflows)

For complex processes requiring orchestration:

# REST API: Domain-driven endpoints only spec: api_endpoints: - name: GetAssignedTasks method: GET path: /tasks description: List tasks assigned to current user - name: CompleteUserTask method: POST path: /tasks/{id}/complete description: Submit human task completion parameters: - name: id type: uuid required: true - name: body type: object schema: type: object properties: decision: {type: string} notes: {type: string} - name: QueryProcessInstance method: GET path: /processes/{id} description: Get process execution state and history

When to use each approach:

Use CaseApproachReason
Standard CRUD (80% of APIs)Auto-generatedNo code needed, instant
CRUD + validation (15%)Refine mechanismCustom logic, auto security
Complex workflows (5%)Domain-drivenOrchestration required

Rate limiting (applied automatically):

anonymous: limit: 100 requests / 15 min burst: 20 authenticated_user: limit: 1,000 requests / 15 min burst: 100 service_account: limit: 50,000 requests / 15 min burst: 2,000

gRPC for internal communication:

  • Service-to-service communication only
  • Not exposed to external clients
  • High throughput (1000+ req/s)
  • Protocol efficiency critical

See API Gateway design →


The Hidden Cost: Complexity Growth Over Time

Research-Backed Reality

Large-scale production orchestration systems don’t stay manageable. Real-world data shows exponential growth in imperative codebases:

Production system complexity:

  • Windows 10: ~50M lines of code
  • Google Chrome: ~6.7M lines of code
  • Linux Kernel: ~27.8M lines of code
  • Typical Kubernetes Operator: 5,000-15,000 lines
  • Enterprise BPM Implementation: 30,000-150,000 lines

When you build orchestration with imperative code, your codebase follows an exponential growth curve. Each new feature, edge case, and failure mode requires new code that creates new interdependencies spawning more code.

Cascade separates business logic from infrastructure. Complexity grows linearly (or stays flat) while the platform absorbs the exponential burden.


The Exponential Growth Crisis

Research on microservices and complex systems shows imperative codebases follow a 2.5-3x multiplier per year, while declarative systems stay linear.

The 390x multiplier: By Year 5, imperative code is 390x larger than Cascade’s declarative approach (390,000 vs 1,800 lines).


Year-by-Year Breakdown

YearImperative LOCCascade LOCKey Milestone
110,0001,000MVP viable, both productive
225,000 (+150%)1,200 (+20%)Divergence begins
365,000 (+160%)1,400 (+17%)Crisis zone entry
4156,000 (+140%)1,600 (+14%)Imperative unsustainable
5390,000 (+150%)1,800 (+13%)Business impact severe

Where the 390,000 Lines Go

By Year 5, imperative codebase allocation:

Compare to Cascade’s 1,800 lines:

  • 500 lines: CDL workflows
  • 300 lines: OPA policies
  • 200 lines: Template definitions
  • 150 lines: Test specifications
  • 150 lines: Integration connectors
  • 500 lines: Schema and validation

Platform handles the other 388,200 lines automatically.


Year 5 Business Impact Comparison

Imperative (390,000 LOC) - UNMAINTAINABLE:

  • Deployment: 8-12 hours (high risk)
  • Team size: 25+ engineers
  • On-call: 8-person rotation
  • Incident rate: ~1 critical per week
  • Mean time to fix: 3-5 days
  • Feature velocity: 80% slowed
  • Test burden: 185,000 lines
  • Technical debt: $2-3M to refactor

Cascade (1,800 LOC) - STILL MAINTAINABLE:

  • Deployment: 5-10 seconds (zero risk)
  • Team size: 1-2 engineers
  • On-call: 1-person rotation
  • Incident rate: ~1 major per year
  • Mean time to fix: 30 minutes
  • Feature velocity: No slowdown
  • Test burden: 2,000 lines
  • Technical debt: None

Net savings: $2.2-4.2M over 5 years, 5-8x productivity lift


The Bifurcation Point

Key insight: The bifurcation happens at Year 2-3. That’s when compounding cost becomes unavoidable and when imperative teams stop shipping features.


Why Cascade Stays Flat

  1. Declarative model: Describe what you want, not how to achieve it
  2. Platform absorption: Infrastructure concerns handled by platform
  3. No framework bloat: No need for custom frameworks
  4. Automatic updates: Platform improvements benefit all workflows
  5. Configuration over code: Thresholds and policies change in YAML

Total Cost of Ownership


Summary: Complete Capability Stack

CapabilityImperativeCDLPlatform Provides
State machine1,000+ lines0 linesTemporal
Retry logic40 lines12 linesCSL Interpreter
Database800-1,200 lines0 linesCapability SDK
Schema5,000-15,000/yr50-100 linesAtlas
Event system500+ lines20 linesNATS
Webhooks150 lines15 linesPlatform
Conditional50 lines8 linesChoice states
Human tasks200 lines10 linesWaitForInput
TOTAL~11,340+ lines~165-500 linesPlatform

Overall reduction: 96-98% less code

Plus automatic security, observability, disaster recovery, multi-tenancy, and compliance built-in.


Next Steps

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