Microservices Choreography with NATS

A complete microservices architecture using event-driven choreography with separate FraiseQL services and NATS for distributed order processing.

System Architecture

Each service publishes a NATS event that triggers the next step — no central orchestrator.

Note

FraiseQL is the write side for the Order Service — it exposes the GraphQL API and publishes NATS events via [observers] TOML config when mutations change order state. The downstream services (inventory, payment, shipping) are external workers that subscribe to NATS using the standard nats.py library. They update state by calling PostgreSQL directly or through their own FraiseQL services.

sequenceDiagram
    participant C as Client
    participant OS as Order Service (FraiseQL)
    participant N as NATS
    participant INV as Inventory Worker (nats.py)
    participant PAY as Payment Worker (nats.py)
    participant SHP as Shipping Worker (nats.py)

    C->>OS: createOrder(items, total)
    OS->>OS: fn_create_order() → status "pending"
    OS->>N: events.order.created (via pg_notify → observer)
    OS-->>C: Order{status: "pending"}

    N->>INV: events.order.created
    INV->>INV: reserve stock in tb_inventory
    INV->>N: events.inventory.reserved

    N->>PAY: events.inventory.reserved
    PAY->>PAY: charge card, insert tb_payment
    PAY->>N: events.payment.charged

    N->>OS: events.payment.charged
    OS->>OS: fn_update_order_status() → "paid"

    N->>SHP: events.payment.charged
    SHP->>SHP: create label, insert tb_shipping
    SHP->>N: events.order.shipped

    N->>OS: events.order.shipped
    OS->>OS: fn_update_order_status() → "shipped"

Running the Full System

docker-compose.yml

version: '3.9'
services:
  postgres:
    image: postgres:16-alpine
    environment:
      POSTGRES_DB: orders
      POSTGRES_USER: fraiseql
      POSTGRES_PASSWORD: password
    ports:
      - "5432:5432"

  nats:
    image: nats:latest
    command: ["-js"]
    ports:
      - "4222:4222"

  order-service:
    build: ./order-service
    depends_on: [postgres, nats]
    environment:
      DATABASE_URL: postgresql://fraiseql:password@postgres:5432/orders
      NATS_URL: nats://nats:4222

  inventory-worker:
    build: ./inventory-worker
    depends_on: [postgres, nats]
    environment:
      DATABASE_URL: postgresql://fraiseql:password@postgres:5432/orders
      NATS_URL: nats://nats:4222

  payment-worker:
    build: ./payment-worker
    depends_on: [postgres, nats]
    environment:
      DATABASE_URL: postgresql://fraiseql:password@postgres:5432/orders
      NATS_URL: nats://nats:4222

  shipping-worker:
    build: ./shipping-worker
    depends_on: [postgres, nats]
    environment:
      DATABASE_URL: postgresql://fraiseql:password@postgres:5432/orders
      NATS_URL: nats://nats:4222

Start all services with:

docker-compose up -d

Order Service — SQL Schema

CREATE TABLE tb_order (
    pk_order    BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    id          UUID DEFAULT gen_random_uuid() UNIQUE NOT NULL,
    identifier  TEXT UNIQUE NOT NULL,
    fk_user BIGINT NOT NULL REFERENCES tb_user(pk_user),
    total       NUMERIC(10, 2) NOT NULL,
    status      TEXT NOT NULL DEFAULT 'pending',
    created_at  TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE UNIQUE INDEX idx_tb_order_id ON tb_order(id);
CREATE INDEX idx_tb_order_fk_user ON tb_order(fk_user);
CREATE INDEX idx_tb_order_status ON tb_order(status);

CREATE TABLE tb_order_item (
    pk_order_item BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    id            UUID DEFAULT gen_random_uuid() UNIQUE NOT NULL,
    identifier    TEXT UNIQUE NOT NULL,
    fk_order BIGINT NOT NULL REFERENCES tb_order(pk_order),
    fk_product BIGINT NOT NULL REFERENCES tb_product(pk_product),
    quantity      INTEGER NOT NULL,
    unit_price    NUMERIC(10, 2) NOT NULL
);

CREATE INDEX idx_tb_order_item_fk_order ON tb_order_item(fk_order);

CREATE TABLE tb_event_log (
    pk_event_log BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    id           UUID DEFAULT gen_random_uuid() UNIQUE NOT NULL,
    identifier   TEXT UNIQUE NOT NULL,
    event_type   TEXT NOT NULL,
    fk_order BIGINT REFERENCES tb_order(pk_order),
    data         JSONB NOT NULL DEFAULT '{}',
    recorded_at  TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE INDEX idx_tb_event_log_fk_order ON tb_event_log(fk_order);

-- Read view: order status
CREATE VIEW v_order AS
SELECT
    o.id,
    jsonb_build_object(
        'id',         o.id::text,
        'identifier', o.identifier,
        'total',      o.total,
        'status',     o.status,
        'created_at', o.created_at
    ) AS data
FROM tb_order o;

-- Mutation function: create order
CREATE FUNCTION fn_create_order(
    p_identifier  TEXT,
    p_fk_user BIGINT,
    p_total       NUMERIC
) RETURNS mutation_response
LANGUAGE plpgsql AS $$
DECLARE
    v_id UUID;
    v_result mutation_response;
BEGIN
    INSERT INTO tb_order (identifier, fk_user, total, status)
    VALUES (p_identifier, p_fk_user, p_total, 'pending')
    RETURNING id INTO v_id;

    v_result.status      := 'success';
    v_result.entity_id   := v_id;
    v_result.entity_type := 'Order';
    RETURN v_result;
END;
$$;

-- Mutation function: update order status (called by NATS observer via SQL)
CREATE FUNCTION fn_update_order_status(
    p_order_id UUID,
    p_status   TEXT
) RETURNS mutation_response
LANGUAGE plpgsql AS $$
DECLARE
    v_result mutation_response;
BEGIN
    UPDATE tb_order SET status = p_status WHERE id = p_order_id;
    v_result.status      := 'success';
    v_result.entity_type := 'Order';
    RETURN v_result;
END;
$$;

Order Service — FraiseQL Python Schema

Tip

The FraiseQL Python SDK is compile-time only. Decorators declare the GraphQL schema; the Rust runtime handles all execution. Mutations have no body — just pass. NATS event publishing happens automatically via [observers] TOML config when pg_notify fires after an INSERT or UPDATE.

order-service/schema.py

import fraiseql
from fraiseql.scalars import ID, DateTime
from decimal import Decimal


@fraiseql.type
class Order:
    """A customer order."""
    id: ID
    identifier: str
    total: Decimal
    status: str
    created_at: DateTime


@fraiseql.input
class CreateOrderInput:
    identifier: str
    total: Decimal


@fraiseql.query
def order(id: ID) -> Order | None:
    """Get a single order by UUID."""
    return fraiseql.config(sql_source="v_order")


@fraiseql.query
def orders(limit: int = 20, offset: int = 0) -> list[Order]:
    """List orders. RLS ensures users see only their own."""
    return fraiseql.config(sql_source="v_order")


@fraiseql.mutation(sql_source="fn_create_order", operation="CREATE")
def create_order(input: CreateOrderInput) -> Order:
    """Create a new order. Rust runtime calls fn_create_order(), pg_notify fires, observer publishes to NATS."""
    pass


@fraiseql.subscription(entity_type="Order", topic="events.order.status_updated")
def order_status_updated(order_id: ID | None = None) -> Order:
    """Subscribe to order status changes in real time."""
    pass


fraiseql.export_schema("schema.json")

Order Service — fraiseql.toml

order-service/fraiseql.toml

[project]
name = "order-service"
version = "1.0.0"

[database]
url = "${DATABASE_URL}"

[fraiseql]
schema_file = "schema.json"
output_file = "schema.compiled.json"

# JWT/OIDC config via environment variables (no [auth] TOML section):
# OIDC_ISSUER_URL, OIDC_CLIENT_ID, OIDC_CLIENT_SECRET

# NATS observer: when tb_order changes, publish to events.order.created
# The Rust runtime receives pg_notify from PostgreSQL and forwards to NATS.
[[observers]]
table   = "tb_order"
event   = "INSERT"
subject = "events.order.created"

[[observers]]
table   = "tb_order"
event   = "UPDATE"
subject = "events.order.status_updated"

[observers]
backend  = "nats"
nats_url = "${NATS_URL}"

[security.enterprise]
enabled   = true
log_level = "info"

Inventory Worker — External nats.py Service

Note

The inventory worker is a separate Python process — not part of FraiseQL. It uses the standard nats.py library to subscribe to NATS subjects and writes directly to PostgreSQL via asyncpg. FraiseQL has no Python NATS subscription API.

inventory-worker/main.py

"""
Inventory worker: subscribes to NATS events using nats.py.
This is a standalone service, not a FraiseQL schema file.
"""
import asyncio
import json
import asyncpg
import nats
from nats.js import JetStreamContext


async def on_order_created(msg):
    """
    React to order creation.
    Reserve inventory or emit failure event.
    """
    nc = msg._client  # nats.py connection reference
    js: JetStreamContext = nc.jetstream()
    data = json.loads(msg.data)
    order_id = data["order_id"]
    items = data["items"]

    conn = await asyncpg.connect(POSTGRES_URL)
    try:
        for item in items:
            row = await conn.fetchrow(
                """
                SELECT pk_inventory, quantity, reserved
                FROM tb_inventory
                WHERE fk_product = (
                    SELECT pk_product FROM tb_product WHERE identifier = $1
                )
                """,
                item["sku"],
            )
            if row is None or (row["quantity"] - row["reserved"]) < item["quantity"]:
                await js.publish(
                    "events.order.failed",
                    json.dumps({
                        "order_id": order_id,
                        "reason": "inventory_unavailable",
                        "sku": item["sku"],
                    }).encode(),
                )
                await msg.nak()
                return

            await conn.execute(
                "UPDATE tb_inventory SET reserved = reserved + $1 WHERE pk_inventory = $2",
                item["quantity"],
                row["pk_inventory"],
            )

        await js.publish(
            "events.inventory.reserved",
            json.dumps({"order_id": order_id}).encode(),
        )
        await msg.ack()
    except Exception as exc:
        await js.publish(
            "events.order.failed",
            json.dumps({"order_id": order_id, "reason": str(exc)}).encode(),
        )
        await msg.nak(delay=5)
    finally:
        await conn.close()


async def main():
    nc = await nats.connect(NATS_URL)
    js = nc.jetstream()

    await js.subscribe(
        "events.order.created",
        durable="inventory_processors",
        cb=on_order_created,
    )
    print("Inventory worker listening on events.order.created")
    await asyncio.sleep(float("inf"))


if __name__ == "__main__":
    import os
    NATS_URL = os.environ["NATS_URL"]
    POSTGRES_URL = os.environ["DATABASE_URL"]
    asyncio.run(main())

Payment Worker — External nats.py Service

payment-worker/main.py

"""
Payment worker: subscribes to NATS events using nats.py.
This is a standalone service, not a FraiseQL schema file.
"""
import asyncio
import json
import asyncpg
import nats


async def on_inventory_reserved(msg):
    """
    Process payment after inventory is reserved.
    Risk: inventory reserved but payment fails — must compensate.
    """
    nc = msg._client
    js = nc.jetstream()
    data = json.loads(msg.data)
    order_id = data["order_id"]

    conn = await asyncpg.connect(POSTGRES_URL)
    try:
        order = await conn.fetchrow(
            "SELECT pk_order, total FROM tb_order WHERE id = $1::uuid",
            order_id,
        )
        if order is None:
            await msg.ack()
            return

        # Charge the card via external payment provider
        payment = await charge_credit_card(
            order_pk=order["pk_order"],
            amount=order["total"],
        )

        await conn.execute(
            """
            INSERT INTO tb_payment (identifier, fk_order, amount, status, provider_id)
            VALUES (gen_random_uuid()::text, $1, $2, 'succeeded', $3)
            """,
            order["pk_order"],
            order["total"],
            payment.id,
        )

        await js.publish(
            "events.payment.charged",
            json.dumps({
                "order_id": order_id,
                "payment_id": payment.id,
                "amount": float(order["total"]),
            }).encode(),
        )
        await msg.ack()

    except Exception as exc:
        # Payment failed — emit compensation event so inventory releases
        await js.publish(
            "events.payment.failed",
            json.dumps({
                "order_id": order_id,
                "reason": str(exc),
            }).encode(),
        )
        await msg.nak(delay=10)
    finally:
        await conn.close()


async def on_payment_failed(msg):
    """Release reserved inventory when payment fails."""
    data = json.loads(msg.data)
    order_id = data["order_id"]

    conn = await asyncpg.connect(POSTGRES_URL)
    try:
        items = await conn.fetch(
            """
            SELECT oi.fk_product, oi.quantity
            FROM tb_order_item oi
            JOIN tb_order o ON o.pk_order = oi.fk_order
            WHERE o.id = $1::uuid
            """,
            order_id,
        )
        for item in items:
            await conn.execute(
                "UPDATE tb_inventory SET reserved = reserved - $1 WHERE fk_product = $2",
                item["quantity"],
                item["fk_product"],
            )

        js = msg._client.jetstream()
        await js.publish(
            "events.order.failed",
            json.dumps({
                "order_id": order_id,
                "reason": "payment_failed",
                "compensated": True,
            }).encode(),
        )
        await msg.ack()
    finally:
        await conn.close()


async def main():
    nc = await nats.connect(NATS_URL)
    js = nc.jetstream()

    await js.subscribe(
        "events.inventory.reserved",
        durable="payment_processors",
        cb=on_inventory_reserved,
    )
    await js.subscribe(
        "events.payment.failed",
        durable="compensation_handlers",
        cb=on_payment_failed,
    )
    print("Payment worker listening")
    await asyncio.sleep(float("inf"))


if __name__ == "__main__":
    import os
    NATS_URL = os.environ["NATS_URL"]
    POSTGRES_URL = os.environ["DATABASE_URL"]
    asyncio.run(main())

Shipping Worker — External nats.py Service

shipping-worker/main.py

"""
Shipping worker: subscribes to NATS events using nats.py.
This is a standalone service, not a FraiseQL schema file.
"""
import asyncio
import json
import asyncpg
import nats


async def on_payment_charged(msg):
    """
    Create shipping label after payment succeeds.
    Last step in the choreography.
    """
    nc = msg._client
    js = nc.jetstream()
    data = json.loads(msg.data)
    order_id = data["order_id"]

    conn = await asyncpg.connect(POSTGRES_URL)
    try:
        order = await conn.fetchrow(
            "SELECT pk_order, shipping_address FROM tb_order WHERE id = $1::uuid",
            order_id,
        )
        if order is None:
            await msg.ack()
            return

        label = await create_shipping_label(
            order_pk=order["pk_order"],
            address=order["shipping_address"],
        )

        await conn.execute(
            """
            INSERT INTO tb_shipping (identifier, fk_order, provider_label_id, carrier, tracking_number)
            VALUES (gen_random_uuid()::text, $1, $2, $3, $4)
            """,
            order["pk_order"],
            label.id,
            label.carrier,
            label.tracking_number,
        )

        await js.publish(
            "events.order.shipped",
            json.dumps({
                "order_id": order_id,
                "tracking_number": label.tracking_number,
            }).encode(),
        )
        await msg.ack()

    except Exception as exc:
        # Log for manual intervention — shipping is the last step
        print(f"[shipping-worker] ERROR order={order_id}: {exc}")
        await msg.nak(delay=60)
    finally:
        await conn.close()


async def main():
    nc = await nats.connect(NATS_URL)
    js = nc.jetstream()

    await js.subscribe(
        "events.payment.charged",
        durable="shipping_handlers",
        cb=on_payment_charged,
    )
    print("Shipping worker listening on events.payment.charged")
    await asyncio.sleep(float("inf"))


if __name__ == "__main__":
    import os
    NATS_URL = os.environ["NATS_URL"]
    POSTGRES_URL = os.environ["DATABASE_URL"]
    asyncio.run(main())

Event Flow Monitoring — External Worker

monitoring-worker/main.py

"""
Event monitor: logs all events to tb_event_log.
Standalone nats.py service — not a FraiseQL schema file.
"""
import asyncio
import json
import asyncpg
import nats


async def monitor_events(msg):
    """Record all events in tb_event_log for audit trail."""
    event_type = msg.subject
    data = json.loads(msg.data)
    order_id = data.get("order_id")

    conn = await asyncpg.connect(POSTGRES_URL)
    try:
        # Look up pk_order for the FK
        order_pk = None
        if order_id:
            row = await conn.fetchrow(
                "SELECT pk_order FROM tb_order WHERE id = $1::uuid",
                order_id,
            )
            if row:
                order_pk = row["pk_order"]

        await conn.execute(
            """
            INSERT INTO tb_event_log (identifier, event_type, fk_order, data)
            VALUES (gen_random_uuid()::text, $1, $2, $3::jsonb)
            """,
            event_type,
            order_pk,
            json.dumps(data),
        )

        if "failed" in event_type:
            await send_alert({"severity": "warning", "event": event_type, "details": data})

        await msg.ack()
    finally:
        await conn.close()


async def main():
    nc = await nats.connect(NATS_URL)
    js = nc.jetstream()

    await js.subscribe("events.>", durable="event_monitoring", cb=monitor_events)
    print("Monitoring worker listening on events.>")
    await asyncio.sleep(float("inf"))


if __name__ == "__main__":
    import os
    NATS_URL = os.environ["NATS_URL"]
    POSTGRES_URL = os.environ["DATABASE_URL"]
    asyncio.run(main())

Testing Choreography

tests/test_choreography.py

import asyncio
import pytest
import httpx


@pytest.mark.asyncio
async def test_full_order_choreography():
    """Test complete happy path through all services via GraphQL API."""
    async with httpx.AsyncClient(base_url="http://localhost:8080") as client:
        # Create order via FraiseQL GraphQL API
        response = await client.post(
            "/graphql",
            json={
                "query": """
                    mutation {
                        createOrder(input: { identifier: "ord-test-1", total: "49.99" }) {
                            id
                            status
                        }
                    }
                """
            },
            headers={"Authorization": f"Bearer {TEST_TOKEN}"},
        )
        data = response.json()
        order_id = data["data"]["createOrder"]["id"]
        assert data["data"]["createOrder"]["status"] == "pending"

    # Give all workers time to process the choreography
    await asyncio.sleep(3.0)

    async with httpx.AsyncClient(base_url="http://localhost:8080") as client:
        response = await client.post(
            "/graphql",
            json={"query": f'query {{ order(id: "{order_id}") {{ id status }} }}'},
            headers={"Authorization": f"Bearer {TEST_TOKEN}"},
        )
        final = response.json()
        assert final["data"]["order"]["status"] == "shipped"


@pytest.mark.asyncio
async def test_payment_failure_compensation():
    """Test inventory release when payment fails."""
    # Use a known-bad order identifier to trigger payment failure
    async with httpx.AsyncClient(base_url="http://localhost:8080") as client:
        response = await client.post(
            "/graphql",
            json={
                "query": """
                    mutation {
                        createOrder(input: { identifier: "ord-bad-card-1", total: "49.99" }) {
                            id
                        }
                    }
                """
            },
            headers={"Authorization": f"Bearer {TEST_TOKEN}"},
        )
        order_id = response.json()["data"]["createOrder"]["id"]

    await asyncio.sleep(3.0)

    async with httpx.AsyncClient(base_url="http://localhost:8080") as client:
        response = await client.post(
            "/graphql",
            json={"query": f'query {{ order(id: "{order_id}") {{ status }} }}'},
            headers={"Authorization": f"Bearer {TEST_TOKEN}"},
        )
        assert response.json()["data"]["order"]["status"] == "failed"

Key Patterns

1. FraiseQL owns the write side: Mutations call SQL functions; the Rust runtime handles GraphQL execution.
2. NATS events via observers: [observers] TOML config routes pg_notify events to NATS — no Python code required.
3. External workers use nats.py: Downstream services are independent processes; they are not FraiseQL schema files.
4. No transactions across services: Each service manages its own state; NATS delivers events asynchronously.
5. Compensation pattern: Payment failure triggers an events.payment.failed event that the compensation handler processes to release inventory.
6. Idempotency: Each worker handler is idempotent — safe to replay if NATS redelivers a message.
7. Audit trail: The monitoring worker logs all events to tb_event_log for observability.

Verify It Works

1. Start all services with Docker Compose:

   docker-compose up -d

   Verify services are running:

   docker-compose ps

   Expected output:

   NAME                                        STATUS         PORTS
   microservices-choreography-postgres-1       Up (healthy)   0.0.0.0:5432->5432/tcp
   microservices-choreography-nats-1           Up (healthy)   0.0.0.0:4222->4222/tcp
   microservices-choreography-order-service-1  Up             0.0.0.0:8080->8080/tcp
   microservices-choreography-inventory-worker-1 Up
   microservices-choreography-payment-worker-1   Up
   microservices-choreography-shipping-worker-1  Up

2. Check NATS is ready:

   docker-compose exec nats nats server info

   Look for jetstream: enabled: true in output.

3. Create a test order:

   curl -X POST http://localhost:8080/graphql \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer $TOKEN" \
     -d '{
       "query": "mutation { createOrder(input: { identifier: \"ord-001\", total: \"49.99\" }) { id status } }"
     }'

   Expected response:

   {
     "data": {
       "createOrder": {
         "id": "550e8400-e29b-41d4-a716-446655440000",
         "status": "pending"
       }
     }
   }

4. Wait for choreography to complete (allow all workers time to process):

   sleep 5

5. Verify order reached final state:

   curl -X POST http://localhost:8080/graphql \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer $TOKEN" \
     -d '{
       "query": "query { order(id: \"550e8400-e29b-41d4-a716-446655440000\") { id status } }"
     }'

   Expected response:

   {
     "data": {
       "order": {
         "id": "550e8400-e29b-41d4-a716-446655440000",
         "status": "shipped"
       }
     }
   }

6. Check event log for complete flow:

   docker-compose exec postgres psql -U fraiseql -d orders -c \
     "SELECT event_type, recorded_at FROM tb_event_log ORDER BY recorded_at;"

   Expected output:

         event_type              |         recorded_at
   --------------------------------+----------------------------
   events.order.created            | 2024-01-15 10:30:00
   events.inventory.reserved       | 2024-01-15 10:30:01
   events.payment.charged          | 2024-01-15 10:30:02
   events.order.shipped            | 2024-01-15 10:30:05

7. Test compensation (failure scenario):

   curl -X POST http://localhost:8080/graphql \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer $TOKEN" \
     -d '{
       "query": "mutation { createOrder(input: { identifier: \"ord-fail-001\", total: \"9999.99\" }) { id } }"
     }'

   Wait and verify order failed:

   sleep 3
   curl -X POST http://localhost:8080/graphql \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer $TOKEN" \
     -d '{"query": "query { order(id: \"<id-from-above>\") { status } }"}'

   Expected: {"status": "failed"}

8. Run integration tests:

   pytest tests/test_choreography.py -v

   Expected output:

   tests/test_choreography.py::test_full_order_choreography PASSED
   tests/test_choreography.py::test_payment_failure_compensation PASSED

Troubleshooting

Services Won’t Start

docker-compose up -d
docker-compose logs order-service

Common issues:

1. Port already in use: Change ports in docker-compose.yml
2. Database not ready: Services have depends_on but PostgreSQL needs time to initialize
3. NATS connection failed: Verify NATS is running: docker-compose logs nats

Order Stuck in “pending”

If order does not progress:

1. Check NATS consumers:

   docker-compose exec nats nats consumer report

2. Verify workers are connected:

   docker-compose exec nats nats consumer info events inventory_processors

3. Check worker logs:

   docker-compose logs inventory-worker | tail -20
   docker-compose logs payment-worker | tail -20

4. Manual event inspection:

   docker-compose exec nats nats sub events.order.created

Events Not Being Published

If tb_event_log is empty:

1. Check the FraiseQL observer configuration:

   docker-compose logs order-service | grep "observer"

2. Verify the [observers] section in fraiseql.toml is correct and nats_url is reachable.

3. Check NATS stream exists:

   docker-compose exec nats nats stream report

Compensation Not Working

If payment failure does not release inventory:

1. Check the payment worker compensation handler log:

   docker-compose logs payment-worker | grep "compensation"

2. Verify events.payment.failed subject is being published:

   docker-compose exec nats nats sub events.payment.failed

Performance Issues

If choreography is slow:

1. Check consumer lag:

   docker-compose exec nats nats consumer info events inventory_processors

   Look for num_pending (should be near 0)

2. Scale workers horizontally: Run multiple instances of each worker with the same durable consumer name — NATS JetStream distributes messages across them.

3. Monitor database connections:

   docker-compose exec postgres psql -U fraiseql -c \
     "SELECT count(*) FROM pg_stat_activity;"

Production Considerations

• Monitor NATS consumer lag (how far behind consumers are)
• Set up a Dead Letter Queue (DLQ) stream for permanently failed messages
• Implement circuit breakers in workers for external service calls (payment provider, shipping API)
• Use durable consumer groups for load distribution across worker replicas
• Enable per-service metrics; instrument each worker with Prometheus counters on ack/nak
• Tune FraiseQL [observers] batch size and flush interval for throughput

Advanced Federation

Cross-service queries and data composition.

Advanced NATS

Reliability patterns and JetStream.

Observers Reference

TOML observer configuration reference.