- Flat src/amqp.rs, src/mongo.rs, src/mariadb.rs promoted to src/services/{amqp,mongo,mariadb}/
- services/amqp/connection.rs: AmqpConnection struct with connect() and declare_exchange()
- services/amqp/error.rs: AmqpError type (thiserror, wraps lapin::Error)
- ipl() made async; #[tokio::main] added to main()
- IPL step 3b: authenticate to RabbitMQ + declare beds.events topic exchange (durable)
- Added lapin = "2" and tokio = { version = "1", features = ["full"] } to Cargo.toml
- 12 unit tests pass
- Docs: README, CLAUDE.md, wiki/04-ipl.md, wiki/06-queue-topology.md updated
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
5.9 KiB
Queue Topology
Overview
BEDS uses a single RabbitMQ topic exchange for all data events. Topic exchanges route messages based on a dotted routing key — this gives BEDS fine-grained control over which brokers receive which events without the overhead of managing multiple exchanges.
The Exchange
Exchange name: beds.events
Exchange type: topic
Durable: true
A single exchange handles all event types. Routing keys determine where messages go.
Routing Key Convention
{store_type}.{operation}
| Routing Key | Description |
|---|---|
rec.read |
MongoDB non-destructive fetch |
rec.write |
MongoDB create / update / delete |
rec.obj |
MongoDB bulk / migration / object operations |
rel.read |
MariaDB non-destructive fetch |
rel.write |
MariaDB create / update / delete |
rel.obj |
MariaDB bulk / migration / object operations |
log |
Log events — routed to admin node |
adm |
Administrative events — node management, config |
mig |
Migration and warehouse operations — segundo node |
Queue Naming Convention
Queue names follow the pattern:
{queue_tag}{routing_key_with_dots_replaced}
Example with queue_tag = "prod_":
prod_rec.read
prod_rec.write
prod_rec.obj
prod_rel.read
prod_rel.write
prod_rel.obj
prod_log
prod_adm
prod_mig
The queue_tag from beds.toml ensures queues from different environments (prod_, qa_, dev_) can coexist on a shared RabbitMQ instance without collision.
Broker-to-Queue Binding
Each broker type binds to one queue and processes events from it:
| Broker Type | Queue Binding | Node |
|---|---|---|
rBroker |
{tag}rec.read, {tag}rel.read |
appServer |
wBroker |
{tag}rec.write, {tag}rel.write |
appServer |
mBroker |
{tag}rec.obj, {tag}rel.obj |
appServer |
adminBrokerIn |
{tag}adm |
admin |
adminBrokerOut |
{tag}adm |
admin |
adminLogsBroker |
{tag}log |
admin |
adminSyslogBroker |
{tag}log |
admin |
adminGraphBroker |
{tag}log |
admin |
whBroker |
{tag}mig |
segundo |
cBroker |
{tag}mig |
segundo |
uBroker |
{tag}rec.read, {tag}rec.write |
tercero |
sBroker |
{tag}rec.read, {tag}rec.write |
tercero |
Log Event Routing
Log events deserve special attention because they are cross-cutting — every node emits them, but only admin consumes them.
Any node
│
│ routing key: log
▼
beds.events exchange
│
│ binding: log → prod_log queue
▼
prod_log queue
│
│ consumer: adminLogsBroker (admin node only)
▼
admin node
│
▼
msLogs collection (MongoDB)
Non-admin nodes never write to MongoDB directly for logging. They publish to the log routing key and trust the admin node to persist the record. If admin is slow, log events queue. If admin is down, log events queue until the RabbitMQ queue limit is reached. Nothing is lost until the queue fills.
This is by design. The log queue is the most important queue in the cluster from an operations standpoint — it should be sized generously.
Why Topic Exchange Over Direct Exchange
A direct exchange routes based on exact routing key match. A topic exchange supports wildcards:
# matches zero or more words
* matches exactly one word
This gives BEDS the option to bind a single consumer to multiple routing keys without multiple queue declarations:
rec.* matches rec.read, rec.write, rec.obj
*.read matches rec.read, rel.read
In the current implementation, brokers bind to specific queues. As the framework grows, the topic exchange flexibility will be used for cross-cutting concerns (audit, metrics) that need visibility across multiple event types without duplicating event payloads.
Queue Declaration Lifecycle
The beds.events exchange is declared during IPL (Step 3b), before any broker task starts. This ensures the routing infrastructure exists before anyone tries to publish to it.
Queues are not declared during IPL. Each broker task declares its own queue when it starts. This is a deliberate design choice:
- Queue presence = service ready. A queue's existence on the broker signals that the task consuming it is alive and ready to process messages. A queue declared at IPL before the consumer starts would be misleading — messages could arrive before the consumer is ready, or worse, before it is confirmed the consumer will start at all.
- No reserved global topology. There is no fixed set of queues that must exist for the cluster to function. The topology emerges from the services that are actually running. An appServer with only rBroker and wBroker running has exactly those two queues — not the full topology diagram.
- Clean restarts. When a broker task restarts, queue declaration is idempotent — RabbitMQ returns success if the queue already exists with matching parameters. Messages queued during the restart interval are waiting for the consumer when it comes back up.
Queue Durability and Persistence
All BEDS queues are:
- Durable — survive RabbitMQ restarts
- Persistent messages — messages survive broker restart (written to disk)
This is non-negotiable for a production framework. The performance cost of persistence (disk write per message) is acceptable given the correctness guarantee.
The vhost Isolation Model
Each environment gets its own RabbitMQ virtual host. A vhost is a completely isolated namespace — queues, exchanges, and bindings in one vhost are invisible to another. A RabbitMQ user is granted access to specific vhosts.
vhost: prod ← production traffic
vhost: qa ← QA / staging traffic
vhost: dev ← development traffic
Even if all three environments share one RabbitMQ instance, they are fully isolated. A message published to prod cannot be consumed by a dev consumer.
This was the operational pattern in the Namaste homelab — one RabbitMQ instance, three vhosts, multiple concurrent dev sessions running without interfering with each other.