we're going to use rabbitmq in our project, but facing a problem that, we want to debug on our dev machines, so the response message have to be send to machine which originally send the request message out. How we're going to achive that, is there an existing solution in spring-rabbitmq framework?
We have considered several solutions. such as declare a set of queues for each machine, the queue name prefix by machine name. Is that feasible?
Define set of queues (debug queue A-Z) and bind them to headers exchange with attributes x-match=any, from=[A-Z], to=[A-Z] respectively to . Then bind headers exchange to you main working exchange (one or more) to receive all messages you interested in, so when your consumer publish response it will be duplicated to your debug exchange and then routed to appropriate queue.
[sender X] [ worker ] [consumer on queue X]
| ^ |
[request] | [response from=X, to=X] [duped request from=X|
\ | | [duplicated response from=X, to=X]
\ [request from=X] | ^
v | V |
[working topic exchange] -------> [debug headers exchange]
/ | \ / | \
{bindings by routing key mask} {bindings by any headers from=[A-Z], to=[A-Z]}
/ | \ / | \
[working queue 1] ... [working queue N] [debug queue A] ... [debug queue Z]
To bind request and response messages you can use applicationId and correlationId message attributes.
Note, that both request and response messages will be duplicated to debug queues. You may also specify separate queue for request and response messages by binding queues to match only specific headers, something like x-match=all, from=[A-Z] or x-match=all, to=[A-Z] and publish response and request messages with only that headers (only from or only to), but it is up to you.
The pros:
easy to implement
requires minimal code changes
easy to turn on/off
may be safely run in production environment
Cons:
use more resource power from RabbitMQ side
Alternatively, you can utilize RPC pattern somehow if you debugging process requires realtime response receiving. But this will block publisher until response processed, which may differ from real-world app usage and break business logic.
Pros:
step-by-step debugging process
Cons:
hard to implement
may require a lot of code changes
break business logic
hard to enable/disable
not production environment safe
p.s.: sorry for ascii graph
Related
I have a Splunk forwarder which sends events to two third-party systems(through TCP) and also index them into a splunk indexer.
The problem I'm facing on is that if any of the two third-pary systems goes down. Splunk stops indexing events and neither sends them to the other system.
The output.conf I have is:
[tcpout]
defaultGroup = default-system1, default-system2
indexAndForward = 0
[tcpout:default-system1]
server = <IP>:<PORT>
[tcpout-server://<IP>:<PORT>]
[tcpout:default-system2]
server = <IP>:<PORT>
sendCookedData = false
Is there any way to avoid such a dependency? If one of the destinatios servers is down, it doesn't affect the other sending. I've been looking at the documentation and there are some options that could be use.
heartbeatFrequency in combination with sendCookedData.
heartbeatFrequency = <integer>
* How often (in seconds) to send a heartbeat packet to the receiving server.
* This setting is a mechanism for the forwarder to know that the receiver
(indexer) is alive. If the indexer does not send a return packet to the
forwarder, the forwarder declares the receiver unreachable and does not
forward data to it.
* The forwarder only sends heartbeats if the 'sendCookedData' setting
is set to "true".
* Default: 30
sendCookedData = <boolean>
* Whether or not to send processed or unprocessed data to the receiving server.
* If set to "true", events are cooked (have been processed by Splunk software).
* If set to "false", events are raw and untouched prior to sending.
* Set to "false" if you are sending events to a third-party system.
* Default: true
But I'm not sure if it's the most correct approach, based on the description of sendCookedData, "Set to "false" if you are sending events to a third-party system."
Don't send cooked data to third-party systems. That way lies sadness.
Unfortunately, the behavior you describe is normal for Splunk. From all outward appearances, there is a single output queue and all destinations are fed from that one queue. To avoid data loss, all sending stops when one destination is unavailable.
I'm trying to achieve a reject/delay loop using Rabbit's operations, i.e. :
I Have:
Main Queue with Main Exchange binded to it and DLX to StandBy Exchange.
StandBy Queue with StandBy Exchange binded to it with 60s TTL and DLX to Main Exchange
Basically I want to:
Consume from Main Queue
Rejects message (under certain circunstances)
Will get redirect it to StandBy Queue because rejection
When TTL expire, re-queue message to Main Queue.
The steps 1, 2 and 3 are OK but the last one drop the message instead of re-queue it.
Some theory from RabbitMQ's docs what I used to design this was:
Messages from a queue can be 'dead-lettered'; that is, republished to another exchange when any of the following events occur:
The message is rejected (basic.reject or basic.nack) with requeue=false,
The TTL for the message expires; or
The queue length limit is exceeded.
...
It is possible to form a cycle of message dead-lettering. For instance, this can happen when a queue dead-letters messages to the default exchange without specifiying a dead-letter routing key. Messages in such cycles (i.e. messages that reach the same queue twice) will be dropped if there was no rejections in the entire cycle.
The theory says that it should be re-queue because it has a rejection in the cycle from step #2, so, can you help me figure it out why it drops the message instead of re-queue it?
UPDATE:
The version I was targeting was 2.8.4 and it seems that in that moment the if there was no rejections in the entire cycle wasn't in the uses cases, anyway you can check this yourselves RabbitMQ 2.8.x Docs
I'll accept #george answer as the original objective can be achieved by this code.
Rafael, I am not sure what client you are using but with the Pika client in Python you could implement something like this. For simplicity I only use one exchange. Are you sure you are setting the exchange and the routing-key properly?
sender.py
import sys
import pika
connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel()
channel.exchange_declare(exchange='cycle', type='direct')
channel.queue_declare(queue='standby_queue',
arguments={
'x-message-ttl': 10000,
'x-dead-letter-exchange': 'cycle',
'x-dead-letter-routing-key': 'main_queue'})
channel.queue_declare(queue='main_queue',
arguments={
'x-dead-letter-exchange': 'cycle',
'x-dead-letter-routing-key': 'standby_queue'})
channel.queue_bind(queue='main_queue', exchange='cycle')
channel.queue_bind(queue='standby_queue', exchange='cycle')
channel.basic_publish(exchange='cycle',
routing_key='main_queue',
body="message body")
connection.close()
receiver.py
import sys
import pika
def callback(ch, method, properties, body):
print "Processing message: {}".format(body)
# replace with condition for rejection
if True:
print "Rejecting message"
ch.basic_nack(method.delivery_tag, False, False)
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.basic_consume(callback, queue='main_queue')
channel.start_consuming()
Two queues are bound to a topic exchange with the following routing keys:
Queue A, bound with routing key pattern match *.foo
Queue B, bound with routing key pattern match *.bar
I'd like to add a third queue to this exchange that receives messages that are neither foo messages nor bar messages. If I bind this queue with a # routing key, I naturally get all messages I need, but including foo's and bar's which I don't want.
Any way to route messages patching a pattern NOT *.foo AND NOT *.bar ?
If you want to catch all messages that doesn't match any bindings, that can be done with Alternate Exchange.
Add alternate exchange for existent one and collect all messages from that alternate exchanges:
standard workflow --> [main exchange (topic)]
| --> via binding *.foo --> [foo queue]
| --> via binding *.bar --> [bar queue]
v
[alternate exchange (let it be topic too)]
--> via binding * --> []
For more specific cases when you have N bindings but you want to catch all messages that doesn't match M bindings (where M < N) it is more problematic, but technically can be done via Dead Letter Exchange and then publish it to custom exchange where you have only M bindings, and then apply case with Alternate Exchange. But it even sounds rusty, not even think about performance degradation (applied only if you have really high messages flow).
we have setup some workflow environment with Rabbit.
It solves our needs but I like to know if it is also good practise to do it like we do for scheduled tasks.
Scheduling means no mission critical 100% adjusted time. So if a job should be retried after 60 seconds, it does mean 60+ seconds, depends on when the queue is handled.
I have created one Q_WAIT and made some headers to transport settings.
Lets do it like:
Worker is running subscribed on Q_ACTION
If the action missed (e.g. smtp server not reachable)
-> (Re-)Publish the message to Q_WAIT and set properties.headers["scheduled"] = time + 60seconds
Another process loops every 15 seconds through all messages in Q_WAIT by method pop() and NOT by subscribed
q_WAIT.pop(:ack => true) do |delivery_info,properties,body|...
if (properties.headers["scheduled"] has reached its time)
-> (Re-)Publish the message back to Q_ACTION
ack(message)
after each loop, the connection is closed so that the NOT (Re-)Published are left in Q_WAIT because they were not acknowledged.
Can someone confirm this as a working (good) practise.
Sure you can use looping process like described in original question.
Also, you can utilize Time-To-Live Extension with Dead Letter Exchanges extension.
First, specify x-dead-letter-exchange Q_WAIT queue argument equal to current exchange and x-dead-letter-routing-key equal to routing key that Q_ACTION bound.
Then set x-message-ttl queue argument set or set message expires property during publishing if you need custom per-message ttl (which is not best practice though while there are some well-known caveats, but it works too).
In this case your messages will be dead-lettered from Q_WAIT to Q_ACTION right after their ttl expires without any additional consumers, which is more reliable and stable.
Note, if you need advanced re-publish logic (change message body, properties) you need additional queue (say Q_PRE_ACTION) to consume messages from, change them and then publish to target queue (say Q_ACTION).
As mentioned here in comments I tried that feature of x-dead-letter-exchange and it worked for most requirements. One question / missunderstandig is TTL-PER-MESSAGE option.
Please look on the example here. From my understanding:
the DLQ has a timeout of 10 seconds
so first message will be available on subscriber 10 seconds after publishing.
the second message is posted 1 second after the first with a message-ttl (expiration) of 3 seconds
I would expect the second message should be prounounced after 3 seconds from publishing and before first message.
But it did not work like that, both are available after 10 seconds.
Q: Shouldn't the message expiration overrule the DLQ ttl?
#!/usr/bin/env ruby
# encoding: utf-8
require 'bunny'
B = Bunny.new ENV['CLOUDAMQP_URL']
B.start
DELAYED_QUEUE='work.later'
DESTINATION_QUEUE='work.now'
def publish
ch = B.create_channel
# declare a queue with the DELAYED_QUEUE name
q = ch.queue(DELAYED_QUEUE, :durable => true, arguments: {
# set the dead-letter exchange to the default queue
'x-dead-letter-exchange' => '',
# when the message expires, set change the routing key into the destination queue name
'x-dead-letter-routing-key' => DESTINATION_QUEUE,
# the time in milliseconds to keep the message in the queue
'x-message-ttl' => 10000,
})
# publish to the default exchange with the the delayed queue name as routing key,
# so that the message ends up in the newly declared delayed queue
ch.basic_publish('message content 1 ' + Time.now.strftime("%H-%M-%S"), "", DELAYED_QUEUE, :persistent => true)
puts "#{Time.now}: Published the message 1"
# wait moment before next publish
sleep 1.0
# puts this with a shorter ttl
ch.basic_publish('message content 2 ' + Time.now.strftime("%H-%M-%S"), "", DELAYED_QUEUE, :persistent => true, :expiration => "3000")
puts "#{Time.now}: Published the message 2"
ch.close
end
def subscribe
ch = B.create_channel
# declare the destination queue
q = ch.queue DESTINATION_QUEUE, durable: true
q.subscribe do |delivery, headers, body|
puts "#{Time.now}: Got the message: #{body}"
end
end
subscribe()
publish()
sleep
I am using celery with a rabbitmq backend. It is producing thousands of queues with 0 or 1 items in them in rabbitmq like this:
$ sudo rabbitmqctl list_queues
Listing queues ...
c2e9b4beefc7468ea7c9005009a57e1d 1
1162a89dd72840b19fbe9151c63a4eaa 0
07638a97896744a190f8131c3ba063de 0
b34f8d6d7402408c92c77ff93cdd7cf8 1
f388839917ff4afa9338ef81c28aad75 0
8b898d0c7c7e4be4aa8007b38ccc00ea 1
3fb4be51aaaa4ac097af535301084b01 1
This seems to be inefficient, but further I have observed that these queues persist long after processing is finished.
I have found the task that appears to be doing this:
#celery.task(ignore_result=True)
def write_pages(page_generator):
g = group(render_page.s(page) for page in page_generator)
res = g.apply_async()
for rendered_page in res:
print rendered_page # TODO: print to file
It seems that because these tasks are being called in a group, they are being thrown into the queue but never being released. However, I am clearly consuming the results (as I can view them being printed when I iterate through res. So, I do not understand why those tasks are persisting in the queue.
Additionally, I am wondering if the large number queues that are being created is some indication that I am doing something wrong.
Thanks for any help with this!
Celery with the AMQP backend will store task tombstones (results) in an AMQP queue named with the task ID that produced the result. These queues will persist even after the results are drained.
A couple recommendations:
Apply ignore_result=True to every task you can. Don't depend on results from other tasks.
Switch to a different backend (perhaps Redis -- it's more efficient anyway): http://docs.celeryproject.org/en/latest/userguide/tasks.html
Use CELERY_TASK_RESULT_EXPIRES (or on 4.1 CELERY_RESULT_EXPIRES) to have a periodic cleanup task remove old data from rabbitmq.
http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-result_expires