After a node is scaled down to a target node, the sf queue in the
target node is not deleted.
Normally this is fine because may be reused when the scaled down
node is back up.
However in cloud environment many drainer pods can be created and
then shutdown in order to drain the messages to a live node (pod).
Each drainer pod will have a different node-id. Over time the sf
queues in the target broker node grows and those sf queues are
no longer reused.
Although use can use management API/console to manually delete
them, it would be nice to have an option to automatically delete
those sf queue/address resources after scale down.
In this PR it added a boolean configuration parameter called
cleanup-sf-queue to scale down policy so that if the parameter
is "true" the broker will send a message to the
target broker signalling that the SF queue is no longer
needed and should be deleted.
If the parameter is not defined (default) or is "false"
the scale down won't remove the sf queue.
When converting from AMQP to core and back again support annotations that
aren't able to be placed into Core message properties by storing the bytes
from encoding the types to AMQP encodings and then decoding them again
when converting back into AMQP messages.
Requires update to proton-j 0.33.2 for encoding fix
The core server session tracks details about producers like what
addresses have had messages sent to them, the most recent message ID
sent to each address, and the number of messages sent to each address.
This information is made available to users via the
listProducersInfoAsJSON method on the various management interfaces
(JMX, web console, etc.). However, in situations where a server session
is long lived (e.g. in a pool) and is used to send to many different
addresses (e.g. randomly named temporary JMS queues) this info can
accumulate to a problematic degree. Therefore, we should limit the
amount of producer details saved by the session.
this test was basically broken, it was silently failing as it was ignoring results and taking a long time to finish.
As this test is multiplied along many options (Netty, Replicated, JDBC) this was taking considerable extra time
on the testsuite.
