The fallback consumer authorization implemented in ARTEMIS-592 needs to
check for an *exact* security-settings match otherwise in certain
configurations a more general and more permissive setting might
be used instead of the intended more specific and more restrictive
setting.
The merge method in AddressSettings should *not* use any getters. It
should reference the relevant variables directly. Using any getters will
return default values in the underlying value is null. This can cause
problems for hierarchical settings.
Also fixed a few potential NPEs exposed by the test-case.
The existing deactivation callback happens *after* several important
services are shutdown (e.g. the remoting service which allows client
connectivity). This commit adds a new callback which is invoked *before*
any services are stopped. This is useful for embedded use-cases where
applications want to stop gracefully before any part of the broker is
stopped.
A default, empty method implementation is provided so that existing
callback implementations don't need to change.
This is to avoid shutting down the server on a critical failure in case the message is a few bytes shy
from beyond the max buffer size.
This will prevent the issue.
When deleting a durable scheduled message via the management API the
message would be removed from memory but it wouldn't be removed from
storage so when the broker restarted the message would reappear.
This commit fixes that by acking the message during the delete
operation.
Using a ThreadLocal for the audit user information works in most cases,
but it can fail when dispatching messages to consumers because threads
are taken out of a pool to do the dispatching and those threads may not
be associated with the proper credentials. This commit fixes that
problem with the following changes:
- Passes the Subject explicitly when logging audit info during dispatch
- Relocates security audit logging from the SecurityManager
implementation(s) to the SecurityStore implementation
- Associates the Subject with the connection properly with the new
security caching
- Adding a paragraph about addressing and distinct queue names
- Renaming match on peers, senders and receivers as "address-match"
- Changing qpid dispatch test to use a single listener
- Fixing reconnect attemps message
This commit is fixing:
- a missing commit that can make leak a connection
- restricting default specific commons-dbcp2 to the default data source
- setting poolPreparedStatements true by default
- configured embedded Derby to be in-memory to speedup tests
It add additional required fixes:
- Fixed uncommitted deleted tx records
- Fixed JDBC authorization on test
- Using property-based version for commons-dbcp2
- stopping thread pool after activation to allow JDBC lease locks to release the lock
- centralize JDBC network timeout configuration and save repeating it
- adding dbcp2 as the default pooled DataSource to be used
Replaces direct jdbc connections with dbcp2 datasource. Adds
configuration options to use alternative datasources and to alter the
parameters. While adding slight overhead, this vastly improves the
management and pooling capabilities with db connections.
This reverts commit dbb3a90fe6.
The org.apache.activemq.artemis.core.server.Queue#getRate method is for
slow-consumer detection and is designed for internal use only.
Furthermore, it's too opaque to be trusted by a remote user as it only
returns the number of message added to the queue since *the last time
it was called*. The problem here is that the user calling it doesn't
know when it was invoked last. Therefore, they could be getting the
rate of messages added for the last 5 minutes or the last 5
milliseconds. This can lead to inconsistent and misleading results.
There are three main ways for users to track rates of message
production and consumption:
1. Use a metrics plugin. This is the most feature-rich and flexible
way to track broker metrics, although it requires tools (e.g.
Prometheus) to store the metrics and display them (e.g. Grafana).
2. Invoke the getMessageCount() and getMessagesAdded() management
methods and store the returned values along with the time they were
retrieved. A time-series database is a great tool for this job. This is
exactly what tools like Prometheus do. That data can then be used to
create informative graphs, etc. using tools like Grafana. Of course, one
can skip all the tools and just do some simple math to calculate rates
based on the last time the counts were retrieved.
3. Use the broker's message counters. Message counters are the broker's
simple way of providing historical information about the queue. They
provide similar results to the previous solutions, but with less
flexibility since they only track data while the broker is up and
there's not really any good options for graphing.
岳豹
Justin Bertram
Marcos Singermann
franz1981
Domenico Francesco Bruscino
Erwin Dondorp
Tomas Hofman
gtully
Clebert Suconic
Ryan Yeats
Sebastian Thomschke
Andy Taylor
Tadayoshi Sato
Luis De Bello
Emmanuel Hugonnet
Luis Alves
Mikko Uoti
Bernd Gutjahr