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.
I am also bringing a test I used to report https://issues.apache.org/jira/browse/PROTON-2297
Even though the issue here is on proton. There's no such thing as enough tests so I am keeping the test.
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
- Fixed an issue where I needed to set connection to null after closing it
- Added more tests on QpidDispatchPeerTest (tests i would have done manually, and reproduced a few issues along the way)
- 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
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.
The queue is missing access to the server,
recent changed functionality on temporary queues namespace needed
the server and now the unit test has to pass in the reference to fix the test.
In a cluster scenario where non durable subscribers fail over to
backup while another live node forwarding messages to it,
there is a chance that the the live node keeps the old remote
binding for the subs and messages go to those
old remote bindings will result in "binding not found".
Both authentication and authorization will hit the underlying security
repository (e.g. files, LDAP, etc.). For example, creating a JMS
connection and a consumer will result in 2 hits with the *same*
authentication request. This can cause unwanted (and unnecessary)
resource utilization, especially in the case of networked configuration
like LDAP.
There is already a rudimentary cache for authorization, but it is
cleared *totally* every 10 seconds by default (controlled via the
security-invalidation-interval setting), and it must be populated
initially which still results in duplicate auth requests.
This commit optimizes authentication and authorization via the following
changes:
- Replace our home-grown cache with Google Guava's cache. This provides
simple caching with both time-based and size-based LRU eviction. See more
at https://github.com/google/guava/wiki/CachesExplained. I also thought
about using Caffeine, but we already have a dependency on Guava and the
cache implementions look to be negligibly different for this use-case.
- Add caching for authentication. Both successful and unsuccessful
authentication attempts will be cached to spare the underlying security
repository as much as possible. Authenticated Subjects will be cached
and re-used whenever possible.
- Authorization will used Subjects cached during authentication. If the
required Subject is not in the cache it will be fetched from the
underlying security repo.
- Caching can be disabled by setting the security-invalidation-interval
to 0.
- Cache sizes are configurable.
- Management operations exist to inspect cache sizes at runtime.
Clebert Suconic
Bartosz Spyrko-Smietanko
Domenico Francesco Bruscino
gtully
Justin Bertram
Luis De Bello
sebthom
franz1981
Robbie Gemmell
Andy Taylor
Emmanuel Hugonnet
Mikko Uoti
Bernd Gutjahr
Howard Gao
Stefan Krutzler