This repository contains a collection of IBM MQ monitoring agents that utilize the IBM MQ Go metric packages to provide programs that can be used with existing monitoring technologies such as Prometheus, AWS CloudWatch, etc. It can also send data to an OpenTelemetry environment. Statistics and status information can be collected from queue managers and made available in databases to enable dashboard and historic reporting.
The repository also includes a program which traces the route a message can take through the MQ network. It is similar
to the dspmqrte program that is part of the MQ product, but writes the output in JSON format. See the dspmqrtj
subdirectory for more information.
This package is provided as-is with no guarantees of support or updates. There are also no guarantees of compatibility with any future versions of the package; interfaces and functions are subject to change based on any feedback. You cannot use IBM formal support channels (Cases/PMRs) for assistance with material in this repository.
These programs use a specific version of the mqmetric and ibmmq golang packages. Those packages are in the
mq-golang repository and are also included in the vendor tree of this
repository. They are referenced in the go.mod file if you wish to reload all of the dependencies by running
go mod vendor.
You will require the following programs:
- Go compiler - version 1.20 is the minimum defined here
- C compiler
The MQ Client SDK for C programs is required in order to compile and run Go programs. You may have this from an MQ Client installation image (eg rpm, deb formats for Linux, msi for Windows).
For Linux x64 and Windows systems, you may also choose to use the MQ Redistributable Client package which is a simple zip/tar file that does not need any privileges to install:
- Download IBM MQ redistributable client
- Unpack archive to fixed directory. E.g.
c:\IBM-MQC-Redist-Win64or/opt/mqm.
See the README file in the mq-golang repository for more information about any environment variables that may be required to point at non-default directories for the MQ C SDK.
- You need to have the MQ client libraries installed first.
- Create a directory where you want to work with the programs.
- Change to that directory.
- Use git to get a copy of this repository into a new directory in the workspace. For example:
git clone https://github.com/ibm-messaging/mq-metric-samples.git src/github.com/ibm-messaging/mq-metric-samples
-
Navigate to the mq-metric-samples root directory (
./src/github.com/ibm-messaging/mq-metric-samples) -
All the prereq packages are already available in the vendor directory, but you can run
go mod vendorto reload them -
From this root directory of the repository you can then compile the code. For example,
cd ./src/github.com/ibm-messaging/mq-metric-samples
export CGO_LDFLAGS_ALLOW='-Wl,-rpath.*'
mkdir -p /tmp/go/bin
go build -mod=vendor -o /tmp/go/bin/mq_prometheus ./cmd/mq_prometheus/*.go
At this point, you should have a compiled copy of the code in /tmp/go/bin. Each monitor agent directory also has
sample scripts, configuration files etc to help with getting the agent running in your specific environment.
The -mod=vendor option is important so that the build process does not need to download additional files from external
repositories.
The Dockerfile in the root directory gives a simple way to both build and run a collector program through containers.
You still need to provide the configuration file at runtime, perhaps as a mounted volume. For example:
docker build -t mqprom:1.0 .
docker run -p 9157:9157 -v <directory>/mq_prometheus.yaml:/opt/config/mq_prometheus.yaml mqprom:1.0
This Dockerfile should work for a variety of platforms. For those with a Redistributable client, it uses curl to
automatically download and unpack the required MQ files. For other platforms, it assumes that you have an MQINST
subdirectory under this root, and then copied the .deb files (or the .tar.gz file for Linux/arm64 systems) from your
real MQ installation tree into it.
As a more flexible example, you can use the buildMonitors.sh script in the scripts subdirectory to build a Docker
container that in turn will build all the binary programs and copy them to a local directory. That script also sets some
extra version-related flags that will be shown when the program starts. The container will automatically download and
install the MQ client runtime files needed for compilation. This might be a preferred approach when you want to run a
collector program alongside a queue manager (perhaps as an MQ SERVICE) and you need to copy the binaries to the target
system.
There is a buildMonitors.bat file to help with building on Windows. It assumes you have the
tdm-gcc-64 64-bit compiler suite installed. It builds all the collectors
and corresponding YAML configuration files into %GOPATH%/bin
When metrics are being collected from the publish/subscribe interface (all platforms except z/OS), there are some considerations:
- MAXHANDS on queue manager: The default configuration of these collectors uses non-durable subscriptions to get information about queue metrics. Each subscription uses an object handle. If many queues are being monitored the default MAXHANDS may need to be increased. A warning is printed if the monitor thinks this attribute appears too low. See below for an alternative option.
- MAXDEPTH on model queues: The model queue used as the basis for publication and reply queues in the monitor must have a MAXDEPTH suitable for the expected amount of data. For published metrics, this is estimated based on holding one minute's amount of publications; the number of monitored channels is also used as an estimate, although that does not need to be time-based as the data is requested directly by the monitor.
- USEDLQ on the admin topic: The USEDLQ attribute on the topic object associated with the metrics publications (usually
SYSTEM.ADMIN.TOPIC) determines what happens if the subscriber's queue is full. You might prefer to set this to NO to avoid filling the system DLQ if the collection program does not read the publications frequently enough.
Connections to the queue manager can be made with either local or client bindings. Running the collector "alongside" the
queue manager is usually preferred, with the collector configured to run as a service. Sample scripts in this repository
show how to define an appropriate MQ SERVICE. Client connections can be made by specifying the channel and connName
information in the basic configuration; in this mode, only plaintext communication is available (similar to the MQSERVER
environment variable). For secure communication using TLS, then you must provide connection information via a CCDT. Use
the ccdtUrl configuration option or environment variables to point at a CCDT that can be in either binary or JSON
format. The runMonitorTLS.sh script gives a simple example of setting up a container to use TLS.
An alternative collection mechanism uses durable subscriptions for the queue metric data. This may avoid needing to increase the MAXHANDS attribute on a queue manager. (Queue manager-level metrics are still collected using non-durable subscriptions.)
To set it up, you must provide suitable configuration options. In the YAML configuration, these are the attributes (command line or environment variable equivalents also exist):
replyQueuemust refer to a local queue (not a model queue)replyQueue2must also be set, referring to a different local queuedurableSubPrefixis a string that is unique across any collectors that might be connected to this queue manager
If you use durable subscriptions, then the named reply queues may continue to receive publications even when the
collector is not running, so that may induce queue-full reports in the error log or events. The subscriptions can be
manually removed using the "DELETE SUB()" MQSC command for all subscriptions where the subscription ids begin with the
durableSubPrefix value. The scripts/cleanDur.sh program can be used for this deletion. You should also clean the
subscriptions when the configuration of which data to collect has changed, particularly the queueSubscriptionSelector
option.
The monitors always collect all of the available queue manager-wide metrics. They can also be configured to collect statistics for specific sets of queues where metrics are published by the queue manager. Object status queries can be used to extract more information about other objects such as channels and subscriptions.
The exporters can have their configuration given on the command line via flags, via environment variables, or in a YAML file described below.
The sets of queues to be monitored can be given either directly on the command line with the -ibmmq.monitoredQueues
flag, put into a separate file which is also named on the command line, with the -ibmmq.monitoredQueuesFile flag, or
in the equivalent YAML configuration.
The parameter can include both positive and negative wildcards. For example ibmmq.monitoredQueues=A*,!AB*" will
collect data on queues beginning with "AC" or "AD" but not "AB". The full rules for expansion can be seen near the
bottom of the discover.go module in the mqmetric package.
The queue patterns are expanded at startup of the program and at regular intervals thereafter. So newly-defined queues
will eventually be monitored if they match the pattern. The rediscovery interval is 1h by default, but can be modified
by the rediscoverInterval parameter.
The monitor programs can process channel status, reporting that back into the database.
The channels to be monitored are set on the command line, similarly to the queue patterns, with
-ibmmq.monitoredChannels or -ibmmq.monitoredChannelFile. Unlike the queue monitoring, wildcards are handled
automatically by the channel status API. So you do not need to restart this monitor in order to pick up newly-defined
channels that match an existing pattern. Only positive wildcards are allowed here; you cannot explicitly exclude
channels.
Another parameter is pollInterval. This determines how frequently the channel status is collected. You may want to
have it collected at a different rate to the queue data, as it may be more expensive to extract the channel status. The
default pollInterval is 0, which means that the channel status is collected every time the exporter processes the queue
and queue manager resource publications. Setting it to 1m means that a minimum time of one minute will elapse between
asking for channel status even if the queue statistics are gathered more frequently.
A short-lived channel that connects and then disconnects in between collection intervals will leave no trace in the status or metrics.
Some the responses from the DISPLAY CHSTATUS command have been selected as metrics. The key values returned include the status and number of messages processed.
The message count for SVRCONN channels is the number of MQI calls made by the client program.
There are actually two versions of the channel status returned. The channel_status metric has the value corresponding
to one of the MQCHS_* values. There are about 15 of these possible values. There is also a channel_status_squash
metric which returns one of only three values, compressing the full set into a simpler value that is easier to put
colours against in Grafana. From this squashed set, you can readily see if a channel is stopped, running, or somewhere
in between.
Channel metrics are given labels to assist in distinguishing them. These can be displayed in Grafana or used as part of the filtering. When there is more than one instance of an active channel, the combination of channel name, connection name and job name will be unique (though see the z/OS section below for caveats on that platform).
The channel type (SENDER, SVRCONN etc) and the name of the remote queue manager are also given as labels on the metric.
An example Grafana dashboard shows how these labels and metrics can be combined to show some channel status from Prometheus. The Channel Status table panel demonstrates a couple of features. It uses the labels to select unique instances of channels. It also uses a simple number-to-text map to show the channel status as a word (and colour the cell) instead of a raw number.
The metrics for the table are selected and have '0' added to them. This may be a workround of a Grafana bug, or it may really be how Grafana is designed to work. But without that '+0' on the metric line, the table was showing multiple versions of the status for each channel. This table combines multiple metrics on the same line now.
Information about channels comes from the DISPLAY CHSTATUS CURRENT command. That only shows channels with a known state and does not report on inactive channels. To also see the inactive channels, then set the showInactiveChannels configuration attribute to true.
When NativeHA is used, the queue manager publishes some metrics on its status. These are automatically collected
whenever available, and can be seen in the metric lists. The metrics are given a prefix or series of "nha". For example,
ibmmq_nha_synchronous_log_sent_bytes is one metric shown in Prometheus. The NativeHA "instance" - the names given to
the replicas - is added as the nhainstance tag to the metrics.
Depending on configuration, the collector may be able to automatically reconnect to the new instance after a failover. If that is not possible, you will need to have a process to restart the collector once the new replica has taken over.
Because the DIS QSTATUS and DIS CHSTATUS commands can be used on z/OS, the monitors support showing some information from a z/OS queue manager. There is nothing special needed to configure it, beyond the client connectivity that allows an application to connect to the z/OS system.
The -ibmmq.useStatus (command line) or useObjectStatus (YAML) parameter must be set to true to use the DIS QSTATUS
command.
There is also support for using the RESET QSTATS command on z/OS. This needs to be explicitly enabled by setting the
-ibmmq.resetQStats (command line) or useResetQStats (YAML) flag to true. While this option allows tracking of the
number of messages put/got to a queue (which is otherwise unavailable from z/OS queue manager status queries), it should
not be used if there are any other active monitoring solutions that are already using that command.
Only one monitor program can reliably use RESET QSTATS on a particular queue manager, to avoid the information being split between them.
Statistics are available for pagesets and bufferpools, similar to the DISPLAY USAGE command.
On z/OS, there is no guaranteed way to distinguish between multiple instances of the same channel name. For example,
multiple users of the same SVRCONN definition. On Distributed platforms, the JOBNAME attribute does that job; for z/OS,
the channel start date/time is used in this package as a discriminator, and used as the jobname label in the metrics.
That may cause the stats to be slightly wrong if two instances of the same channel are started simultaneously from the
same remote address. The sample dashboard showing z/OS status includes counts of the unique channels seen over the
monitoring period.
Monitors can be configured to authenticate to the queue manager, sending a userid and password.
The userid is configured using the -ibmmq.userid flag. The password can be set either by using the -ibmmq.password
flag, or by passing it via stdin. That allows it to be piped from an external stash file or some other mechanism. Using
the command line flags for controlling passwords is not recommended for security-sensitive environments.
Where authentication is needed for access to a database, passwords for those can also be passed via stdin.
Instead of providing all of the configuration for the exporters via command-line flags, you can also provide the
configuration in a YAML file. Then only the -f command-line option is required for the exporter to point at the file.
All of the exporters support the same configuration options for how to connect to MQ and which objects are monitored. There is then an exporter-specific section for additional configuration such as how to contact the back-end database. The common options are shown in a template in this directory; the exporter-specific options are in individual files in each directory. Combine the two pieces into a single file to get a complete deployable configuration.
Unlike the command line flags, lists are provided in a more natural format instead of comma-separated values in a single string. If an option is provided on both the command line and in the file, it is the file that takes precedence. Not all strings need to be surrounded by quote characters in the file, but some (eg "!SYSTEM*") seem to need it. The example files have used quotes where they have been found to be necessary.
The field names are slightly different in the YAML file to try to make them a bit more consistent and structured. The command flags are not being changed to preserve compatibility with previous versions.
User passwords can be provided in the file, but it is not recommended that you do that. Instead provide the password either on the command line or piped via stdin to the program.
As a further alternative for configuration, parameters can be set by environment variables. This may be more convenient when running collectors in a container as the variables may be easier to modify for each container than setting up different YAML files. The names of the variables follow the YAML naming pattern with an IBMMQ prefix, underscore separators, and in uppercase.
For example, the queue manager name can be set with IBMMQ_CONNECTION_QUEUEMANAGER. You can use the "-h" parameter to
the collector to see the complete set of options.
The command line flags are highest precedence. Environment variables override settings in the YAML file, And the YAML overrides the hardcoded default values.
For all the collectors, you can configure additional metadata that is replicated into the tags or labels produced on
each metric. These might indicate, for example, whether a queue manager is DEV or PROD level. These are defined using
the -ibmmq.metadataTags and -ibmmq.metadataValues command line flags (comma-separated), corresponding
IBMMQ_CONNECTION-level environment variables, or as arrays within the YAML configuration file.
Each of the sample monitor programs has its own README file describing any particular considerations. The metrics.txt file in this directory has a summary of the available metrics for each object type.
See CHANGELOG in this directory.
For feedback and issues relating specifically to this package, please use the GitHub issue tracker.
Contributions to this package can be accepted under the terms of the Developer's Certificate of Origin, found in the DCO file of this repository. When submitting a pull request, you must include a statement stating you accept the terms in the DCO.
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