Monitoring

Information that the condor_collector collects can be used to monitor a pool. The condor_status command can be used to display snapshot of the current state of the pool. Monitoring systems can be set up to track the state over time, and they might go further, to alert the system administrator about exceptional conditions.

Ganglia

Support for the Ganglia monitoring system (http://ganglia.info/) is integral to HTCondor. Nagios (http://www.nagios.org/) is often used to provide alerts based on data from the Ganglia monitoring system. The condor_gangliad daemon provides an efficient way to take information from an HTCondor pool and supply it to the Ganglia monitoring system.

The condor_gangliad gathers up data as specified by its configuration, and it streamlines getting that data to the Ganglia monitoring system. Updates sent to Ganglia are done using the Ganglia shared libraries for efficiency.

If Ganglia is already deployed in the pool, the monitoring of HTCondor is enabled by running the condor_gangliad daemon on a single machine within the pool. If the machine chosen is the one running Ganglia’s gmetad, then the HTCondor configuration consists of adding GANGLIAD to the definition of configuration variable DAEMON_LIST on that machine. It may be advantageous to run the condor_gangliad daemon on the same machine as is running the condor_collector daemon, because on a large pool with many ClassAds, there is likely to be less network traffic. If the condor_gangliad daemon is to run on a different machine than the one running Ganglia’s gmetad, modify configuration variable GANGLIA_GSTAT_COMMAND to get the list of monitored hosts from the master gmond program.

If the pool does not use Ganglia, the pool can still be monitored by a separate server running Ganglia.

By default, the condor_gangliad will only propagate metrics to hosts that are already monitored by Ganglia. Set configuration variable GANGLIA_SEND_DATA_FOR_ALL_HOSTS to True to set up a Ganglia host to monitor a pool not monitored by Ganglia or have a heterogeneous pool where some hosts are not monitored. In this case, default graphs that Ganglia provides will not be present. However, the HTCondor metrics will appear.

On large pools, setting configuration variable GANGLIAD_PER_EXECUTE_NODE_METRICS to False will reduce the amount of data sent to Ganglia. The execute node data is the least important to monitor. One can also limit the amount of data by setting configuration variable GANGLIAD_REQUIREMENTS . Be aware that aggregate sums over the entire pool will not be accurate if this variable limits the ClassAds queried.

Metrics to be sent to Ganglia are specified in all files within the directory specified by configuration variable GANGLIAD_METRICS_CONFIG_DIR . Each file in the directory is read, and the format within each file is that of New ClassAds. Here is an example of a single metric definition given as a New ClassAd:

[
  Name   = "JobsSubmitted";
  Desc   = "Number of jobs submitted";
  Units  = "jobs";
  TargetType = "Scheduler";
]

A nice set of default metrics is in file: $(GANGLIAD_METRICS_CONFIG_DIR)/00_default_metrics.

Recognized metric attribute names and their use:

Name

The name of this metric, which corresponds to the ClassAd attribute name. Metrics published for the same machine must have unique names.

Value

A ClassAd expression that produces the value when evaluated. The default value is the value in the daemon ClassAd of the attribute with the same name as this metric.

Desc

A brief description of the metric. This string is displayed when the user holds the mouse over the Ganglia graph for the metric.

Verbosity

The integer verbosity level of this metric. Metrics with a higher verbosity level than that specified by configuration variable GANGLIA_VERBOSITY will not be published.

TargetType

A string containing a comma-separated list of daemon ClassAd types that this metric monitors. The specified values should match the value of MyType of the daemon ClassAd. In addition, there are special values that may be included. “Machine_slot1” may be specified to monitor the machine ClassAd for slot 1 only. This is useful when monitoring machine-wide attributes. The special value “ANY” matches any type of ClassAd.

Requirements

A boolean expression that may restrict how this metric is incorporated. It defaults to True, which places no restrictions on the collection of this ClassAd metric.

Title

The graph title used for this metric. The default is the metric name.

Group

A string specifying the name of this metric’s group. Metrics are arranged by group within a Ganglia web page. The default is determined by the daemon type. Metrics in different groups must have unique names.

Cluster

A string specifying the cluster name for this metric. The default cluster name is taken from the configuration variable GANGLIAD_DEFAULT_CLUSTER .

Units

A string describing the units of this metric.

Scale

A scaling factor that is multiplied by the value of the Value attribute. The scale factor is used when the value is not in the basic unit or a human-interpretable unit. For example, duty cycle is commonly expressed as a percent, but the HTCondor value ranges from 0 to 1. So, duty cycle is scaled by 100. Some metrics are reported in KiB. Scaling by 1024 allows Ganglia to pick the appropriate units, such as number of bytes rather than number of KiB. When scaling by large values, converting to the “float” type is recommended.

Derivative

A boolean value that specifies if Ganglia should graph the derivative of this metric. Ganglia versions prior to 3.4 do not support this.

Type

A string specifying the type of the metric. Possible values are “double”, “float”, “int32”, “uint32”, “int16”, “uint16”, “int8”, “uint8”, and “string”. The default is “string” for string values, the default is “int32” for integer values, the default is “float” for real values, and the default is “int8” for boolean values. Integer values can be coerced to “float” or “double”. This is especially important for values stored internally as 64-bit values.

Regex

This string value specifies a regular expression that matches attributes to be monitored by this metric. This is useful for dynamic attributes that cannot be enumerated in advance, because their names depend on dynamic information such as the users who are currently running jobs. When this is specified, one metric per matching attribute is created. The default metric name is the name of the matched attribute, and the default value is the value of that attribute. As usual, the Value expression may be used when the raw attribute value needs to be manipulated before publication. However, since the name of the attribute is not known in advance, a special ClassAd attribute in the daemon ClassAd is provided to allow the Value expression to refer to it. This special attribute is named Regex. Another special feature is the ability to refer to text matched by regular expression groups defined by parentheses within the regular expression. These may be substituted into the values of other string attributes such as Name and Desc. This is done by putting macros in the string values. “\\1” is replaced by the first group, “\\2” by the second group, and so on.

Aggregate

This string value specifies an aggregation function to apply, instead of publishing individual metrics for each daemon ClassAd. Possible values are “sum”, “avg”, “max”, and “min”.

AggregateGroup

When an aggregate function has been specified, this string value specifies which aggregation group the current daemon ClassAd belongs to. The default is the metric Name. This feature works like GROUP BY in SQL. The aggregation function produces one result per value of AggregateGroup. A single aggregate group would therefore be appropriate for a pool-wide metric. As an example, to publish the sum of an attribute across different types of slot ClassAds, make the metric name an expression that is unique to each type. The default AggregateGroup would be set accordingly. Note that the assumption is still that the result is a pool-wide metric, so by default it is associated with the condor_collector daemon’s host. To group by machine and publish the result into the Ganglia page associated with each machine, make the AggregateGroup contain the machine name and override the default Machine attribute to be the daemon’s machine name, rather than the condor_collector daemon’s machine name.

Machine

The name of the host associated with this metric. If configuration variable GANGLIAD_DEFAULT_MACHINE is not specified, the default is taken from the Machine attribute of the daemon ClassAd. If the daemon name is of the form name@hostname, this may indicate that there are multiple instances of HTCondor running on the same machine. To avoid the metrics from these instances overwriting each other, the default machine name is set to the daemon name in this case. For aggregate metrics, the default value of Machine will be the name of the condor_collector host.

IP

A string containing the IP address of the host associated with this metric. If GANGLIAD_DEFAULT_IP is not specified, the default is extracted from the MyAddress attribute of the daemon ClassAd. This value must be unique for each machine published to Ganglia. It need not be a valid IP address. If the value of Machine contains an “@” sign, the default IP value will be set to the same value as Machine in order to make the IP value unique to each instance of HTCondor running on the same host.

Absent ClassAds

By default, HTCondor assumes that resources are transient: the condor_collector will discard ClassAds older than CLASSAD_LIFETIME seconds. Its default configuration value is 15 minutes, and as such, the default value for UPDATE_INTERVAL will pass three times before HTCondor forgets about a resource. In some pools, especially those with dedicated resources, this approach may make it unnecessarily difficult to determine what the composition of the pool ought to be, in the sense of knowing which machines would be in the pool, if HTCondor were properly functioning on all of them.

This assumption of transient machines can be modified by the use of absent ClassAds. When a machine ClassAd would otherwise expire, the condor_collector evaluates the configuration variable ABSENT_REQUIREMENTS against the machine ClassAd. If True, the machine ClassAd will be saved in a persistent manner and be marked as absent; this causes the machine to appear in the output of condor_status -absent. When the machine returns to the pool, its first update to the condor_collector will invalidate the absent machine ClassAd.

Absent ClassAds, like offline ClassAds, are stored to disk to ensure that they are remembered, even across condor_collector crashes. The configuration variable COLLECTOR_PERSISTENT_AD_LOG defines the file in which the ClassAds are stored, and replaces the no longer used variable OFFLINE_LOG. Absent ClassAds are retained on disk as maintained by the condor_collector for a length of time in seconds defined by the configuration variable ABSENT_EXPIRE_ADS_AFTER . A value of 0 for this variable means that the ClassAds are never discarded, and the default value is thirty days.

Absent ClassAds are only returned by the condor_collector and displayed when the -absent option to condor_status is specified, or when the absent machine ClassAd attribute is mentioned on the condor_status command line. This renders absent ClassAds invisible to the rest of the HTCondor infrastructure.

A daemon may inform the condor_collector that the daemon’s ClassAd should not expire, but should be removed right away; the daemon asks for its ClassAd to be invalidated. It may be useful to place an invalidated ClassAd in the absent state, instead of having it removed as an invalidated ClassAd. An example of a ClassAd that could benefit from being absent is a system with an uninterruptible power supply that shuts down cleanly but unexpectedly as a result of a power outage. To cause all invalidated ClassAds to become absent instead of invalidated, set EXPIRE_INVALIDATED_ADS to True. Invalidated ClassAds will instead be treated as if they expired, including when evaluating ABSENT_REQUIREMENTS.

GPUs

HTCondor supports monitoring GPU utilization for NVidia GPUs. This feature is enabled by default if you set use feature : GPUs in your configuration file.

Doing so will cause the startd to run the condor_gpu_utilization tool. This tool polls the (NVidia) GPU device(s) in the system and records their utilization and memory usage values. At regular intervals, the tool reports these values to the condor_startd, assigning them to each device’s usage to the slot(s) to which those devices have been assigned.

Please note that condor_gpu_utilization can not presently assign GPU utilization directly to HTCondor jobs. As a result, jobs sharing a GPU device, or a GPU device being used by from outside HTCondor, will result in GPU usage and utilization being misreported accordingly.

However, this approach does simplify monitoring for the owner/administrator of the GPUs, because usage is reported by the condor_startd in addition to the jobs themselves.

DeviceGPUsAverageUsage

The number of seconds executed by GPUs assigned to this slot, divided by the number of seconds since the startd started up.

DeviceGPUsMemoryPeakUsage

The largest amount of GPU memory used GPUs assigned to this slot, since the startd started up.

Elasticsearch

HTCondor supports pushing condor_schedd and condor_startd job history ClassAds to Elasticsearch via the condor_adstash tool/daemon. condor_adstash collects job history ClassAds as specified by its configuration, converts each ClassAd to a JSON document, and pushes each doc to the configured Elasticsearch index. The index is automatically created if it does not exist, and fields are added and configured based on well known job ClassAd attributes. (Custom attributes are also pushed, though always as keyword fields.)

condor_adstash is a Python 3.6+ script that uses the HTCondor Python Bindings and the Python Elasticsearch Client, both of which must be available to the system Python 3 installation if using the daemonized version of condor_adstash. condor_adstash can also be run as a standalone tool (e.g. in a Python 3 virtual environment containing the necessary libraries).

Running condor_adstash as a daemon (i.e. under the watch of the condor_master) can be enabled by adding use feature : adstash to your HTCondor configuration. By default, this configuration will poll all condor_schedds that report to the $(CONDOR_HOST) condor_collector every 20 minutes and push the contents of the job history ClassAds to an Elasticsearch instance running on localhost to an index named htcondor-000001. Your situation and monitoring needs are likely different! See the condor_config.local.adstash example configuration file in the examples/ directory for detailed information on how to modify your configuration.

If you prefer to run condor_adstash in standalone mode, see the condor_adstash man page for more details.