Chapter 2. Analyzing Metrics
Kibana offers two ways of analyzing metrics:
- Build your own visualizations such as charts, graphs, and tables.
- Load and use predefined sets of visualizations
Red Hat suggests that you start off by using the predefined visualizations. Each set is known as a dashboard. Dashboards have the advantage of enabling you to quickly access a wide range of metrics while offering the flexibility of changing them to match your individual needs.
2.1. Using Dashboards
A dashboard displays a set of saved visualizations. Dashboards have the advantage of enabling you to quickly access a wide range of metrics while offering the flexibility of changing them to match your individual needs.
You can use the Dashboard tab to create your own dashboards. Alternatively, Red Hat provides the following dashboard examples, which you can import into Kibana and use as is or customize to suit your specific needs:
- System dashboard
- Hosts dashboard
- VMs dashboard
Importing Dashboard Examples
The dashboard examples are only available after completing the procedure for Deploying collectd and rsyslog.
-
Copy the
/etc/ovirt-engine-metrics/dashboards-examplesdirectory from the Manager virtual machine to your local machine. -
Log in to the Kibana console using the URL (
https://kibana.example.com) that you recorded during the Metrics Store installation process. Use the defaultadminuser, and the password you defined during the installation. - Open Kibana and click the Management tab.
- Click the Saved Objects tab.
-
Click Import and import Searches from your local copy of
/etc/ovirt-engine-metrics/dashboards-examples. Click Import and import Visualizations.
NoteIf you see an error message while importing the visualizations, check your hosts to ensure that collectd and rsyslog are running without errors.
Click Import and import Dashboards.
NoteIf you are logged in as the
adminuser, you may see a message regarding missing index patterns while importing the visualizations. Select the project.* index pattern instead.In the Elasticsearch Kibana portal, go to
, select , and add the following index patterns: project.ovirt-metrics-<ovirt_env_name>*project.ovirt-logs-<ovirt_env_name>*- Click for each of the new index patterns.
The imported dashboards are now stored in the system.
Loading Saved Dashboards
Once you have created and saved a dashboard, or imported Red Hat’s sample dashboards, you can display them in the Dashboard tab:
- Click the Dashboards tab to display a list of saved dashboards.
- Click a saved dashboard to load it.
2.2. Creating a New Visualization
Use the Visualize page to design data visualizations based on the metrics or log data collected by Metrics Store. You can save these visualizations, use them individually, or combine visualizations into a dashboard. A visualization can be based on one of the following data source types:
- A new interactive search
- A saved search
- An existing saved visualization
Visualizations are based on Elasticsearch’s aggregation feature.
Creating a New Visualization
Kibana guides you through the creation process with the help of a visualization wizard.
- To start the new visualization wizard, click the Visualize tab.
- In step 1, Create a new visualization table, select the type of visualization you want to create.
In step 2, Select a search source, select whether you want to create a new search or reuse a saved search:
- To create a new search, select From a new search and enter the indexes to use as the source. Use project.ovirt-logs prefix for log data or project.ovirt-metrics prefix for metric data.
-
To create a visualization from a saved search, select From a saved search and enter the name of the search.
The visualization editor appears.
2.3. Graphic User Interface Elements
The visualization editor consists of three main areas:
-
The toolbar
-
The aggregation builder
-
The preview pane
Visualization Editor
2.4. Using the Visualization Editor
Use the visualization editor to create visualizations by:
2.4.1. Submitting Search Queries
Use the toolbar to perform search queries based on the Lucene query parser syntax. For a detailed explanation of this syntax, see Apache Lucene - Query Parser Syntax.
2.4.2. Selecting Metrics and Aggregations
Use the aggregation builder to define which metrics to display, how to aggregate the data, and how to group the results.
The aggregation builder performs two types of aggregations, metric and bucket, which differ depending on the type of visualization you are creating:
- Bar, line, or area chart visualizations use metrics for the y-axis and buckets for the x-axis, segment bar colors, and row/column splits.
- Pie charts use metrics for the slice size and buckets to define the number of slices.
To define a visualization from the aggregation bar:
- Select the metric aggregation for your visualization’s y-axis from the Aggregation drop-down list in the metrics section, for example, count, average, sum, min, max, or unique count. For more information about how these aggregations are calculated, see Metrics Aggregation in the Elasticsearch Reference documentation.
Use the buckets area to select the aggregations for the visualization’s x-axis, color slices, and row/column splits:
Use the Aggregation drop-down list to define how to aggregate the bucket. Common bucket aggregations include date histogram, range, terms, filters, and significant terms.
The order in which you define the buckets determines the order in which they will be executed, so the first aggregation determines the data set for any subsequent aggregations. For more information, see Aggregation Builder in the Kibana documentation.
- Select the metric you want to display from the Field drop-down list. For details about each of the available metrics, see Metrics Schema.
- Select the required interval from the Interval field.
-
Click Apply Changes
.
2.5. Metrics Schema
The following sections describe the metrics that are available from the Field menu when creating visualizations.
All metric values are collected at 10 second intervals.
2.5.1. Aggregation Metrics
The Aggregation metric aggregates several values into one using aggregation functions such as sum, average, min, and max. It is used to provide a combined value for average and total CPU statistics.
The following table describes the aggregation metrics reported by the Aggregation plugin.
| Metric Name | collectd.type_instance | Description |
|---|---|---|
| collectd.aggregation.percent |
| The average and total CPU usage, as an aggregated percentage, for each of the collectd.type_instance states. |
Additional Values
- collectd.plugin: Aggregation
- collectd.type_instance: cpu-average / cpu-sum
- collectd.plugin_instance:
- collectd.type: percent
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Gauge
2.5.2. CPU Metrics
CPU metrics display the amount of time spent by the hosts' CPUs, as a percentage.
The following table describes CPU metrics as reported by the CPU plugin.
| Metric Name | collectd.type_instance | Description |
|---|---|---|
| collectd.cpu.percent |
| The percentage of time spent, per CPU, in the collectd.type_instance states. |
Additional Values
- collectd.plugin: CPU
- collectd.plugin_instance: The CPU’s number
- collectd.type: percent
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Gauge
2.5.3. CPU Load Average Metrics
CPU load represents CPU contention, that is, the average number of schedulable processes at any given time. This is reported as an average value for all CPU cores on the host. Each CPU core can only execute one process at a time. Therefore, a CPU load average above 1.0 indicates that the CPUs have more work than they can perform, and the system is overloaded.
CPU load is reported over short term (last one minute), medium term (last five minutes) and long term (last fifteen minutes). While it is normal for a host’s short term load average to exceed 1.0 (for a single CPU), sustained load average above 1.0 on a host may indicate a problem.
On multi-processor systems, the load is relative to the number of processor cores available. The "100% utilization" mark is 1.00 on a single-core, 2.00 on a dual-core, 4.00 on a quad-core system.
Red Hat recommends looking at CPU load in conjunction with CPU Metrics.
The following table describes the CPU load metrics reported by the Load plugin.
| Metric Name | Description |
|---|---|
| collectd.load.load.longterm | Average number of schedulable processes per CPU core over the last 15 minutes. A value above 1.0 indicates the system was overloaded during the last 15 minutes. |
| collectd.load.load.midterm | Average number of schedulable processes per CPU core over the last five minutes. A value above 1.0 indicates the system was overloaded during the last 5 minutes. |
| collectd.load.load.shortterm | Average number of schedulable processes per CPU core over the last one minute. A value above 1.0 indicates the system was overloaded during the last minute. |
Additional Values
- collectd.plugin: Load
- collectd.type: load
- collectd.type_instance: None
- collectd.plugin_instance: None
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Gauge
2.5.4. Disk Consumption Metrics
Disk consumption (DF) metrics enable you to monitor metrics about disk consumption, such as the used, reserved, and free space for each mounted file system.
The following table describes the disk consumption metrics reported by the DF plugin.
| Metric Name | Description |
|---|---|
| collectd.df.df_complex | The amount of free, used, and reserved disk space, in bytes, on this file system. |
| collectd.df.percent_bytes | The amount of free, used, and reserved disk space, as a percentage of total disk space, on this file system. |
Additional Values
- collectd.plugin: DF
- collectd.type_instance: free, used, reserved
- collectd.plugin_instance: A mounted partition
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Gauge
2.5.5. Disk Operation Metrics
Disk operation metrics are reported per physical disk on the host, and per partition.
The following table describes the disk operation metrics reported by the Disk plugin.
| Metric Name | Description | collectd.dstypes |
|---|---|---|
| collectd.disk.disk_ops.read | The number of disk read operations. | |
| collectd.disk.disk_ops.write | The number of disk write operations. | |
| collectd.disk.disk_merged.read | The number of disk reads that have been merged into single physical disk access operations. In other words, this metric measures the number of instances in which one physical disk access served multiple disk reads. The higher the number, the better. | |
| collectd.disk.disk_merged.write | The number of disk writes that were merged into single physical disk access operations. In other words, this metric measures the number of instances in which one physical disk access served multiple write operations. The higher the number, the better. | |
| collectd.disk.disk_time.read | The average amount of time it took to do a read operation, in milliseconds. | |
| collectd.disk.disk_time.write | The average amount of time it took to do a write operation, in milliseconds. | |
| collectd.disk.pending_operations | The queue size of pending I/O operations. | |
| collectd.disk.disk_io_time.io_time | The time spent doing I/Os in milliseconds. This can be used as a device load percentage, where a value of 1 second of time spent represents a 100% load. | |
| collectd.disk.disk_io_time.weighted_io_time | A measure of both I/O completion time and the backlog that may be accumulating. |
Additional Values
- collectd.plugin: Disk
- collectd.type_instance: None
- collectd.plugin_instance: The disk’s name
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
2.5.6. Entropy Metrics
Entropy metrics display the available entropy pool size on the host. Entropy is important for generating random numbers, which are used for encryption, authorization, and similar tasks.
The following table describes the entropy metrics reported by the Entropy plugin.
| Metric Name | Description |
|---|---|
| collectd.entropy.entropy | The entropy pool size, in bits, on the host. |
Additional Values
- collectd.plugin: Entropy
- collectd.type_instance: None
- collectd.plugin_instance: None
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Gauge
2.5.7. Network Interface Metrics
The following types of metrics are reported from physical and virtual network interfaces on the host:
- Bytes (octets) transmitted and received (total, or per second)
- Packets transmitted and received (total, or per second)
- Interface errors (total, or per second)
The following table describes the network interface metrics reported by the Interface plugin.
| collectd.type | Metric Name | Description |
|---|---|---|
| if_octets | collectd.interface.if_octets.rx | A count of the bytes received by the interface. You can view this metric as a Rate/sec or a cumulative count (Max): * Rate/sec: Provides the current traffic level on the interface in bytes/sec. * Max: Provides the cumulative count of bytes received. Note that since this metric is a cumulative counter, its value will periodically restart from zero when the maximum possible value of the counter is exceeded. |
| if_octets | collectd.interface.if_octets.tx | A count of the bytes transmitted by the interface. You can view this metric as a Rate/sec or a cumulative count (Max): * Rate/sec: Provides the current traffic level on the interface in bytes/sec. * Max: Provides the cumulative count of bytes transmitted. Note that since this metric is a cumulative counter, its value will periodically restart from zero when the maximum possible value of the counter is exceeded. |
| if_packets | collectd.interface.if_packets.rx | A count of the packets received by the interface. You can view this metric as a Rate/sec or a cumulative count (Max): * Rate/sec: Provides the current traffic level on the interface in bytes/sec. * Max: Provides the cumulative count of packets received. Note that since this metric is a cumulative counter, its value will periodically restart from zero when the maximum possible value of the counter is exceeded. |
| if_packets | collectd.interface.if_packets.tx | A count of the packets transmitted by the interface. You can view this metric as a Rate/sec or a cumulative count (Max): * Rate/sec: Provides the current traffic level on the interface in packets/sec. * Max: Provides the cumulative count of packets transmitted. Note that since this metric is a cumulative counter, its value will periodically restart from zero when the maximum possible value of the counter is exceeded. |
| if_errors | collectd.interface.if_errors.rx | A count of errors received on the interface. You can view this metric as a Rate/sec or a cumulative count (Max). * Rate/sec rollup provides the current rate of errors received on the interface in errors/sec. * Max rollup provides the total number of errors received since the beginning. Note that since this is a cumulative counter, its value will periodically restart from zero when the maximum possible value of the counter is exceeded. |
| if_errors | collectd.interface.if_errors.tx | A count of errors transmitted on the interface. You can view this metric as a Rate/sec or a cumulative count (Max). * Rate/sec rollup provides the current rate of errors transmitted on the interface in errors/sec. * Max rollup provides the total number of errors transmitted since the beginning. Note that since this is a cumulative counter, its value will periodically restart from zero when the maximum possible value of the counter is exceeded. |
| if_dropped | collectd.interface.if_dropped.rx | A count of dropped packets received on the interface. You can view this metric as a Rate/sec or a cumulative count (Max). * Rate/sec rollup provides the current rate of dropped packets received on the interface in packets/sec. * Max rollup provides the total number of dropped packets received since the beginning. Note that since this is a cumulative counter, its value will periodically restart from zero when the maximum possible value of the counter is exceeded. |
| if_dropped | collectd.interface.if_dropped.tx | A count of dropped packets transmitted on the interface. You can view this metric as a Rate/sec or a cumulative count (Max). * Rate/sec rollup provides the current rate of dropped packets transmitted on the interface in packets/sec. * Max rollup provides the total number of dropped packets transmitted since the beginning. Note that since this is a cumulative counter, its value will periodically restart from zero when the maximum possible value of the counter is exceeded. |
Additional Values
- collectd.plugin: Interface
- collectd.type_instance: None
- collectd.plugin_instance: The network’s name
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Derive
2.5.8. Memory Metrics
Metrics collected about memory usage.
The following table describes the memory usage metrics reported by the Memory plugin.
| Metric Name | collectd.type | collectd.type_instance | Description |
|---|---|---|---|
| collectd.memory.memory | memory | used | The total amount of memory used. |
| free | The total amount of unused memory. | ||
| cached | The amount of memory used for caching disk data for reads, memory-mapped files, or tmpfs data. | ||
| buffered | The amount of memory used for buffering, mostly for I/O operations. | ||
| slab_recl | The amount of reclaimable memory used for slab kernel allocations. | ||
| slab_unrecl | Amount of unreclaimable memory used for slab kernel allocations. | ||
| collectd.memory.percent | percent | used | The total amount of memory used, as a percentage. |
| free | The total amount of unused memory, as a percentage. | ||
| cached | The amount of memory used for caching disk data for reads, memory-mapped files, or tmpfs data, as a percentage. | ||
| buffered | The amount of memory used for buffering I/O operations, as a percentage. | ||
| slab_recl | The amount of reclaimable memory used for slab kernel allocations, as a percentage. | ||
| slab_unrecl | The amount of unreclaimable memory used for slab kernel allocations, as a percentage. |
Additional Values
- collectd.plugin: Memory
- collectd.plugin_instance: None
- ovirt.entity: Host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Gauge
2.5.9. NFS Metrics
NFS metrics enable you to analyze the use of NFS procedures.
The following table describes the NFS metrics reported by the NFS plugin.
| Metric Name | collectd.type_instance | Description | ||
|---|---|---|---|---|
| collectd.nfs.nfs_procedure | null / getattr / lookup / access / readlink / read / write / create / mkdir / symlink / mknod / rename / readdir / remove / link / fsstat / fsinfo / readdirplus / pathconf / rmdir / commit / compound / reserved / access / close / delegpurge / putfh / putpubfh putrootfh / renew / restorefh / savefh / secinfo | / setattr / setclientid / setcltid_confirm / verify / open / openattr / open_confirm / exchange_id / create_session / destroy_session / bind_conn_to_session / delegreturn / getattr / getfh / lock / lockt / locku / lookupp / open_downgrade / nverify | / release_lockowner / backchannel_ctl / free_stateid / get_dir_delegation / getdeviceinfo / getdevicelist / layoutcommit / layoutget / layoutreturn / secinfo_no_name / sequence / set_ssv / test_stateid / want_delegation / destroy_clientid / reclaim_complete | The number of processes per collectd.type_instance state. |
Additional Values
- collectd.plugin: NFS
- collectd.plugin_instance: File system + server or client (for example: v3client)
- collectd.type: nfs_procedure
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Derive
2.5.10. PostgreSQL Metrics
PostgreSQL data collected by executing SQL statements on a PostgreSQL database.
The following table describes the PostgreSQL metrics reported by the PostgreSQL plugin.
| Metric Name | collectd.type_instance | Description |
|---|---|---|
| collectd.postgresql.pg_numbackends | N/A | How many server processes this database is using. |
| collectd.postgresql.pg_n_tup_g | live | The number of live rows in the database. |
| dead | The number of dead rows in the database. Rows that are deleted or obsoleted by an update are not physically removed from their table; they remain present as dead rows until a VACUUM is performed. | |
| collectd.postgresql.pg_n_tup_c | del | The number of delete operations. |
| upd | The number of update operations. | |
| hot_upd | The number of update operations that have been performed without requiring an index update. | |
| ins | The number of insert operations. | |
| collectd.postgresql.pg_xact | num_deadlocks | The number of deadlocks that have been detected by the database. Deadlocks are caused by two or more competing actions that are unable to finish because each is waiting for the other’s resources to be unlocked. |
| collectd.postgresql.pg_db_size | N/A | The size of the database on disk, in bytes. |
| collectd.postgresql.pg_blks | heap_read | How many disk blocks have been read. |
| heap_hit | How many read operations were served from the buffer in memory, so that a disk read was not necessary. This only includes hits in the PostgreSQL buffer cache, not the operating system’s file system cache. | |
| idx_read | How many disk blocks have been read by index access operations. | |
| idx_hit | How many index access operations have been served from the buffer in memory. | |
| toast_read | How many disk blocks have been read on TOAST tables. | |
| toast_hit | How many TOAST table reads have been served from buffer in memory. | |
| tidx_read | How many disk blocks have been read by index access operations on TOAST tables. |
Additional Values
- collectd.plugin: Postgresql
- collectd.plugin_instance: Database’s Name
- ovirt.entity: engine
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Gauge
2.5.11. Process Metrics
The following table describes the process metrics reported by the Processes plugin.
| Metric Name | collectd.type | Description | collectd.dstypes |
|---|---|---|---|
| collectd.processes.ps_state | ps_state | The number of processes in each state. | |
| collectd.processes.ps_disk_ops.read | ps_disk_ops | The process’s I/O read operations. | |
| collectd.processes.ps_disk_ops.write | ps_disk_ops | The process’s I/O write operations. | |
| collectd.processes.ps_vm | ps_vm | The total amount of memory including swap. | |
| collectd.processes.ps_rss | ps_rss | The amount of physical memory assigned to the process. | |
| collectd.processes.ps_data | ps_data | ||
| collectd.processes.ps_code | ps_code | ||
| collectd.processes.ps_stacksize | ps_stacksize | ||
| collectd.processes.ps_cputime.syst | ps_cputime | The amount of time spent by the matching processes in kernel mode. The values are scaled to microseconds per second to match collectd’s numbers. | |
| collectd.processes.ps_cputime.user | ps_cputime | The amount of time spent by the matching processes in user mode. The values are scaled to microseconds per second. | |
| collectd.processes.ps_count.processes | ps_count | The number of processes for the defined process. | |
| collectd.processes.ps_count.threads | ps_count | The number of threads for the defined process. | |
| collectd.processes.ps_pagefaults.majfltadd | ps_pagefaults | The number of major page faults caused by the process. | |
| collectd.processes.ps_pagefaults.minflt | ps_pagefaults | The number of major page faults caused by the process. | |
| collectd.processes.ps_disk_octets.write | ps_disk_octets | The process’s I/O write operations in transferred bytes. | |
| collectd.processes.ps_disk_octets.read | ps_disk_octets | The process’s I/O read operations in transferred bytes. | |
| collectd.processes.fork_rate | fork_rate | The system’s fork rate. |
Additional Values
- collectd.plugin: Processes
- collectd.type_instance: N/A (except for collectd.processes.ps_state=running/ zombies/ stopped/ paging/ blocked/ sleeping)
- ovirt.entity: host
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
2.5.12. StatsD Metrics
The following table describes the StatsD metrics reported by the StatsD plugin.
| Metric Name | collectd.type | collectd.type_instance | Description |
|---|---|---|---|
| collectd.statsd.host_storage | host_storage | storage uuid | The latency for writing to the storage domain. |
| collectd.statsd.vm_balloon_cur | vm_balloon_cur | N/A | The current amount of memory available to the guest virtual machine (in KB). |
| collectd.statsd.vm_balloon_max | vm_balloon_max | N/A | The maximum amount of memory available to the guest virtual machine (in KB). |
| collectd.statsd.vm_balloon_min | vm_balloon_min | N/A | The minimum amount of memory guaranteed to the guest virtual machine (in KB). |
| collectd.statsd.vm_balloon_target | vm_balloon_target | N/A | The amount of memory requested (in KB). |
| collectd.statsd.vm_cpu_sys | vm_cpu_sys | N/A | Ratio of non-guest virtual machine CPU time to total CPU time spent by QEMU. |
| collectd.statsd.vm_cpu_usage | vm_cpu_usage | N/A | Total CPU usage since VM start in (ns). |
| collectd.statsd.vm_cpu_user | vm_cpu_user | N/A | Ratio of guest virtual machine CPU time to total CPU time spent by QEMU. |
| collectd.statsd.vm_disk_apparent_size | vm_disk_apparent_size | disk name | The size of the disk (in bytes). |
| collectd.statsd.vm_disk_flush_latency | vm_disk_flush_latency | disk name | The virtual disk’s flush latency (in seconds). |
| collectd.statsd.vm_disk_read_bytes | vm_disk_read_bytes | disk name | The read rate from disk (in bytes per second). |
| collectd.statsd.vm_disk_read_latency | vm_disk_read_latency | disk name | The virtual disk’s read latency (in seconds). |
| collectd.statsd.vm_disk_read_ops | vm_disk_read_ops | disk name | The number of read operations since the virtual machine was started. |
| collectd.statsd.vm_disk_read_rate | vm_disk_read_rate | disk name | The virtual machine’s read activity rate (in bytes per second). |
| collectd.statsd.vm_disk_true_size | vm_disk_true_size | disk name | The amount of underlying storage allocated (in bytes). |
| collectd.statsd.vm_disk_write_latency | vm_disk_write_latency | disk name | The virtual disk’s write latency (in seconds). |
| collectd.statsd.vm_disk_write_ops | vm_disk_write_ops | disk name | The number of write operations since the virtual machine was started. |
| collectd.statsd.vm_disk_write_rate | vm_disk_write_rate | disk name | The virtual machine’s write activity rate (in bytes per second). |
| collectd.statsd.vm_nic_rx_bytes | vm_nic_rx_bytes | network name | The total number of incoming bytes. |
| collectd.statsd.vm_nic_rx_dropped | vm_nic_rx_dropped | network name | The number of incoming packets that have been dropped. |
| collectd.statsd.vm_nic_rx_errors | vm_nic_rx_errors | network name | The number of incoming packets that contained errors. |
| collectd.statsd.vm_nic_speed | vm_nic_speed | network name | The interface speed (in Mbps). |
| collectd.statsd.vm_nic_tx_bytes | vm_nic_tx_bytes | network name | The total number of outgoing bytes. |
| collectd.statsd.vm_nic_tx_dropped | vm_nic_tx_dropped | network name | The number of outgoing packets that were dropped. |
| collectd.statsd.vm_nic_tx_errors | vm_nic_tx_errors | network name | The number of outgoing packets that contained errors. |
Additional Values
- collectd.plugin: StatsD
- collectd.plugin_instance: The virtual machine’s name (except for collectd.statsd.host_storage=N/A)
- ovirt.entity: vm (except for collectd.statsd.host_storage=host)
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
- collectd.dstypes: Gauge
2.5.13. Swap Metrics
Swap metrics enable you to view the amount of memory currently written onto the hard disk, in bytes, according to available, used, and cached swap space.
The following table describes the Swap metrics reported by the Swap plugin.
| Metric Name | collectd.type | collectd.type_instance | collectd.dstypes | Description |
|---|---|---|---|---|
| collectd.swap.swap | swap | used / free / cached | The used, available, and cached swap space (in bytes). | |
| collectd.swap.swap_io | swap_io | in / out | The number of swap pages written and read per second. | |
| collectd.swap.percent | percent | used / free / cached | The percentage of used, available, and cached swap space. |
Additional Fields
- collectd.plugin: Swap
- collectd.plugin_instance: None
- ovirt.entity: host or Manager
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
2.5.14. Virtual Machine Metrics
The following table describes the virtual machine metrics reported by the Virt plugin.
| Metric Name | collectd.type | collectd.type_instance | collectd.dstypes |
|---|---|---|---|
| collectd.virt.ps_cputime.syst | ps_cputime.syst | N/A | |
| collectd.virt.percent | percent | virt_cpu_total | |
| collectd.virt.ps_cputime.user | ps_cputime.user | N/A | |
| collectd.virt.virt_cpu_total | virt_cpu_total | CPU number | |
| collectd.virt.virt_vcpu | virt_vcpu | CPU number | |
| collectd.virt.disk_octets.read | disk_octets.read | disk name | |
| collectd.virt.disk_ops.read | disk_ops.read | disk name | |
| collectd.virt.disk_octets.write | disk_octets.write | disk name | |
| collectd.virt.disk_ops.write | disk_ops.write | disk name | |
| collectd.virt.if_octets.rx | if_octets.rx | network name | |
| collectd.virt.if_dropped.rx | if_dropped.rx | network name | |
| collectd.virt.if_errors.rx | if_errors.rx | network name | |
| collectd.virt.if_octets.tx | if_octets.tx | network name | |
| collectd.virt.if_dropped.tx | if_dropped.tx | network name | |
| collectd.virt.if_errors.tx | if_errors.tx | network name | |
| collectd.virt.if_packets.rx | if_packets.rx | network name | |
| collectd.virt.if_packets.tx | if_packets.tx | network name | |
| collectd.virt.memory | memory | rss / total /actual_balloon / available / unused / usable / last_update / major_fault / minor_fault / swap_in / swap_out | |
| collectd.virt.total_requests | total_requests | flush-DISK | |
| collectd.virt.total_time_in_ms | total_time_in_ms | flush-DISK | |
| collectd.virt.total_time_in_ms | total_time_in_ms | flush-DISK |
Additional Values
- collectd.plugin: virt
- collectd.plugin_instance: The virtual machine’s name
- ovirt.entity: vm
- ovirt.cluster.name.raw: The cluster’s name
- ovirt.engine_fqdn.raw: The Manager’s FQDN
- hostname: The host’s FQDN
- ipaddr4: IP address
- interval: 10
2.5.15. Gauge and Derive Data Source Types
Each metric includes a collectd.dstypes value that defines the data source’s type:
- Gauge: A gauge value is simply stored as-is and is used for values that may increase or decrease, such as the amount of memory used.
Derive: These data sources assume that the change of the value is interesting, i.e., the derivative. Such data sources are very common for events that can be counted, for example the number of disk read operations. The total number of disk read operations is not interesting, but rather the change since the value was last read. The value is therefore converted to a rate using the following formula:
rate = value(new)-value(old)\ time(new)-time(old)NoteIf value(new) is less than value (old), the resulting rate will be negative. If the minimum value to zero, such data points will be discarded.
2.6. Working with Metrics Store Indexes
Metrics Store creates the following two indexes per day:
- project.ovirt-metrics-<ovirt-env-name>.uuid.yyyy.mm.dd
- project.ovirt-logs-<ovirt-env-name>.uuid.yyyy.mm.dd
When using the Discover page, select the index named project.ovirt-logs-<ovirt-env-name>.uuid.
In the Visualization page select project.ovirt-metrics-<ovirt-env-name>.uuid for metrics data or project.ovirt-logs-<ovirt-env-name>.uuid for log data.
