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Cassandra 🔗

Description
Installation
Configuration
Usage
Metrics

DESCRIPTION 🔗

This integration primarily consists of the Smart Agent monitor collectd/cassandra. Below is an overview of that monitor.

Smart Agent Monitor 🔗

Monitors Cassandra using the Collectd GenericJMX plugin. This is essentially a wrapper around the collectd-genericjmx monitor that comes with a set of predefined MBean definitions that a standard Cassandra deployment will expose.

Use this integration to monitor the following types of information from Cassandra nodes:

read/write/range-slice requests
read/write/range-slice errors (timeouts and unavailable)
read/write/range-slice latency (median, 99th percentile, maximum)
compaction activity
hint activity

Supports Cassandra 2.0.10+.

INSTALLATION 🔗

This integration is part of the SignalFx Smart Agent as the collectd/cassandra monitor. You should first deploy the Smart Agent to the same host as the service you want to monitor, and then continue with the configuration instructions below.

CONFIGURATION 🔗

To activate this monitor in the Smart Agent, add the following to your agent config:

monitors:  # All monitor config goes under this key
 - type: collectd/cassandra
   ...  # Additional config

For a list of monitor options that are common to all monitors, see Common Configuration.

Config option	Required	Type	Description
`host`	yes	`string`	Host to connect to -- JMX must be configured for remote access and accessible from the agent
`port`	yes	`integer`	JMX connection port (NOT the RMI port) on the application. This correponds to the `com.sun.management.jmxremote.port` Java property that should be set on the JVM when running the application.
`name`	no	`string`
`serviceName`	no	`string`	This is how the service type is identified in the SignalFx UI so that you can get built-in content for it. For custom JMX integrations, it can be set to whatever you like and metrics will get the special property `sf_hostHasService` set to this value.
`serviceURL`	no	`string`	The JMX connection string. This is rendered as a Go template and has access to the other values in this config. NOTE: under normal circumstances it is not advised to set this string directly - setting the host and port as specified above is preferred. (default: `service:jmx:rmi:///jndi/rmi://{{.Host}}:{{.Port}}/jmxrmi`)
`instancePrefix`	no	`string`	Prefixes the generated plugin instance with prefix. If a second `instancePrefix` is specified in a referenced MBean block, the prefix specified in the Connection block will appear at the beginning of the plugin instance, and the prefix specified in the MBean block will be appended to it
`username`	no	`string`	Username to authenticate to the server
`password`	no	`string`	User password to authenticate to the server
`customDimensions`	no	`map of strings`	Takes in key-values pairs of custom dimensions at the connection level.
`mBeansToCollect`	no	`list of strings`	A list of the MBeans defined in `mBeanDefinitions` to actually collect. If not provided, then all defined MBeans will be collected.
`mBeansToOmit`	no	`list of strings`	A list of the MBeans to omit. This will come handy in cases where only a few MBeans need to omitted from the default list
`mBeanDefinitions`	no	`map of objects (see below)`	Specifies how to map JMX MBean values to metrics. If using a specific service monitor such as cassandra, kafka, or activemq, they come pre-loaded with a set of mappings, and any that you add in this option will be merged with those. See collectd GenericJMX for more details.

The nested mBeanDefinitions config object has the following fields:

Config option	Required	Type	Description
`objectName`	no	`string`	Sets the pattern which is used to retrieve MBeans from the MBeanServer. If more than one MBean is returned you should use the `instanceFrom` option to make the identifiers unique
`instancePrefix`	no	`string`	Prefixes the generated plugin instance with prefix
`instanceFrom`	no	`list of strings`	The object names used by JMX to identify MBeans include so called "properties" which are basically key-value-pairs. If the given object name is not unique and multiple MBeans are returned, the values of those properties usually differ. You can use this option to build the plugin instance from the appropriate property values. This option is optional and may be repeated to generate the plugin instance from multiple property values
`values`	no	`list of objects (see below)`	The `value` blocks map one or more attributes of an MBean to a value list in collectd. There must be at least one `value` block within each MBean block
`dimensions`	no	`list of strings`

The nested values config object has the following fields:

Config option	Required	Type	Description
`type`	no	`string`	Sets the data set used within collectd to handle the values of the MBean attribute
`table`	no	`bool`	Set this to true if the returned attribute is a composite type. If set to true, the keys within the composite type is appended to the type instance. (default: `false`)
`instancePrefix`	no	`string`	Works like the option of the same name directly beneath the MBean block, but sets the type instance instead
`instanceFrom`	no	`list of strings`	Works like the option of the same name directly beneath the MBean block, but sets the type instance instead
`attribute`	no	`string`	Sets the name of the attribute from which to read the value. You can access the keys of composite types by using a dot to concatenate the key name to the attribute name. For example: “attrib0.key42”. If `table` is set to true, path must point to a composite type, otherwise it must point to a numeric type.
`attributes`	no	`list of strings`	The plural form of the `attribute` config above. Used to derive multiple metrics from a single MBean.

USAGE 🔗

Sample of built-in dashboard in SignalFx:

METRICS 🔗

Metric Name	Description	Type
counter.cassandra.ClientRequest.CASRead.Latency.Count	Count of transactional read operations since server start	cumulative
counter.cassandra.ClientRequest.CASRead.TotalLatency.Count	The total number of microseconds elapsed in servicing client transactional read requests	cumulative
counter.cassandra.ClientRequest.CASWrite.Latency.Count	Count of transactional write operations since server start	cumulative
counter.cassandra.ClientRequest.CASWrite.TotalLatency.Count	The total number of microseconds elapsed in servicing client transactional write requests	cumulative
counter.cassandra.ClientRequest.RangeSlice.Latency.Count	Count of range slice operations since server start	cumulative
counter.cassandra.ClientRequest.RangeSlice.Timeouts.Count	Count of range slice timeouts since server start	cumulative
counter.cassandra.ClientRequest.RangeSlice.TotalLatency.Count	The total number of microseconds elapsed in servicing range slice requests	cumulative
counter.cassandra.ClientRequest.RangeSlice.Unavailables.Count	Count of range slice unavailables since server start	cumulative
counter.cassandra.ClientRequest.Read.Latency.Count	Count of read operations since server start	cumulative
counter.cassandra.ClientRequest.Read.Timeouts.Count	Count of read timeouts since server start	cumulative
counter.cassandra.ClientRequest.Read.TotalLatency.Count	The total number of microseconds elapsed in servicing client read requests	cumulative
counter.cassandra.ClientRequest.Read.Unavailables.Count	Count of read unavailables since server start	cumulative
counter.cassandra.ClientRequest.Write.Latency.Count	Count of write operations since server start	cumulative
counter.cassandra.ClientRequest.Write.Timeouts.Count	Count of write timeouts since server start	cumulative
counter.cassandra.ClientRequest.Write.TotalLatency.Count	The total number of microseconds elapsed in servicing client write requests	cumulative
counter.cassandra.ClientRequest.Write.Unavailables.Count	Count of write unavailables since server start	cumulative
counter.cassandra.Compaction.TotalCompactionsCompleted.Count	Number of compaction operations since node start	cumulative
counter.cassandra.Storage.Exceptions.Count	Number of internal exceptions caught	cumulative
counter.cassandra.Storage.Load.Count	Storage used for Cassandra data in bytes	cumulative
counter.cassandra.Storage.TotalHints.Count	Total hints since node start	cumulative
counter.cassandra.Storage.TotalHintsInProgress.Count	Total pending hints	cumulative
gauge.cassandra.ClientRequest.CASRead.Latency.50thPercentile	50th percentile (median) of Cassandra transactional read latency	gauge
gauge.cassandra.ClientRequest.CASRead.Latency.99thPercentile	99th percentile of Cassandra transactional read latency	gauge
gauge.cassandra.ClientRequest.CASRead.Latency.Max	Maximum Cassandra transactional read latency	gauge
gauge.cassandra.ClientRequest.CASWrite.Latency.50thPercentile	50th percentile (median) of Cassandra transactional write latency	gauge
gauge.cassandra.ClientRequest.CASWrite.Latency.99thPercentile	99th percentile of Cassandra transactional write latency	gauge
gauge.cassandra.ClientRequest.CASWrite.Latency.Max	Maximum Cassandra transactional write latency	gauge
gauge.cassandra.ClientRequest.RangeSlice.Latency.50thPercentile	50th percentile (median) of Cassandra range slice latency	gauge
gauge.cassandra.ClientRequest.RangeSlice.Latency.99thPercentile	99th percentile of Cassandra range slice latency	gauge
gauge.cassandra.ClientRequest.RangeSlice.Latency.Max	Maximum Cassandra range slice latency	gauge
gauge.cassandra.ClientRequest.Read.Latency.50thPercentile	50th percentile (median) of Cassandra read latency	gauge
gauge.cassandra.ClientRequest.Read.Latency.99thPercentile	99th percentile of Cassandra read latency	gauge
gauge.cassandra.ClientRequest.Read.Latency.Max	Maximum Cassandra read latency	gauge
gauge.cassandra.ClientRequest.Write.Latency.50thPercentile	50th percentile (median) of Cassandra write latency	gauge
gauge.cassandra.ClientRequest.Write.Latency.99thPercentile	99th percentile of Cassandra write latency	gauge
gauge.cassandra.ClientRequest.Write.Latency.Max	Maximum Cassandra write latency	gauge
gauge.cassandra.Compaction.PendingTasks.Value	Number of compaction operations waiting to run	gauge
gauge.jvm.threads.count	Number of JVM threads	gauge
gauge.loaded_classes	Number of classes loaded in the JVM	gauge
invocations	Total number of garbage collection events	cumulative
jmx_memory.committed	Amount of memory guaranteed to be available in bytes	gauge
jmx_memory.init	Amount of initial memory at startup in bytes	gauge
jmx_memory.max	Maximum amount of memory that can be used in bytes	gauge
jmx_memory.used	Current memory usage in bytes	gauge
total_time_in_ms.collection_time	Amount of time spent garbage collecting in milliseconds	cumulative

counter.cassandra.ClientRequest.CASRead.Latency.Count 🔗

cumulative

Count of transactional read operations since server start.

counter.cassandra.ClientRequest.CASRead.TotalLatency.Count 🔗

cumulative

The total number of microseconds elapsed in servicing client transactional read requests.

It can be devided by counter.cassandra.ClientRequest.CASRead.Latency.Count to find the real time transactional read latency.

counter.cassandra.ClientRequest.CASWrite.Latency.Count 🔗

cumulative

Count of transactional write operations since server start.

counter.cassandra.ClientRequest.CASWrite.TotalLatency.Count 🔗

cumulative

The total number of microseconds elapsed in servicing client transactional write requests.

It can be devided by counter.cassandra.ClientRequest.CASWrite.Latency.Count to find the real time transactional write latency.

counter.cassandra.ClientRequest.RangeSlice.Latency.Count 🔗

cumulative

Count of range slice operations since server start. This typically indicates a server overload condition.

If this value is increasing across the cluster then the cluster is too small for the application range slice load.

If this value is increasing for a single server in a cluster, then one of the following conditions may be true:

one or more clients are directing more load to this server than the others
the server is experiencing hardware or software issues and may require maintenance.

counter.cassandra.ClientRequest.RangeSlice.Timeouts.Count 🔗

cumulative

Count of range slice timeouts since server start. This typically indicates a server overload condition.

If this value is increasing across the cluster then the cluster is too small for the application range slice load.

If this value is increasing for a single server in a cluster, then one of the following conditions may be true:

one or more clients are directing more load to this server than the others
the server is experiencing hardware or software issues and may require maintenance.

counter.cassandra.ClientRequest.RangeSlice.TotalLatency.Count 🔗

cumulative

The total number of microseconds elapsed in servicing range slice requests.

counter.cassandra.ClientRequest.RangeSlice.Unavailables.Count 🔗

cumulative

Count of range slice unavailables since server start. A non-zero value means that insufficient replicas were available to fulfil a range slice request at the requested consistency level.

This typically means that one or more nodes are down. To fix this condition, any down nodes must be restarted, or removed from the cluster.

counter.cassandra.ClientRequest.Read.Latency.Count 🔗

cumulative

Count of read operations since server start.

counter.cassandra.ClientRequest.Read.Timeouts.Count 🔗

cumulative

Count of read timeouts since server start. This typically indicates a server overload condition.

If this value is increasing across the cluster then the cluster is too small for the application read load.

If this value is increasing for a single server in a cluster, then one of the following conditions may be true:

one or more clients are directing more load to this server than the others
the server is experiencing hardware or software issues and may require maintenance.

counter.cassandra.ClientRequest.Read.TotalLatency.Count 🔗

cumulative

The total number of microseconds elapsed in servicing client read requests.

It can be devided by counter.cassandra.ClientRequest.Read.Latency.Count to find the real time read latency.

counter.cassandra.ClientRequest.Read.Unavailables.Count 🔗

cumulative

Count of read unavailables since server start. A non-zero value means that insufficient replicas were available to fulfil a read request at the requested consistency level. This typically means that one or more nodes are down. To fix this condition, any down nodes must be restarted, or removed from the cluster.

counter.cassandra.ClientRequest.Write.Latency.Count 🔗

cumulative

Count of write operations since server start.

counter.cassandra.ClientRequest.Write.Timeouts.Count 🔗

cumulative

Count of write timeouts since server start. This typically indicates a server overload condition.

If this value is increasing across the cluster then the cluster is too small for the application write load.

If this value is increasing for a single server in a cluster, then one of the following conditions may be true:

one or more clients are directing more load to this server than the others
the server is experiencing hardware or software issues and may require maintenance.

counter.cassandra.ClientRequest.Write.TotalLatency.Count 🔗

cumulative

The total number of microseconds elapsed in servicing client write requests.

It can be devided by counter.cassandra.ClientRequest.Write.Latency.Count to find the real time write latency.

counter.cassandra.ClientRequest.Write.Unavailables.Count 🔗

cumulative

Count of write unavailables since server start. A non-zero value means that insufficient replicas were available to fulfil a write request at the requested consistency level.

This typically means that one or more nodes are down. To fix this condition, any down nodes must be restarted, or removed from the cluster.

counter.cassandra.Compaction.TotalCompactionsCompleted.Count 🔗

cumulative

Number of compaction operations since node start. If this value does not increase steadily over time then the node may be experiencing problems completing compaction operations.

counter.cassandra.Storage.Exceptions.Count 🔗

cumulative

Number of internal exceptions caught. Under normal exceptions this should be zero.

counter.cassandra.Storage.Load.Count 🔗

cumulative

Storage used for Cassandra data in bytes. Use this metric to see how much storage is being used for data by a Cassandra node.

The value of this metric is influenced by:

Total data stored into the database
compaction behavior

counter.cassandra.Storage.TotalHints.Count 🔗

cumulative

Total hints since node start. Indicates that write operations cannot be delivered to a node, usually because a node is down. If this value is increasing and all nodes are up then there may be some connectivity issue between nodes in the cluster.

counter.cassandra.Storage.TotalHintsInProgress.Count 🔗

cumulative

Total pending hints. Indicates that write operations cannot be delivered to a node, usually because a node is down. If this value is increasing and all nodes are up then there may be some connectivity issue between nodes in the cluster.

gauge.cassandra.ClientRequest.CASRead.Latency.50thPercentile 🔗

gauge

50th percentile (median) of Cassandra transactional read latency.

gauge.cassandra.ClientRequest.CASRead.Latency.99thPercentile 🔗

gauge

99th percentile of Cassandra transactional read latency.

gauge.cassandra.ClientRequest.CASRead.Latency.Max 🔗

gauge

Maximum Cassandra transactional read latency.

gauge.cassandra.ClientRequest.CASWrite.Latency.50thPercentile 🔗

gauge

50th percentile (median) of Cassandra transactional write latency.

gauge.cassandra.ClientRequest.CASWrite.Latency.99thPercentile 🔗

gauge

99th percentile of Cassandra transactional write latency.

gauge.cassandra.ClientRequest.CASWrite.Latency.Max 🔗

gauge

Maximum Cassandra transactional write latency.

gauge.cassandra.ClientRequest.RangeSlice.Latency.50thPercentile 🔗

gauge

50th percentile (median) of Cassandra range slice latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.

gauge.cassandra.ClientRequest.RangeSlice.Latency.99thPercentile 🔗

gauge

99th percentile of Cassandra range slice latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.

gauge.cassandra.ClientRequest.RangeSlice.Latency.Max 🔗

gauge

Maximum Cassandra range slice latency.

gauge.cassandra.ClientRequest.Read.Latency.50thPercentile 🔗

gauge

50th percentile (median) of Cassandra read latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.

gauge.cassandra.ClientRequest.Read.Latency.99thPercentile 🔗

gauge

99th percentile of Cassandra read latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.

gauge.cassandra.ClientRequest.Read.Latency.Max 🔗

gauge

Maximum Cassandra read latency.

gauge.cassandra.ClientRequest.Write.Latency.50thPercentile 🔗

gauge

50th percentile (median) of Cassandra write latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.

gauge.cassandra.ClientRequest.Write.Latency.99thPercentile 🔗

gauge

99th percentile of Cassandra write latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.

gauge.cassandra.ClientRequest.Write.Latency.Max 🔗

gauge

Maximum Cassandra write latency

gauge.cassandra.Compaction.PendingTasks.Value 🔗

gauge

Number of compaction operations waiting to run. If this value is continually increasing then the node may be experiencing problems completing compaction operations.

gauge.jvm.threads.count 🔗

gauge

Number of JVM threads

gauge.loaded_classes 🔗

gauge

Number of classes loaded in the JVM

invocations 🔗

cumulative

Total number of garbage collection events

jmx_memory.committed 🔗

gauge

Amount of memory guaranteed to be available in bytes

jmx_memory.init 🔗

gauge

Amount of initial memory at startup in bytes

jmx_memory.max 🔗

gauge

Maximum amount of memory that can be used in bytes

jmx_memory.used 🔗

gauge

Current memory usage in bytes

total_time_in_ms.collection_time 🔗

cumulative

Amount of time spent garbage collecting in milliseconds

Metrics that are categorized as container/host (default) are in bold and italics in the list below.

These are the metrics available for this integration.

counter.cassandra.ClientRequest.CASRead.Latency.Count (cumulative)
Count of transactional read operations since server start.
counter.cassandra.ClientRequest.CASRead.TotalLatency.Count (cumulative)
The total number of microseconds elapsed in servicing client transactional read requests.

It can be devided by counter.cassandra.ClientRequest.CASRead.Latency.Count to find the real time transactional read latency.
counter.cassandra.ClientRequest.CASWrite.Latency.Count (cumulative)
Count of transactional write operations since server start.
counter.cassandra.ClientRequest.CASWrite.TotalLatency.Count (cumulative)
The total number of microseconds elapsed in servicing client transactional write requests.

It can be devided by counter.cassandra.ClientRequest.CASWrite.Latency.Count to find the real time transactional write latency.
counter.cassandra.ClientRequest.RangeSlice.Latency.Count (cumulative)
Count of range slice operations since server start. This typically indicates a server overload condition.

If this value is increasing across the cluster then the cluster is too small for the application range slice load.

If this value is increasing for a single server in a cluster, then one of the following conditions may be true:
- one or more clients are directing more load to this server than the others
- the server is experiencing hardware or software issues and may require maintenance.
counter.cassandra.ClientRequest.RangeSlice.Timeouts.Count (cumulative)
Count of range slice timeouts since server start. This typically indicates a server overload condition.

If this value is increasing across the cluster then the cluster is too small for the application range slice load.

If this value is increasing for a single server in a cluster, then one of the following conditions may be true:
- one or more clients are directing more load to this server than the others
- the server is experiencing hardware or software issues and may require maintenance.
counter.cassandra.ClientRequest.RangeSlice.TotalLatency.Count (cumulative)
The total number of microseconds elapsed in servicing range slice requests.
counter.cassandra.ClientRequest.RangeSlice.Unavailables.Count (cumulative)
Count of range slice unavailables since server start. A non-zero value means that insufficient replicas were available to fulfil a range slice request at the requested consistency level.

This typically means that one or more nodes are down. To fix this condition, any down nodes must be restarted, or removed from the cluster.
counter.cassandra.ClientRequest.Read.Latency.Count (cumulative)
Count of read operations since server start.
counter.cassandra.ClientRequest.Read.Timeouts.Count (cumulative)
Count of read timeouts since server start. This typically indicates a server overload condition.

If this value is increasing across the cluster then the cluster is too small for the application read load.

If this value is increasing for a single server in a cluster, then one of the following conditions may be true:
- one or more clients are directing more load to this server than the others
- the server is experiencing hardware or software issues and may require maintenance.
counter.cassandra.ClientRequest.Read.TotalLatency.Count (cumulative)
The total number of microseconds elapsed in servicing client read requests.

It can be devided by counter.cassandra.ClientRequest.Read.Latency.Count to find the real time read latency.
counter.cassandra.ClientRequest.Read.Unavailables.Count (cumulative)
Count of read unavailables since server start. A non-zero value means that insufficient replicas were available to fulfil a read request at the requested consistency level. This typically means that one or more nodes are down. To fix this condition, any down nodes must be restarted, or removed from the cluster.
counter.cassandra.ClientRequest.Write.Latency.Count (cumulative)
Count of write operations since server start.
counter.cassandra.ClientRequest.Write.Timeouts.Count (cumulative)
Count of write timeouts since server start. This typically indicates a server overload condition.

If this value is increasing across the cluster then the cluster is too small for the application write load.

If this value is increasing for a single server in a cluster, then one of the following conditions may be true:
- one or more clients are directing more load to this server than the others
- the server is experiencing hardware or software issues and may require maintenance.
counter.cassandra.ClientRequest.Write.TotalLatency.Count (cumulative)
The total number of microseconds elapsed in servicing client write requests.

It can be devided by counter.cassandra.ClientRequest.Write.Latency.Count to find the real time write latency.
counter.cassandra.ClientRequest.Write.Unavailables.Count (cumulative)
Count of write unavailables since server start. A non-zero value means that insufficient replicas were available to fulfil a write request at the requested consistency level.

This typically means that one or more nodes are down. To fix this condition, any down nodes must be restarted, or removed from the cluster.
counter.cassandra.Compaction.TotalCompactionsCompleted.Count (cumulative)
Number of compaction operations since node start. If this value does not increase steadily over time then the node may be experiencing problems completing compaction operations.
counter.cassandra.Storage.Exceptions.Count (cumulative)
Number of internal exceptions caught. Under normal exceptions this should be zero.
counter.cassandra.Storage.Load.Count (cumulative)
Storage used for Cassandra data in bytes. Use this metric to see how much storage is being used for data by a Cassandra node.

The value of this metric is influenced by:
- Total data stored into the database
- compaction behavior
counter.cassandra.Storage.TotalHints.Count (cumulative)
Total hints since node start. Indicates that write operations cannot be delivered to a node, usually because a node is down. If this value is increasing and all nodes are up then there may be some connectivity issue between nodes in the cluster.
counter.cassandra.Storage.TotalHintsInProgress.Count (cumulative)
Total pending hints. Indicates that write operations cannot be delivered to a node, usually because a node is down. If this value is increasing and all nodes are up then there may be some connectivity issue between nodes in the cluster.
gauge.cassandra.ClientRequest.CASRead.Latency.50thPercentile (gauge)
50th percentile (median) of Cassandra transactional read latency.
gauge.cassandra.ClientRequest.CASRead.Latency.99thPercentile (gauge)
99th percentile of Cassandra transactional read latency.
gauge.cassandra.ClientRequest.CASRead.Latency.Max (gauge)
Maximum Cassandra transactional read latency.
gauge.cassandra.ClientRequest.CASWrite.Latency.50thPercentile (gauge)
50th percentile (median) of Cassandra transactional write latency.
gauge.cassandra.ClientRequest.CASWrite.Latency.99thPercentile (gauge)
99th percentile of Cassandra transactional write latency.
gauge.cassandra.ClientRequest.CASWrite.Latency.Max (gauge)
Maximum Cassandra transactional write latency.
gauge.cassandra.ClientRequest.RangeSlice.Latency.50thPercentile (gauge)
50th percentile (median) of Cassandra range slice latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.
gauge.cassandra.ClientRequest.RangeSlice.Latency.99thPercentile (gauge)
99th percentile of Cassandra range slice latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.
gauge.cassandra.ClientRequest.RangeSlice.Latency.Max (gauge)
Maximum Cassandra range slice latency.
gauge.cassandra.ClientRequest.Read.Latency.50thPercentile (gauge)
50th percentile (median) of Cassandra read latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.
gauge.cassandra.ClientRequest.Read.Latency.99thPercentile (gauge)
99th percentile of Cassandra read latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.
gauge.cassandra.ClientRequest.Read.Latency.Max (gauge)
Maximum Cassandra read latency.
gauge.cassandra.ClientRequest.Write.Latency.50thPercentile (gauge)
50th percentile (median) of Cassandra write latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.
gauge.cassandra.ClientRequest.Write.Latency.99thPercentile (gauge)
99th percentile of Cassandra write latency. This value should be similar across all nodes in the cluster. If some nodes have higher values than the rest of the cluster then they may have more connected clients or may be experiencing heavier than usual compaction load.
gauge.cassandra.ClientRequest.Write.Latency.Max (gauge)
Maximum Cassandra write latency
gauge.cassandra.Compaction.PendingTasks.Value (gauge)
Number of compaction operations waiting to run. If this value is continually increasing then the node may be experiencing problems completing compaction operations.

Group jvm 🔗

All of the following metrics are part of the jvm metric group. All of the non-default metrics below can be turned on by adding jvm to the monitor config option extraGroups:

gauge.jvm.threads.count (gauge)
Number of JVM threads
gauge.loaded_classes (gauge)
Number of classes loaded in the JVM
invocations (cumulative)
Total number of garbage collection events
jmx_memory.committed (gauge)
Amount of memory guaranteed to be available in bytes
jmx_memory.init (gauge)
Amount of initial memory at startup in bytes
jmx_memory.max (gauge)
Maximum amount of memory that can be used in bytes
jmx_memory.used (gauge)
Current memory usage in bytes
total_time_in_ms.collection_time (cumulative)
Amount of time spent garbage collecting in milliseconds

Non-default metrics (version 4.7.0+) 🔗

The following information applies to the agent version 4.7.0+ that has enableBuiltInFiltering: true set on the top level of the agent config.

To emit metrics that are not default, you can add those metrics in the generic monitor-level extraMetrics config option. Metrics that are derived from specific configuration options that do not appear in the above list of metrics do not need to be added to extraMetrics.

To see a list of metrics that will be emitted you can run agent-status monitors after configuring this monitor in a running agent instance.

Legacy non-default metrics (version < 4.7.0) 🔗

The following information only applies to agent versions prior to 4.7.0. If you have a newer agent and have set enableBuiltInFiltering: true at the top level of your agent config, see the section above. See upgrade instructions in Old-style inclusion list filtering.

If you have a reference to the whitelist.json in your agent’s top-level metricsToExclude config option, and you want to emit metrics that are not in that allow list, then you need to add an item to the top-level metricsToInclude config option to override that allow list (see Inclusion filtering. Or you can just copy the whitelist.json, modify it, and reference that in metricsToExclude.