Skip to content

monitoring.md

Latest commit

 

History

History
766 lines (564 loc) · 42.5 KB

monitoring.md

File metadata and controls

766 lines (564 loc) · 42.5 KB
copyright
years
2026
lastupdated 2026-03-09
keywords monitoring, metrics, cost, billing, opting in
subcollection EventStreams

{{site.data.keyword.attribute-definition-list}}

Monitoring {{site.data.keyword.messagehub}} service metrics by using {{site.data.keyword.mon_full_notm}}

{: #metrics}

{{site.data.keyword.mon_full}} is a third-party cloud-native, and container-intelligence management system that you can include as part of your {{site.data.keyword.cloud_notm}} architecture. Use it to gain operational visibility into the performance and health of your applications, services, and platforms. It offers administrators, DevOps teams, and developers full stack telemetry with advanced features to monitor and troubleshoot, define alerts, and design custom dashboards. {: shortdesc}

While you monitor service metrics with {{site.data.keyword.mon_full_notm}}, Kafka clients (producers and consumers) have their own set of metrics ato monitor their performance and health.

Opting in to and enabling {{site.data.keyword.messagehub}} service metrics

{: #opt_in_metrics}

{{site.data.keyword.messagehub}} service metrics can broadly be categorized into two different groups: Default and Enhanced.

Enabling default {{site.data.keyword.messagehub}} service metrics

{: #enabling_default_metrics}

Before you can start to use {{site.data.keyword.messagehub}} {{site.data.keyword.mon_full_notm}} metrics, you must first opt in, and then enable platform metrics by completing the following steps:

  1. Enable platform metrics for {{site.data.keyword.messagehub}}. For more information, see Enabling platform metrics{: external}.

    The owner of the account has full access to this metrics data. For more information about managing access for other users, see Getting started with {{site.data.keyword.mon_full_notm}} - manage user access{: external}.

  2. To navigate from the {{site.data.keyword.messagehub}} instance page to the {{site.data.keyword.monitoringshort}} dashboard, click Actions on the instance page and select Monitoring.

    On your first usage, you might see a welcome wizard. To advance to the dashboard selection menu, select Next and then Skip on the Choosing an installation method page. Accept the prompts that follow. You can then select the IBM {{site.data.keyword.messagehub}} or IBM {{site.data.keyword.messagehub}} (Enterprise) dashboard, depending on the plan that you use.

Enabling enhanced {{site.data.keyword.messagehub}} metrics

{: #opt_in_enhanced_metrics}

The enhanced {{site.data.keyword.messagehub}} metrics consist of three groups; topic, partition and consumers. You can opt in to either one, two, or all. The metrics available are described in the topic, partition and consumers tables.

Enabling enhanced metrics introduces more global gauge metrics and therefore increases the costs.

Before you can start to use enhanced {{site.data.keyword.messagehub}} metrics, you must first enable them by completing the following step:

  • Run the following command to update the service instance to start using enhanced metrics:

    ibmcloud resource service-instance-update <instance-name> -p '{"metrics":["topic","partition","consumers"]}'

    {: codeblock}

When enhanced metrics are enabled, depending on the selection, the following new dashboards are available; IBM {{site.data.keyword.messagehub}}(Topic), IBM {{site.data.keyword.messagehub}}(Partitions) and IBM {{site.data.keyword.messagehub}}(Consumers).

To opt out of enhanced metrics, run the following command:

ibmcloud resource service-instance-update <instance-name> -p '{"metrics":[]}'

{: codeblock}

Dashboards are available only after metrics started to be recorded; it might take a few minutes to initialize. {: note}

{{site.data.keyword.messagehub}} service metrics cost information

{: #metric_costs}

Before you opt in to using {{site.data.keyword.monitoringshort}} metrics, be aware of the cost of doing so. The estimated cost depends on the following considerations:

  • The {{site.data.keyword.messagehub}} plan that you use.
  • How many unique time series are sent for each plan.
  • The number of topics that you created.
  • The number of partitions that you created.
  • Whether you have topics, partitions, consumers, or all enabled.

Enabling mirroring for Enterprise clusters introduces one more global gauge metric and an extra gauge metric per topic in the target cluster (with the target cluster already emitting metrics in accordance with the preceding table), therefore increasing the costs.

For more information, see {{site.data.keyword.monitoringshort}} pricing.

{{site.data.keyword.messagehub}} service metrics details

{: #metric_details}

The following tables describe the specific metrics that are provided by {{site.data.keyword.messagehub}} for each plan.

Service metrics available by service plan

{: #metrics-by-plan}

Metric name Enterprise Lite Standard
Authentication failures Checkmark icon
Connected clients software name and version Checkmark icon
Consume message conversion time Checkmark icon
Estimated connected clients percentage Checkmark icon Checkmark icon Checkmark icon
IAM ID bytes in per second Checkmark icon
IAM ID bytes out per second Checkmark icon
Inactive consumer groups Checkmark icon
Instance bytes in per second Checkmark icon Checkmark icon Checkmark icon
Instance bytes out per second Checkmark icon Checkmark icon Checkmark icon
Instance utilization Checkmark icon
Missing SNI connections Checkmark icon
Number of offline partitions Checkmark icon
Number of partitions Checkmark icon Checkmark icon Checkmark icon
Number of topics Checkmark icon Checkmark icon Checkmark icon
Number of under in-sync replica partitions Checkmark icon
Oversized request drop total Checkmark icon
Produce message conversion time Checkmark icon
Rebalancing consumer groups Checkmark icon
Reserved disk space percentage Checkmark icon
Reserved disk space percentage per topic Checkmark icon
Rest-producer requests per second Checkmark icon
Schema Registry request rate Checkmark icon
Schema greatest version percentage Checkmark icon
Schema used percentage Checkmark icon
Stable consumer groups Checkmark icon
Topic bytes in per second Checkmark icon Checkmark icon Checkmark icon
Topic bytes out per second Checkmark icon Checkmark icon Checkmark icon
Used disk space percentage Checkmark icon
{: caption="Metrics Available by Plan Names" caption-side="top"}

Enhanced service metrics available with mirroring enabled

{: #metrics-mirroring}

Metric name Enterprise Lite Standard
Mirroring latency Checkmark icon
Mirroring throughput Checkmark icon
{: caption="Metrics available for mirroring" caption-side="bottom"}

Enhanced service metrics available with topic enabled

{: #metrics-topic}

Metric name Enterprise Lite Standard
Available disk space for topic operations Checkmark icon
Maximum partition retention percentage Checkmark icon
Reserved disk space percentage per topic Checkmark icon
Topic size Checkmark icon
{: caption="Metrics available for topic" caption-side="bottom"}

Enhanced service metrics available with consumers enabled

{: #metrics-consumers}

Metric name Enterprise Lite Standard
Consumer groups lag Checkmark icon
{: caption="Metrics available for consumers" caption-side="bottom"}

Enhanced service metrics available with partition enabled

{: #metrics-partition}

Metric name Enterprise Lite Standard
Message rate per partition Checkmark icon
{: caption="Metrics available for partition" caption-side="bottom"}

This information is useful for detecting if the distribution of message activity across the partitions in a topic is unbalanced and if the number of partitions a topic is scaled appropriately.

Enhanced service metrics available with quotas enabled

{: #metrics-quotas}

Metric name Enterprise Lite Standard
IAM ID bytes in quota used percentage Checkmark icon
IAM ID bytes out quota used percentage Checkmark icon
{: caption="Metrics available for quotas" caption-side="bottom"}

Kafka quotas use sampling to determine how long clients should be paused before they can send or receive more data. For unpredictable workloads, or configurations that result in quota decisions being made using only a few samples, you might observe the percentage quota used metric going above 100%.

Authentication failures

{: #ibm_eventstreams_kafka_authentication_failure_total}

Incrementing count of the number of authentication failures.

Metadata Description
Metric Name ibm_eventstreams_kafka_authentication_failure_total
Metric Type counter
Value Type none
Segment By Service instance, Service instance name
{: caption="Authentication failures metric metadata" caption-side="bottom"}

Ideally zero. A nonzero value on this indicates that clients attempt to connect by using invalid credentials. Ensure that all clients are using valid credentials.

Available disk space for topic operations

{: #ibm_eventstreams_instance_minimum_available_disk_space}

The amount of disk space available for creating topics, creating partitions, or changing topic configurations.

Metadata Description
Metric Name ibm_eventstreams_instance_minimum_available_disk_space
Metric Type gauge
Value Type byte
Segment By Service instance, Service instance name
{: caption="Available disk space for topic operations metadata" caption-side="bottom"}

This value is the lowest amount of disk space available across all storage volumes in your Event Streams instance. It provides a safe indication of how much additional data or configuration change your cluster can support before more storage is required.

Connected clients software name and version

{: #ibm_eventstreams_instance_connected_clients_software_name_and_version}

The number of connected clients with a particular client software name and version.

Metadata Description
Metric Name ibm_eventstreams_instance_connected_clients_software_name_and_version
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name, Client software name, Client software version
{: caption="Connected clients software name and version metric metadata" caption-side="bottom"}

This is for information to help you monitor the software name and version data of the active clients that are connected to the {{site.data.keyword.messagehub}} instance.

Client software name and version are available for the Kafka client (Java version 2.4 or later, and other implementations that support software name and version) as described in KIP-8855. If the client software name and version are not available, these are set as unknown.

Consume message conversion time

{: #ibm_eventstreams_instance_consume_conversions_time_quantile}

Indicates that the accumulated time spent performing message conversion from clients that are consuming by using older protocol versions.

Metadata Description
Metric Name ibm_eventstreams_instance_consume_conversions_time_quantile
Metric Type gauge
Value Type second
Segment By Service instance, Quantile, Service instance name
{: caption="Consume message conversion time metric metadata" caption-side="bottom"}

Ideally zero, as nonzero indicates that clients are experiencing more latency because of using an older protocol level. Those clients are down-level and must be upgraded. Ensure that all clients are at the latest levels.

Consumer groups lag

{: #ibm_eventstreams_instance_consumer_groups_lag}

Lag for each consumer group for each topic-partition in an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_consumer_groups_lag
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name, IBM {{site.data.keyword.messagehub}} Kafka topic, IBM {{site.data.keyword.messagehub}} Kafka partition, IBM {{site.data.keyword.messagehub}} Consumer Group
{: caption="Consumer groups lag metric metadata" caption-side="bottom"}

An increasing lag might highlight that the consumers in the group are not keeping pace with the rate that messages are being produced. This might require you to scale the number of consumers that process messages for the group.

It is normal for this metric to fluctuate when viewed over short time periods because of sampling and batch processing effects. {: note}

Estimated connected clients percentage

{: #ibm_eventstreams_kafka_recommended_max_connected_clients_percent}

The percentage of maximum number of connected clients.

Metadata Description
Metric Name ibm_eventstreams_kafka_recommended_max_connected_clients_percent
Metric Type gauge
Value Type percent
Segment By Service instance, Service instance name
{: caption="Estimated connected clients percentage metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage. See Choosing your plan{: external} to determine what the recommended limits are for your plan and instance.

IAM ID bytes in per second

{: #ibm_eventstreams_iam_id_bytes_in_per_second}

The number of bytes in per second from an IAM ID.

Metadata Description
Metric Name ibm_eventstreams_iam_id_bytes_in_per_second
Metric Type gauge
Value Type byte
Segment By Service instance name, IBM IAM Id, Service instance
{: caption="IAM ID bytes in per second metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage, particularly if any IAM IDs are producing unusually more throughput than expected.

This metric allows any differences in the amount of data being sent to the service from different users (IAM IDs) to be seen, and if required, guide the setting of any quotas needed.

IAM ID bytes in quota used percentage

{: #ibm_eventstreams_iam_id_bytes_in_quota_used_percentage}

The percentage of bytes in quota used per IAM ID.

Metadata Description
Metric Name ibm_eventstreams_iam_id_bytes_in_quota_used_percentage
Metric Type gauge
Value Type percent
Segment By Service instance name, IBM IAM Id, Service instance
{: caption="IAM ID bytes in quota used percentage metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage, particularly if any IAM IDs are producing close to their quota limits.

Quota metrics might sometimes exceed 100%. Kafka quotas use sampling and are applied asynchronously. For some workloads, especially where data is sent in large batches, this might result in small deviations from the limit.

IAM ID bytes out per second

{: #ibm_eventstreams_iam_id_bytes_out_per_second}

The number of bytes out per second from an IAM ID.

Metadata Description
Metric Name ibm_eventstreams_iam_id_bytes_out_per_second
Metric Type gauge
Value Type byte
Segment By Service instance name, IBM IAM Id, Service instance
{: caption="IAM ID bytes out per second metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage, particularly if any IAM IDs are consuming unusually more throughput than expected.

This metric allows you to see any differences in the amount of data that is sent from the service to different users (IAM IDs), and if required, guides the setting of any quotas needed.

IAM ID bytes out quota used percentage

{: #ibm_eventstreams_iam_id_bytes_out_quota_used_percentage}

The percentage of bytes out quota used per IAM ID.

Metadata Description
Metric Name ibm_eventstreams_iam_id_bytes_out_quota_used_percentage
Metric Type gauge
Value Type percent
Segment By Service instance name, IBM IAM Id, Service instance
{: caption="IAM ID bytes out quota used percentage metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage, particularly if any IAM IDs are consuming close to their quota limits.

Quota metrics might sometimes exceed 100%. Kafka quotas use sampling and are applied asynchronously. For some workloads, especially where data is sent in large batches, this might result in small deviations from the limit.

Inactive consumer groups

{: #ibm_eventstreams_instance_inactive_consumergroups}

The number of inactive consumer groups in an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_inactive_consumergroups
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Inactive consumer groups metric metadata" caption-side="bottom"}

This is for information only and is not an issue. Spikes indicate that a set of consumer groups stopped sending messages.

Instance bytes in per second

{: #ibm_eventstreams_instance_bytes_in_per_second}

The number of bytes produced per second to an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_bytes_in_per_second
Metric Type gauge
Value Type byte
Segment By Service instance, Service instance name
{: caption="Instance bytes in per second metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage of how many incoming or outgoing MB/s your clients are transferring to and from your cluster. Refer to {{site.data.keyword.messagehub}}{: external} to determine what the recommended limits are for your plan and instance.

Instance bytes out per second

{: #ibm_eventstreams_instance_bytes_out_per_second}

The number of bytes consumed per second from an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_bytes_out_per_second
Metric Type gauge
Value Type byte
Segment By Service instance, Service instance name
{: caption="Instance bytes out per second metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage of how many incoming or outgoing MB/s your clients are transferring to and from your cluster. Refer to {{site.data.keyword.messagehub}}{: external} to determine what the recommended limits are for your plan and instance.

Instance utilization

{: #ibm_eventstreams_instance_utilization}

The level of utilization of an {{site.data.keyword.messagehub}} instance. This is a numeric value between zero and two (inclusive):

  • 0 indicates that the workload being processed by this instance is within the capacity of the instance. More precisely, the utilization level is under 80%.
  • 1 indicates that the workload being processed by this instance is approaching the capacity limit for the instance. Review whether it is appropriate to scale the service instance. More precisely, the utilization level is over 80% and under 95%.
  • 2 indicates the workload being processed by this instance is at the capacity limit for the instance. As a result of this, messaging latency might increase. Review whether it is appropriate to scale the service instance. More precisely, the utilization level is over 95%.
Metadata Description
Metric Name ibm_eventstreams_instance_utilization
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Instance utilization metric metadata" caption-side="bottom"}

Maximum partition retention percentage

{: #ibm_eventstreams_instance_max_partition_retention_percent}

Maximum partition retention percentage indicates the percentage of the configured retention size that is used by the partition with the most data in the topic. For example, if a topic has a retention size of 10GB, one partition has 4GB and another partition has 6GB, this metric will report 60%. This helps you monitor if a single partition within a topic is approaching its retention size which could trigger a log segment deletion or impact performance.

Metadata Description
Metric Name ibm_eventstreams_instance_max_partition_retention_percent
Metric Type gauge
Value Type percent
Segment By Service instance, Service instance name, IBM {{site.data.keyword.messagehub}} Kafka topic
{: caption="Maximum partition retention percentage metric metadata" caption-side="bottom"}

Message rate per partition

{: #ibm_eventstreams_instance_message_rate_per_partition}

The rate of change of this metric gives the message per second that is incoming in to a partition of a {{site.data.keyword.messagehub}} instance topic.

Metadata Description
Metric Name ibm_eventstreams_instance_message_rate_per_partition
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name, IBM {{site.data.keyword.messagehub}} Kafka topic, IBM {{site.data.keyword.messagehub}} Kafka partition
{: caption="Message rate per partition metric metadata" caption-side="bottom"}

Mirroring latency

{: #ibm_eventstreams_instance_mirroring_latency_seconds}

The per-topic mirroring latency in seconds from the source {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_mirroring_latency_seconds
Metric Type gauge
Value Type second
Segment By Service instance, Service instance name, IBM {{site.data.keyword.messagehub}} Kafka topic
{: caption="Mirroring latency metric metadata" caption-side="bottom"}

This is useful to determine how far behind a topic on the target cluster is.

Mirroring throughput

{: #ibm_eventstreams_instance_mirroring_throughput_bytes_per_second}

The bytes per second of mirroring throughput from source {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_mirroring_throughput_bytes_per_second
Metric Type gauge
Value Type byte
Segment By Service instance, Service instance name
{: caption="Mirroring throughput metric metadata" caption-side="bottom"}

This is useful to see whether mirroring is active and for capacity planning.

Missing SNI connections

{: #ibm_eventstreams_kafka_missing_sni_host_total}

Incrementing count of the number of connections rejected due to not supporting the SNI extension to TLS.

Metadata Description
Metric Name ibm_eventstreams_kafka_missing_sni_host_total
Metric Type counter
Value Type none
Segment By Service instance, Service instance name
{: caption="Missing SNI connections metric metadata" caption-side="bottom"}

Ideally this should be zero. It indicates clients that are not configured correctly. Clients must use the SNI extension for TLS to connect to the service. If this value is nonzero, ensure that all clients are at correct level and configured correctly for SNI{: external}.

Number of offline partitions

{: #ibm_eventstreams_kafka_offline_partitions}

The number of partitions offline in an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_kafka_offline_partitions
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Number of offline partitions metric metadata" caption-side="bottom"}

Ideally this value should be zero. A nonzero value might indicate to a temporary issue with the cluster. It might also indicate to a Kafka partition leader election difficulty.

Number of partitions

{: #ibm_eventstreams_instance_partitions}

The number of leader partitions in an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_partitions
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Number of partitions metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage. Refer to {{site.data.keyword.messagehub}}{: external} to determine what the recommended limits are for your plan and instance.

Number of topics

{: #ibm_eventstreams_instance_topics}

The number of topics in an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_topics
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Number of topics metric metadata" caption-side="bottom"}

Number of under in-sync replica partitions

{: #ibm_eventstreams_kafka_under_minisr_partitions}

The number of partitions with fewer than two in-sync replicas.

Metadata Description
Metric Name ibm_eventstreams_kafka_under_minisr_partitions
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Number of under in-sync replica partitions metric metadata" caption-side="bottom"}

Ideally this value should be zero. A nonzero value might highlight a temporary issue with the cluster.

Oversize request drop total

{: #ibm_eventstreams_kafka_oversize_request_drop_total}

Total number of oversized Kafka requests that are dropped.

Metadata Description
Metric Name ibm_eventstreams_kafka_oversize_request_drop_total
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Total number of dropped oversized Kafka requests" caption-side="bottom"}

Produce message conversion time

{: #ibm_eventstreams_instance_produce_conversions_time_quantile}

Indicates that the accumulated time spent performing message conversion from clients that are producing by using older protocol versions.

Metadata Description
Metric Name ibm_eventstreams_instance_produce_conversions_time_quantile
Metric Type gauge
Value Type second
Segment By Service instance, Quantile, Service instance name
{: caption="Produce message conversion time metric metadata" caption-side="bottom"}

Ideally zero. A consistent growth in this indicates that some clients are down-level and should be upgraded. Ensure that all clients are at the latest levels.

Rebalancing consumer groups

{: #ibm_eventstreams_instance_rebalancing_consumergroups}

The number of rebalancing consumer groups in an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_rebalancing_consumergroups
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Rebalancing consumer groups metric metadata" caption-side="bottom"}

While it is expected that this figure is occasionally >0 (as broker restarts happen frequently,) sustained high levels suggest that consumers might be restarting frequently and leaving or rejoining the consumer groups. Check you client logs.

Reserved disk space percentage

{: #ibm_eventstreams_instance_reserved_disk_space_percent}

The percentage of reserved disk space that is required for all allocated partitions if fully used.

Metadata Description
Metric Name ibm_eventstreams_instance_reserved_disk_space_percent
Metric Type gauge
Value Type percent
Segment By Service instance, Service instance name
{: caption="Reserved disk space percentage metric metadata" caption-side="bottom"}

Shows the percentage of disk space that would be used if your topics were filled to the extent of their configured retention size.

Reserved disk space percentage per topic

{: #ibm_eventstreams_instance_reserved_disk_space_per_topic_percent}

The percentage of reserved disk space that is required for each topic if all of the topics allocated partitions are fully used. You can use this metric to plan your disk space requirements for Event Streams and also identify misconfigured topics that are reserving an unnecessarily large amount of disk space.

Metadata Description
Metric Name ibm_eventstreams_instance_reserved_disk_space_per_topic_percent
Metric Type gauge
Value Type percent
Segment By Service instance, Service instance name, topic
{: caption="Reserved disk space percentage per topic metric metadata" caption-side="bottom"}

Shows the percentage of disk space that would be used if your topics were filled to the extent of their configured retention size.

Rest-producer requests per second

{: #ibm_eventstreams_instance_rest_producer_requests_per_sec}

Number of requests per second made to the rest-producer API.

Metadata Description
Metric Name ibm_eventstreams_instance_rest_producer_requests_per_sec
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Rest-producer requests per second metric metadata" caption-side="bottom"}

This is for information to help you monitor usage of the REST Producer API, including use of schema encoders.

Schema Registry request rate

{: #ibm_eventstreams_instance_schema_registry_serdes_requests_per_sec}

Total number of requests made to any HTTP SerDes endpoint per second.

Metadata Description
Metric Name ibm_eventstreams_instance_schema_registry_serdes_requests_per_sec
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Schema Registry request rate metric metadata" caption-side="bottom"}

This is for information to help you monitor the serialization and deserialization rates on your schema registry.

Schema greatest version percentage

{: #ibm_eventstreams_instance_schema_registry_schema_versions_greatest_percentage}

The percentage of schema version capacity used for the schema with the greatest number of versions in the registry.

Metadata Description
Metric Name ibm_eventstreams_instance_schema_registry_schema_versions_greatest_percentage
Metric Type gauge
Value Type percent
Segment By Service instance, Service instance name
{: caption="Schema greatest version percentage metric metadata" caption-side="bottom"}

Schema used percentage

{: #ibm_eventstreams_instance_schema_registry_schemas_used_percentage}

The percentage of schema capacity used in the schema registry.

Metadata Description
Metric Name ibm_eventstreams_instance_schema_registry_schemas_used_percentage
Metric Type gauge
Value Type percent
Segment By Service instance, Service instance name
{: caption="Schema used percentage metric metadata" caption-side="bottom"}

Stable consumer groups

{: #ibm_eventstreams_instance_stable_consumergroups}

The number of stable consumer groups in an {{site.data.keyword.messagehub}} instance.

Metadata Description
Metric Name ibm_eventstreams_instance_stable_consumergroups
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name
{: caption="Stable consumer groups metric metadata" caption-side="bottom"}

Use along with rebalancing consumer groups. If this is consistently zero and rebalancing high, then it indicates a cluster problem. If this is nonzero and rebalancing high, it indicates a consumer group issue.

Topic bytes in per second

{: #ibm_eventstreams_instance_topic_bytes_in_per_second}

The number of bytes produced per second to a topic.

Metadata Description
Metric Name ibm_eventstreams_instance_topic_bytes_in_per_second
Metric Type gauge
Value Type byte
Segment By Service instance, IBM {{site.data.keyword.messagehub}} Kafka topic, Service instance name
{: caption="Topic bytes in per second metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage, particularly if any topics are producing unusual throughput, which is more or less than expected.

Topic bytes out per second

{: #ibm_eventstreams_instance_topic_bytes_out_per_second}

The number of bytes consumed per second from a topic.

Metadata Description
Metric Name ibm_eventstreams_instance_topic_bytes_out_per_second
Metric Type gauge
Value Type byte
Segment By Service instance, IBM {{site.data.keyword.messagehub}} Kafka topic, Service instance name
{: caption="Topic bytes out per second metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage, particularly if any topics are consuming unusually more or less throughput than expected.

Topic size

{: #ibm_eventstreams_instance_topic_size}

Total disk size currently being used by partitions of a topic e.g if a topic has two partitions, one with 2MB of data and one with 4MB of data, the metric will report the size as 6MB. This can be used to monitor storage usage and optimise partitioning.

Metadata Description
Metric Name ibm_eventstreams_instance_topic_size
Metric Type gauge
Value Type none
Segment By Service instance, Service instance name, IBM {{site.data.keyword.messagehub}} Kafka topic
{: caption="Topic size metric metadata" caption-side="bottom"}

Used disk space percentage

{: #ibm_eventstreams_instance_utilised_disk_space_percent}

The percentage of currently used disk space.

Metadata Description
Metric Name ibm_eventstreams_instance_utilised_disk_space_percent
Metric Type gauge
Value Type percent
Segment By Service instance, Service instance name
{: caption="Used disk space percentage metric metadata" caption-side="bottom"}

This is for information to help you monitor trends in your usage. Refer to {{site.data.keyword.messagehub}}{: external} to determine what the recommended limits are for your plan and instance.

Attributes for Segmentation

{: attributes}

Global attributes

{: #global-attributes}

The following attributes are available for segmenting all of the listed metrics.

Attribute Attribute name Attribute description
Cloud Type ibm_ctype The cloud type is a value of public, dedicated, or local.
Location ibm_location The location of the monitored resource - this might be a region, data center or global.
Scope ibm_scope The scope is the account, organization, or space GUID associated with this metric.
Service name ibm_service_name Name of the service that generates this metric.
Service instance ibm_service_instance The service instance GUID identifies the instance that the metric is associated with.
Service instance name ibm_service_instance_name The service instance name provides the user-provided name of the service instance that isn't necessarily a unique value that depends on the name that is provided by the user.
Resource ibm_resource The resource that is measured by the service - typically an identifying name or GUID.
Resource Type ibm_resource_type The type of the resource that is measured by the service.
Resource group ibm_resource_group_name The resource group name where the service instance was created.
{: caption="Global attributes" caption-side="bottom"}

Additional attributes

{: #additional-attributes}

The following attributes are available for segmenting one or more attributes. See the individual metrics for segmentation options.

Attribute Attribute name Attribute description
Client software name ibm_eventstreams_clientsoftwarename Client software name.
Client software version ibm_eventstreams_clientsoftwareversion Client software version.
IBM IAM Id ibm_eventstreams_iam_id IBM IAM Id.
IBM {{site.data.keyword.messagehub}} Consumer Group ibm_eventstreams_consumergroup IBM Event Streams Consumer Group.
IBM {{site.data.keyword.messagehub}} Kafka partition ibm_eventstreams_partition IBM Event Streams Kafka partition.
IBM {{site.data.keyword.messagehub}} Kafka topic ibm_eventstreams_topic IBM Event Streams Kafka topic.
Quantile ibm_quantile The quantile represented when a metric supports segmenting by quantile
{: caption="Additional attributes" caption-side="bottom"}

For more information about enabling platform metrics from the {{site.data.keyword.messagehub}} dashboard and viewing metrics, see Monitoring {{site.data.keyword.messagehub}} metrics{: external}.