vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Working with platform metrics [link](https://cloud.ibm.com/docs/monitoring?topic=monitoring-platform_metrics_working) 
 Controlling what data is visible [link](https://cloud.ibm.com/docs/monitoring?topic=monitoring-platform_metrics_workingcontrolling-what-data-is-visible) 
 Monitoring metrics using dashboards [link](https://cloud.ibm.com/docs/monitoring?topic=monitoring-platform_metrics_workingplatform_metrics_working_dash) 
 Configuring an alert for a platform metric [link](https://cloud.ibm.com/docs/monitoring?topic=monitoring-platform_metrics_workingplatform_metrics_working_alert) 
 Controlling access by using teams [link](https://cloud.ibm.com/docs/monitoring?topic=monitoring-platform_metrics_workingplatform_metrics_working_team) 







 Metrics 

IBM Cloud Metrics Routing exposes the following metrics:



* [Successful data points](https://cloud.ibm.com/docs/metrics-router?topic=metrics-router-platform-metricsibm_metrics_router_successful_data_points)
* [Target mode](https://cloud.ibm.com/docs/metrics-router?topic=metrics-router-platform-metricsibm_metrics_router_target_mode)





 Total number of data points successfully sent to the storage target 

Total number of data points that IBM Cloud Metrics Routing sent successfully to the destination target.



Table 1. Total number of metrics successfully sent to the storage target

 Metadata Description 

 Metric Name ibm_metrics_router_successful_data_points 
 Metric Type gauge 
 Value Type none 
 Segment By ibm_ctype,ibm_location, ibm_scope, ibm_service_name, ibm_resource_type, ibm_resource_name, ibm_resource, ibm_metrics_router_target_type 
 Frequency 60 seconds 







 The current target mode 

The current mode for IBM Cloud Metrics Routing targets.
subject: string; // asset url for the evidence
subjects: string[]; // a piece of evidence can be relevant for multiple asset in v2
artifacts: Artifact[]; // additional artifacts related to the evidence
tool: string; | undefined // the tool related to the evidence (optional)

[origin]: {
url: string; // origin of the evidence, e.g. path github where the task resides;
version: string; // version of the task, e.g. git commit representing the code level used for that task
}

[key: string]: any; // additional fields, described by evidence_type_id and evidence_type_version
}

interface Artifact {
url: string;
hash: string;
}
Show more

Some of the fields in the schema are specific to toolchains and Tekton pipelines:

toolchain_crn: string;
pipeline_id: string;
pipeline_run_id: string;

These fields identify the specific continuous integration or continuous deployment job that produced the evidence. They can also contain information from any continuous integration or continuous deployment job if they serve the same purpose. These fields identify scope size from the widest to the narrowest in the following order: toolchain_crn -> pipeline_id -> pipeline_run_id.

This schema is saved to the evidence locker by using JSON format. The evidence format that is collected in different pipeline stages does not differ. Currently, their saved path indicates the pipeline stage, such as continuous integration or continuous deployment.



 Example 

{
"evidence_type_id": "com.ibm.dynamic_scan",
"evidence_type_version": "1.0.0",
"date": "2022-04-01T09:00:59.703Z",
"result": "failure",
"pipeline_id": "f9ab90d6-31a8-4b02-99b7-d2055ef0ee3c",
Each of the aggregations returns the following values as keys of aggregated document sets:<-- <ul> --> * term(model_name): Accord, CR-V * term(component_name): engine, brake, radiator<-- </ul> -->The calculated relevancy values of combinations of each of the document sets might look like this:<-- <ul> --> * Accord x engine * Accord x brake * Accord x radiator * CR-V x engine * CR-V x brake * CR-V x radiator<-- </ul> -->The response defines a two-dimensional array of aggregation results, which can be represented in a table.<-- <table "class="row-headers comparison-table" "> -->Table 4. Pair aggregation example This table has row and column headers. The row headers identify car models. The column headers identify car component parts. Each cell calculates a relevancy score by multiplying the car model (row) relevancy value times the car component (header) relevancy value.| Car model | Component: engine | Component: brake | Component: radiator || --------- | ----------------- | ---------------- | ------------------- || Accord | Accord x engine | Accord x brake | Accord x radiator || CR-V | CR-V x engine | CR-V x brake | CR-V x radiator |<-- </table "class="row-headers comparison-table" "> -->Each array of columns and rows of the table is sorted in the same order of the results of the first and second aggregations. For example, if you specify the term aggregation as the first argument, the resulting column arrays are sorted by frequency of terms.
Metric Name ibm_mq_qmgr_failed_mqset_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Failed MQSUBRQ count 

Total number of failed MQSUBRQ for a queue manager



Table 18: Failed MQSUBRQ count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_failed_mqsubrq_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Failed browse count 

Total number of failed browses for a queue manager



Table 19: Failed browse count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_failed_browse_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Failed create/alter/resume subscription count 

Total number of failed create/alter/resume subscriptions for a queue manager



Table 20: Failed create/alter/resume subscription count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_failed_subscription_create_alter_resume_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Failed topic MQPUT/MQPUT1 count 

Total number of failed topic MQPUT/MQPUT1 for a queue manager



Table 21: Failed topic MQPUT/MQPUT1 count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_failed_topic_mqput_mqput1_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Got non-persistent messages - byte count 

Total non-persistent message get count for a queue manager



Table 22: Got non-persistent messages - byte count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_non_persistent_message_destructive_get_total 
 Metric Type counter 
 Value Type byte 
 Segment By Service instance, The queue manager name 







 Got persistent messages - byte count 

Total persistent messages get byte count for a queue manager



Table 23: Got persistent messages - byte count metric metadata
How much wal log file uses, in bytes



Table 37: WAL logs used bytes metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_wal_used_bytes 
 Metric Type gauge 
 Value Type count 
 Segment By Service instance 









 Attributes for Segmentation 



 Global Attributes 

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



Table 38: Global Attributes Metadata

 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 may be a region, data center, or global. 
 Resource ibm_resource The resource being measured by the service - typically an identifying name or GUID. 
 Resource Type ibm_resource_type The type of the resource being measured by the service. 
 Scope ibm_scope The scope is the account, organization, or space GUID associated with this metric. 







 Additional Attributes 

The following attributes are available for segmenting one or more attributes as described in the reference above. Please see the individual metrics for segmentation options.



Table 39: Additional Attributes Metadata

 Attribute Attribute Name Attribute Description 

 Service instance ibm_service_instance The service instance segment 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, which isn't necessarily a unique value depending on the name that is provided by the user. 
 Resource group ibm_resource_group_name The resource group where the service instance was created.
See also [testing data](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-glossarygloss_T) and [training data](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-glossarygloss_T).







 C 



* concordance

Provides a way to ensure that the same mention is annotated with the same entity type throughout a document and across annotation sets. Concordance helps ensure consistency across multiple occurrences of a mention without requiring the human annotator to manually label each occurrence.
* confusion matrix

A table that provides a detailed numeric breakdown of annotated document sets. The table is used to compare the annotations that were added by a machine learning model to the annotations in the ground truth. The table reports the number of [false positives](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-glossarygloss_F), [false negatives](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-glossarygloss_F), [true positives](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-glossarygloss_T), and [true negatives](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-glossarygloss_T).
* coreference

A relationship between two words or phrases in which both refer to the same person or thing and one stands as a linguistic antecedent of the other. For example, there is a coreference between the two pronouns in the phrase "She taught herself" but not in the phrase "She taught her". A coreference links two equivalent entities in the same text.
* coreference chain

A list of entities that were annotated as coreferences.
For more information, see [Removed features](https://docs.sysdig.com/en/docs/release-notes/enhanced-metric-store/removed-featurees).

Impact to your environment:



* Your existing dashboards, alerts and notifications will be automatically migrated to the new metric naming convention.
* Notifications that are sent by alerts (webhooks, PagerDuty, and so on) will use the new metric and label convention. If you are doing further processing to parse metrics or labels, you will need to update your scripts accordingly.
* Terraform or configuration scripts that are used for dashboards, alerts and notifications might need updating.



For more information, see [Enhanced metric store](https://cloud.ibm.com/docs/monitoring?topic=monitoring-metrics_store).





 18 May 2022 

Availability of Advisor
: IBM Cloud® Monitoring has released Advisor to help and simplify how you monitor and troubleshoot the health, risk, and capacity of hosts and Kubernetes clusters. [Learn more](https://cloud.ibm.com/docs/monitoring?topic=monitoring-monitoringmonitoring_advisor).





 14 December 2021 

SMS event notification support
: IBM Cloud Monitoring now supports SMS notifications based on events.





 2 November 2021 

Email event notification support
: IBM Cloud Monitoring now supports email notifications based on events.





 30 September 2021 

Metrics streaming support
: You can use IBM Cloud Monitoring to push a set of selected metrics to a Kafka service such as Event Streams. [Learn more about metric streaming](https://cloud.ibm.com/docs/monitoring?topic=monitoring-data_streaming)





 8 September 2021 

Secure support for new and updated compliance standards
: New and updates compliance standards are supported.



* Updates to PCI DSS V3.2.1.
* New NIST 800-171 rev2 compliance.







 31 August 2021 

Agent update
: Added support for agent version 11.4.1.
: Returns a count-min sketch of a column with the indicated esp, confidence, and seed. The result is an array of bytes, which can be de-serialized to a CountMinSketch before usage. Count-min sketch is a probabilistic data structure that is used for cardinality estimation by using sublinear space.





 covar_pop 

covar_pop(expr1, expr2)
: Returns the population covariance of a set of number pairs.





 covar_samp 

covar_samp(expr1, expr2)
: Returns the sample covariance of a set of number pairs.





 crc32 

crc32(expr)
: Returns a cyclic redundancy check value of the expr as a bigint.
: Example of an SQL function usage fragment

> SELECT crc32('Spark')
: Result value

1557323817





 cube 





 cume_dist 

cume_dist()
: Computes the position of a value relative to all values in the partition.





 current_database 

current_database()
: Returns the current database.
: Example of an SQL function usage fragment

> SELECT current_database()
: Result value

default





 current_date 

current_date()
: Returns the current date at the start of query evaluation.





 current_timestamp 

current_timestamp()
: Returns the current timestamp at the start of query evaluation.





 date 

date(expr)
: Casts the value expr to the target data type date.





 date_add 

date_add(start_date, num_days)
: Returns the date that is num_days after start_date.
: Example of an SQL function usage fragment

> SELECT date_add('2016-07-30', 1)
: Result value

2016-07-31





 date_format 

date_format(timestamp, fmt)
: Converts timestamp to a value of string in the format specified by the date format fmt.
[Dashboard configuration](https://cloud.ibm.com/docs-content/v1/content/63322f2f3a72050206eb05ba7f590fdcb741105c/cloud-object-storage/images/SysDig-pre-built-reports.png)

Figure 2. Choose a pre-built report





 View your data in Monitoring 

Once you've configured your dashboard, you can view your data. Figures 3-5 show different views of your usage.

Zoom

![View bucket metrics](https://cloud.ibm.com/docs-content/v1/content/63322f2f3a72050206eb05ba7f590fdcb741105c/cloud-object-storage/images/SysDig-COS-metrics.jpg)

Figure 3. View sample data showing space used and number of objects

Zoom

![View metrics by location](https://cloud.ibm.com/docs-content/v1/content/63322f2f3a72050206eb05ba7f590fdcb741105c/cloud-object-storage/images/SysDig-COS-metrics-2.jpg)

Figure 4. View space used by location

Zoom

![View metrics by storage class](https://cloud.ibm.com/docs-content/v1/content/63322f2f3a72050206eb05ba7f590fdcb741105c/cloud-object-storage/images/SysDig-COS-metrics-3.jpg)

Figure 5. View used space by storage class







 Cloud Object Storage metrics details 



 Usage metrics 

There are a set of basic metrics that track usage:



* ibm_cos_bucket_used_bytes
* ibm_cos_bucket_object_count
* ibm_cos_bucket_hard_quota_bytes







 Request metrics
![Dashboard showing API counter metrics](https://cloud.ibm.com/docs-content/v1/content/f84e2238288b7f0355715a4c355609ce7ac48fb0/solution-tutorials/images/solution12/wolam_api_counter_total.png)

Figure 2. Dashboard showing API counter metrics



1. Under Explore, select All workloads.
2. Expand your cluster name on the left pane, then expand default namespace and then click on app-log-analysis-deployment.
3. In the list of metrics, expand wolam.
4. Select wolam_api_counter_total to monitor the calls to API endpoints.
5. Configure the query



1. Set Metric to sum
2. Set Group by to sum
3. Set label to endpoint



6. Go back to the application running at http://$MYINGRESSSUBDOMAIN/ and click on the Monitoring tab, generate a few metrics after changing the region.
7. To monitor the calls to a given API endpoint of the application by region, change the Group by label to region.







 Create a custom dashboard 

Along with the pre-defined dashboards, you can create your own custom dashboard to display the most useful/relevant views and metrics for the containers running your app in a single location. Each dashboard is comprised of a series of panels configured to display specific data in a number of different formats.

To create a dashboard with a first panel:



1. Under Dashboards, click New Dashboard.
2. In the New Panel:



1. Set the Metric to sysdig_container_net_http_request_time.
2. Set Group by to container_id.



3. Edit the Dashboard scope, set the filter to container_image, is and icr.io/solution-tutorials/tutorial-application-log-analysis:latest.
4. Save the dashboard.



Zoom

!
correlation_ID The unique identifier that is used to track and correlate transactions. 



To protect the confidentiality of your personal data, avoid entering personally identifiable information (PII), such as your name or location, when you create a key alias. For more examples of PII, see section 2.2 of the [NIST Special Publication 800-122](https://www.nist.gov/publications/guide-protecting-confidentiality-personally-identifiable-information-pii).

A successful POST api/v2/keys/<key_ID>/aliases/<key_alias> response returns the alias for your key, along with other metadata. The alias is a unique name that is assigned to your key and can be used for to retrieve more information about the associated key.

{
"metadata": {
"collectionType": "application/vnd.ibm.kms.key+json",
"collectionTotal": 1
},
"resources": [
{
"keyId": "02fd6835-6001-4482-a892-13bd2085f75d",
"alias": "test-alias",
"creationDate": "2020-03-12T03:37:32Z",
"createdBy": "..."
}
]
}

For a detailed description of the response parameters, see the Hyper Protect Crypto Services [REST API reference doc](https://cloud.ibm.com/apidocs/hs-crypto).









 Deleting key aliases 

To remove a key alias for a key, you can use either the console or the key management service API.



 Deleting key aliases with the console 

Delete a key alias with the console by completing the following steps:



1. [Log in to the IBM Cloud console](https://cloud.ibm.com/login).
2. Go to Menu > Resource list to view a list of your resources.
3. From your IBM Cloud resource list, select your provisioned instance of Hyper Protect Crypto Services.
Using ellipsoidal metrics 

You can use ellipsoidal metrics to calculate the distance between points.

Examples of metrics you can compute:



*  Compute the radians between two points using azimuth:

>>> p1 = stc.point(47.1, -73.5)
>>> p2 = stc.point(47.6, -72.9)
>>> stc.eg_metric.azimuth(p1, p2)
0,6802979449118038
*  Compute the distance between two points in the units of the underlying data (typically in meters):

>>> eg_metric.distance(p1, p2)
71730,66213673435
*  Compute the landing point given a starting point, a heading (in radians), and the distance in the units of the underlying data (typically in meters):

>>> point = p1
>>> heading = eg_metric.azimuth(p1, p2)
>>> distance = eg_metric.distance(p1, p2)
>>> eg_metric.destination_point(p1, heading, distance)
Point(47.60000000001233, -72.89999999998498)
"metrics_monitoring": ],
"resource_instance_id": true,
"retention_rule": ],
"s3_endpoint_private": true,
"s3_endpoint_public": true
},
"before_sensitive": false,
"after_sensitive": {
"activity_tracking": ],
"archive_rule": ],
"expire_rule": ],
"metrics_monitoring": ],
"retention_rule": ]
}
}
},
{
"address": "module.instance_config.ibm_cos_bucket.bad_resource_cos_bucket_missing_all_configuration1]",
"module_address": "module.instance_config",
"mode": "managed",
"type": "ibm_cos_bucket",
"name": "bad_resource_cos_bucket_missing_all_configuration",
"index": 1,
"provider_name": "registry.terraform.io/ibm-cloud/ibm",
"change": {
"actions":
"create"
],
"before": null,
"after": {
"activity_tracking": ],
"allowed_ip": null,
"archive_rule": ],
"cross_region_location": null,
"endpoint_type": "public",
"expire_rule": ],
"force_delete": true,
"key_protect": null,
"metrics_monitoring": ],
"region_location": "us-south",
"retention_rule": ],
"single_site_location": null,
"storage_class": "standard",
"timeouts": null
},
"after_unknown": {
"activity_tracking": ],
"archive_rule": ],
They can also help you distinguish the absence of results due to



* Lack of audio.
* Lack of speech in submitted audio.
* Engine stalls at the server and network stalls between the client and the server. To differentiate between engine and network stalls, results are periodic rather than event-based.



The metrics can also help you estimate response jitter by examining the periodic arrival times. Metrics are generated at a constant interval, so any difference in arrival times is caused by jitter.



 Requesting processing metrics 

To request processing metrics, use the following optional parameters:



* processing_metrics is a boolean that indicates whether the service is to return processing metrics. Specify true to request the metrics. By default, the service returns no metrics.
* processing_metrics_interval is a float that specifies the interval in seconds of real wall-clock time at which the service is to return metrics. By default, the service returns metrics once per second. The service ignores this parameter unless the processing_metrics parameter is set to true.

The parameter accepts a minimum value of 0.1 seconds. The level of precision is not restricted, so you can specify values such as 0.25 and 0.125. The service does not impose a maximum value.



How you provide the parameters and how the service returns processing metrics differ by interface:



* With the WebSocket interface, you specify the parameters with the JSON start message for a speech recognition request. The service calculates and returns metrics in real-time at the requested interval.
* With the asynchronous HTTP interface, you specify query parameters with a speech recognition request. The service calculates the metrics at the requested interval, but it returns all metrics for the audio with the final transcription results.



The service also returns processing metrics for transcription events. If you request interim results with the WebSocket interface, you can receive metrics with greater frequency than the requested interval.

To receive processing metrics only for transcription events instead of at periodic intervals, set the processing interval to a large number. If the interval is larger than the duration of the audio, the service returns processing metrics only for transcription events.





 Understanding processing metrics
<br> <br>For more information, see [Retrieving an instance ID](https://cloud.ibm.com/docs/key-protect?topic=key-protect-retrieve-instance-ID). 
 correlation_ID Optional.The unique identifier that is used to track and correlate transactions. 



To protect the confidentiality of your personal data, avoid entering personally identifiable information (PII), such as your name or location, when you create a key alias. For more examples of PII, see section 2.2 of the [NIST Special Publication 800-122](https://www.nist.gov/publications/guide-protecting-confidentiality-personally-identifiable-information-pii).

A successful POST api/v2/keys/<keyID_or_alias>/aliases/<key_alias> response returns the alias for your key, along with other metadata. The alias is a unique name that is assigned to your key and can be used for to retrieve more information about the associated key.

{
"metadata": {
"collectionType": "application/vnd.ibm.kms.key+json",
"collectionTotal": 1
},
"resources": [
{
"keyId": "02fd6835-6001-4482-a892-13bd2085f75d",
"alias": "test-alias",
"creationDate": "2020-03-12T03:37:32Z",
"createdBy": "..."
}
]
}

For a detailed description of the response parameters, see the Key Protect [REST API reference doc](https://cloud.ibm.com/apidocs/key-protect).

Each key can have up to five aliases. There is a limit of 1,000 aliases per instance.





 Deleting key aliases with the API 

Delete a key alias by making a DELETE call to the following endpoint.