vania-janet/multiturn-rag-retrieval-data huggingface dataset:
[IO utilization in percent 5-minute average](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_disk_io_utilization_percent_average_5m) 
 [IO utilization in percent 15-minute average](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_disk_io_utilization_percent_average_15m) 
 [IO utilization in percent 30-minute average](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_disk_io_utilization_percent_average_30m) 
 [IO utilization in percent 60-minute average](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_disk_io_utilization_percent_average_60m) 
 [IOPS read and write total count for an instance.](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_disk_iops_read_write_total) 
 [Max allowed memory for an instance.](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_memory_limit_bytes) 
 [The total number of connections used.](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_total_connections) 
 [Total disk space for an instance.](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_disk_total_bytes) 
 [Used CPU for an instance.]
curl -X POST https://us-south.monitoring.cloud.ibm.com/api/data -H "Authorization: Bearer $AUTH_TOKEN" -H "IBMInstanceID: $GUID" -H "content-type: application/json" -d @metrics.json

The following example of the metrics.json file shows how to configure the file to extract data by instance ID, app container age, and number of bytes the app is using:

{
"metrics": [
{
"id": "ibm_resource"
},
{
"id": "ibm_cloudfoundry_app_container_age",
"aggregations": {"group": "max", "time": "max"}
},
{
"id": "ibm_cloudfoundry_app_memory_bytes_used",
"aggregations": {"group": "avg", "time": "avg"}
}
],
"filter": "ibm_location = "us-south"",
"sampling": 86400,
"last": 86400
}
Show more

The result for extracting data returns the following information:

{
"data": [
{
"d":
"73a9d202-7e97-45b2-a107-3b1528856be3",
316660942465643,
18809015.575
],
"t": 1587772800
}
],
"end": 1587772800,
"start": 1587686400
}





 Sample: Extract CPU data 

This example shows how to extract CPU data by host with start, end, and, sampling limit.

The following example of the metrics.json file shows how to configure the file to extract CPU data:

{
"start":"1555404790",
","value":0.0018456263984895185},{"date":"2022-03-29T16:20:11Z","name":"zz_89","unit":"GHz","value":-0.00004880017432284111},{"date":"2022-03-29T16:20:11Z","name":"jq_1718","unit":"GHz","value":0.0018450164797376928},{"date":"2022-03-29T16:20:11Z","name":"zz_1718","unit":"GHz","value":-0.000049801394905237715},{"date":"2022-03-29T16:20:11Z","name":"jq_1114","unit":"GHz","value":0.001835082276886779},{"date":"2022-03-29T16:20:11Z","name":"zz_1114","unit":"GHz","value":-0.00004752850851691654},{"date":
.<-- </section "id="section-term-example" "> --><-- </section "id="section-term" "> --><-- <section "id="section-timeslice" "> --> timeslice A specialized histogram that uses dates to create interval segments.<-- <section "id="section-timeslice-syntax" "> --> Syntax The syntax is timeslice({field},{interval},{time_zone}).<-- <ul> --> * The field that you specify must have a date data type. For more information about date field, see How dates are handled]] ! ! ! ! ! ! ! ! ! . * Valid interval values are 1second or {n}seconds, 1minute or {n}minutes, 1hour or {n}hours, 1day or {n}days, 1week or {n}weeks, 1month or {n}months, and 1year or {n}years where {n} is a number. * You can optionally include a custom name by including a name parameter.<-- </ul> --><-- </section "id="section-timeslice-syntax" "> --><-- <section "id="section-timeslice-example" "> --> Example The following example shows the number of matches for each day value. timeslice(field:DATEA,interval:1day)
The results look as follows. "aggregations":
{
"type": "timeslice",
"field": "DATEA",
"interval": "1d",
"results":
{
"key": 1262304000000,
"key_as_string": "2010-01-01T00:00:00.000Z",
"matching_results": 5
},
{
"key": 1262390400000,
yaxis9: [ Hits with hits type 401 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis10: [ Hits with hits type 403 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis11: [ Hits with hits type 404 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis12: [ Hits with hits type 412 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis13: [ Hits with hits type 4XX divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis14: [ Hits with hits type 5XX divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis15: [ Hits with hits type Error divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis16: [ Hits with hits type Others divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
* datetimeDay

<-- </ul> -->

<-- </ul> -->

--order: Output order. (default: "datetime_ASC") The following list is usable order options for corresponding dataset and all of order options support ASC and DESC action. Combine these order options and action with _. For example date_ASC is order by date ascending.
<-- <ul> -->

* Common order options for every http dataset.

<-- <ul> -->

* orderByParams
* date
* sum_bytes
* sum_cachedBytes
* sum_cachedRequests
* sum_requests

<-- </ul> -->

* httpRequests1dGroups order options.

<-- <ul> -->

* avg_bytes
* sum_encryptedBytes
* sum_encryptedRequests
* sum_pageViews
* sum_threats
* uniq_uniques

<-- </ul> -->

* httpRequests1hGroups order options.

<-- <ul> -->

* avg_bytes
* sum_encryptedBytes
* sum_encryptedRequests
* sum_pageViews
* sum_threats
* uniq_uniques
* datetime

<-- </ul> -->

* httpRequests1mGroups order options.

<-- <ul> -->

* avg_bytes
* sum_encryptedBytes
* sum_encryptedRequests
* sum_pageViews
* sum_threats
* uniq_uniques
* datetime
* datetimeFifteenMinutes
* datetimeHour
* datetimeFifteenMinutes
* datetimeHour
* datetimeDay

<-- </ul> -->
[IO utilization as a percent - 15-minute average](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_disk_io_utilization_percent_average_15m) 
 [IO utilization as a percent - 30-minute average](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_disk_io_utilization_percent_average_30m) 
 [IO utilization as a percent - 60-minute average](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_disk_io_utilization_percent_average_60m) 
 [IOPS read and write total count for an instance.](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_disk_iops_read_write_total) 
 [Max allowed memory for an instance.](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_memory_limit_bytes) 
 [Total disk space for an instance.](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_disk_total_bytes) 
 [Used CPU for an instance.](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_cpu_used_percent) 
 [Used disk space for an instance.](https://cloud.ibm.com/docs/databases-for-etcd?topic=databases-for-etcd-monitoringibm_databases_for_etcd_disk_used_bytes)
[Tuples inserted count](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_tuples_inserted_count) 
 [Tuples inserted rate](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_tuples_inserted_rate) 
 [Tuples returned rate](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_tuples_returned_rate) 
 [Tuples updated count](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_tuples_updated_count) 
 [Tuples updated rate](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_tuples_updated_rate) 
 [Used CPU for an instance.](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases-for-enterprisedb_cpu_used_percent) 
 [Used disk space for an instance.](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases-for-enterprisedb_disk_used_bytes) 
 [Used memory for an instance.](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases-for-enterprisedb_memory_used_bytes)
[Non-persistent - topic MQPUT/MQPUT1 count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_non_persistent_topic_mqput_mqput1_total) 
 [Non-persistent message MQPUT count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_non_persistent_message_mqput_total) 
 [Non-persistent message MQPUT1 count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_non_persistent_message_mqput1_total) 
 [Non-persistent message browse - byte count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_non_persistent_message_browse_bytes_total) 
 [Non-persistent message browse count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_non_persistent_message_browse_total) 
 [Non-persistent message get - byte count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_non_persistent_message_get_bytes_total) 
 [Persistent - topic MQPUT/MQPUT1 count](https://cloud.ibm.com/docs/mqcloud?topic=mqcloud-monitor_sysdigibm_mq_qmgr_persistent_topic_mqput_mqput1_total)
[Cloud databases dashboard in monitoring](https://cloud.ibm.com/docs-content/v1/content/3505da55c475bf1edbe3eb3496324ba13941b783/databases-for-postgresql/images/monitoring-ibm-list.png)

Figure 2. Cloud databases dashboard in monitoring



 Monitoring Availability 

IBM Cloud Monitoring monitoring is available for deployments in every region. Deployments in Multi-zone Regions (MZRs) - eu-gb, eu-de, us-east, us-south, jp-tok, jp-osaau-syd - have their metrics in the corresponding region.

If you have deployments that are in Single-zone Region (SZR) che01 then your logs are forwarded to a IBM Cloud Monitoring instance in another region. You need to provision monitoring instances in the region where your metrics are forwarded to. Metrics for deployments in che01 go to jp-tok.





 Available Metrics 



Table 1. Available Metrics

 Metric Name 

 [Blocks hit rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_blocks_hit_rate) 
 [Blocks read rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_blocks_read_rate) 
 [Buffers backend rate](https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-monitoringibm_databases_for_postgresql_buffers_backend_rate)
(https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases-for-enterprisedb_memory_limit_bytes) 
 [Read replica replication lag.](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases-for-enterprisedb_read_replica_replication_lag_bytes) 
 [Successful archive rate](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_successful_archive_rate) 
 [Temporary files size in bytes](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_temp_bytes_count) 
 [Total number of PostgreSQL connections being used.](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases-for-enterprisedb_total_connections) 
 [Total disk space for an instance.](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases-for-enterprisedb_disk_total_bytes) 
 [Transaction commit count](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_transaction_commit_count) 
 [Transaction commit rate](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_transaction_commit_rate) 
 [Transaction rollback count](https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-monitoringibm_databases_for_enterprisedb_transaction_rollback_count)
"text": "6 hours ago was <? now().minusHours(6).reformatDateTime('h:mm a') ?>."
}
],
"response_type": "text",
"selection_policy": "sequential"
}
]
}
}

Result if it is 2:19 PM: 6 hours ago was 8:19 AM.



 Working with time spans 

To show a response based on whether today's date falls within a certain time frame, you can use a combination of time-related methods. For example, if you run a special offer during the holiday season every year, you can check whether today's date falls between November 25 and December 24 of this year. First, define the dates of interest as context variables.

In the following start and end date context variable expressions, the date is being constructed by concatenating the dynamically-derived current year value with hard-coded month and day values.

"context": {
"end_date": "<? now().reformatDateTime('Y') + '-12-24' ?>",
"start_date": "<? now().reformatDateTime('Y') + '-11-25' ?>"
}

In the response condition, you can indicate that you want to show the response only if the current date falls between the start and end dates that you defined as context variables.

now().after($start_date) && now().before($end_date)





 java.util.Date support 

In addition to the built-in methods, you can use standard methods of the java.util.Date class.

To get the date of the day that falls a week from today, you can use the following syntax.

{
"context": {
"week_from_today": "<? new Date(new Date().getTime() +
(7 * (2460601000L))) ?>"
}
}
[Tuples updated rate](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_tuples_updated_rate) 
 [Used CPU for an instance.](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_cpu_used_percent) 
 [Used disk space for an instance.](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_disk_used_bytes) 
 [Used memory for an instance.](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_memory_used_bytes) 
 [WAL logs used bytes](https://cloud.ibm.com/docs/databases-for-mysql?topic=databases-for-mysql-monitoringibm_databases_for_mysql_wal_used_bytes) 





 Blocks hit rate 

Blocks hit rate



Table 2: Blocks hit rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_blocks_hit_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Blocks read rate 

Blocks read rate



Table 3: Blocks read rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_blocks_read_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Buffers backend rate 

Buffers backend rate



Table 4: Buffers backend rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_buffers_backend_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance
Click the Boards icon ![Boards icon](https://cloud.ibm.com/docs-content/v1/content/f61dbc5d84bf0c89e477a05cab04c72f429e1b7a/log-analysis/images/boards.png).
3. Click NEW BOARD.
4. Click Add Graph.
5. Select app For Field.
6. Select ANY for Field Value.
7. Click Add Graph. A trend line showing the count of app log events is listed.
8. Click the graph line and click Show logs to see the log at point in time.
9. Open the subplot view ![Open the subplot view](https://cloud.ibm.com/docs-content/v1/content/f61dbc5d84bf0c89e477a05cab04c72f429e1b7a/log-analysis/images/opensubplot.png) and select Histogram for Choose breakdown type and host for SYSTEM FIELD.
10. Click Add Breakdown. A histogram of the data by host is added to the dashboard.
11. Click Add and add an additional histogram and specify verb for SYSTEM FIELD.
12. Try adding additional plots by clicking Add Plot.
13. Multiple plot lines can be hidden or displayed on the graph. Select your plot and click Show to show the line or Hide to hide it.
14. Click the Filter icon ![Filter icon](https://cloud.ibm.com/docs-content/v1/content/f61dbc5d84bf0c89e477a05cab04c72f429e1b7a/log-analysis/images/filter.png) to filter the graph by a specific field's data. For example, host:myhost.
15. Click the Pencil icon !
"2022-03-29T16:20:11Z","name":"jq_1012","unit":"GHz","value":0.0018810647156439844},{"date":"2022-03-29T16:20:11Z","name":"zz_1012","unit":"GHz","value":-0.000050802013978420064},{"date":"2022-03-29T16:20:11Z","name":"jq_1314","unit":"GHz","value":0.0019353641563545731},{"date":"2022-03-29T16:20:11Z","name":"zz_1314","unit":"GHz","value":-0.000055527771046030225},{"date":"2022-03-29T16:20:11Z","name":"jq_1619","unit":"GHz","value":0.0026560337819497093},{"date":"2022-03-29T16:20:
curl "<ENDPOINT>/v1/usage/apps/<SERVICE_NAME>/<APP_NAME>?from=<FROM_DATE>&to=<TO_DATE>&limit=<LIMIT_NUMBER>" -u <SERVICE_KEY>:
* For 1 tag during a time period, you can get the aggregated usage.

curl "<ENDPOINT>/v1/usage/tags/<SERVICE_NAME>/<TAG_NAME>?from=<FROM_DATE>&to=<TO_DATE>&limit=<LIMIT_NUMBER>" -u <SERVICE_KEY>:
* For 1 host during a time period, you can get the aggregated usage.

curl "<ENDPOINT>/v1/usage/hosts/<SERVICE_NAME>/<HOST_NAME>?from=<FROM_DATE>&to=<TO_DATE>&limit=<LIMIT_NUMBER>" -u <SERVICE_KEY>:
* Replace <ENDPOINT> with the IBM Cloud Activity Tracker API endpoint. To see the list of endpoints, see either [Public API endpoints](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-endpointsendpoints_api_public) or [Private API endpoints](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-endpointsendpoints_api_private) as appropriate for your environment.
* Replace <SERVICE_NAME> with the [CRN service name](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-cloud_services) for the service you are interested in. If not specified, data for all services meeting the other criteria will be returned.
* Replace <APP_NAME> with the name of the app.
* Replace <TAG_NAME> with the name of the tag.
* Replace <HOST_NAME> with the name of the host.

For platform logs, the <HOST_NAME> is the [CRN service name](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-cloud_services) for the service.
How much disk I/O has been used over 60 minutes as a percentage of total disk I/O available.



Table 14. IO utilization in percent 60 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_disk_io_utilization_percent_average_60m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IOPS read and write total count for an instance 

How many input/output operations per second your deployment is performing.



Table 15. IOPS read & write total count for an instance metric metadata

 Metadata Description 

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







 Max allowed memory for an instance 

The maximum amount of memory available to your deployment.



Table 16. Max allowed memory for an instance metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_memory_limit_bytes 
 Metric Type gauge 
 Value Type byte 
 Segment By Service instance 







 Read replica replication lag 

How far behind a mysql read-only replica is, in bytes.



Table 17. Read replica replication lag metric metadata

 Metadata Description 

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







 Successful archive rate 

Successful archive rate



Table 18: Successful archive rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_successful_archive_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Temporary files size in bytes 

Temporary files size in bytes



Table 19: Temporary files size in bytes metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_temp_bytes_count 
 Metric Type gauge 
 Value Type none 
 Segment By Service instance 







 Total disk space for an instance 

Represents the total amount of disk available to your deployment.



Table 20: Total disk space for an instance metric metadata

 Metadata Description
(https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_total_connections) 
 [Total disk space for an instance.](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_disk_total_bytes) 
 [Used CPU for an instance.](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_cpu_used_percent) 
 [Used disk space for an instance.](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_disk_used_bytes) 
 [Used memory for an instance.](https://cloud.ibm.com/docs/databases-for-cassandra?topic=databases-for-cassandra-monitoringibm_databases-for-cassandra_memory_used_bytes) 





 Disk read latency mean 

Disk read latency mean



Table 2. Disk read latency mean metric metadata

 Metadata Description 

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







 Disk write latency mean 

Disk write latency mean



Table 3. Disk write latency mean metric metadata

 Metadata Description 

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







 IO utilization in percent 5-minute average 

How much disk I/O was used over 5 minutes as a percentage of total disk I/O available.



Table 4. IO utilization in percent 5 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-cassandra_disk_io_utilization_percent_average_5m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 15-minute average