vania-janet/multiturn-rag-retrieval-data huggingface dataset:
{
"key": 1577836800000,
"key_as_string": "2020-01-01T00:00:00.000Z",
"matching_results": 4,
"topic_indicator": -0.027972712,
"total_matching_documents": 7,
"estimated_matching_results": 4.056338028
},
{
"key": 1588291200000,
"key_as_string": "2020-05-01T00:00:00.000Z",
"matching_results": 10,
"topic_indicator": -0.621777032,
"total_matching_documents": 12,
"estimated_matching_results": 12.16901408
},
{
"key": 1590969600000,
"key_as_string": "2020-06-01T00:00:00.000Z",
"matching_results": 5,
"topic_indicator": -0.787665504,
"total_matching_documents": 11,
"estimated_matching_results": 7.098591549
}
]
}
] ! ! ! ! ! !
},
{
"key": "chocolate",
...
},
{
"key": "mint",
...
}
}
Show more
<-- </section "id="section-topic-example" "> --><-- </section "id="section-topic" "> --><-- <section "id="section-unique_count" "> --> unique_count Returns a count of the unique instances of the specified field in the collection.<-- <section "id="section-unique_count-syntax" "> --> Syntax unique_count(field)
Specifying a larger chunk is recommended (for example, 1 hour instead of 10 seconds).



curl -H 'Authorization: Bearer <API_TOKEN>’ -H 'Content-Type: application/json' https://us-south.monitoring.cloud.ibm.com/api/data/batch -d '{
"requests": [
{
"format": {
"type": "data"
},
"scope": "ibm_resource="908461"",
"metrics": {
"k0": "timestamp",
“v1”: "ibm_cloud_load_balancer_throughput"
},
"time": {
"from": 1584396900000000,
"to": 1584402600000000,
“sampling”: 600000000
},
"group": {
"by":
{
"metric": "k0",
“value” : 600000000
}
],
"aggregations": {
“v1”: "sum"
},
"groupAggregations": {
“v1”: "sum"
}
}
}
]
}
Show more
3324167 |     1794615
(1 row)
* To compare how many passengers travelled between 1:00 AM and 6:00 PM in 2021 and 2022, run:

select
(
select
sum("passenger_count")
from
YELLOW_TAXI_JANUARY_2022_LOADED
where
"tpep_pickup_datetime"::time > '1:00am'
and "tpep_pickup_datetime"::time < '6:00am') as "overnight passengers 2022",
(
select
sum("passenger_count")
from
YELLOW_TAXI_JANUARY_2021
where
"tpep_pickup_datetime"::time > '1:00am'
and "tpep_pickup_datetime"::time < '6:00am') as "overnight passengers 2021"; 
Show more

Output:

overnight passengers 2022 | overnight passengers 2021
---------------------------+---------------------------
163058 | 33469
(1 row)
(https://cloud.ibm.com/docs-content/v1/content/f61dbc5d84bf0c89e477a05cab04c72f429e1b7a/log-analysis/images/archive-monitor-1.png)

Then, customize the metric to see data per storage class.

![Customize metric.](https://cloud.ibm.com/docs-content/v1/content/f61dbc5d84bf0c89e477a05cab04c72f429e1b7a/log-analysis/images/archive-monitor-2.png)

Check that the resolution is set to 10M.



For more information, see [Using IBM Cloud Monitoring with IBM Cloud Object Storage](https://cloud.ibm.com/docs/cloud-object-storage?topic=cloud-object-storage-mm-cos-integration).
20.05, Total bandwidth from the start time to the end time
100, Total Hits with hits type 200
200, Total Hits with hits type 206
200, Total Hits with hits type 2XX
300, Total Hits with hits type 302
100, Total Hits with hits type 304
200, Total Hits with hits type 3XX
300, Total Hits with hits type 401
100, Total Hits with hits type 403
200, Total Hits with hits type 404
300, Total Hits with hits type 412
100, Total Hits with hits type 4XX
200, Total Hits with hits type 5XX
200, Total Hits with hits type Error
200 Total Hits with hits type Others
],
time: [
1576627200,
1576630800,
1576634400,
1576638000,
1576641600,
1576645200,
1576648800,
1576652400,
1576656000,
1576659600,
1576663200
],
yaxis1: [ Total hits divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis2: [ Total bandwidth divided with the given time interval
1.21,
2.32,
0.45,
1.03,
3.01,
2.55,
4.98,
2.45,
10.23,
2.12,
8.97
],
yaxis3: [ Hits with hits type 200 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis4: [ Hits with hits type 206 divided with the given time interval
123,
23,
345,
12,
33,
78,
This is not a useful sampling due to the large amount of data returned. Specifying a larger chunk is recommended (for example, 1 hour instead of 10 seconds).

curl -H 'Authorization: Bearer <API_TOKEN>’ -H 'Content-Type: application/json' https://us-south.monitoring.cloud.ibm.com/api/data/batch -d '{
"requests": [
{
"format": {
"type": "data"
},
"scope": "ibm_resource = "cfc851b1-f30b-4a06-b354-5b64526c0a69"",
"metrics": {
"k0": "timestamp",
“v1”: "ibm_is_load_balancer_active_connections"
},
"time": {
"from": 1584396900000000,
"to": 1584402600000000,
“sampling”: 600000000
},
"group": {
"by":
{
"metric": "k0",
“value” : 600000000
}
],
"aggregations": {
“v1”: "sum"
},
"groupAggregations": {
“v1”: "sum"
}
}
}
]
}'
 more
1436195790
]
]
},
{
"target": "myclustername DELETE",
"datapoints":

null,
1436195500
],

0.0,
1436195510
],
...

0.0,
1436195790
]
]
},
{
"target": "myclustername COPY",
"datapoints":

null,
1436195500
],

0.0,
1436195510
],
...

0.0,
1436195790
]
]
},
{
"target": "myclustername HEAD",
"datapoints":

null,
1436195500
],

0.0,
1436195510
],
...

0.0,
1436195790
]
]
}
]
}
Show more





 response_time 

See an example of a response_time monitoring request:

curl "https://$ACCOUNT.cloudant.com/_api/v2/monitoring/response_time?cluster=myclustername&format=json"

See example results (abbreviated) from a response_time monitoring request:

{
"start": 1523984559,
"end": 1523984859,
"target_responses": [
{
"datapoints":

118.1668472290039,
1523984540
],

90.57628631591797,
1523984550
],

142.6778106689453,
1523984560
],

118.42487335205078,
1523984570
],
This month there are only 4 thousand requests but 200 GB is retrieved. Now the bucket is classified as cool.
3. In the third month activity slows to a near stop. There are only 400 requests and 10 GB is retrieved. This month the bucket is classified as cold.



Let's see how the costs might compare to the other storage classes.



Table 3. Cost comparison

 Month storage requests retrieval Classification Standard Vault Cold Vault Smart Tier 

 1 1,000 GB 4,000,000 100 GB Hot $41 $53 $111 $41 
 2 1,000 GB 4,000 200 GB Cool $21 $14 $16 $12 
 3 1,000 GB 400 10 GB Cold $21 $12 $7 $8 
 Total <br><br> * <br><br><br> <br><br> * <br><br><br> <br><br> * <br><br><br> <br><br> * <br><br><br> $83 $79 $134 $61 



Note that in situations where data is very cold, it is possible to get a lower rate with a Cold Vault bucket, although unexpected spikes in access could accrue significant costs. In this scenario, if the data doesn't require on-demand access, it might be better to archive the objects instead.







 Get bucket metadata 

In order to determine your current usage, you may wish to query a bucket to see bytes_used and object_count. Use of this command returns metadata containing that information for the specified bucket.

curl https://config.cloud-object-storage.cloud.ibm.com/v1/b/{my-bucket} 
-H 'authorization: bearer <IAM_token>'

The appropriate response to the request should contain bytes_used and object_count.

{
"name": "{my-bucket}",
1523984700
],

106.68751525878906,
1523984710
],

144.12387084960938,
1523984720
],

103.8598861694336,
1523984730
],

136.84429931640625,
1523984740
],

110.58084106445312,
1523984750
],

94.69702911376953,
1523984760
],

126.85747528076172,
1523984770
],

100.8759994506836,
1523984780
],

145.0876922607422,
1523984790
],

100.77622985839844,
1523984800
],

null,
1523984810
],

null,
1523984820
],

null,
1523984830
]
],
"target": "myclustername Response Time (ms)"
}
]
}
Show more





 rps 

See an example of an rps monitoring request:

curl "https://$ACCOUNT.cloudant.com/_api/v2/monitoring/rps?cluster=myclustername&format=json"

See example results (abbreviated) from an rps monitoring request:

{
"start": 1436195908,
"end": 1436196208,
"target_responses": [
{
"target": "myclustername Document Reads Per Second",
"datapoints":

0.20000000001164153,
1436195910
],

0.10000000000582077,
"datetime": "2020-05-04T18:10:34Z",
"scriptName": "edge-function-subrequest-test-client",
"status": "success"
},
"quantiles": {
"cpuTimeP50": 291,
"cpuTimeP99": 291
},
"sum": {
"errors": 0,
"requests": 1,
"subrequests": 0
}
},
{
"dimensions": {
"datetime": "2020-05-04T18:10:49Z",
"scriptName": "edge-function-subrequest-test-client",
"status": "success"
},
"quantiles": {
"cpuTimeP50": 212.5,
"cpuTimeP99": 261.19
},
"sum": {
"errors": 0,
"requests": 4,
"subrequests": 0
}
}
]
}
]
}
},
"errors": null
}
1436194920
],
...

null,
1436195200
]
]
}
]
}
Show more





 rate/verb 

See an example of a rate/verb monitoring request:

curl "https://$ACCOUNT.cloudant.com/_api/v2/monitoring/rate/verb?cluster=myclustername&format=json"

See example results (abbreviated) from a rate/verb monitoring request:

{
"start": 1436195497,
"end": 1436195797,
"target_responses": [
{
"target": "myclustername GET",
"datapoints":

null,
1436195500
],

36.0,
1436195510
],
...

49.5,
1436195790
]
]
},
{
"target": "myclustername POST",
"datapoints":

null,
1436195500
],

0.0,
1436195510
],
...

0.0,
1436195790
]
]
},
{
"target": "myclustername PUT",
"datapoints":

null,
1436195500
],

0.0,
1436195510
],
...

0.0,
1436195790
]
]
},
{
"target": "myclustername DELETE",
"datapoints":

null,
1436195500
],

0.0,
1436195510
],
...

0.0,
1436195790
]
]
},
{
"start": 1436195908,
"end": 1436196208,
"target_responses": [
{
"target": "myclustername Document Reads Per Second",
"datapoints":

0.20000000001164153,
1436195910
],

0.10000000000582077,
1436195925
],
...

null,
1436196195
]
]
}
]
}
Show more





 wps 

See an example of a wps monitoring request:

curl "https://$ACCOUNT.cloudant.com/_api/v2/monitoring/wps?cluster=myclustername&format=json"

See example results (abbreviated) from a wps monitoring request:

{
"start": 1436195999,
"end": 1436196299,
"target_responses": [
{
"target": "myclustername Document Writes Per Second",
"datapoints":

1.2999999999992724,
1436196000
],

0.5,
1436196015
],
...

null,
1436196285
]
]
}
]
}
Show more
* Reduce the time series to a value or collection of values
* Perform save or print operations



For example, to collect and return all of the values of a timeseries:

observations = ts.collect()
observations

This results in:

[(0,5.0),(1,2.0),(2,4.0),(3,6.0),(4,6.0),(5,7.0)]

To collect a range from a time series, use:

observations = ts[1:3] same as ts.get_values(1, 3)
observations

Here the output is:

[(1,2.0),(2,4.0),(3,6.0)]

Note that a time series is optimized for range queries if the time series is periodic in nature.

Using the describe on a current time series, also graphs the time series:

describe_obj = ts.describe()
describe_obj

The output is:

min inter-arrival-time: 1
max inter-arrival-time: 1
mean inter-arrival-time: 1.0
top: 6.0
unique: 5
frequency: 2
first: TimeStamp: 0 Value: 5.0
last: TimeStamp: 5 Value: 7.0
count: 6
mean:5.0
std:1.632993161855452
min:2.0
max:7.0
25%:3.5
50%:5.5
75%:6.25
Show more





 Learn more 



* [Time series key functionality](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-time-series-key-functionality)
* [Time series functions](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-time-series-functions)
2. Modify the Total number of Applications number panel and modify the query to display the actual number of instances by using the [ibm_codeengine_application_actual_instances](https://cloud.ibm.com/docs/codeengine?topic=codeengine-monitoribm_codeengine_application_actual_instances) metric.



1. Click the pencil icon to edit the panel.
2. From the Query tab, click the ibm_codeengine_application_service_count metric to modify the metric. Type ibm_codeengine_ for a list of Code Engine metrics. For this example, select the ibm_codeengine_application_actual_instances metric and specify a Maximum time aggregation and a Maximum group rollup.
3. From the Panel tab, rename this panel to Total number of Application Instances.
4. Click Save to save the changes for this panel.



3. Modify the Total number of Application revisions number panel and change the query to display the number of job runs by using the [ibm_codeengine_jobruns](https://cloud.ibm.com/docs/codeengine?topic=codeengine-monitoribm_codeengine_jobruns) metric.



1. Click the pencil icon to edit the panel.
2. From the Query tab, click the ibm_codeengine_application_revision_count metric to modify the metric. Type ibm_codeengine_ for a list of Code Engine metrics. For this example, select the ibm_codeengine_jobruns metric and specify a Maximum time aggregation and a Maximum group rollup.
3. From the Panel tab, rename this panel to Total number of Job Runs.
4. Click Save to save the changes for this panel.



4. Delete the Total number of Application routes number panel by using the action menu.
5.
This results in an exception if the underlying time series is not associated with a TRS (if TRS is None). Assuming the underlying time series has a TRS, the datetime objects are mapped to a numeric range using the TRS.

 Jan 1 2019, 12 midnight GMT
dt_beg = datetime.datetime(2019,1,1,0,0,0,0,tzinfo=datetime.timezone.utc)
 Jan 1 2019, 12:02 AM GMT
dt_end = datetime.datetime(2019,1,1,0,2,0,0,tzinfo=datetime.timezone.utc)

print(y.get_values(dt_beg, dt_end))
print(y_minutely_1970.get_values(dt_beg, dt_end))
print(y_minutely.get_values(dt_beg, dt_end))

 get_values on y in UTC millis
[(1546300800,1.0),(1546300860,2.0), (1546300920,4.0)]
 get_values on y_minutely_1970 in UTC minutes
[(25771680,1.0),(25771681,2.0),(25771682,4.0)]
 get_values on y_minutely in minutes offset by 1 Jan 2019, 12 midnight
[(0,1.0),(1,2.0),(2,4.0)]





 Duplicate timestamps 

Changing the TRS can result in duplicate timestamps. The following example changes the time tick to one hour which results in duplicate timestamps. The time series library handles duplicate timestamps seamlessly and provides convenience combiners to reduce values associated with duplicate timestamps into a single value, for example by calculating an average of the values grouped by duplicate timestamps.

y_hourly = y_minutely.with_trs(time_tick=datetime.timedelta(hours=1), start_time=zdt2)
print(y_minutely)
yaxis3: [ Hits with hits type 200 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis4: [ Hits with hits type 206 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis5: [ Hits with hits type 2XX divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis6: [ Hits with hits type 302 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis7: [ Hits with hits type 304 divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
yaxis8: [ Hits with hits type 3XX divided with the given time interval
123,
23,
345,
12,
33,
78,
89,
87,
234,
12,
43
],
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,
Metric Name ibm_databases_for_mysql_disk_read_latency_mean 
 Metric Type gauge 
 Value Type count 
 Segment By Service instance 







 Disk write latency mean 

Disk write latency mean



Table 10: Disk write latency mean metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_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 has been used over 5 minutes as a percentage of total disk I/O available.



Table 11. IO utilization in percent 5 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_disk_io_utilization_percent_average_5m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 15-minute average 

How much disk I/O has been used over 15 minutes as a percentage of total disk I/O available.



Table 12. IO utilization in percent 15 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_disk_io_utilization_percent_average_15m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 30-minute average 

How much disk I/O has been used over 30 minutes as a percentage of total disk I/O available.



Table 13. IO utilization in percent 30 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_disk_io_utilization_percent_average_30m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 60-minute average 

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
IO utilization in percent 15 minute average 

How much disk I/O has been used over 15 minutes as a percentage of total disk I/O available.



Table 12. IO utilization in percent 15 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-enterprisedb_disk_io_utilization_percent_average_15m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 30 minute average 

How much disk I/O was used over 30 minutes as a percentage of total disk I/O available.



Table 13. IO utilization in percent 30 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-enterprisedb_disk_io_utilization_percent_average_30m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 60 minute average 

How much disk I/O was 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-enterprisedb_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 and write total count for an instance metric metadata

 Metadata Description 

 Metric Name ibm_databases-for-enterprisedb_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-enterprisedb_memory_limit_bytes 
 Metric Type gauge 
 Value Type byte 
 Segment By Service instance 







 Read replica replication lag 

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



Table 17. Read replica replication lag metric metadata

 Metadata Description
* 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.
* Replace the <SERVICE_KEY> with a valid service key for the IBM Log Analysis instance. For more information, see [Service keys by using the API](https://cloud.ibm.com/log-analysis?topic=log-analysis-service_keysservice_keys_api).
* Replace <LIMIT_NUMBER> with the number of services to be returned from the highest usage to the lowest. If not specified, only the highest-usage service will be returned. <LIMIT_NUMBER> is an integer number.
* Replace <FROM_DATE> with your desired starting date in Unix epoch format. For example, $(($(date +%s)-864000)) would be 10 days ago and $(($(date +%s)-86400)) is 1 day ago. $(date +%s) is today. Dates can be specified as relative values as shown, or a specific integer such as 1633046400.
* Replace <TO_DATE> with your desired ending date in Unix epoch format.



[This website](https://www.epochconverter.com/) can help you calculate the epoch date.





 Output 

The method returns the requested data in JSON format.

[
{
"name": "host or app or tag name 1",
"current_total": 1418178,
"percentage_of_total": 59.35177415676025
},
{
"name": "host or app or tag name 1",
"current_total": 287299,
"percentage_of_total": 12.023670768735
}
]
[Used disk space for an instance (percent)](https://cloud.ibm.com/docs/databases-for-redis?topic=databases-for-redis-monitoringibm_databases_for_redis_disk_used_percent) 
 [Used memory for an instance (byte)](https://cloud.ibm.com/docs/databases-for-redis?topic=databases-for-redis-monitoringibm_databases_for_redis_memory_used_bytes) 
 [Used memory for an instance (percent)](https://cloud.ibm.com/docs/databases-for-redis?topic=databases-for-redis-monitoringibm_databases_for_redis_memory_used_percent) 





 IO utilization in percent 5 minute average 

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



Table 2. IO utilization in percent 5 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_redis_disk_io_utilization_percent_average_5m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 15 minute average 

How much disk I/O has been used over 15 minutes as a percentage of total disk I/O available.



Table 3. IO utilization in percent 15 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_redis_disk_io_utilization_percent_average_15m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 30 minute average 

How much disk I/O has been used over 30 minutes as a percentage of total disk I/O available.



Table 4. IO utilization in percent 30 minute average metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_redis_disk_io_utilization_percent_average_30m 
 Metric Type gauge 
 Value Type percent 
 Segment By Service instance 







 IO utilization in percent 60 minute average 

How much disk I/O has been used over 60 minutes as a percentage of total disk I/O available.



Table 5.
"name": "host or app or tag name 1",
"current_total": 1418178,
"percentage_of_total": 59.35177415676025
},
{
"name": "host or app or tag name 1",
"current_total": 287299,
"percentage_of_total": 12.023670768735
}
]

In this case, usage for 2 services has been returned. The current_total value reports the number of ingested lines during the queried time period. The percentage_of_total is the percentage of total lines ingested during the time period by all services.





 Additional examples 

The following provide additional, specific usage examples.



 List usage data for all hosts for the past 6 months 

This command list the aggregated usage information for all hosts for the past 6 months.

curl "https://api.us-south.logging.cloud.ibm.com/v1/usage/hosts" -u <SERVICE_KEY>:





 List usage data for the top N hosts for the past 6 months 

This command lists the aggregated usage information for the highest-usage N hosts for the past 6 months where N is an integer number.

curl "https://api.us-south.logging.cloud.ibm.com/v1/usage/hosts?limit=N" -u <SERVICE_KEY>:

Specifying 5 for N will return the information for the top 5 highest-usage hosts as shown in this example:

[
{
"name": "cloudcerts",
"current_total": 1418178,
"percentage_of_total": 59.35177415676025
},
{
"name": "containers-kubernetes",
"current_total": 287299,
"percentage_of_total": 12.023670768735
},
{
"name": "container-registry",
"current_total": 218164,