Metrics

Import the metrics resource module and instantiate the client

from peak import metrics
from peak.constants import ArtifactInfo

metric_client: metrics.Metric = metrics.get_client()

Possible field values

Collection Scope

The scope of the collection. The following scopes are supported: PUBLIC and PRIVATE.

Publish a semantic model metrics

publish_body = {
    "namespace": "dev",
}

artifact: ArtifactInfo = {
    "path": "metrics",
}

collection_id: str = "bc8b6ef5-d2f6-4b7f-9365-0261b43997c9"

# Publish metrics using artifact and namespace
publish_artifact_metrics_response = metric_client.publish(
    body=publish_body,
    artifact=artifact,
)

# Publish metrics using collection id and namespace
publish_collection_metrics_response = metric_client.publish(
    body=publish_body,
    collection_id=collection_id,
)

Query published metrics

query_data = metric_client.query(
    namespace="dev",
    generate_sql=False,
    measures=["product.count"],
    dimensions=["product.name"],
    filters=[{"dimension": "product.name", "operator": "equals", "values": ["car", "bike"]}],
    time_dimensions=[
        {
            "dimension": "product.created_at",
            "granularity": "day",
            "date_range": {"start": "2021-01-01", "end": "2021-01-31"},
        },
    ],
    order={"product.count": "asc"},
    segments=["product.segment1", "product.segment2"],
    limit=100,
    offset=0,
)

"""
query_data = {
    "data": [
        {
            "product.name": "car",
            "product.count": 100,
        },
        {
            "product.name": "bike",
            "product.count": 50,
        },
    ],
}
"""

sql_query = metric_client.query(
    namespace="dev",
    generate_sql=True,
    measures=["product.count"],
    dimensions=["product.name"],
    filters=[{"dimension": "product.name", "operator": "equals", "values": ["car", "bike"]}],
    time_dimensions=[
        {
            "dimension": "product.created_at",
            "granularity": "day",
            "date_range": ["2021-01-01", "2021-01-31"],
        },
    ],
    order={"product.count": "asc"},
    segments=["product.segment1", "product.segment2"],
    limit=100,
    offset=0,
)

"""
sql_query = {
    "sql": "SELECT product.name, product.count FROM table WHERE product.name IN ('car', 'bike') AND created_at BETWEEN '2021-01-01' AND '2021-01-31' ORDER BY product.count ASC LIMIT 100 OFFSET 0",
}
"""

List published metrics

metrics_list_iterator = metric_client.list(
    namespace="dev",
    type="all",
)

metrics_list_iterated = next(metrics_list_iterator)

"""
{
    "data": [
        {
            "name": "product",
            "type": "cube",
            "public": true,
            "measures": [
                {
                    "name": "product.max_price",
                    "type": "number",
                    "aggType": "max",
                    "public": true
                },
                {
                    "name": "product.max_discount",
                    "type": "number",
                    "aggType": "max",
                    "public": true
                },
            ],
            "dimensions": [
                {
                    "name": "product.sale",
                    "type": "string",
                    "public": true,
                    "primaryKey": true
                },
                {
                    "name": "order.created_at",
                    "type": "time",
                    "public": true,
                    "primaryKey": false
                },
            ],
            "segments": [],
            "collectionId": "b8d8308e-4f45-42e5-9c08-d4c4ba6db7f6",
            "publicationId": "299e7d07-db2f-4050-88c3-40afd7603807"
        }
    ],
    "pageCount": 1,
    "pageNumber": 1,
    "pageSize": 25,
    "totalCount": 2
}
"""

Delete Metrics

# Delete all metrics related to a publication id
metric_client.delete(
    publication_id="299e7d07-db2f-4050-88c3-40afd7603807",
)

# Delete given metrics from a namespace
metric_client.delete(
    namespace="dev",
    measures=["product"],
)

Create Metrics Collection

metrics_collection_body = {
    "name": "test",
    "scope": "public",
    "description": "This collection is used for product metrics",
}

artifact: ArtifactInfo = {
    "path": "metrics",
}

create_collection_response = metric_client.publish(
    body=metrics_collection_body,
    artifact=artifact,
)

"""
create_collection_response  = {
    "artifactId": "7dc0feaa-be90-467b-9c3a-009a234e4b2b",
    "collectionId": "bc8b6ef5-d2f6-4b7f-9365-0261b43997c9",
}
"""

Delete Metrics Collection

metric_client.delete_collection(collection_id="bc8b6ef5-d2f6-4b7f-9365-0261b43997c9")

List Metrics Collections

collections_list_iterator = metric_client.list_collections()

collections_list_iterated = next(collections_list_iterator)

"""
{
    "id": "b8d8308e-4f45-42e5-9c08-d4c4ba6db7f6",
    "name": "product",
    "tenant": "tenant-name",
    "scope": "PUBLIC",
    "description": "Product metrics",
    "artifactId": "7dc0feaa-be90-467b-9c3a-009a234e4b2b",
    "createdAt": "2024-01-26T00:00:00Z",
    "createdBy": "someone@peak.ai",
    "updatedAt": "2024-01-26T00:00:00Z",
    "updatedBy": "someone@peak.ai"
}
"""

List Namespaces

namespaces_list_iterator = metric_client.list_namespaces()

namespaces_list_iterated = next(namespaces_list_iterator)

"""
  {
      "name": "sktest-it-all",
      "models": [
          {
              "name": "products",
              "publicationId": "3ebf5556-fd48-43b5-8fa3-a74b8c96ee58",
              "type": "cube"
          },
          {
              "name": "sales",
              "publicationId": "3ebf5556-fd48-43b5-8fa3-a74b8c96ee58",
              "type": "cube"
          }
      ]
  }
"""