Services → Cortex, serverless tasks, clustering, data transfer queries
If match found: Adapt the verified query SQL (adjust time filters, object names, limits). Replace __table_name with SNOWFLAKE.ACCOUNT_USAGE.TABLE_NAME from base_table definition.
If no match: Generate SQL using semantic model table definitions and similar verified queries as structural reference.
Step 3: SQL Construction Guidelines
All tables in SNOWFLAKE.ACCOUNT_USAGE schema
Use ROUND() for credit amounts (2 decimal places)
For anomaly queries, ALWAYS filter on IS_ANOMALY = TRUE (not actual > forecasted)
JOIN TAG_REFERENCES for cost attribution
Apply time filters (common: last 7/30 days, month-to-date)
Order by cost/credits DESC, use LIMIT for top-N queries
anomaly identifier
description: System-generated identifier for the anomaly detection record
expr: anomaly_id
data_type: VARCHAR(16777216)
name: DATE
synonyms:
anomaly date
detection date
description: Day in UTC when the consumption occurred
expr: date
data_type: DATE
name: IS_ANOMALY
synonyms:
anomaly flag
is cost anomaly
anomaly detected
true anomaly
actual anomaly
description: "CRITICAL: This boolean field is the authoritative indicator of anomalies. Only rows where is_anomaly = TRUE represent actual cost anomalies. Do not assume actual_value > forecasted_value means anomaly - use this field instead."
expr: is_anomaly
data_type: BOOLEAN
facts:
name: ACTUAL_VALUE
description: Amount of consumption measured in credits
expr: actual_value
data_type: NUMBER(38,2)
name: FORECASTED_VALUE
description: Predicted consumption based on the anomaly-detecting algorithm, measured in credits
expr: forecasted_value
data_type: NUMBER(38,2)
name: LOWER_BOUND
description: Predicted lowest level of consumption based on the anomaly-detecting algorithm, measured in credits
expr: lower_bound
data_type: NUMBER(38,2)
name: UPPER_BOUND
description: Predicted highest level of consumption based on the anomaly-detecting algorithm, measured in credits
expr: upper_bound
data_type: NUMBER(38,2)
metrics:
name: ANOMALY_PERCENTAGE
description: Percentage of days that were identified as cost anomalies
expr: (SUM(CASE WHEN anomalies_daily.is_anomaly THEN 1 ELSE 0 END) * 100.0 / COUNT(anomalies_daily.date))
name: AVERAGE_VARIANCE_FROM_FORECAST
description: Average absolute variance between actual and forecasted consumption in credits
expr: AVG(ABS(anomalies_daily.actual_value - anomalies_daily.forecasted_value))
name: TOTAL_ANOMALOUS_CONSUMPTION
description: Total consumption in credits during anomalous periods
expr: SUM(CASE WHEN anomalies_daily.is_anomaly THEN anomalies_daily.actual_value ELSE 0 END)
name: AUTOMATIC_CLUSTERING_HISTORY
synonyms:
clustering history
automatic clustering
clustering usage
clustering costs
table clustering
description: Historical record of automatic clustering operations including credits consumed, bytes and rows reclustered for each table clustering event
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: AUTOMATIC_CLUSTERING_HISTORY
primary_key:
columns:
START_TIME
TABLE_ID
dimensions:
name: DATABASE_ID
synonyms:
clustering database id
description: Internal identifier for the database containing the clustered table
expr: database_id
data_type: NUMBER(38,0)
name: DATABASE_NAME
synonyms:
clustering database
description: Name of the database containing the clustered table
expr: database_name
data_type: VARCHAR(134217728)
name: END_TIME
synonyms:
clustering end time
clustering end
description: End of the automatic clustering time range
expr: end_time
data_type: TIMESTAMP_LTZ(9)
name: INSTANCE_ID
synonyms:
clustering instance id
description: Internal identifier for the instance that the table belongs to
expr: instance_id
data_type: NUMBER(38,0)
name: SCHEMA_ID
synonyms:
clustering schema id
description: Internal identifier for the schema containing the clustered table
expr: schema_id
data_type: NUMBER(38,0)
name: SCHEMA_NAME
synonyms:
clustering schema
description: Name of the schema containing the clustered table
expr: schema_name
data_type: VARCHAR(134217728)
name: START_TIME
synonyms:
clustering start time
clustering start
description: Start of the automatic clustering time range
expr: start_time
data_type: TIMESTAMP_LTZ(9)
name: TABLE_ID
synonyms:
clustering table id
description: Internal identifier for the table that was clustered
expr: table_id
data_type: NUMBER(38,0)
name: TABLE_NAME
synonyms:
clustering table
clustered table
description: Name of the table that was clustered
expr: table_name
data_type: VARCHAR(134217728)
facts:
name: CREDITS_USED
synonyms:
clustering cost
clustering spend
clustering charges
clustering money
clustering billing
description: Credits billed for automatic clustering during the time window
expr: credits_used
data_type: NUMBER(38,9)
name: NUM_BYTES_RECLUSTERED
synonyms:
bytes clustered
clustering bytes
reclustered bytes
description: Number of bytes reclustered during the clustering operation
expr: num_bytes_reclustered
data_type: NUMBER(38,0)
name: NUM_ROWS_RECLUSTERED
synonyms:
rows clustered
clustering rows
reclustered rows
description: Number of rows reclustered during the clustering operation
expr: num_rows_reclustered
data_type: NUMBER(38,0)
metrics:
name: AVERAGE_CLUSTERING_CREDITS
synonyms:
average clustering cost
avg clustering spend
clustering cost per operation
description: Average credits consumed per automatic clustering operation
expr: AVG(automatic_clustering_history.credits_used)
name: TOTAL_BYTES_RECLUSTERED
synonyms:
total clustering bytes
total bytes clustered
clustering data volume
description: Total bytes reclustered across all automatic clustering operations
expr: SUM(automatic_clustering_history.num_bytes_reclustered)
name: TOTAL_CLUSTERING_CREDITS
synonyms:
total clustering cost
total clustering spend
clustering spending
clustering billing
description: Total credits consumed by automatic clustering across all tables and time periods
expr: SUM(automatic_clustering_history.credits_used)
name: TOTAL_ROWS_RECLUSTERED
synonyms:
total clustering rows
total rows clustered
clustering row count
description: Total rows reclustered across all automatic clustering operations
expr: SUM(automatic_clustering_history.num_rows_reclustered)
name: CORTEX_ANALYST_USAGE_HISTORY
synonyms:
cortex analyst
analyst usage
data assistant
conversational AI
AI analyst assistant
analyst requests
description: Historical usage and credits consumed by Cortex Analyst (conversational data analysis assistant) requests, aggregated hourly by user
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: CORTEX_ANALYST_USAGE_HISTORY
primary_key:
columns:
START_TIME
USERNAME
dimensions:
name: END_TIME
synonyms:
analyst end time
cortex end time
description: End of the time range when Cortex Analyst message responses were sent
expr: end_time
data_type: TIMESTAMP_LTZ(9)
name: START_TIME
synonyms:
analyst start time
cortex start time
description: Start of the time range when Cortex Analyst message requests were received
expr: start_time
data_type: TIMESTAMP_LTZ(9)
name: USERNAME
synonyms:
analyst user
data assistant user
conversational AI user
analyst requesting user
description: Username of the user who sent Cortex Analyst message requests
expr: username
data_type: VARCHAR(16777216)
facts:
name: CREDITS
synonyms:
analyst cost
analyst spend
analyst money
analyst charges
conversational AI cost
data assistant cost
analyst billing
description: Credits consumed by Cortex Analyst (conversational data analysis assistant) requests during the time window
expr: credits
data_type: NUMBER(38,9)
name: REQUEST_COUNT
synonyms:
analyst requests
analyst messages
conversational requests
data assistant requests
description: Number of messages sent to Cortex Analyst during the time window
expr: request_count
data_type: NUMBER(18,0)
metrics:
name: AVERAGE_CREDITS_PER_REQUEST
synonyms:
analyst cost per request
cost per analyst request
analyst cost efficiency
conversational AI cost efficiency
description: Average credits consumed per Cortex Analyst message request
expr: SUM(cortex_analyst_usage_history.credits) / SUM(cortex_analyst_usage_history.request_count)
name: TOTAL_CORTEX_ANALYST_CREDITS
synonyms:
total analyst cost
total analyst spend
total analyst money
analyst spending
conversational AI spending
data assistant billing
description: Total credits consumed by Cortex Analyst (conversational data analysis assistant) across all users and time periods
expr: SUM(cortex_analyst_usage_history.credits)
name: CORTEX_FUNCTIONS_USAGE_HISTORY
synonyms:
cortex functions
function usage
AI functions
cortex function usage
function usage history
AI function costs
description: Hourly aggregated usage history of Cortex Functions (AI_COMPLETE, AI_TRANSLATE, etc.) including tokens and credits consumed by function and model with time dimensions for trend analysis
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: CORTEX_FUNCTIONS_USAGE_HISTORY
primary_key:
columns:
START_TIME
FUNCTION_NAME
MODEL_NAME
WAREHOUSE_ID
dimensions:
name: END_TIME
synonyms:
function end time
AI function end
cortex function end
function end
description: End of the hour-long time window in which the Cortex function usage took place
expr: end_time
data_type: TIMESTAMP_LTZ(9)
name: FUNCTION_NAME
synonyms:
function name
AI function name
cortex function
function type
description: Name of the Cortex function (e.g., COMPLETE, TRANSLATE) used during the time window
expr: function_name
data_type: VARCHAR(134217728)
name: MODEL_NAME
synonyms:
model name
AI model name
function model
model type
description: Name of the AI model used during the time window (empty for functions where model is not specified)
expr: model_name
data_type: VARCHAR(134217728)
name: START_TIME
synonyms:
function start time
AI function start
cortex function start
function start
description: Start of the hour-long time window in which the Cortex function usage took place
expr: start_time
data_type: TIMESTAMP_LTZ(9)
name: WAREHOUSE_ID
synonyms:
function warehouse
AI function warehouse
cortex warehouse
function warehouse id
description: ID of the warehouse that executed the Cortex functions during the time window
expr: warehouse_id
data_type: NUMBER(38,0)
facts:
name: TOKENS
synonyms:
function tokens
AI function tokens
cortex tokens
function token usage
description: Number of tokens consumed by Cortex functions during the hour-long time window for the function, model, and warehouse combination
expr: tokens
data_type: NUMBER(38,0)
name: TOKEN_CREDITS
synonyms:
function cost
AI function cost
function spend
name: DATABASE_STORAGE
synonyms:
database storage
storage usage
historical database storage
description: Average daily storage usage per database, including table, Time Travel, Fail-safe, and hybrid table storage
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: DATABASE_STORAGE_USAGE_HISTORY
primary_key:
columns:
USAGE_DATE
DATABASE_ID
dimensions:
name: DATABASE_ID
synonyms:
db id
description: Internal database ID
expr: database_id
data_type: NUMBER(38,0)
name: DATABASE_NAME
synonyms:
db name
description: Name of the database
expr: database_name
data_type: VARCHAR(134217728)
name: USAGE_DATE
synonyms:
storage date
description: Date of the storage usage record
expr: usage_date
data_type: DATE
facts:
name: AVERAGE_DATABASE_BYTES
description: Average daily bytes used in database tables and Time Travel
expr: average_database_bytes
data_type: FLOAT
name: AVERAGE_FAILSAFE_BYTES
description: Average daily bytes stored in Fail-safe
expr: average_failsafe_bytes
data_type: FLOAT
name: AVERAGE_HYBRID_TABLE_STORAGE_BYTES
description: Average daily bytes used by hybrid table storage
expr: average_hybrid_table_storage_bytes
data_type: FLOAT
metrics:
name: TOTAL_AVERAGE_DATABASE_BYTES
description: Sum of average daily database storage usage (table + Time Travel)
expr: SUM(database_storage.average_database_bytes)
name: TOTAL_AVERAGE_FAILSAFE_BYTES
description: Sum of average daily Fail-safe storage
expr: SUM(database_storage.average_failsafe_bytes)
name: TOTAL_AVERAGE_HYBRID_TABLE_STORAGE_BYTES
description: Sum of average daily hybrid table storage
expr: SUM(database_storage.average_hybrid_table_storage_bytes)
name: DATA_TRANSFER
synonyms:
data movement
cross-region transfer
cloud egress
network transfer
description: Cross-region, cross-cloud, or external data transfer history
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: DATA_TRANSFER_HISTORY
primary_key:
columns:
START_TIME
TARGET_REGION
dimensions:
name: END_TIME
synonyms:
transfer end time
description: End of the data transfer window
expr: end_time
data_type: TIMESTAMP_LTZ(9)
name: SOURCE_CLOUD
synonyms:
origin cloud
source cloud provider
description: Cloud provider of the source region
expr: source_cloud
data_type: VARCHAR(134217728)
name: SOURCE_REGION
synonyms:
origin region
description: Region where the data originated
expr: source_region
data_type: VARCHAR(134217728)
name: START_TIME
synonyms:
transfer start time
description: Start of the data transfer window
expr: start_time
data_type: TIMESTAMP_LTZ(9)
name: TARGET_CLOUD
synonyms:
destination cloud
target cloud provider
description: Cloud provider of the target region
expr: target_cloud
data_type: VARCHAR(134217728)
name: TARGET_REGION
synonyms:
destination region
description: Destination region of the data transfer
expr: target_region
data_type: VARCHAR(134217728)
name: TRANSFER_TYPE
synonyms:
type of transfer
transfer classification
description: "Classification of transfer: cross-region, cross-cloud, or external"
expr: transfer_type
data_type: VARCHAR(134217728)
facts:
name: BYTES_TRANSFERRED
description: Total bytes transferred during the interval
expr: bytes_transferred
data_type: FLOAT
metrics:
name: TOTAL_BYTES_TRANSFERRED
description: Total bytes transferred between regions, clouds, or externally
expr: SUM(data_transfer.bytes_transferred)
name: TOTAL_GB_TRANSFERRED
description: Total data transferred in gigabytes
expr: SUM(data_transfer.bytes_transferred) / POW(1024, 3)
name: TOTAL_TB_TRANSFERRED
description: Total data transferred in terabytes
expr: SUM(data_transfer.bytes_transferred) / POW(1024, 4)
overhead spending
description: Total cloud services credits used
expr: SUM(metering.credits_used_cloud_services)
name: TOTAL_COMPUTE_CREDITS
synonyms:
total compute cost
total compute spend
total compute money
compute spending
warehouse spending
description: Total compute credits used
expr: SUM(metering.credits_used_compute)
name: QUERY_ACCELERATION_HISTORY
synonyms:
query acceleration
QAS history
query acceleration service
accelerated queries
description: Historical usage and credits consumed by the Query Acceleration Service for warehouse-level acceleration
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: QUERY_ACCELERATION_HISTORY
primary_key:
columns:
START_TIME
WAREHOUSE_ID
dimensions:
name: END_TIME
synonyms:
acceleration end time
QAS end time
description: End of the time range when Query Acceleration Service was active
expr: end_time
data_type: TIMESTAMP_LTZ(9)
name: START_TIME
synonyms:
acceleration start time
QAS start time
description: Start of the time range when Query Acceleration Service was active
expr: start_time
data_type: TIMESTAMP_LTZ(9)
name: WAREHOUSE_ID
synonyms:
acceleration warehouse id
QAS warehouse id
description: Internal identifier of the warehouse that used Query Acceleration Service
expr: warehouse_id
data_type: NUMBER(38,0)
name: WAREHOUSE_NAME
synonyms:
acceleration warehouse
QAS warehouse name
description: Name of the warehouse that consumed Query Acceleration Service credits
expr: warehouse_name
data_type: VARCHAR(134217728)
facts:
name: CREDITS_USED
synonyms:
acceleration cost
acceleration spend
QAS money
acceleration charges
query acceleration billing
description: Credits consumed by the Query Acceleration Service during the time window (Enterprise Edition feature)
expr: credits_used
data_type: NUMBER(38,9)
metrics:
name: AVERAGE_CREDITS_PER_WINDOW
synonyms:
average acceleration cost
average QAS spend
QAS cost per window
description: Average Query Acceleration Service credits consumed per time window
expr: AVG(query_acceleration_history.credits_used)
name: TOTAL_ACCELERATION_CREDITS
description: Total credits consumed by Query Acceleration Service across all warehouses and time periods
expr: SUM(query_acceleration_history.credits_used)
name: QUERY_ATTRIBUTION
synonyms:
query cost
query attribution
query credits
expensive queries
top queries
costliest queries
description: Compute credits attributed to each query execution over the past 365 days
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: QUERY_ATTRIBUTION_HISTORY
primary_key:
columns:
QUERY_ID
dimensions:
name: END_TIME
synonyms:
query end time
description: Timestamp when query execution ended
expr: end_time
data_type: TIMESTAMP_LTZ(3)
name: PARENT_QUERY_ID
synonyms:
parent query
description: ID of the parent query if part of a multi-statement execution
expr: parent_query_id
data_type: VARCHAR(16777216)
query
description: Unique identifier for the query
expr: query_id
data_type: VARCHAR(16777216)
name: QUERY_PARAMETERIZED_HASH
synonyms:
parameterized query hash
query family
description: Hash of the query with parameters abstracted
expr: query_parameterized_hash
data_type: VARCHAR(16777216)
name: QUERY_TAG
synonyms:
query tag
description: Custom tag associated with the query (for attribution)
expr: query_tag
data_type: VARCHAR(16777216)
name: ROOT_QUERY_ID
synonyms:
root query
description: ID of the root query in a query call chain
expr: root_query_id
data_type: VARCHAR(16777216)
name: START_TIME
synonyms:
query start time
description: Timestamp when query execution started
expr: start_time
data_type: TIMESTAMP_LTZ(3)
name: USER_NAME
synonyms:
user
description: Username that executed the query
expr: user_name
data_type: VARCHAR(16777216)
name: WAREHOUSE_ID
synonyms:
query warehouse id
description: ID of the warehouse used for the query
expr: warehouse_id
data_type: NUMBER(38,0)
name: WAREHOUSE_NAME
synonyms:
query warehouse
description: Name of the warehouse used for the query
expr: warehouse_name
data_type: VARCHAR(134217728)
facts:
name: CREDITS_ATTRIBUTED_COMPUTE
synonyms:
query cost
query spend
query charges
query money
individual query cost
description: Compute credits attributed to this query execution
expr: credits_attributed_compute
data_type: FLOAT
name: QUERY_HISTORY
synonyms:
query logs
query history
query execution
description: Detailed history of executed queries, including text, execution time, and performance metadata
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: QUERY_HISTORY
primary_key:
columns:
QUERY_ID
dimensions:
name: DATABASE_NAME
synonyms:
execution db
description: Name of the database in use when query was executed
expr: database_name
data_type: VARCHAR(134217728)
name: END_TIME
synonyms:
query end
description: Timestamp when the query finished execution
expr: end_time
data_type: TIMESTAMP_LTZ(6)
name: QUERY_ID
synonyms:
query
description: Unique identifier for the query
expr: query_id
data_type: VARCHAR(134217728)
name: QUERY_TEXT
synonyms:
sql text
query body
description: Text of the executed query
expr: query_text
data_type: VARCHAR(134217728)
name: QUERY_TYPE
synonyms:
sql type
query kind
description: Type of query (e.g., SELECT, INSERT)
expr: query_type
data_type: VARCHAR(134217728)
name: ROLE_NAME
synonyms:
active role
description: Role active during query execution
expr: role_name
data_type: VARCHAR(134217728)
name: SCHEMA_NAME
synonyms:
execution schema
description: Name of the schema in use when query was executed
expr: schema_name
data_type: VARCHAR(134217728)
name: START_TIME
synonyms:
query start
description: Timestamp when the query started execution
expr: start_time
data_type: TIMESTAMP_LTZ(6)
name: USER_NAME
synonyms:
query user
description: Username of the user who ran the query
expr: user_name
data_type: VARCHAR(134217728)
name: WAREHOUSE_ID
synonyms:
execution warehouse id
description: ID of the warehouse used to run the query
expr: warehouse_id
data_type: NUMBER(38,0)
name: WAREHOUSE_NAME
synonyms:
execution warehouse
description: Name of the warehouse used to run the query
expr: warehouse_name
data_type: VARCHAR(134217728)
facts:
name: BYTES_SCANNED
description: Number of bytes scanned by the query
expr: bytes_scanned
data_type: NUMBER(38,0)
name: BYTES_WRITTEN
description: Number of bytes written by the query
expr: bytes_written
data_type: NUMBER(38,0)
name: ERROR_CODE
description: Error code if the query failed, NULL if successful
expr: error_code
data_type: VARCHAR(134217728)
name: SERVERLESS_TASK_HISTORY
synonyms:
serverless tasks
task usage
serverless task usage
task history
description: Historical usage and credits consumed by serverless tasks with task-level and schema/database attribution
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: SERVERLESS_TASK_HISTORY
primary_key:
columns:
START_TIME
TASK_ID
dimensions:
name: DATABASE_ID
synonyms:
task database id
description: Internal identifier for the database containing the serverless task
expr: database_id
data_type: NUMBER(38,0)
name: DATABASE_NAME
synonyms:
task database
description: Name of the database containing the serverless task
expr: database_name
data_type: VARCHAR(134217728)
name: END_TIME
synonyms:
task end time
serverless task end
description: End of the time range when serverless task usage occurred
expr: end_time
data_type: TIMESTAMP_LTZ(9)
name: INSTANCE_ID
synonyms:
task instance id
description: Internal identifier for the instance which the serverless task belongs to
expr: instance_id
data_type: NUMBER(38,0)
name: SCHEMA_ID
synonyms:
task schema id
description: Internal identifier for the schema containing the serverless task
expr: schema_id
data_type: NUMBER(38,0)
name: SCHEMA_NAME
synonyms:
task schema
description: Name of the schema containing the serverless task
expr: schema_name
data_type: VARCHAR(134217728)
name: START_TIME
synonyms:
task start time
serverless task start
description: Start of the time range when serverless task usage occurred
expr: start_time
data_type: TIMESTAMP_LTZ(9)
name: TASK_ID
synonyms:
serverless task id
description: Internal identifier for the serverless task
expr: task_id
data_type: NUMBER(38,0)
name: TASK_NAME
synonyms:
task
serverless task name
description: Name of the serverless task
expr: task_name
data_type: VARCHAR(134217728)
facts:
name: CREDITS_USED
synonyms:
task cost
task spend
task money
task charges
serverless cost
serverless spend
description: Credits consumed by serverless tasks during the time window
expr: credits_used
data_type: NUMBER(38,9)
metrics:
name: AVERAGE_CREDITS_PER_TASK_WINDOW
synonyms:
average task cost
average task spend
name: SNOWPARK_CONTAINER_SERVICES
synonyms:
container services
snowpark usage
container usage
spcs
description: Snowpark Container Services resource usage per compute pool and time window
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: SNOWPARK_CONTAINER_SERVICES_HISTORY
primary_key:
columns:
START_TIME
COMPUTE_POOL_NAME
dimensions:
name: APPLICATION_ID
synonyms:
app id
description: ID of the application that owns the compute pool (if any)
expr: application_id
data_type: NUMBER(38,0)
name: APPLICATION_NAME
synonyms:
app name
description: Name of the application associated with the compute pool (if any)
expr: application_name
data_type: VARCHAR(134217728)
name: COMPUTE_POOL_NAME
synonyms:
pool
container pool
spcs pool
description: Name of the compute pool that incurred the credit usage
expr: compute_pool_name
data_type: VARCHAR(16777216)
name: END_TIME
synonyms:
spcs end time
description: End time of the usage interval
expr: end_time
data_type: TIMESTAMP_LTZ(0)
name: IS_EXCLUSIVE
synonyms:
exclusive usage
description: TRUE if the compute pool was created exclusively for an application
expr: is_exclusive
data_type: BOOLEAN
name: START_TIME
synonyms:
spcs start time
description: Start time of the usage interval
expr: start_time
data_type: TIMESTAMP_LTZ(0)
facts:
name: CREDITS_USED
synonyms:
container cost
container spend
container charges
SPCS cost
container service money
description: Total credits consumed by the container service during the hour
expr: credits_used
data_type: NUMBER(38,9)
metrics:
name: TOTAL_CREDITS_USED
synonyms:
total container cost
total container spend
total SPCS money
container service spending
container billing
description: Total Snowflake credits consumed by Snowpark Container Services
expr: SUM(snowpark_container_services.credits_used)
stage usage date
description: Date of the stage storage usage record
expr: usage_date
data_type: DATE
facts:
name: AVERAGE_STAGE_BYTES
description: Average daily bytes used by internal stages (named and default)
expr: average_stage_bytes
data_type: NUMBER(38,6)
metrics:
name: TOTAL_AVERAGE_STAGE_BYTES
description: Sum of average daily internal stage storage
expr: SUM(stage_storage.average_stage_bytes)
name: TAG_REFERENCES
synonyms:
tags
object tags
tag mapping
description: "Tag associations for Snowflake objects used for attributing cost or grouping usage. Contains apply_method field to distinguish between MANUAL (direct), INHERITED (from parent objects), PROPAGATED (automatic), and CLASSIFIED (auto-applied) tags. Note: Inheritance behavior may vary - test with your specific tagging setup."
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: TAG_REFERENCES
primary_key:
columns:
OBJECT_NAME
TAG_NAME
TAG_SCHEMA
TAG_DATABASE
dimensions:
name: APPLY_METHOD
synonyms:
tag application method
tag source
how tag applied
description: "How the tag was applied: MANUAL (directly set), INHERITED (from parent object), PROPAGATED (automatic propagation), CLASSIFIED (auto-applied for sensitive data)"
expr: apply_method
data_type: VARCHAR(16777216)
name: DOMAIN
synonyms:
tag domain
object type
resource type
description: Object type to which the tag is applied (e.g., WAREHOUSE)
expr: domain
data_type: VARCHAR(16777216)
name: OBJECT_NAME
synonyms:
tagged object
tagged resource
resource name
description: Name of the tagged object (e.g., warehouse name)
expr: object_name
data_type: VARCHAR(16777216)
name: TAG_DATABASE
synonyms:
tag db
tag database
description: Database where the tag is defined
expr: tag_database
data_type: VARCHAR(16777216)
name: TAG_NAME
synonyms:
tag
team
team name
cost center
department
owner
group
classification
category
description: Name of the tag applied to the object. Tags are schema-level objects used for data governance, cost attribution, compliance, and resource monitoring. Common examples include team, cost_center, data_classification, environment, or project.
expr: tag_name
data_type: VARCHAR(16777216)
name: TAG_SCHEMA
synonyms:
tag namespace
tag schema
description: Schema where the tag is defined
expr: tag_schema
data_type: VARCHAR(16777216)
name: TAG_VALUE
synonyms:
tag value
team value
cost center value
department value
team name value
classification value
description: String value associated with the tag name, forming a key-value pair. Values can be duplicated across objects (e.g., multiple tables tagged with team=engineering) or unique. Used for data governance, cost allocation, sensitive data classification, and policy enforcement.
expr: tag_value
data_type: VARCHAR(16777216)
name: WAREHOUSE_EVENTS_HISTORY
synonyms:
warehouse events
warehouse lifecycle
warehouse operations
warehouse status changes
description: Historical events for warehouse operations including resume, suspend, resize, and cluster management activities
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: WAREHOUSE_EVENTS_HISTORY
primary_key:
columns:
TIMESTAMP
WAREHOUSE_ID
CLUSTER_NUMBER
EVENT_NAME
dimensions:
name: EVENT_NAME
synonyms:
event type
warehouse operation
operation type
description: Type of warehouse event (e.g., RESUME_WAREHOUSE, SUSPEND_WAREHOUSE, ALTER_WAREHOUSE)
expr: event_name
data_type: VARCHAR(134217728)
name: EVENT_REASON
synonyms:
event trigger
operation reason
why event occurred
description: Reason for the event (e.g., WAREHOUSE_AUTORESUME, WAREHOUSE_AUTOSUSPEND, WAREHOUSE_RESIZE)
expr: event_reason
data_type: VARCHAR(134217728)
name: EVENT_STATE
synonyms:
event status
operation status
description: "State of the event: STARTED or COMPLETED"
expr: event_state
data_type: VARCHAR(134217728)
name: QUERY_ID
synonyms:
triggering query
event query
description: ID of the query that triggered the event (NULL for manual or system-initiated events)
expr: query_id
data_type: VARCHAR(134217728)
name: ROLE_NAME
synonyms:
event role
user role
description: Role used when the event was initiated (NULL for system-initiated events)
expr: role_name
data_type: VARCHAR(134217728)
name: SIZE
synonyms:
warehouse size
compute size
instance size
description: Warehouse size at the time of the event (e.g., X-SMALL, SMALL, MEDIUM, LARGE, X-LARGE, 2X-LARGE, etc.)
expr: size
data_type: VARCHAR(134217728)
name: TIMESTAMP
synonyms:
event time
event timestamp
warehouse event time
description: Date and time when the warehouse event occurred
expr: timestamp
data_type: TIMESTAMP_LTZ(6)
name: USER_NAME
synonyms:
event user
triggering user
description: Name of the user who initiated the event (NULL for system-initiated events)
expr: user_name
data_type: VARCHAR(134217728)
name: WAREHOUSE_ID
synonyms:
event warehouse id
description: Unique identifier of the warehouse involved in the event
expr: warehouse_id
data_type: NUMBER(38,0)
warehouse cloud money
description: Total cloud services credits for all warehouses
expr: SUM(warehouse_metering.credits_used_cloud_services)
name: TOTAL_WAREHOUSE_COMPUTE_CREDITS
synonyms:
total warehouse compute cost
warehouse compute spending
warehouse compute money
description: Total compute credits for all warehouses
expr: SUM(warehouse_metering.credits_used_compute)
name: TOTAL_WAREHOUSE_CREDITS_USED
synonyms:
total warehouse cost
total warehouse spend
name: BUDGET_DETAILS
synonyms:
budgets
budget details
spending budgets
description: Budget definitions and current month spending by database and schema
base_table:
database: SNOWFLAKE
schema: ACCOUNT_USAGE
table: BUDGET_DETAILS
primary_key:
columns:
BUDGET_NAME
DATABASE_NAME
SCHEMA_NAME
CREATED_ON
dimensions:
name: BUDGET_NAME
synonyms:
budget
budget identifier
description: Name of the budget
expr: budget_name
data_type: VARCHAR(16777216)
name: DATABASE_NAME
synonyms:
database
description: Database scope for the budget (if applicable)
expr: database_name
data_type: VARCHAR(16777216)
name: SCHEMA_NAME
synonyms:
schema
description: Schema scope for the budget (if applicable)
expr: schema_name
data_type: VARCHAR(16777216)
name: CREATED_ON
synonyms:
created at
creation time
description: Timestamp when the budget was created
expr: created_on
data_type: TIMESTAMP_LTZ(6)
facts:
name: CREDIT_LIMIT
synonyms:
budget credit limit
budget cap
description: Credit limit defined for the budget
expr: credit_limit
data_type: NUMBER(38,0)
name: CURRENT_MONTH_SPENDING
synonyms:
current month spend
month-to-date spending
description: Credits spent in the current month under the budget
expr: current_month_spending
data_type: NUMBER(38,2)
relationships:
name: Where is my money going - service breakdown
question: Where is my money going? Show me a complete breakdown of my Snowflake spending by service type.
sql: SELECT service_type, ROUND(SUM(credits_used), 2) AS total_credits, ROUND(SUM(credits_used) / SUM(SUM(credits_used)) OVER () * 100, 1) AS percentage_of_total FROM __metering WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() GROUP BY service_type ORDER BY total_credits DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Total credits used (UI internal)
question: How many credits were used last week?
sql: SELECT 'current_period' AS time, ROUND(IFNULL(SUM(credits_used), 0), 2) AS credits_used FROM __metering WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE()
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Credits used by service type (UI internal)
question: How much did I spend by service type last week?
sql: SELECT service_type, ROUND(SUM(credits_used), 2) AS credits_used FROM __metering WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() GROUP BY service_type ORDER BY credits_used DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Week-over-week credit usage comparison
question: How did my Snowflake spend change compared to the previous week?
sql: SELECT 'current_week' AS period, ROUND(IFNULL(SUM(credits_used), 0), 2) AS credits_used FROM __metering WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() UNION ALL SELECT 'previous_week' AS period, ROUND(IFNULL(SUM(credits_used), 0), 2) AS credits_used FROM __metering WHERE start_time >= DATEADD(DAY, -14, CURRENT_DATE()) AND start_time < DATEADD(DAY, -7, CURRENT_DATE())
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Month-over-month credit usage comparison
question: How did my Snowflake spend change compared to last month?
sql: SELECT 'current_month' AS period, ROUND(IFNULL(SUM(credits_used), 0), 2) AS credits_used FROM __metering WHERE start_time >= DATE_TRUNC('MONTH', CURRENT_DATE()) AND start_time < DATE_TRUNC('MONTH', DATEADD(MONTH, 1, CURRENT_DATE())) UNION ALL SELECT 'previous_month' AS period, ROUND(IFNULL(SUM(credits_used), 0), 2) AS credits_used FROM __metering WHERE start_time >= DATE_TRUNC('MONTH', DATEADD(MONTH, -1, CURRENT_DATE())) AND start_time < DATE_TRUNC('MONTH', CURRENT_DATE())
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Cost anomaly analysis
question: Which days had cost anomalies and how much did consumption exceed forecasts? Were there any spend anomalies? Were there any spend anomalies in the past month? Any cost anomalies recently?
sql: SELECT date, COUNT(*) AS anomaly_count, ROUND(SUM(actual_value), 2) AS total_consumption, ROUND(SUM(forecasted_value), 2) AS total_forecast, ROUND(SUM(actual_value - forecasted_value), 2) AS variance_amount, ROUND((SUM(actual_value) - SUM(forecasted_value)) / SUM(forecasted_value) * 100, 2) AS variance_percent FROM __anomalies_daily WHERE is_anomaly = TRUE AND date >= CURRENT_DATE - 30 GROUP BY date ORDER BY variance_percent DESC LIMIT 10
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Anomaly trends over time
question: What is the trend of cost anomalies by week over the past 3 months?
sql: SELECT DATE_TRUNC('WEEK', date) AS week_start, COUNT(DISTINCT date) AS days_with_anomalies, COUNT(*) AS total_anomalies, ROUND(AVG(actual_value - forecasted_value), 2) AS avg_variance_credits, ROUND(AVG((actual_value - forecasted_value) / forecasted_value * 100), 2) AS avg_variance_percent FROM __anomalies_daily WHERE is_anomaly = TRUE AND date >= CURRENT_DATE - 90 GROUP BY week_start ORDER BY week_start DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Correct anomaly detection vs incorrect filtering
question: What is the difference between actual anomalies (is_anomaly=TRUE) and just high consumption days (actual > forecast)?
sql: WITH actual_anomalies AS (SELECT COUNT() AS true_anomaly_days FROM __anomalies_daily WHERE is_anomaly = TRUE AND date >= CURRENT_DATE - 30), high_consumption AS (SELECT COUNT() AS high_consumption_days FROM __anomalies_daily WHERE actual_value > forecasted_value AND date >= CURRENT_DATE - 30) SELECT aa.true_anomaly_days, hc.high_consumption_days, (hc.high_consumption_days - aa.true_anomaly_days) AS difference_in_counts, 'Always use is_anomaly = TRUE for anomaly detection' AS important_note FROM actual_anomalies aa CROSS JOIN high_consumption hc
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Team cost breakdown across all services
question: What did each team spend in total? Show me detailed team costs by service type. What is the cost breakdown by team for the past month? How much did each team spend across all Snowflake services?
sql: SELECT tr.tag_name, tr.tag_value AS team, m.service_type, COUNT(DISTINCT m.name) AS resource_count, ROUND(SUM(m.credits_used), 2) AS total_credits, ROUND(SUM(m.credits_used_compute), 2) AS compute_credits, ROUND(SUM(m.credits_used_cloud_services), 2) AS cloud_services_credits, ROUND(AVG(m.credits_used), 2) AS avg_credits_per_resource FROM __metering m JOIN __tag_references tr ON m.name = tr.object_name WHERE m.start_time >= DATEADD(MONTH, -1, CURRENT_DATE()) AND m.start_time < CURRENT_DATE() AND tr.tag_value IS NOT NULL AND tr.tag_value != '' GROUP BY tr.tag_name, tr.tag_value, m.service_type ORDER BY total_credits DESC
verified_at: 1753987877
verified_by: Spencer Moritz
name: Team cost breakdown by warehouse
question: What is the total cost breakdown by team across all warehouses?
sql: SELECT tr.tag_name, tr.tag_value AS team, COUNT(DISTINCT wm.warehouse_name) AS warehouse_count, ROUND(SUM(wm.credits_used), 2) AS total_credits, ROUND(AVG(wm.credits_used), 2) AS avg_credits_per_warehouse FROM __warehouse_metering wm JOIN __tag_references tr ON wm.warehouse_name = tr.object_name WHERE tr.domain = 'WAREHOUSE' AND tr.tag_value IS NOT NULL AND tr.tag_value != '' AND wm.start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND wm.start_time < CURRENT_DATE() GROUP BY tr.tag_name, tr.tag_value ORDER BY total_credits DESC
verified_at: 1753987877
verified_by: Spencer Moritz
name: Department resource utilization
question: How many resources does each department own and what do they cost?
sql: SELECT tag_name AS department, domain AS resource_type, COUNT(DISTINCT object_name) AS resource_count, CASE WHEN domain = 'WAREHOUSE' THEN 'See warehouse costs in other queries' ELSE 'Storage/Other resources' END AS cost_note FROM __tag_references GROUP BY tag_name, domain ORDER BY tag_name, resource_count DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top teams by warehouse spend
question: Which teams or cost centers have the highest warehouse costs?
sql: SELECT tr.tag_name, tr.tag_value AS team, wm.warehouse_name, ROUND(SUM(wm.credits_used), 2) AS credits FROM __warehouse_metering wm JOIN __tag_references tr ON wm.warehouse_name = tr.object_name WHERE tr.domain = 'WAREHOUSE' AND tr.tag_value IS NOT NULL AND tr.tag_value != '' AND wm.start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND wm.start_time < CURRENT_DATE() GROUP BY tr.tag_name, tr.tag_value, wm.warehouse_name ORDER BY credits DESC LIMIT 100
verified_at: 1753987877
verified_by: Spencer Moritz
name: Top users by query costs
question: What users are spending the most? Which users are spending the most? Who is spending the most?
sql: SELECT user_name, COUNT(DISTINCT query_id) AS query_count, ROUND(SUM(credits_attributed_compute + COALESCE(credits_used_query_acceleration, 0)), 2) AS total_credits FROM __query_attribution WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() GROUP BY user_name ORDER BY total_credits DESC LIMIT 20
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Credits by service type and entity
question: How much compute and cloud services credit has each entity used by service type?
sql: SELECT DATE_TRUNC('DAY', CONVERT_TIMEZONE('UTC', start_time)) AS start_time, entity_id, name, service_type, ROUND(SUM(credits_used), 2) AS credits_used, ROUND(SUM(credits_used_compute), 2) AS credits_compute, ROUND(SUM(credits_used_cloud_services), 2) AS credits_cloud FROM __metering WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() AND service_type = 'WAREHOUSE_METERING' GROUP BY 1, 2, 3, 4
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top warehouses by credit usage
question: Which warehouses consumed the most credits last week?
sql: SELECT warehouse_name, warehouse_id, ROUND(SUM(credits_used), 2) AS credits FROM __warehouse_metering WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() GROUP BY 1, 2 ORDER BY 3 DESC LIMIT 100
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top databases by storage usage
question: Which databases used the most storage on average?
sql: WITH daily AS (SELECT database_name, usage_date, MAX(database_id) AS object_id, MAX(average_database_bytes + average_failsafe_bytes + average_hybrid_table_storage_bytes) AS database_storage_bytes FROM __database_storage WHERE usage_date >= DATEADD(DAY, -7, CURRENT_DATE()) AND usage_date < CURRENT_DATE() GROUP BY ALL) SELECT database_name, AVG(database_storage_bytes) AS database_storage_bytes FROM daily GROUP BY 1 ORDER BY 2 DESC LIMIT 100
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Storage usage by object type
question: What is the database and stage storage usage for each day?
sql: SELECT usage_date, database_name AS object_name, 'DATABASE' AS object_type, MAX(average_database_bytes) AS database_bytes, MAX(average_failsafe_bytes) AS failsafe_bytes, 0 AS stage_bytes, MAX(database_id) AS object_id, 0 AS hybrid_bytes FROM __database_storage WHERE usage_date >= DATEADD(DAY, -7, CURRENT_DATE()) AND usage_date < CURRENT_DATE() GROUP BY 1, 2, 3 UNION ALL SELECT usage_date, 'Stages' AS object_name, 'STAGE' AS object_type, 0, 0, MAX(average_stage_bytes), 0, 0 FROM __stage_storage WHERE usage_date >= DATEADD(DAY, -7, CURRENT_DATE()) AND usage_date < CURRENT_DATE() GROUP BY 1, 2, 3
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Storage growth trend analysis
question: How is storage usage trending across databases over time?
sql: SELECT database_name, usage_date, ROUND(average_database_bytes / POW(1024, 4), 3) AS database_tb, ROUND(average_failsafe_bytes / POW(1024, 4), 3) AS failsafe_tb, ROUND(average_hybrid_table_storage_bytes / POW(1024, 4), 3) AS hybrid_tb, ROUND((average_database_bytes + average_failsafe_bytes + average_hybrid_table_storage_bytes) / POW(1024, 4), 3) AS total_tb, LAG(ROUND((average_database_bytes + average_failsafe_bytes + average_hybrid_table_storage_bytes) / POW(1024, 4), 3)) OVER (PARTITION BY database_name ORDER BY usage_date) AS prev_day_tb FROM __database_storage WHERE usage_date >= CURRENT_DATE - 30 AND database_name IN (SELECT database_name FROM __database_storage WHERE usage_date = CURRENT_DATE - 1 ORDER BY (average_database_bytes + average_failsafe_bytes + average_hybrid_table_storage_bytes) DESC LIMIT 10) ORDER BY database_name, usage_date DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Most expensive individual queries
question: Which individual queries consumed the most compute credits last week?
sql: SELECT qh.query_id, qh.warehouse_name, qh.user_name, ROUND(qa.credits_attributed_compute, 2) AS credits_attributed_compute, ROUND(qa.credits_used_query_acceleration, 2) AS credits_used_query_acceleration, ROUND((qh.total_elapsed_time / 1000), 2) AS duration_seconds, LEFT(qh.query_text, 100) AS query_preview FROM __query_history qh JOIN __query_attribution qa ON qh.query_id = qa.query_id WHERE qh.start_time >= CURRENT_DATE - 7 AND qa.credits_attributed_compute > 0 ORDER BY qa.credits_attributed_compute DESC LIMIT 20
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top parameterized queries with text
question: Which parameterized queries were the most expensive last week, and what did they do?
sql: WITH expensive_queries AS (SELECT query_parameterized_hash, COUNT(query_id) AS num_queries FROM __query_attribution WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() GROUP BY query_parameterized_hash) SELECT eq.query_parameterized_hash, eq.num_queries, qh.query_id, qh.query_text FROM expensive_queries AS eq JOIN __query_history AS qh ON eq.query_parameterized_hash = qh.query_parameterized_hash QUALIFY ROW_NUMBER() OVER (PARTITION BY eq.query_parameterized_hash ORDER BY qh.end_time DESC) = 1 ORDER BY eq.num_queries DESC LIMIT 5
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Data transfer by region and cloud
question: How much data was transferred by region, cloud, and transfer type?
sql: SELECT DATE_TRUNC('DAY', CONVERT_TIMEZONE('UTC', start_time)) AS start_time, target_cloud, target_region, transfer_type, SUM(bytes_transferred) AS bytes_transferred FROM __data_transfer WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() GROUP BY 1, 2, 3, 4
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Data transfer cost analysis
question: What are the top data transfer costs by region and cloud provider?
sql: SELECT target_cloud, target_region, source_cloud, source_region, transfer_type, COUNT(*) AS transfer_count, ROUND(SUM(bytes_transferred) / POW(1024, 4), 3) AS total_tb_transferred, DATE_TRUNC('DAY', start_time) AS transfer_date FROM __data_transfer WHERE start_time >= CURRENT_DATE - 30 GROUP BY target_cloud, target_region, source_cloud, source_region, transfer_type, transfer_date HAVING total_tb_transferred > 0.1 ORDER BY total_tb_transferred DESC LIMIT 25
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top compute pools by spend
question: Which compute pools used the most credits over the past week?
sql: SELECT compute_pool_name, ROUND(SUM(credits_used), 2) AS total_credits FROM __snowpark_container_services WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() GROUP BY compute_pool_name ORDER BY total_credits DESC LIMIT 10
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Total Snowpark container services credits
question: How many total credits were used by Snowpark container services last week?
sql: SELECT ROUND(SUM(credits_used), 2) AS total_credits FROM __snowpark_container_services WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE()
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top applications by Snowpark container spend
question: Which applications used the most Snowpark container credits last week?
sql: SELECT application_name, application_id, ROUND(SUM(credits_used), 2) AS total_credits FROM __snowpark_container_services WHERE start_time >= DATEADD(DAY, -7, CURRENT_DATE()) AND start_time < CURRENT_DATE() AND application_id IS NOT NULL AND application_id <> '' GROUP BY application_name, application_id ORDER BY total_credits DESC LIMIT 10
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Warehouse resize events
question: Have any warehouses been resized recently?
sql: WITH warehouse_states AS (SELECT warehouse_name, timestamp, size, LAG(size) OVER (PARTITION BY warehouse_name ORDER BY timestamp) as previous_size FROM __warehouse_events_history WHERE event_name = 'WAREHOUSE_CONSISTENT' AND timestamp >= CURRENT_DATE - 7) SELECT warehouse_name, timestamp, previous_size, size as new_size FROM warehouse_states WHERE previous_size IS NOT NULL AND previous_size != size ORDER BY timestamp DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Query acceleration credits by warehouse (month-to-date)
question: Which warehouses used the most query acceleration service credits this month?
sql: SELECT warehouse_name, ROUND(SUM(credits_used), 2) AS total_credits_used FROM __query_acceleration_history WHERE start_time >= DATE_TRUNC('month', CURRENT_DATE) GROUP BY warehouse_name ORDER BY total_credits_used DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Serverless task credits last week
question: How many credits did serverless tasks consume last week?
sql: SELECT task_name, database_name, schema_name, ROUND(SUM(credits_used), 2) AS total_credits FROM __serverless_task_history WHERE start_time >= DATEADD('D', -7, CURRENT_DATE) AND start_time < CURRENT_DATE GROUP BY task_name, database_name, schema_name ORDER BY total_credits DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top serverless tasks by database
question: Which databases have the highest serverless task credit usage?
sql: SELECT database_name, COUNT(DISTINCT task_name) AS task_count, ROUND(SUM(credits_used), 2) AS total_credits FROM __serverless_task_history WHERE start_time >= DATEADD('D', -30, CURRENT_DATE) GROUP BY database_name ORDER BY total_credits DESC LIMIT 10
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Serverless task usage last 12 hours
question: What serverless task activity happened in the last 12 hours?
sql: SELECT task_name, database_name, schema_name, start_time, end_time, ROUND(credits_used, 2) AS credits_used FROM __serverless_task_history WHERE start_time >= DATEADD('H', -12, CURRENT_TIMESTAMP) ORDER BY start_time DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top Cortex Analyst users by credits
question: Which users consumed the most Cortex Analyst credits this month?
sql: SELECT username, ROUND(SUM(credits), 2) AS total_credits, SUM(request_count) AS total_requests, ROUND(SUM(credits) / NULLIF(SUM(request_count), 0), 4) AS avg_credits_per_request FROM __cortex_analyst_usage_history WHERE start_time >= DATE_TRUNC('month', CURRENT_DATE) GROUP BY username ORDER BY total_credits DESC LIMIT 10
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Cortex Analyst usage trend last 30 days
question: How has Cortex Analyst usage trended over the past month?
sql: SELECT DATE_TRUNC('DAY', start_time) AS usage_date, ROUND(SUM(credits), 2) AS daily_credits, SUM(request_count) AS daily_requests, COUNT(DISTINCT username) AS unique_users FROM __cortex_analyst_usage_history WHERE start_time >= DATEADD('D', -30, CURRENT_DATE) GROUP BY usage_date ORDER BY usage_date DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Cortex function usage trends
question: Show me the daily trend of Cortex function usage and costs over the past week
sql: SELECT DATE(start_time) AS usage_date, function_name, SUM(tokens) AS total_tokens, ROUND(SUM(token_credits), 2) AS total_credits FROM __cortex_functions_usage_history WHERE start_time >= DATEADD('D', -7, CURRENT_DATE) GROUP BY DATE(start_time), function_name ORDER BY usage_date DESC, total_credits DESC
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top Cortex functions by cost
question: Which Cortex functions are the most expensive? What are my top AI function costs? Which AI models consumed the most credits?
sql: SELECT function_name, model_name, SUM(tokens) AS total_tokens, ROUND(SUM(token_credits), 2) AS total_credits, COUNT(*) AS time_windows FROM __cortex_functions_usage_history GROUP BY function_name, model_name ORDER BY total_credits DESC LIMIT 10
verified_at: 1742476800
verified_by: Nathan Scharfe
name: Top 5 most expensive databases by storage
question: Which 5 databases are contributing the most to storage costs?
sql: SELECT database_name, database_id, COUNT() as days_tracked, AVG((average_database_bytes + average_failsafe_bytes + COALESCE(average_hybrid_table_storage_bytes, 0))) / (102410241024) AS avg_total_storage_gb, AVG(average_database_bytes) / (102410241024) AS avg_database_storage_gb, AVG(average_failsafe_bytes) / (102410241024) AS avg_failsafe_storage_gb, AVG(COALESCE(average_hybrid_table_storage_bytes, 0)) / (10241024*1024) AS avg_hybrid_storage_gb, MIN(usage_date) as first_date, MAX(usage_date) as last_date FROM __database_storage WHERE usage_date >= DATEADD(days, -30, CURRENT_DATE()) GROUP BY database_name, database_id ORDER BY avg_total_storage_gb DESC LIMIT 5
verified_at: 1752164792
verified_by: Spencer Moritz
name: Most expensive warehouses on date of anomalies
question: Over the past 6 months what were the most expensive warehouses on the date that had anomalies?
analyst request count
description: Total number of messages sent to Cortex Analyst across all users and time periods
expr: SUM(cortex_analyst_usage_history.request_count)
name: UNIQUE_CORTEX_USERS
synonyms:
unique analyst users
analyst user count
conversational AI users
data assistant users
description: Number of unique users who have sent requests to Cortex Analyst
expr: COUNT(DISTINCT cortex_analyst_usage_history.username)
function charges
function billing
description: Credits consumed by Cortex functions during the hour-long time window for the function, model, and warehouse combination
expr: token_credits
data_type: NUMBER(38,9)
metrics:
name: AVERAGE_FUNCTION_CREDITS
synonyms:
average function cost
cost per hour
function cost efficiency
function cost average
description: Average credits consumed per hour by Cortex functions
expr: AVG(cortex_functions_usage_history.token_credits)
name: AVERAGE_FUNCTION_TOKENS
synonyms:
average function tokens
tokens per hour
function token efficiency
function token average
description: Average tokens consumed per hour by Cortex functions
expr: AVG(cortex_functions_usage_history.tokens)
name: TOTAL_FUNCTION_CREDITS
synonyms:
total function cost
total AI function cost
total function spend
function spending
function billing
description: Total credits consumed by Cortex functions across all time windows, functions, and models
expr: SUM(cortex_functions_usage_history.token_credits)
name: TOTAL_FUNCTION_TOKENS
synonyms:
total function tokens
total AI function tokens
total cortex tokens
function token usage
description: Total tokens consumed by Cortex functions across all time windows, functions, and models
expr: SUM(cortex_functions_usage_history.tokens)
name: TOTAL_FUNCTION_WINDOWS
synonyms:
total function windows
function time window count
function time periods
description: Total number of hourly time windows with Cortex function usage recorded
expr: COUNT(cortex_functions_usage_history.start_time)
name: TOTAL_CREDITS_USED
synonyms:
total money
total cost
total spend
total spending
total charges
total bill
total billing
where is my money going
what am I spending on
cost breakdown
spending breakdown
description: Total Snowflake credits used
expr: SUM(metering.credits_used)
name: CREDITS_USED_QUERY_ACCELERATION
synonyms:
query acceleration cost
QAS cost
query acceleration spend
query acceleration money
description: Credits used by Query Acceleration Service for this query (if applicable)
expr: credits_used_query_acceleration
data_type: FLOAT
metrics:
name: TOTAL_QUERY_ACCELERATION_CREDITS
synonyms:
total QAS cost
total acceleration spend
total acceleration money
acceleration spending
description: Total credits used by Query Acceleration Service for queries
expr: SUM(query_attribution.credits_used_query_acceleration)
name: TOTAL_QUERY_COMPUTE_CREDITS
synonyms:
total query cost
total query spend
total query money
query spending
query cost breakdown
description: Total compute credits attributed to query execution
expr: SUM(query_attribution.credits_attributed_compute)
name: EXECUTION_TIME
description: Actual time in milliseconds spent executing the query
expr: execution_time
data_type: NUMBER(38,0)
name: QUERY_PARAMETERIZED_HASH
description: Hash value of the parameterized query
expr: query_parameterized_hash
data_type: VARCHAR(134217728)
name: ROWS_DELETED
description: Number of rows deleted by the query (if applicable)
expr: rows_deleted
data_type: NUMBER(38,0)
name: ROWS_INSERTED
description: Number of rows inserted by the query (if applicable)
expr: rows_inserted
data_type: NUMBER(38,0)
name: ROWS_PRODUCED
description: Number of rows returned by the query
expr: rows_produced
data_type: NUMBER(38,0)
name: ROWS_UPDATED
description: Number of rows updated by the query (if applicable)
expr: rows_updated
data_type: NUMBER(38,0)
name: TOTAL_ELAPSED_TIME
description: Total time in milliseconds the query spent executing, including compilation, queueing, and execution
expr: total_elapsed_time
data_type: NUMBER(38,0)
metrics:
name: TOTAL_BYTES_SCANNED
description: Total bytes scanned by queries
expr: SUM(query_history.bytes_scanned)
name: TOTAL_BYTES_WRITTEN
description: Total bytes written by queries
expr: SUM(query_history.bytes_written)
name: TOTAL_ELAPSED_TIME_MS
description: Total elapsed time including compilation, queueing, and execution (milliseconds)
expr: SUM(query_history.total_elapsed_time)
name: TOTAL_EXECUTION_TIME_MS
description: Total execution time across all queries (milliseconds)
expr: SUM(query_history.execution_time)
name: TOTAL_FAILED_QUERIES
description: Total number of failed queries
expr: SUM(CASE WHEN query_history.error_code IS NOT NULL THEN 1 ELSE 0 END)
name: TOTAL_QUERIES
description: Total number of queries executed
expr: COUNT(query_history.query_id)
name: TOTAL_ROWS_PRODUCED
description: Total rows produced by queries
expr: SUM(query_history.rows_produced)
task cost per execution
description: Average serverless task credits consumed per task execution window
expr: AVG(serverless_task_history.credits_used)
name: TOTAL_SERVERLESS_TASK_CREDITS
synonyms:
total task cost
total task spend
total task money
serverless spending
task billing
description: Total credits consumed by serverless tasks across all tasks and time periods
expr: SUM(serverless_task_history.credits_used)
name: TOTAL_TASK_EXECUTIONS
description: Total number of serverless task execution windows recorded
expr: COUNT(serverless_task_history.task_id)
name: WAREHOUSE_NAME
synonyms:
event warehouse
warehouse
description: Name of the warehouse involved in the event
expr: warehouse_name
data_type: VARCHAR(134217728)
facts:
name: CLUSTER_COUNT
description: Total number of clusters in the warehouse at the time of the event (useful for tracking scaling patterns)
expr: cluster_count
data_type: VARCHAR(134217728)
name: CLUSTER_NUMBER
description: Numeric identifier of the specific cluster involved in the event (always 1 for single-cluster warehouses)
expr: cluster_number
data_type: NUMBER(38,0)
metrics:
name: AVERAGE_CLUSTER_COUNT
description: Average number of clusters across all warehouse events (multi-cluster scaling indicator)
expr: AVG(warehouse_events_history.cluster_count)
name: TOTAL_RESIZE_EVENTS
description: Total number of resize initiation events (for actual size changes, use WAREHOUSE_CONSISTENT events with LAG analysis)
expr: SUM(CASE WHEN warehouse_events_history.event_name = 'RESIZE_WAREHOUSE' THEN 1 ELSE 0 END)
name: TOTAL_WAREHOUSE_EVENTS
description: Total number of warehouse events recorded
expr: COUNT(warehouse_events_history.timestamp)
total warehouse money
warehouse spending breakdown
description: Total warehouse credits used
expr: SUM(warehouse_metering.credits_used)
