The activities data table associated with your workspace logs record enrolments, node executions, and success/failure outcomes across all your tools and plays.
These logs provide real-time visibility into how your workflows are performing—helping you identify bottlenecks, debug errors, and discover optimization opportunities.
You can query activities data from your data warehouse’s query editor, a
Cargo data model, or the Sandbox query editor in the System of Record
section of your workspace settings.
Adapt query syntax for your database (BigQuery or Snowflake). If using your
own data warehouse, the dataset name matches the Dataset name from your
System of Record configuration. If using Cargo’s managed BigQuery, contact
the Cargo team for connection details.
Query from the CLI
Run these queries directly against your system of record with the CLI:
cargo-ai storage query execute \
"SELECT kind, COUNT(*) AS count FROM activities
WHERE kind IN ('workflowEntered','workflowLeft') GROUP BY kind"
storage query, reference the activities table simply as activities — Cargo rewrites it to the correct physical table for your warehouse. The physical compute__activities / _compute.activities names in the Table naming conventions section below are only needed when querying your warehouse directly.
Understanding the activities table
The activities data is stored in the compute__activities table with the following structure:
| Column | Description |
|---|
_id | Unique identifier for each activity |
_time | Timestamp when the activity occurred |
kind | Type of activity (see below) |
model_uuid | Reference to the data model (if applicable) |
workflow_uuid | Reference to the workflow |
play_uuid | Reference to the play (if applicable) |
tool_uuid | Reference to the tool (if applicable) |
segment_uuid | Reference to the segment (for segment activities) |
context | JSON object containing detailed activity information |
Activity types
| Kind | Description |
|---|
workflowNodeExecuted | A specific node in a workflow completed execution |
workflowEntered | A record entered a workflow |
workflowLeft | A record exited a workflow |
segmentEntered | A record entered a segment |
segmentLeft | A record left a segment |
Example: Track workflow node executions
Query all node executions for a specific workflow, including their status and any error messages:
SELECT
_time,
JSON_VALUE(context, '$.nodeSlug') AS node_name,
JSON_VALUE(context, '$.nodeKind') AS node_type,
JSON_VALUE(context, '$.nodeStatus') AS status,
JSON_VALUE(context, '$.nodeErrorMessage') AS error_message
FROM [DATASET].compute__activities
WHERE kind = 'workflowNodeExecuted'
AND JSON_VALUE(context, '$.workflowUuid') = 'your-workflow-uuid-here'
ORDER BY _time DESC;
SELECT
_time,
context:nodeSlug AS node_name,
context:nodeKind AS node_type,
context:nodeStatus AS status,
context:nodeErrorMessage AS error_message
FROM [DATABASE]._compute.activities
WHERE kind = 'workflowNodeExecuted'
AND context:workflowUuid = 'your-workflow-uuid-here'
ORDER BY _time DESC;
Example: Track workflow throughput
Monitor how many records enter and exit your workflows over time:
SELECT
DATE(_time) AS date,
kind,
COUNT(*) AS count
FROM [DATASET].compute__activities
WHERE kind IN ('workflowEntered', 'workflowLeft')
AND workflow_uuid = 'your-workflow-uuid-here'
GROUP BY date, kind
ORDER BY date DESC;
SELECT
DATE(_time) AS date,
kind,
COUNT(*) AS count
FROM [DATABASE]._compute.activities
WHERE kind IN ('workflowEntered', 'workflowLeft')
AND workflow_uuid = 'your-workflow-uuid-here'
GROUP BY date, kind
ORDER BY date DESC;
Example: Identify failing nodes
Find nodes with the highest error rates:
SELECT
JSON_VALUE(context, '$.nodeSlug') AS node_name,
COUNT(*) AS total_executions,
COUNTIF(JSON_VALUE(context, '$.nodeStatus') = 'failed') AS failures,
ROUND(
COUNTIF(JSON_VALUE(context, '$.nodeStatus') = 'failed') * 100.0 / COUNT(*),
2
) AS failure_rate
FROM [DATASET].compute__activities
WHERE kind = 'workflowNodeExecuted'
AND _time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
GROUP BY node_name
HAVING failures > 0
ORDER BY failure_rate DESC;
SELECT
context:nodeSlug AS node_name,
COUNT(*) AS total_executions,
COUNT_IF(context:nodeStatus = 'failed') AS failures,
ROUND(COUNT_IF(context:nodeStatus = 'failed') * 100.0 / COUNT(*), 2) AS failure_rate
FROM [DATABASE]._compute.activities
WHERE kind = 'workflowNodeExecuted'
AND _time >= DATEADD(DAY, -7, CURRENT_TIMESTAMP())
GROUP BY node_name
HAVING failures > 0
ORDER BY failure_rate DESC;
Table naming conventions
Depending on your System of Record scope, use the appropriate table reference:
| Scope | Table reference |
|---|
| BigQuery project-level | [PROJECT_ID].[DATASET].compute__activities |
| BigQuery dataset-level | [DATASET].compute__activities |
| Snowflake database-level | [DATABASE]._compute.activities |
| Snowflake schema-level | [DATABASE].[SCHEMA]._compute__activities |
Replace bracketed values (e.g., [PROJECT_ID], [DATASET]) with your actual
configuration values from the System of Record settings.
