# The QueryInfo object

## Attributes

- `query_id` (string)
  The query ID.
  Example: `f996b47c-6672-4763-9668-d491a82099f5`
- `status` (string)
  Query status with one the following values:
   
   - `QUEUED`: Query has been received and queued.
   - `RUNNING`: Query has started.
   - `CANCELED`: Query has been cancelled by the user.
   - `FAILED`: Query has failed.
   - `FINISHED`: Query has completed.
  Possible values: `QUEUED`, `STARTED`, `COMPILING`, `COMPILED`, `RUNNING`, `CANCELED`, `FAILED`, `FINISHED`
  Example: `FINISHED`
- `query_text` (string)
  The text of the query.
  Example: `SELECT * FROM customers;`
- `query_start_time_ms` (int64)
  The time the query started.
  Example: `1595357086200`
- `execution_end_time_ms` (int64)
  The time execution of the query ended.
  Example: `1595357086373`
- `query_end_time_ms` (int64)
  The time the query ended.
  Example: `1595357087200`
- `user_id` (int64)
  The ID of the user who ran the query.
  Example: `1234567890123456`
- `user_name` (string)
  The email address or username of the user who ran the query.
  Example: `user@example.com`
- `spark_ui_url` (string)
  URL to the Spark UI query plan.
  Example: `https://<databricks-instance>/sparkui/1234-567890-test123/driver-1234567890123456789/SQL/execution/?id=0`
- `endpoint_id` (string)
  Alias for `warehouse_id`.
- `rows_produced` (int64)
  The number of results returned by the query.
  Example: `100`
- `error_message` (string)
  Message describing why the query could not complete.
- `lookup_key` (string)
  A key that can be used to look up query details.
  Example: `CiQ3OGFkYmQ2Zi00ZGUwLTRlNTYtOTkxZC05Y2I5OTNlZTViYjcQ4N6r/dguGhBlM2VlYTVlOTExMjFkMzNjILPbh9OK6uoL`
- `metrics` (object)
  Metrics about query execution.
  - `total_time_ms` (int64)
    Total execution time of the query from the client’s point of view, in milliseconds.
  - `read_bytes` (int64)
    Total size of data read by the query, in bytes.
  - `rows_produced_count` (int64)
    Total number of rows returned by the query.
  - `compilation_time_ms` (int64)
    Time spent loading metadata and optimizing the query, in milliseconds.
  - `execution_time_ms` (int64)
    Time spent executing the query, in milliseconds.
  - `read_remote_bytes` (int64)
    Size of persistent data read from cloud object storage on your cloud tenant, in bytes.
  - `write_remote_bytes` (int64)
    Size pf persistent data written to cloud object storage in your cloud tenant, in bytes.
  - `read_cache_bytes` (int64)
    Size of persistent data read from the cache, in bytes.
  - `spill_to_disk_bytes` (int64)
    Size of data temporarily written to disk while executing the query, in bytes.
  - `task_total_time_ms` (int64)
    Sum of execution time for all of the query’s tasks, in milliseconds.
  - `read_files_count` (int64)
    Number of files read after pruning
  - `read_partitions_count` (int64)
    Number of partitions read after pruning.
  - `photon_total_time_ms` (int64)
    Total execution time for all individual Photon query engine tasks in the query, in milliseconds.
  - `rows_read_count` (int64)
    Total number of rows read by the query.
  - `result_fetch_time_ms` (int64)
    Time spent fetching the query results after the execution finished, in milliseconds.
  - `network_sent_bytes` (int64)
    Total amount of data sent over the network between executor nodes during shuffle, in bytes.
  - `result_from_cache` (boolean)
    `true` if the query result was fetched from cache, `false` otherwise.
  - `pruned_bytes` (int64)
    Total number of file bytes in all tables not read due to pruning
  - `pruned_files_count` (int64)
    Total number of files from all tables not read due to pruning
  - `provisioning_queue_start_timestamp` (int64)
    Timestamp of when the query was enqueued waiting for a cluster to be provisioned for the warehouse.
     This field is optional and will not appear if the query skipped the provisioning queue.
  - `overloading_queue_start_timestamp` (int64)
    Timestamp of when the query was enqueued waiting while the warehouse was at max load.
     This field is optional and will not appear if the query skipped the overloading queue.
  - `query_compilation_start_timestamp` (int64)
    Timestamp of when the underlying compute started compilation of the query.
  - `task_time_over_time_range` (object)
    sum of task times completed in a range of wall clock time, approximated to a configurable number of points
     aggregated over all stages and jobs in the query (based on task_total_time_ms)
    - `entries` (array of object)
      - `task_completed_time_ms` (int64)
        total task completion time in this time range, aggregated over all stages and jobs in the query
    - `interval` (int64)
      interval length for all entries (difference in start time and end time of an entry range)
       the same for all entries
       start time of first interval is query_start_time_ms
  - `work_to_be_done` (int64)
    remaining work to be done across all stages in the query, calculated by autoscaler StatementAnalysis.scala, in milliseconds
     deprecated: using projected_remaining_task_total_time_ms instead
  - `runnable_tasks` (int64)
    number of remaining tasks to complete, calculated by autoscaler StatementAnalysis.scala
     deprecated: use remaining_task_count instead
  - `projected_remaining_task_total_time_ms` (int64)
    projected remaining work to be done aggregated across all stages in the query, in milliseconds
  - `remaining_task_count` (int64)
    number of remaining tasks to complete
     this is based on the current status and could be bigger or smaller in the future based on future updates
  - `projected_remaining_wallclock_time_ms` (int64)
    projected lower bound on remaining total task time based on projected_remaining_task_total_time_ms / maximum concurrency
  - `read_files_bytes` (int64)
    Total number of file bytes in all tables read
- `executed_as_user_id` (int64)
  The ID of the user whose credentials were used to run the query.
- `executed_as_user_name` (string)
  The email address or username of the user whose credentials were used to run the query.
- `session_id` (string, GA)
  The spark session UUID that query ran on.
   This is either the Spark Connect, DBSQL, or SDP session ID.
  Example: `f996b47c-6672-4763-9668-d491a82099f5`
- `is_final` (boolean)
  Whether more updates for the query are expected.
  Example: `true`
- `channel_used` (object)
  SQL Warehouse channel information at the time of query execution
  - `name` (string)
    Name of the channel
    Possible values: `CHANNEL_NAME_UNSPECIFIED`, `CHANNEL_NAME_PREVIEW`, `CHANNEL_NAME_CURRENT`, `CHANNEL_NAME_PREVIOUS`, `CHANNEL_NAME_CUSTOM`
    Example: `CHANNEL_NAME_CURRENT`
  - `dbsql_version` (string)
    DB SQL Version the Channel is mapped to.
    Example: `2022.30`
- `plans_state` (string)
  Whether plans exist for the execution, or the reason why they are missing
  Possible values: `IGNORED_SMALL_DURATION`, `IGNORED_LARGE_PLANS_SIZE`, `EXISTS`, `UNKNOWN`, `EMPTY`, `IGNORED_SPARK_PLAN_TYPE`
  Example: `EXISTS`
- `statement_type` (string)
  Type of statement for this query
  Possible values: `OTHER`, `ALTER`, `ANALYZE`, `COPY`, `CREATE`, `DELETE`, `DESCRIBE`, `DROP`, `EXPLAIN`, `GRANT`, `INSERT`, `MERGE`, `OPTIMIZE`, `REFRESH`, `REPLACE`, `REVOKE`, `SELECT`, `SET`, `SHOW`, `TRUNCATE`, `UPDATE`, `USE`
  Example: `SELECT`
- `warehouse_id` (string)
  Warehouse ID.
  Example: `098765321fedcba`
- `duration` (int64)
  Total time of the statement execution. This value does not include the time taken to retrieve the results, which
   can result in a discrepancy between this value and the start-to-finish wall-clock time.
  Example: `1000`
- `client_application` (string)
  Client application that ran the statement. For example: Databricks SQL Editor, Tableau, and Power BI. 
   This field is derived from information provided by client applications. While values are expected to 
   remain static over time, this cannot be guaranteed.
  Example: `Power BI`
- `query_source` (object)
  A struct that contains key-value pairs representing <Databricks> entities that were involved in the execution 
   of this statement, such as jobs, notebooks, or dashboards. This field only records <Databricks> entities.
  - `dashboard_id` (string)
    The canonical identifier for this Lakeview dashboard
    Example: `b1efe7f5891c1815b65e21c873fdaf4e`
  - `legacy_dashboard_id` (string)
    The canonical identifier for this legacy dashboard
    Example: `caf1e170-d14e-4bcc-8019-8c3b26ca46e4`
  - `alert_id` (string)
    The canonical identifier for this SQL alert
    Example: `d789836c-56ef-4c89-b951-d7186c4ad3ee`
  - `notebook_id` (string)
    The canonical identifier for this notebook
    Example: `1335125300829196`
  - `sql_query_id` (string)
    The canonical identifier for this SQL query
    Example: `be6df0a0-c317-44df-9659-b4f206c5d027`
  - `job_info` (object)
    - `job_id` (string)
      The canonical identifier for this job.
      Example: `445923364221868`
    - `job_run_id` (string)
      The canonical identifier of the run. This ID is unique across all runs of all jobs.
    - `job_task_run_id` (string)
      The canonical identifier of the task run.
      Example: `870588346649939`
  - `genie_space_id` (string)
    The canonical identifier for this Genie space
    Example: `a1f008dd4daf1340a7d59c66c2bdc5a8`
- `cache_query_id` (string)
  The ID of the cached query if this result retrieved from cache
  Example: `f996b47c-6672-4763-9668-d491a82099f5`
- `query_tags` (array of object, Public Preview)
  A query execution can be optionally annotated with query tags
  - `key` (string, Public Preview)
  - `value` (string, Public Preview)

## Example

```json
{
  "query_id": "f996b47c-6672-4763-9668-d491a82099f5",
  "status": "FINISHED",
  "query_text": "SELECT * FROM customers;",
  "query_start_time_ms": 1595357086200,
  "execution_end_time_ms": 1595357086373,
  "query_end_time_ms": 1595357087200,
  "user_id": 1234567890123456,
  "user_name": "user@example.com",
  "spark_ui_url": "https://\u003cdatabricks-instance\u003e/sparkui/1234-567890-test123/driver-1234567890123456789/SQL/execution/?id=0",
  "endpoint_id": "string",
  "rows_produced": 100,
  "error_message": "Table or view not found: customers; line 1 pos 14;\n'GlobalLimit 1000\n+- 'LocalLimit 1000\n +- 'Project [*]\n +- 'UnresolvedRelation [sales]\n",
  "lookup_key": "CiQ3OGFkYmQ2Zi00ZGUwLTRlNTYtOTkxZC05Y2I5OTNlZTViYjcQ4N6r/dguGhBlM2VlYTVlOTExMjFkMzNjILPbh9OK6uoL",
  "metrics": {
    "total_time_ms": 0,
    "read_bytes": 0,
    "rows_produced_count": 0,
    "compilation_time_ms": 0,
    "execution_time_ms": 0,
    "read_remote_bytes": 0,
    "write_remote_bytes": 0,
    "read_cache_bytes": 0,
    "spill_to_disk_bytes": 0,
    "task_total_time_ms": 0,
    "read_files_count": 0,
    "read_partitions_count": 0,
    "photon_total_time_ms": 0,
    "rows_read_count": 0,
    "result_fetch_time_ms": 0,
    "network_sent_bytes": 0,
    "result_from_cache": true,
    "pruned_bytes": 0,
    "pruned_files_count": 0,
    "provisioning_queue_start_timestamp": 0,
    "overloading_queue_start_timestamp": 0,
    "query_compilation_start_timestamp": 0,
    "task_time_over_time_range": {},
    "work_to_be_done": 0,
    "runnable_tasks": 0,
    "projected_remaining_task_total_time_ms": 0,
    "remaining_task_count": 0,
    "projected_remaining_wallclock_time_ms": 0,
    "read_files_bytes": 0
  },
  "executed_as_user_id": 0,
  "executed_as_user_name": "string",
  "session_id": "f996b47c-6672-4763-9668-d491a82099f5",
  "is_final": true,
  "channel_used": {
    "name": "CHANNEL_NAME_CURRENT",
    "dbsql_version": "2022.30"
  },
  "plans_state": "EXISTS",
  "statement_type": "SELECT",
  "warehouse_id": "098765321fedcba",
  "duration": 1000,
  "client_application": "Power BI",
  "query_source": {
    "dashboard_id": "b1efe7f5891c1815b65e21c873fdaf4e",
    "legacy_dashboard_id": "caf1e170-d14e-4bcc-8019-8c3b26ca46e4",
    "alert_id": "d789836c-56ef-4c89-b951-d7186c4ad3ee",
    "notebook_id": "1335125300829196",
    "sql_query_id": "be6df0a0-c317-44df-9659-b4f206c5d027",
    "job_info": {},
    "genie_space_id": "a1f008dd4daf1340a7d59c66c2bdc5a8"
  },
  "cache_query_id": "f996b47c-6672-4763-9668-d491a82099f5",
  "query_tags": [
    {
      "key": "string",
      "value": "string"
    }
  ]
}
```