JOBS_TIMELINE_BY_FOLDER view
The INFORMATION_SCHEMA.JOBS_TIMELINE_BY_FOLDER
view contains near real-time
BigQuery metadata by timeslice for all jobs submitted in the
parent folder of the current project, including the jobs in subfolders under it.
This view contains both running and completed jobs.
Required permissions
To query the INFORMATION_SCHEMA.JOBS_TIMELINE_BY_FOLDER
view, you need
the bigquery.jobs.listAll
Identity and Access Management (IAM) permission for the parent
folder. Each of the following predefined IAM roles includes the
required permission:
- Folder Admin
- BigQuery Admin
For more information about BigQuery permissions, see Access control with IAM.
Schema
When you query the INFORMATION_SCHEMA.JOBS_TIMELINE_BY_*
views, the query
results contain one row for every second of execution of every
BigQuery job. Each period starts on a whole-second interval and
lasts exactly one second.
The INFORMATION_SCHEMA.JOBS_TIMELINE_BY_*
view has the following schema:
Column name | Data type | Value |
---|---|---|
period_start |
TIMESTAMP |
Start time of this period. |
period_slot_ms |
INTEGER |
Slot milliseconds consumed in this period. |
project_id |
STRING |
(Clustering column) ID of the project. |
project_number |
INTEGER |
Number of the project. |
folder_numbers |
REPEATED INTEGER |
Number IDs of the folders that contain the project, starting with the folder that immediately contains the project, followed by the folder that contains the child folder, and so forth. For example, if `folder_numbers` is `[1, 2, 3]`, then folder `1` immediately contains the project, folder `2` contains `1`, and folder `3` contains `2`. |
user_email |
STRING |
(Clustering column) Email address or service account of the user who ran the job. |
job_id |
STRING |
ID of the job. For example, bquxjob_1234 . |
job_type |
STRING |
The type of the job. Can be QUERY , LOAD ,
EXTRACT , COPY , or null . Job
type null indicates an internal job, such as script job
statement evaluation or materialized view refresh. |
statement_type |
STRING |
The type of query statement, if valid. For example,
SELECT , INSERT , UPDATE , or
DELETE . |
priority |
STRING |
The priority of this job. Valid values include INTERACTIVE and
BATCH . |
parent_job_id |
STRING |
ID of the parent job, if any. |
job_creation_time |
TIMESTAMP |
(Partitioning column) Creation time of this job. Partitioning is based on the UTC time of this timestamp. |
job_start_time |
TIMESTAMP |
Start time of this job. |
job_end_time |
TIMESTAMP |
End time of this job. |
state |
STRING |
Running state of the job at the end of this period. Valid states
include PENDING , RUNNING , and
DONE . |
reservation_id |
STRING |
Name of the primary reservation assigned to this job at the end of this period, if applicable. |
edition |
STRING |
The edition associated with the reservation assigned to this job. For more information about editions, see Introduction to BigQuery editions. |
total_bytes_billed |
INTEGER |
If the project is configured to use on-demand pricing, then this field contains the total bytes billed for the job. If the project is configured to use flat-rate pricing, then you are not billed for bytes and this field is informational only. |
total_bytes_processed |
INTEGER |
Total bytes processed by the job. |
error_result |
RECORD |
Details of error (if any) as an
ErrorProto.
|
cache_hit |
BOOLEAN |
Whether the query results of this job were from a cache. |
period_shuffle_ram_usage_ratio |
FLOAT |
Shuffle usage ratio in the selected time period. |
period_estimated_runnable_units |
INTEGER |
Units of work that can be scheduled immediately in this period. Additional slots for these units of work accelerate your query, provided no other query in the reservation needs additional slots. |
transaction_id |
STRING |
ID of the transaction in which this job ran, if any. (Preview) |
Data retention
This view contains currently running jobs and the job history for the past 180 days.
Scope and syntax
Queries against this view must include a region qualifier. If you don't specify a regional qualifier, metadata is retrieved from all regions. The following table explains the region scope for this view:
View name | Resource scope | Region scope |
---|---|---|
[PROJECT_ID.]`region-REGION`.INFORMATION_SCHEMA.JOBS_TIMELINE_BY_FOLDER |
Project level | REGION |
Optional: PROJECT_ID
: the ID of your
Google Cloud project. If not specified, the default project is used.
REGION
: any dataset region name.
For example, `region-us`
.
Examples
The following examples show how to query the
INFORMATION_SCHEMA.JOBS_TIMELINE_BY_FOLDER
view.
Get the number of unique jobs
The following query displays the number of unique jobs running per minute in the designated project's folder:
SELECT TIMESTAMP_TRUNC(period_start, MINUTE) AS per_start, COUNT(DISTINCT job_id) AS unique_jobs FROM `region-us`.INFORMATION_SCHEMA.JOBS_TIMELINE_BY_FOLDER, UNNEST(folder_numbers) f WHERE my_folder_number = f GROUP BY per_start ORDER BY per_start DESC;
The result is similar to the following:
+---------------------------+---------------------------------+ | per_start | unique_jobs | +---------------------------+---------------------------------+ | 2019-10-10 00:04:00 UTC | 5 | | 2019-10-10 00:03:00 UTC | 2 | | 2019-10-10 00:02:00 UTC | 3 | | 2019-10-10 00:01:00 UTC | 4 | | 2019-10-10 00:00:00 UTC | 4 | +---------------------------+---------------------------------+
Calculate the slot-time used
The following query displays the slot-time used per minute in the designated project's folder:
SELECT TIMESTAMP_TRUNC(period_start, MINUTE) AS per_start, SUM(period_slot_ms) AS slot_ms FROM `region-us`.INFORMATION_SCHEMA.JOBS_TIMELINE_BY_FOLDER, UNNEST(folder_numbers) f WHERE my_folder_number = f AND reservation_id = "my reservation id" AND statement_type != "SCRIPT" GROUP BY per_start ORDER BY per_start DESC;
The result is similar to the following:
+---------------------------+---------------------------------+ | per_start | slot_ms | +---------------------------+---------------------------------+ | 2019-10-10 00:04:00 UTC | 500 | | 2019-10-10 00:03:00 UTC | 1000 | | 2019-10-10 00:02:00 UTC | 3000 | | 2019-10-10 00:01:00 UTC | 4000 | | 2019-10-10 00:00:00 UTC | 4000 | +---------------------------+---------------------------------+