Is there any system tables in Google Bigquery to check all the current running queries? I am looking similar to V$SQL table and v$Session tables in Oracle.
You can query the INFORMATION_SCHEMA.JOBS_BY_* view to retrieve real-time metadata about BigQuery jobs. This view contains currently running jobs, as well as the last 180 days of history of completed jobs.
for example
SELECT
job_id,
creation_time,
query
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_USER
WHERE state != "DONE"
Note: Valid states include PENDING, RUNNING, and DONE.
Related
I have lake dataset which take data from a OLTP system, with the nature of transactions we have lot of updates the next day, so to keep track of the latest record we are using active_flag = '1'.
We also created a update script which retires old records and updates active_flag = '0'.
Now the main question: how can i execute a update statement by changing table name automatically(programmatically).
I know we have a option of using cloudfunctions but it'll expire in 9 mins and I have atleast 350 tables to update.
Has anyone faced this situation earlier??
You can easily do this with Cloud Workflows.
There you setup the template calls to Bigquery as a substeps, and then you pass a list of tables, and loop through the items and invoke the BigQuery step for each item/table.
I wrote an article with samples that you can adapt: Automate the execution of BigQuery queries with Cloud Workflows
Where/how can I easily see how many BigQuery analysis queries have been run per month. How about storage usage overall/changes-over-time(monthly)?
I've had a quick look at "Monitoring > Dashboards > Bigquery". Is that the best place to explore? It only seems to go back to early October - was that when it was released or does it only display the last X weeks of data? Trying metrics explorer for Queries Count (Metric:bigquery.googleapis.com/job/num_in_flight) was giving me a weird unlabelled y-axis, e.g. a scale of 0 to 0.08? Odd as I expect to see a few hundred queries run per week.
Context: It would be good to have a high level summary of BigQuery, as the the months progress, to give an idea to the wider organisation and management on the scale of usage.
You can track your bytes billed by exporting BigQuery usage logs.
Setup logs export (this is using the Legacy Logs Viewer)
Open Logging -> Logs Viewer
Click Create Sink
Enter "Sink Name"
For "Sink service" choose "BigQuery dataset"
Select your BigQuery dataset to monitor
Create sink
Create sink
Once Logs is enabled, all queries to be executed will store data usage logs in table "cloudaudit_googleapis_com_data_access_YYYYMMDD" under the BigQuery dataset you selected in your sink.
Created cloudaudit_googleapis_com_* tables
Here is a sample query to get bytes used per user
#standardSQL
WITH data as
(
SELECT
protopayload_auditlog.authenticationInfo.principalEmail as principalEmail,
protopayload_auditlog.metadataJson AS metadataJson,
CAST(JSON_EXTRACT_SCALAR(protopayload_auditlog.metadataJson,
"$.jobChange.job.jobStats.queryStats.totalBilledBytes") AS INT64) AS totalBilledBytes,
FROM
`myproject_id.training_big_query.cloudaudit_googleapis_com_data_access_*`
)
SELECT
principalEmail,
SUM(totalBilledBytes) AS billed_bytes
FROM
data
WHERE
JSON_EXTRACT_SCALAR(metadataJson, "$.jobChange.job.jobConfig.type") = "QUERY"
GROUP BY principalEmail
ORDER BY billed_bytes DESC
Query results
NOTES:
You can only track the usage starting at the date when you set up the logs export
Table "cloudaudit_googleapis_com_data_access_YYYYMMDD" is created daily to track all logs
I think Cloud Monitoring is the only place to create and view metrics. If you are not happy with what they provide for BigQuery by default, the only other alternative is to create your own customized carts and dashboards that satisfy your need. You can achieve that using Monitoring Query Language. Using MQL you can achieve the stuff you described in you question. Here are the links for more detailed information.
Introduction to BigQuery monitoring
Introduction to Monitoring Query Language
I want to retrieve data from BigQuery that arrived every hour and do some processing and pull the new calculate variables in a new BigQuery table. The things is that I've never worked with gcp before and I have to for my job now.
I already have my code in python to process the data but it's work only with a "static" dataset
As your source and sink of that are both in BigQuery, I would recommend you to do your transformations inside BigQuery.
If you need a scheduled job that runs in a pre determined time, you can use Scheduled Queries.
With Scheduled Queries you are able to save some query, execute it periodically and save the results to another table.
To create a scheduled query follow the steps:
In BigQuery Console, write your query
After writing the correct query, click in Schedule query and then in Create new scheduled query as you can see in the image below
Pay attention in this two fields:
Schedule options: there are some pre-configured schedules such as daily, monthly, etc.. If you need to execute it every two hours, for example, you can set the Repeat option as Custom and set your Custom schedule as 'every 2 hours'. In the Start date and run time field, select the time and data when your query should start being executed.
Destination for query results: here you can set the dataset and table where your query's results will be saved. Please keep in mind that this option is not available if you use scripting. In other words, you should use only SQL and not scripting in your transformations.
Click on Schedule
After that your query will start being executed according to your schedule and destination table configurations.
According with Google recommendation, when your data are in BigQuery and when you want to transform them to store them in BigQuery, it's always quicker and cheaper to do this in BigQuery if you can express your processing in SQL.
That's why, I don't recommend you dataflow for your use case. If you don't want, or you can't use directly the SQL, you can create User Defined Function (UDF) in BigQuery in Javascript.
EDIT
If you have no information when the data are updated into BigQuery, Dataflow won't help you on this. Dataflow can process realtime data only if these data are present into PubSub. If not, it's not magic!!
Because you haven't the information of when a load is performed, you have to run your process on a schedule. For this, Scheduled Queries is the right solution is you use BigQuery for your processing.
We are trying to insert data into bigquery (streaming) using dataflow. Is there a way where we can keep a check on the number of records inserted into Bigquery? We need this data for reconciliation purpose.
Add a step to your dataflow which calls Google API Tables.get OR run this query before and after the flow (Both are equally good).
select row_count, table_id from `dataset.__TABLES__` where table_id = 'audit'
As an example, the query returns this
You also may be able to examine the "Elements added" by clicking on the step writing to bigquery in the Dataflow UI.
Let's consider I have multiple jobs which are updating/loading the same table. As per the semaphore concept, if any 1 process is loading data to the table other processes will wait till the resource for that table gets free. I would like to know is there any semaphore concepts for loading data into BigQuery table using dataflow? if yes, then how to handle such scenario for BigQuery table load using dataflow?
I don't believe that dataflow has knowledge of the table activity, they just send the requested update as a Job to bigquery.
Bigquery receives the job and then sends it to a queue of the given table. So all the "semaphore concept" is handled internally by bigquery and the given table.
So for example, imagine that in parallel you are running three queries that update a table, two of them run via dataflow and the other via script.
The three ones go to the same queue and bigquery process one by one(one after the other completed) in the order they arrived to bigquery.