Saving the data in one function call, where I am sending the data as 2019-02-01T00:00:00.000Z but the fiddler shows the different date in JSON as 2019-01-31T18:30:00.000Z. I am not aware that why it is giving the difference of 5 hrs 30 minutes.
On the local case, date saved in the database is 01-02-2019, while on production environment the previous date is getting saved in the table records.
Thanks in advance. Please help me with the solutions.
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I have noticed that my big query database was configured to clear data more than 60 days old. The setting has been changed on the dataset for Default table expiry = Never but came with the warning "existing tables will not be affected"
What does this mean for future data preservation? It looks as though each day is its own table, so the 60 day expiry will only stop being a problem 60 days from now?
any clarification on what the wording of this warning means and how it will affect our data preservation in big query would be great
thanks
Aaron
If you have already created tables in that dataset, then these table still have 60 days expiry. You need to clear this setting on each table individually. Otherwise all data older than 60 days will keep deleting every day.
If your system creates new table everyday, then yes, the expiry will stop being a problem after 60 days, because new tables will have no expiry. All old tables will be emptied though.
By default how long a temp table will be on to get data? I know that we can set up the expiration, but what is the default?
And about the job? What's the default expiration time if it has?
I tried to find these in the documentation but i couldn't find, we return the jobId to the client so he can get the data when the job is complete, but some of them like to store and tries to fetch data with a jobId from 2 weeks ago, 1 month ago.
What's the default time here so i can explain them better?
Query results are stored for 24 hours:
All query results, including both interactive and batch queries, are cached in temporary tables for approximately 24 hours with some exceptions.
https://cloud.google.com/bigquery/docs/cached-results
As was mentioned by Alexey, they query results are stored for 24 hours when using cache.
Regarding the timelife for BigQuery jobs, you can get the job history for the last six months
By the other side, based on your description, creating a new table from your query results with expiration time seems to be the most appropriate strategy. Also, you could check if using materialized views could help you to store some results of recurring queries.
I have my MySQL database inserting timestamp when I upload a record, so what's entered is something like 2020-04-02 16:59:29. Is there a Vue.Js way to convert that into something like 10 Days Ago? If so, can anyone give the code for conversion in Vue.Js?
I hold the fetched DB records in an object called data_local as in my bellow code.
Last Activity : {{data_local.updated_at}} days ago
Use moment.js, you can get your desired data the way you want. First do install moment and then import & use.
Below are ways to get days:
Ex-1:( This will provide you No_of_Days days ago ( e.g- 10 days ago)
moment("2020-04-02 16:59:29").fromNow()
Ex-2: (If you want only 10 days without ago then use below)
moment("2020-04-02 16:59:29").fromNow(true)
For more information visit https://momentjs.com/
I am looking to pull data between two time periods at only 15 to 30 mins apart. I want to be able to rerun the code multiple times to constantly update the data I had already pulled. I know there is a function for current system time but I am unable to use it effectively in SQL developer.
I have tried using the function CURRENT_TIMESTAMP but could not get it to work effectively.
Currently i am using the following code and just pulling over a broad time frame, but i would like to shrink that down to 15 to 30 minute intervals that could be used to continue to pull updated data.
I expect to be able to pull current data within 15 to 30 minute segments of time.
I've been using the Table Range Decorators feature daily since May in order to only query the data from the last 7 days in some of my tables.
Since 2 weeks, I've noticed that sometimes some data is missing when I use that feature. For example, if do a query to get the results for the last 7 days (by adding "#-604800000--1" to table), some data will be missing as opposed to if I query on the whole table (without a table decorator).
I wonder what could explain this and if there is a fix coming soon to address this?
If this can help the BigQuery team, I've noticed that when using Table Decorators some data was missing for us for October 16th between around 16:00 and 20:00 UTC time.
For the BigQuery team here are 2 jobs ids where some data is missing: job_-xtL4PlIYhNjQ5weMnssvqDmd6U , job_9ASNxqq_swjCd1eMmiQ6SmPpxlQ
and 1 job id where data is correct(without decorators): job_QbcRwYGbQv0BZdHreQEvRlYh-mM
This is a known issue with table decorators containing a time range. Due to a bug in BigQuery, it is possible for certain time ranges to omit data that should be included within the time range.
We're working on a fix and plan to have it released next week. After this fix is deployed time range decorators should again work as expected.