I have a couple of years of data on a big query partitioned table by day. I want to replace the data of just the last 30 days; however, when I use the create or replace table function on bigquery, it replaces the entire table with just the new dates partitions. Is there any way to update only those partitions without losing the previous data?
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I have an external hive table, partitioned on the date column. Each date has multiple AB test experiment’s data.
I need to create a job that drops experiments which have ended more than 6 months ago.
Since dropping data in an external hive partitioned table, drops the entire partition. In this case, data for one whole date. Is there a way to only drop part of a date?
I have schedule query which runs hourly I want to partition the table hourly so in the destinaltion I have provided this mytable_{run_time|"%Y%m%d%H"}, but this is creating a new table for every run in my BigQuery datasets , when I change the destination to mytable_{run_time|"%Y%m%d"}, it's partition the data correctly based on date
How to enable hourly partition in big query ?
What you are doing is aligned to table sharding, which you can do but it is not as performant and involves more management. In theory it acts similarly to a partition but is not the same. What you are likely seeing when you use the format mytable_{run_time|"%Y%m%d"} is that you're inserting multiple hours into the same day, and depending on what your table definition is may be partitioned within a single day.
You will want to define the partition in the creation of the table see below:
https://cloud.google.com/bigquery/docs/creating-partitioned-tables#create_a_time-unit_column-partitioned_table
Recently, I have been working on converting date suffixed tables into partitioned tables using ingestion time. However, in partition tables, how do we know whether certain date contains no data or the table was not created successfully?
Here is more details,
Previously, daily tables were created, but it is OK that some tables were empty because no result met the criteria. For example,
daily_table_20200601 (100 rows)
daily_table_20200602 (0 rows)
daily_table_20200603 (10 rows)
In this case, I can see table daily_table_20200602 exists, so I know my scheduled job runs successfully.
When switching to partitioned tables using ingestion time, I am writing into the table daily_table every day, for example,
daily_table$20200601 (100 rows)
daily_table$20200602 (0 rows)
daily_table$20200603 (10 rows)
But how do we know the whether table daily_table$20200602 was created successfully or it is just empty?
Also, there is something interesting. I am using API to check whether partition table exist, see the following code,
dataset_ref = client.dataset('dataset_name')
table_ref = dataset_ref.table("daily_table$20210101")
client.get_table(table_ref)
The result shows the table exist. So are we able to check whether the certain date table exist or not?
there's no separate (date table) for every partition, because the partitioning doesn't create a separate partition table, it's similar to relational database partitioning
ingestion time partitioning method adds a pseudo columns for day partitioning (_PARTITIONTIME,_PARTITIONDATE) and for hourly partitioning (_PARTITIONTIME) which will contains the timestamp of the beginning of the insertion data or hour and partition the table accordingly,
for this code:
dataset_ref = client.dataset('dataset_name')
table_ref = dataset_ref.table("daily_table$20210101")
client.get_table(table_ref)
This will success as long as the partitioned table exists
I have an external hive table which is partitioned on load_date (DD-MM-YYYY). however the very first period lets say 01-01-2000 has all the data from 1980 till 2000. How can I further create partitions on year for the previous data while keeping the existing data (data for load date greater than 01-01-2000) still available
First load the data of '01-01-2000' into a table and create a dynamic partition table partitioned by data '01-01-2000'. This might solve your problem.
My question is about table partitioning in SQL Server 2008.
I have a program that loads data into a table every 10 mins or so. Approx 40 million rows per day.
The data is bcp'ed into the table and needs to be able to be loaded very quickly.
I would like to partition this table based on the date the data is inserted into the table. Each partition would contain the data loaded in one particular day.
The table should hold the last 50 days of data, so every night I need to drop any partitions older than 50 days.
I would like to have a process that aggregates data loaded into the current partition every hour into some aggregation tables. The summary will only ever run on the latest partition (since all other partitions will already be summarised) so it is important it is partitioned on insert_date.
Generally when querying the data, the insert date is specified (or multiple insert dates). The detailed data is queried by drilling down from the summarised data and as this is summarised based on insert date, the insert date is always specified when querying the detailed data in the partitioned table.
Can I create a default column in the table "Insert_date" that gets a value of Getdate() and then partition on this somehow?
OR
I can create a column in the table "insert_date" and put a hard coded value of today's date.
What would the partition function look like?
Would seperate tables and a partitioned view be better suited?
I have tried both, and even though I think partition tables are cooler. But after trying to teach how to maintain the code afterwards it just wasten't justified. In that scenario we used a hard coded field date field that was in the insert statement.
Now I use different tables ( 31 days / 31 tables ) + aggrigation table and there is an ugly union all query that joins togeather the monthly data.
Advantage. Super timple sql, and simple c# code for bcp and nobody has complained about complexity.
But if you have the infrastructure and a gaggle of .net / sql gurus I would choose the partitioning strategy.