I have a use case where I have to replace the data in a partition of a table in BigQuery every time 15 mins. Are there any functions available in Bigquery similar to partition exchange in Bigquery or any provision to truncate data of a partition.
Regarding your requirement to load new data every fifteen minutes into a partitioned table you could use Data Manipulation Language (DML).
In order to update rows in a partitioned table you could use the UPDATE statement as stated in the documentation.
Also, in case that you wanted to overwrite partitions you could also use a load job using the CLI as stated here. Using --noreplace or --replace you can specify if you want to append or truncate the given partition.
Related
Is it even possible to add a partition to an existing table in Athena that currently is without partitions? If so, please also write syntax for doing so in the answer.
For example:
ALTER TABLE table1 ADD PARTITION (ourDateStringCol = '2021-01-01')
The above command will give the following error:
FAILED: SemanticException table is not partitioned but partition spec exists
Note: I have done a web-search, and variants exist for SQL server, or adding a partition to an already partitioned table. However, I personally could not find a case where one could successfully add a partition to an existing non-partitioned table.
This is extremely similar to:
SemanticException adding partiton Hive table
However, the answer given there requires re-creating the table.
I want to do so without re-creating the table.
Partitions in Athena are based on folder structure in S3. Unlike standard RDBMS that are loading the data into their disks or memory, Athena is based on scanning data in S3. This is how you enjoy the scale and low cost of the service.
What it means is that you have to have your data in different folders in a meaningful structure such as year=2019, year=2020, and make sure that the data for each year is all and only in that folder.
The simple solution is to run a CREATE TABLE AS SELECT (CTAS) query that will copy the data and create a new table that can be optimized for your analytical queries. You can choose the table format (Parquet, for example), the compression (SNAPPY, for example), and also the partition schema (per year, for example).
Using the Bigquery Schedule functionality, is it possible to write truncate a partition using the run_time parameters like #run_time?
What I am trying to do is to give the destination table_name like mytable${run_time|"%Y%m%d"}, but this gives me an error saying:
Invalid partition decorator in column-partitioned table mytable$20200124 with partitioning field { value: "event_date" }
If I don't give the partition decorate the whole table is written truncated irrespective of the partition
Yes, it is possible. I just replicated it with the following configuration
Notice that the Partitioning field is empty. In addition, I found this issue where is provided the following workaround:
[...] If you just want to overwrite on partition, you could use MERGE statement on the query and set the "Destination table" to the column-partitioned table.
We can insert data into specific partition of partitioned table, here we need to specify partition value.But my requirement is to overwrite all partitions in a table in one query using UI. Can we perform this operation?
Consulted bigquery team member. You can NOT write to all partitions in one query.
You can only write to a partition at a time.
As YY has pointed out, you would not be able to do this directly in BigQuery/SQL with one query (you could script something to run N queries). However, if you spun up a Cloud Dataflow pipeline and configured it to have multiple BigQueryIO sinks, with each one (sink) overwriting a partition, this could be one way I can think of doing it in one shot. It would be a straightforward pipeline to spin up and run.
At this time, BigQuery allows updating upto 2000 partitions in a single statement. If you need to just insert data into a partitioned table, you can use the INSERT DML statement to write to upto 2000 partitions in one statement. If you are updating or deleting existing partitions you can use the UPDATE or DELETE statements respectively. If you are both updating and inserting new data, you can use the MERGE DML statements to achieve this.
Note: this is nearly a duplicate of this question with the distinction that in this case, the source table is date partitioned and the destination table does not yet exist. Also, the accepted solution to that question didn't work in this case.
I'm trying to copy a single day's worth of data from one date partitioned table into a new date partitoined table that I have not yet created. My hope is that BigQuery would simply create the date-partitioned destination table for me like it usually does for the non-date-partitioned case.
Using BigQuery CLI, here's my command:
bq cp mydataset.sourcetable\$20161231 mydataset.desttable\$20161231
Here's the output of that command:
BigQuery error in cp operation: Error processing job
'myproject:bqjob_bqjobid': Partitioning specification must be provided
in order to create partitioned table
I've tried doing something similar using the python SDK: running a select command on a date partitioned table (which selects data from only one date partition) and saving the results into a new destination table (which I hope would also be date partitioned). The job fails with the same error:
{u'message': u'Partitioning specification must be provided in order to
create partitioned table', u'reason': u'invalid'}
Clearly I need to add a partitioning specification, but I couldn't find any documentation on how to do so.
You need to create the partitioned destination table first (as per the docs):
If you want to copy a partitioned table into another partitioned
table, the partition specifications for the source and destination
tables must match.
So, just create the destination partitioned table before you start copying. If you can't be bothered specifying the schema, you can create the destination partitioned table like so:
bq mk --time_partitioning_type=DAY mydataset.temps
Then, use a query instead of a copy to write to the destination table. The schema will be copied with it:
bq query --allow_large_results --replace --destination_table 'mydataset.temps$20160101''SELECT * from `source`'
Can anyone please suggest how to create partition table in Big Query ?.
Example: Suppose I have one log data in google storage for the year of 2016. I stored all data in one bucket partitioned by year , month and date wise. Here I want create table with partitioned by date.
Thanks in Advance
Documentation for partitioned tables is here:
https://cloud.google.com/bigquery/docs/creating-partitioned-tables
In this case, you'd create a partitioned table and populate the partitions with the data. You can run a query job that reads from GCS (and filters data for the specific date) and writes to the corresponding partition of a table. For example, to load data for May 1st, 2016 -- you'd specify the destination_table as table$20160501.
Currently, you'll have to run several query jobs to achieve this process. Please note that you'll be charged for each query job based on bytes processed.
Please see this post for some more details:
Migrating from non-partitioned to Partitioned tables
There are two options:
Option 1
You can load each daily file into separate respective table with name as YourLogs_YYYYMMDD
See details on how to Load Data from Cloud Storage
After tables created, you can access them either using Table wildcard functions (Legacy SQL) or using Wildcard Table (Standar SQL). See also Querying Multiple Tables Using a Wildcard Table for more examples
Option 2
You can create Date-Partitioned Table (just one table - YourLogs) - but you still will need to load each daily file into respective partition - see Creating and Updating Date-Partitioned Tables
After table is loaded you can easily Query Date-Partitioned Tables
Having partitions for an External Table is not allowed as for now. There is a Feature Request for it:
https://issuetracker.google.com/issues/62993684
(please vote for it if you're interested in it!)
Google says that they are considering it.