How to create a date partitioned table while using a loadjob in google Bigquery?

user_12560657 0
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Would someone be able to explain how to create date partitioned table while using a loadjob in google Bigquery using JobConfig.

https://cloud.google.com/bigquery/docs/creating-column-partitions#creating_a_partitioned_table_when_loading_data

I couldnt understand the documentation, if someone could explain with an example it would be very helpful.

Edited: So I thought I figured out the object thanks to @irvifa, but I am still not able to create a TimePartitioned Table, here is the code am trying to use.

import pandas
from google.cloud import bigquery


def load_df(self, df):
  project_id="ProjectID"
  dataset_id="Dataset"
  table_id="TableName"
  table_ref=project_id+"."+dataset_id+"."+table_id
  time_partitioning = bigquery.table.TimePartitioning(field="PartitionColumn")
  job_config = bigquery.LoadJobConfig(
                         schema="Schema",
                         destinationTable=table_ref
                         write_disposition="WRITE_TRUNCATE",
                         timePartitioning=time_partitioning
                         )
  Job = Client.load_table_from_dataframe(df, table_ref, 
                                         job_config=job_config)
  Job.result()
table-partitioning google-bigquery python
2 Answers

I don't know whether it will help, but you can use the following sample to load job with partition:

from datetime import datetime, time
from concurrent import futures
import math
from pathlib import Path
from google.cloud import bigquery

def run_query(self, query_job_config):
  time_partitioning = bigquery.table.TimePartitioning(field="partition_date")
  job_config = bigquery.QueryJobConfig()
  job_config.destination = query_job_config['destination_dataset_table']
  job_config.time_partitioning = time_partitioning
  job_config.use_legacy_sql = False
  job_config.allow_large_results = True
  job_config.write_disposition = 'WRITE_APPEND'
  sql = query_job_config['sql']
  query_job = self.client.query(sql, job_config=job_config)
  query_job.result()
user_43343760

Thanks to irvifa.

I was trying to load dataframe and was looking for LoadJobConfig, but it was very similar.

I would post my answer just in case someone needs any example for LoadJob. 

import pandas
from google.cloud import bigquery


def load_df(self, df):
  project_id="ProjectID"
  dataset_id="Dataset"
  table_id="TableName"
  table_ref=project_id+"."+dataset_id+"."+table_id
  time_partitioning = bigquery.table.TimePartitioning(field="PartitionColumn")
  job_config = bigquery.LoadJobConfig(
                         schema="Schema",
                         write_disposition="WRITE_TRUNCATE",
                         time_partitioning=time_partitioning
                         )
  Job = Client.load_table_from_dataframe(df, table_ref, 
                                         job_config=job_config)
  Job.result()
user_125606570
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