CODEWITHSUNDEEP
pythongoogle-cloud-platformgoogle-bigquery
RashidLadj_Winux
RashidLadj_Winux

Reputation: 911

how to load data into BigQuery from GCS (using load_table_from_uri or load_table_from_dataframe) without duplicating existing data in the BQ table

I would like to retrieve from google cloud storage (Bucket) CSV files, and load the data from these files into a bigquery table without having duplicate data. The goal being to have a code rather optimal in performance that in cost. My current code is the following:

def load_data_in_BQT():
    job_config = bigquery.LoadJobConfig(
        schema=[
            bigquery.SchemaField("id", "INTEGER"),
            bigquery.SchemaField("name", "STRING"),
        ],
        
        # The source format defaults to CSV, so the line below is optional.
        source_format=bigquery.SourceFormat.CSV,
        skip_leading_rows=1,
        autodetect=True,
        write_disposition=bigquery.WriteDisposition.WRITE_APPEND, # (Addition of the data (possibility of having duplications)
        # write_disposition=bigquery.WriteDisposition.WRITE_TRUNCATE, # (Formatting of the table and insertion of the new data (Loss of the old data))
    )

    uri = "gs://mybucket/myfolder/myfile.csv"
  
    load_job = self.client.load_table_from_uri(
        uri, self.table_ref["object"], job_config=job_config, 
    )

Currently my idea is to read the CSV file in pandas in order to have a dataframe, to load also the data of the bigquery table and to transform them into dataframe, to make my treatment on the whole of the data in order to remove the duplicates, and at the end to reinsert (with the option Truncate) the whole of the cleaned data. However, I find this method harmful if we have a huge set of data that we will have to load at each new input file in our bucket.

What could you suggest? Thank you in advance

Upvotes: 0

Views: 2842

Answers (1)

Mazlum Tosun
Mazlum Tosun

Reputation: 6572

You can use merge query with Bigquery :

https://cloud.google.com/bigquery/docs/reference/standard-sql/dml-syntax?hl=en#merge_statement

The idea behind is :

  • Create a staging table with the same structure as final table
  • Truncate the staging table (empty the table), before you execute your script
  • Excute your Python script to ingest data in the staging table
  • Add a merge query between the staging and final table : If the element doesn't exist in the final table, you can insert it, otherwise you can update it.

Example of merge query :

MERGE dataset.Inventory T
USING dataset.NewArrivals S
ON T.product = S.product
WHEN MATCHED THEN
  UPDATE SET quantity = T.quantity + S.quantity
WHEN NOT MATCHED THEN
  INSERT (product, quantity) VALUES(product, quantity)

Orchestrator like Airflow or Cloud workflow can easily chain these steps for example.

Upvotes: 1

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