Usage

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Applying column-wise filtering on the data being ingested

By default, the existing source and resource functions, sql_database and sql_table, ingest all of the records from the source table. But by using query_adapter_callback, it is possible to pass a WHERE clause inside the underlying SELECT statement using the SQLAlchemy syntax. Thich enables filtering the data based on specific columns before extract.

The example below uses query_adapter_callback to filter on the column customer_id for the table orders:

from dlt.sources.sql_database import sql_database

def query_adapter_callback(query, table):
    if table.name == "orders":
        # Only select rows where the column customer_id has value 1
        return query.where(table.c.customer_id==1)
    # Use the original query for other tables
    return query

source = sql_database(
    query_adapter_callback=query_adapter_callback
).with_resources("orders")

Transforming the data before load

You have direct access to the extracted data through the resource objects (sql_table() or sql_database().with_resource())), each of which represents a single SQL table. These objects are generators that yield individual rows of the table which can be modified by using custom python functions. These functions can be applied to the resource using add_map.

note

The PyArrow backend does not yield individual rows rather loads chunks of data as ndarray. In this case, the transformation function that goes into add_map should be configured to expect an ndarray input.

Examples:

1. Pseudonymizing data to hide personally identifiable information (PII) before loading it to the destination. (See here for more information on pseudonymizing data with dlt)

   import dlt
   import hashlib
   from dlt.sources.sql_database import sql_database
   
   def pseudonymize_name(doc):
       '''
       Pseudonmyisation is a deterministic type of PII-obscuring
       Its role is to allow identifying users by their hash,
       without revealing the underlying info.
       '''
       # add a constant salt to generate
       salt = 'WI@N57%zZrmk#88c'
       salted_string = doc['rfam_acc'] + salt
       sh = hashlib.sha256()
       sh.update(salted_string.encode())
       hashed_string = sh.digest().hex()
       doc['rfam_acc'] = hashed_string
       return doc
   
   pipeline = dlt.pipeline(
       # Configure the pipeline
   )
   # using sql_database source to load family table and pseudonymize the column "rfam_acc"
   source = sql_database().with_resources("family")
   # modify this source instance's resource
   source.family.add_map(pseudonymize_name)
   # Run the pipeline. For a large db this may take a while
   info = pipeline.run(source, write_disposition="replace")
   print(info)

2. Excluding unnecessary columns before load

   import dlt
   from dlt.sources.sql_database import sql_database
   
   def remove_columns(doc):
       del doc["rfam_id"]
       return doc
   
   pipeline = dlt.pipeline(
       # Configure the pipeline
   )
   # using sql_database source to load family table and remove the column "rfam_id"
   source = sql_database().with_resources("family")
   # modify this source instance's resource
   source.family.add_map(remove_columns)
   # Run the pipeline. For a large db this may take a while
   info = pipeline.run(source, write_disposition="replace")
   print(info)

Deploying the sql_database pipeline

You can deploy the sql_database pipeline with any of the dlt deployment methods, such as GitHub Actions, Airflow, Dagster etc. See here for a full list of deployment methods.

Running on Airflow

When running on Airflow:

1. Use the dlt Airflow Helper to create tasks from the sql_database source. (If you want to run table extraction in parallel, then you can do this by setting decompose = "parallel-isolated" when doing the source->DAG conversion. See here for code example.)
2. Reflect tables at runtime with defer_table_reflect argument.
3. Set allow_external_schedulers to load data using Airflow intervals.