python-dlt 0.1.0rc3

DLT is an open-source python-native scalable data loading framework that does not require any devops efforts to run.

Follow this quick guide to implement DLT in your project

Simple loading of two rows:

Install DLT

DLT is available in PyPi and can be installed with pip install python-dlt. Support for target warehouses is provided in extra packages:

pip install python-dlt[redshift] for Redshift

pip install python-dlt[gcp] for BigQuery

1. Configuraton: name your schema, table, pass credentials

from dlt.pipeline import Pipeline, PostgresPipelineCredentials

schema_prefix = 'demo_'
schema_name = 'example'
parent_table = 'my_json_doc'

# Credentials for Redshift or Bigquery

# Redshift: you can pass password as last parameter or via PG_PASSWORD env variable.
# credential = PostgresPipelineCredentials("redshift", "dbname", "schemaname", "username", "3.73.90.3", "dolphins")

# Bigquery:
gcp_credential_json_file_path = "scalevector-1235ac340b0b.json"
credential = Pipeline.load_gcp_credentials(gcp_credential_json_file_path, schema_prefix)

# optionally save and reuse schema
# schema_file_path = "examples/schemas/quickstart.yml"

2. Create a pipeline

pipeline = Pipeline(schema_name)
pipeline.create_pipeline(credential)

# Optionally uncomment to re-use a schema
# schema = Pipeline.load_schema_from_file(schema_file_path)
# pipeline.create_pipeline(credential, schema=schema)

3. Pass the data to the pipeline and give it a table name.

rows = [{"name":"Ana", "age":30, "id":456, "children":[{"name": "Bill", "id": 625},
                                                       {"name": "Elli", "id": 591}
                                                      ]},

        {"name":"Bob", "age":30, "id":455, "children":[{"name": "Bill", "id": 625},
                                                       {"name": "Dave", "id": 621}
                                                      ]}
       ]

pipeline.extract(iter(rows), table_name=parent_table)

# Optionally the pipeline to un-nest the json into a relational structure
pipeline.unpack()

# Optionally save the schema for manual edits/future use.
# schema = pipeline.get_default_schema()
# schema_yaml = schema.as_yaml()
# f = open(data_schema_file_path, "a")
# f.write(schema_yaml)
# f.close()

4. Load

pipeline.load()

5. Error capture - print, raise or handle.

# now enumerate all complete loads to check if we have any failed packages
# complete but failed job will not raise any exceptions
completed_loads = pipeline.list_completed_loads()
# print(completed_loads)
for load_id in completed_loads:
    print(f"Checking failed jobs in {load_id}")
    for job, failed_message in pipeline.list_failed_jobs(load_id):
        print(f"JOB: {job}\nMSG: {failed_message}")

6. Use your data

Tables created:

 SELECT *  FROM `scalevector.demo__example.my_json_doc`
RESULT:
{  "name": "Ana",  "age": "30",  "id": "456",  "_load_id": "1654787700.406905",  "_record_hash": "5b018c1ba3364279a0ca1a231fbd8d90"}
{  "name": "Bob",  "age": "30",  "id": "455",  "_load_id": "1654787700.406905",  "_record_hash": "afc8506472a14a529bf3e6ebba3e0a9e"}


 SELECT * FROM `scalevector.demo__example.my_json_doc__children` LIMIT 1000
RESULT:
{  "name": "Bill",  "id": "625",  "_parent_hash": "5b018c1ba3364279a0ca1a231fbd8d90",  "_pos": "0",  "_root_hash": "5b018c1ba3364279a0ca1a231fbd8d90",  "_record_hash": "7993452627a98814cc7091f2c51faf5c"}
{  "name": "Bill",  "id": "625",  "_parent_hash": "afc8506472a14a529bf3e6ebba3e0a9e",  "_pos": "0",  "_root_hash": "afc8506472a14a529bf3e6ebba3e0a9e",  "_record_hash": "9a2fd144227e70e3aa09467e2358f934"}
{  "name": "Dave",  "id": "621",  "_parent_hash": "afc8506472a14a529bf3e6ebba3e0a9e",  "_pos": "1",  "_root_hash": "afc8506472a14a529bf3e6ebba3e0a9e",  "_record_hash": "28002ed6792470ea8caf2d6b6393b4f9"}
{  "name": "Elli",  "id": "591",  "_parent_hash": "5b018c1ba3364279a0ca1a231fbd8d90",  "_pos": "1",  "_root_hash": "5b018c1ba3364279a0ca1a231fbd8d90",  "_record_hash": "d18172353fba1a492c739a7789a786cf"}

Join your data via recursively created join keys.

 select p.name, p.age, p.id as parent_id,
        c.name as child_name, c.id as child_id, c._pos as child_order_in_list
 from `scalevector.demo__example.my_json_doc` as p
 left join `scalevector.demo__example.my_json_doc__children`  as c
     on p._record_hash = c._parent_hash
RESULT:
{  "name": "Ana",  "age": "30",  "parent_id": "456",  "child_name": "Bill",  "child_id": "625",  "child_order_in_list": "0"}
{  "name": "Ana",  "age": "30",  "parent_id": "456",  "child_name": "Elli",  "child_id": "591",  "child_order_in_list": "1"}
{  "name": "Bob",  "age": "30",  "parent_id": "455",  "child_name": "Bill",  "child_id": "625",  "child_order_in_list": "0"}
{  "name": "Bob",  "age": "30",  "parent_id": "455",  "child_name": "Dave",  "child_id": "621",  "child_order_in_list": "1"}

7. Run it yourself - plug your own iterator or generator.

Working example: quickstart.py