pycarol.staging

class pycarol.staging.Staging(carol)

Class to send data to Carol.

create_schema(fields_dict=None, staging_name=None, connector_id=None, connector_name=None, mdm_flexible=False, crosswalk_name=None, crosswalk_list=None, overwrite=False, auto_send=True, export_data=False, data=None)

Args:

fields_dict: dict, list of dicts, pandas.DataFrame, default None

Data to create schema from. fields_dict will be removed in the future. Use parameter data

staging_name: str,

Staging name to send the data.

connector_id: str, default None

Connector name where the staging should be.

connector_name: str, default None

Connector name where the staging should be.

mdm_flexible: bool, default False

If flexible schema.

crosswalk_name: None, default staging_name

Crosswalk name. Most of the time it should be the staging name.

crosswalk_list: list, default None

Crosswalk list of fields.

overwrite: bool, default False

if already exists, overwrite current schema

auto_send: bool, default True

Send the schema after creating.

export_data: bool, default False

Export data to CDS for this staging. This is a manual export.

data: json, list of dicts, pandas.DataFrame, default None

Data to create schema from.

drop_staging(staging_name=None, connector_id=None, connector_name=None, reject_on_no_schema=False, reject_on_etl_existence=False, reject_on_mapping_existence=False)

Args:

staging_name: str,

Staging name.

connector_name: str, default None

Connector name.

connector_id: str, default None

Connector Id.

reject_on_no_schema: bool default False

Do not drop if no schema found.

reject_on_etl_existence: bool default False

Raise an error if there exists a ETL for this staging table

reject_on_mapping_existence: bool default False

Raise an error if there exists a mapping for this staging table

Returns:

Carol response

export(staging_name, connector_id=None, connector_name=None, sync_staging=True, full_export=False, delete_previous=False)

@DEPRECATED. This function was removed in pycarol 3.34

Export Staging from RT to CDS

This method will trigger or pause the export of the data in the staging to CDS.

Args:

staging_name: str, default None

Staging name to send the data. If empty it will get from the schema

connector_id: str, default None

Connector name. If empty it will get from the schema

connector_name: str, default None

Connector name. If empty it will get from the schema

sync_staging: bool default True

Start export for this staging

full_export: bool default False

Full export sync for this staging

delete_previous: bool default False

Delete previous data.

Returns: None

Usage: To trigger the export the first time:

from pycarol.staging import Staging
from pycarol.auth.PwdAuth import PwdAuth
from pycarol.carol import Carol
login = Carol()
stag  = Staging(login)
stag.export(staging, connector_name=connector_name,sync_staging=True)

#To do a resync, that is, start the sync from the begining without delete old data
stag.export(staging, connector_name=connector_name,sync_staging=True, full_export=True)

#To delete the old data:
stag.export(staging, connector_name=connector_name,sync_staging=True, full_export=True, delete_previous=True)

#To Pause a sync:
stag.export(staging, connector_name=connector_name,sync_staging=False)

export_all(connector_id=None, connector_name=None, sync_staging=True, full_export=False, delete_previous=False)

@DEPRECATED. This function was removed in pycarol 3.34

Export all Stagings from a connector to s3

This method will trigger or pause the export of all stagings to s3.

Parameters:

• sync_staging – bool, default True Sync the data model
• connector_name – str Connector name
• connector_id – str Connector id
• full_export – bool, default True Do a resync of the data model
• delete_previous – bool, default False Delete previous exported files.

Returns:

None

Usage: See Staging.export()

fetch_parquet(staging_name, connector_id=None, connector_name=None, backend='pandas', merge_records=True, return_dask_graph=False, columns=None, max_hits=None, return_metadata=False, callback=None, cds=True, max_workers=None, file_pattern=None, return_callback_result=False)

Fetch parquet from a staging table.

Args:

staging_name: str,

Staging name to fetch parquet of

connector_id: str, default None

Connector id to fetch parquet of

connector_name: str, default None

Connector name to fetch parquet of

backend: [‘dask’,’pandas’], default dask

if to use either dask or pandas to fetch the data

merge_records: bool, default True

This will keep only the most recent record exported. Sometimes there are updates and/or deletions and one should keep only the last records.

return_dask_graph: bool, default false

If to return the dask graph or the dataframe.

columns: list, default None

List of columns to fetch.

max_hits: int, default None

Number of records to get. This only should be user for tests.

return_metadata: bool, default False

To return or not the fields like [‘mdmId’, ‘mdmCounterForEntity’, etc.]

callback: callable, default None

Function to be called each downloaded file.

cds: bool, default True

Get staging data from CDS.

max_workers: int default None

Number of workers to use when downloading parquet files with pandas back-end.

file_pattern: str default None

File pattern to filter data when fetching from CDS. e.g. file_pattern=’2019-11-25’ will fetch only CDS files that start with 2019-11-25.

return_callback_result bool default False

If a callback is used, it will return the result of the response of the callback. This will skip all the operation to merge records and return selected columns.

Returns: pandas.DataFrame

DataFrame with the staging data.

generate_SQL_tables(connector_id=None, connector_name=None)

Generates SQL tables from a connector.

The tables created will be named: stg_{connector_id}_{staging_name}.

Args:

connector_id (str, optional): Connector ID. Defaults to None. connector_name (str, optional): Connector name. Defaults to None.

Returns:

dict: Carol’s response

get_schema(staging_name, connector_name=None, connector_id=None)

Return the staging table schema.

Args:

staging_name: str,

Staging name

connector_name: str, default None

Connector name. Either connector_name or connector_id need to be set.

connector_id: str, default None

Connector Id. Either connector_name or connector_id need to be set.

Returns: dict

Staging table schema

remove_SQL_tables(connector_id=None, connector_name=None)

Remove all SQL tables from a connector

Args:

connector_id (str, optional): Connector ID. Defaults to None. connector_name (str, optional): Connector name. Defaults to None.

Returns:

dict: Carol’s response

send_data(staging_name, data=None, connector_name=None, connector_id=None, step_size=500, print_stats=True, gzip=True, auto_create_schema=False, crosswalk_auto_create=None, flexible_schema=False, force=False, max_workers=2, dm_to_delete=None, async_send=False, carol_data_storage=False, storage_only=True, carol_sync=False)

Send data to a staging table in Carol.

Args:

staging_name: str,

Staging name to send the data.

data: pandas data frame, json. default None

Data to be send to Carol

connector_name: str, default None

Connector name where the staging should be. Either connector_name or connector_id need to be set.

connector_id: str, default None

Connector Id where the staging should be. Either connector_name or connector_id need to be set.

step_size: int, default 500

Number of records to be sent in each iteration. Max size for each batch is 10MB.

print_stats:bool, default True

If print the number of records sent

gzip:bool, default True

If send each batch as a gzip file.

auto_create_schema:bool, default False

If to auto create the schema for the data being sent.

crosswalk_auto_create: list, default None

If auto_create_schema=True, crosswalk list of fields.

flexible_schema: bool, default False

If auto_create_schema=True, to use a flexible schema.

force: bool, default False

pycarol will check for duplicated values given the crosswalk. If force=True it will not check. If False it will check for duplicates and raise an error.

max_workers: int, default 2

To be used with async_send=True. Number of threads to use when sending.

dm_to_delete: str, default None DEPRECATED.

Name of the data model to be erased before send the data.

async_send: bool, default False

To use async to send the data. This is much faster than a sequential send. It can conflict with jupyter notebooks process. To run this inside a jupyter notebook, use

import nest_asyncio
nest_asyncio.apply()

carol_data_storage: bool, default False

Deprecated, Use storage_only

storage_only: bool, default False

Send data only to CDS.

carol_sync: bool, default False

Send and wait data to be processed in Carol

send_schema(schema, staging_name=None, connector_id=None, connector_name=None, overwrite=False)

Send schema to Carol

Args:

schema: dict

Dictianary with the schema to be sent

staging_name: str, default None

Staging name to send the data. If empty it will get from the schema

connector_id: str, default None

Connector name. If empty it will get from the schema

connector_name: str, default None

Connector name. If empty it will get from the schema

overwrite: bool, default False

if already exists, overwrite current schema

Returns: None