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tableau_external_table_extractor.py
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tableau_external_table_extractor.py
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# Copyright Contributors to the Amundsen project.
# SPDX-License-Identifier: Apache-2.0
import logging
from typing import (
Any, Dict, Iterator,
)
from pyhocon import ConfigFactory, ConfigTree
import databuilder.extractor.dashboard.tableau.tableau_dashboard_constants as const
from databuilder import Scoped
from databuilder.extractor.base_extractor import Extractor
from databuilder.extractor.dashboard.tableau.tableau_dashboard_utils import (
TableauDashboardUtils, TableauGraphQLApiExtractor,
)
from databuilder.transformer.base_transformer import ChainedTransformer
from databuilder.transformer.dict_to_model import MODEL_CLASS, DictToModel
LOGGER = logging.getLogger(__name__)
class TableauGraphQLExternalTableExtractor(TableauGraphQLApiExtractor):
"""
Implements the extraction-time logic for parsing the GraphQL result and transforming into a dict
that fills the TableMetadata model.
"""
EXTERNAL_CLUSTER_NAME = const.EXTERNAL_CLUSTER_NAME
EXTERNAL_SCHEMA_NAME = const.EXTERNAL_SCHEMA_NAME
def execute(self) -> Iterator[Dict[str, Any]]:
response = self.execute_query()
for table in response['databases']:
if table['connectionType'] in ['google-sheets', 'salesforce', 'excel-direct']:
for downstreamTable in table['tables']:
data = {
'cluster': self._conf.get_string(TableauGraphQLExternalTableExtractor.EXTERNAL_CLUSTER_NAME),
'database': TableauDashboardUtils.sanitize_database_name(
table['connectionType']
),
'schema': TableauDashboardUtils.sanitize_schema_name(table['name']),
'name': TableauDashboardUtils.sanitize_table_name(downstreamTable['name']),
'description': table['description']
}
yield data
else:
data = {
'cluster': self._conf.get_string(TableauGraphQLExternalTableExtractor.EXTERNAL_CLUSTER_NAME),
'database': TableauDashboardUtils.sanitize_database_name(table['connectionType']),
'schema': self._conf.get_string(TableauGraphQLExternalTableExtractor.EXTERNAL_SCHEMA_NAME),
'name': TableauDashboardUtils.sanitize_table_name(table['name']),
'description': table['description']
}
yield data
class TableauDashboardExternalTableExtractor(Extractor):
"""
Creates the "external" Tableau tables.
In this context, "external" tables are "tables" that are not from a typical database, and are loaded
using some other data format, like CSV files.
This extractor has been tested with the following types of external tables:
Excel spreadsheets
Text files (including CSV files)
Salesforce connections
Google Sheets connections
Excel spreadsheets, Salesforce connections, and Google Sheets connections are all classified as
"databases" in terms of Tableau's Metadata API, with their "subsheets" forming their "tables" when
present. However, these tables are not assigned a schema, this extractor chooses to use the name
parent sheet as the schema, and assign a new table to each subsheet. The connection type is
always used as the database, and for text files, the schema is set using the EXTERNAL_SCHEMA_NAME
config option. Since these external tables are usually named for human consumption only and often
contain a wider range of characters, all inputs are transformed to remove any problematic
occurences before they are inserted: see the sanitize methods TableauDashboardUtils for specifics.
A more concrete example: if one had a Google Sheet titled "Growth by Region & County" with 2 subsheets called
"FY19 Report" and "FY20 Report", two tables would be generated with the following keys:
googlesheets://external.growth_by_region_county/FY_19_Report
googlesheets://external.growth_by_region_county/FY_20_Report
"""
API_BASE_URL = const.API_BASE_URL
API_VERSION = const.API_VERSION
CLUSTER = const.CLUSTER
EXCLUDED_PROJECTS = const.EXCLUDED_PROJECTS
EXTERNAL_CLUSTER_NAME = const.EXTERNAL_CLUSTER_NAME
EXTERNAL_SCHEMA_NAME = const.EXTERNAL_SCHEMA_NAME
EXTERNAL_TABLE_TYPES = const.EXTERNAL_TABLE_TYPES
SITE_NAME = const.SITE_NAME
TABLEAU_ACCESS_TOKEN_NAME = const.TABLEAU_ACCESS_TOKEN_NAME
TABLEAU_ACCESS_TOKEN_SECRET = const.TABLEAU_ACCESS_TOKEN_SECRET
VERIFY_REQUEST = const.VERIFY_REQUEST
def init(self, conf: ConfigTree) -> None:
self._conf = conf
self.query = """query externalTables($externalTableTypes: [String]) {
databases (filter: {connectionTypeWithin: $externalTableTypes}) {
name
connectionType
description
tables {
name
}
}
}"""
self.query_variables = {
'externalTableTypes': self._conf.get_list(TableauDashboardExternalTableExtractor.EXTERNAL_TABLE_TYPES)}
self._extractor = self._build_extractor()
transformers = []
dict_to_model_transformer = DictToModel()
dict_to_model_transformer.init(
conf=Scoped.get_scoped_conf(self._conf, dict_to_model_transformer.get_scope()).with_fallback(
ConfigFactory.from_dict(
{MODEL_CLASS: 'databuilder.models.table_metadata.TableMetadata'})))
transformers.append(dict_to_model_transformer)
self._transformer = ChainedTransformer(transformers=transformers)
def extract(self) -> Any:
record = self._extractor.extract()
if not record:
return None
return self._transformer.transform(record=record)
def get_scope(self) -> str:
return 'extractor.tableau_external_table'
def _build_extractor(self) -> TableauGraphQLExternalTableExtractor:
"""
Builds a TableauGraphQLExternalTableExtractor. All data required can be retrieved with a single GraphQL call.
:return: A TableauGraphQLExternalTableExtractor that creates external table metadata entities.
"""
extractor = TableauGraphQLExternalTableExtractor()
config_dict = {
TableauGraphQLApiExtractor.QUERY_VARIABLES: self.query_variables,
TableauGraphQLApiExtractor.QUERY: self.query}
tableau_extractor_conf = \
Scoped.get_scoped_conf(self._conf, extractor.get_scope())\
.with_fallback(self._conf)\
.with_fallback(ConfigFactory.from_dict(config_dict))
extractor.init(conf=tableau_extractor_conf)
return extractor