Python
Esta API é composta por funções com 2 tipos de funcionalidade:
-
Módulos para requisição de dados: para aquele(as) que desejam somente consultar os dados e metadados do nosso projeto (ou qualquer outro projeto no Google Cloud).
-
Classes para gerenciamento de dados no Google Cloud: para aqueles(as) que desejam subir dados no nosso projeto (ou qualquer outro projeto no Google Cloud, seguindo a nossa metodologia e infraestrutura).
Toda documentação do código abaixo está em inglês
Módulos (Requisição de dados)
Functions for managing downloads
download(savepath, query=None, dataset_id=None, table_id=None, billing_project_id=None, query_project_id='basedosdados', limit=None, from_file=False, reauth=False, compression='GZIP')
Download table or query result from basedosdados BigQuery (or other).
-
Using a query:
download('select * frombasedosdados.br_suporte.diretorio_municipioslimit 10') -
Using dataset_id & table_id:
download(dataset_id='br_suporte', table_id='diretorio_municipios')
You can also add arguments to modify save parameters:
download(dataset_id='br_suporte', table_id='diretorio_municipios', index=False, sep='|')
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
savepath |
str, pathlib.PosixPath |
savepath must be a file path. Only supports |
required |
query |
str |
Optional. Valid SQL Standard Query to basedosdados. If query is available, dataset_id and table_id are not required. |
None |
dataset_id |
str |
Optional. Dataset id available in basedosdados. It should always come with table_id. |
None |
table_id |
str |
Optional. Table id available in basedosdados.dataset_id. It should always come with dataset_id. |
None |
billing_project_id |
str |
Optional. Project that will be billed. Find your Project ID here https://console.cloud.google.com/projectselector2/home/dashboard |
None |
query_project_id |
str |
Optional. Which project the table lives. You can change this you want to query different projects. |
'basedosdados' |
limit |
int |
Optional Number of rows. |
None |
from_file |
boolean |
Optional. Uses the credentials from file, located in `~/.basedosdados/credentials/ |
False |
reauth |
boolean |
Optional. Re-authorize Google Cloud Project in case you need to change user or reset configurations. |
False |
compression |
str |
Optional.
Compression type. Only |
'GZIP' |
Exceptions:
| Type | Description |
|---|---|
Exception |
If either table_id, dataset_id or query are empty. |
Source code in basedosdados/download/download.py
def download(
savepath,
query=None,
dataset_id=None,
table_id=None,
billing_project_id=None,
query_project_id="basedosdados",
limit=None,
from_file=False,
reauth=False,
compression="GZIP",
):
"""Download table or query result from basedosdados BigQuery (or other).
* Using a **query**:
`download('select * from `basedosdados.br_suporte.diretorio_municipios` limit 10')`
* Using **dataset_id & table_id**:
`download(dataset_id='br_suporte', table_id='diretorio_municipios')`
You can also add arguments to modify save parameters:
`download(dataset_id='br_suporte', table_id='diretorio_municipios', index=False, sep='|')`
Args:
savepath (str, pathlib.PosixPath):
savepath must be a file path. Only supports `.csv`.
query (str): Optional.
Valid SQL Standard Query to basedosdados. If query is available,
dataset_id and table_id are not required.
dataset_id (str): Optional.
Dataset id available in basedosdados. It should always come with table_id.
table_id (str): Optional.
Table id available in basedosdados.dataset_id.
It should always come with dataset_id.
billing_project_id (str): Optional.
Project that will be billed. Find your Project ID here https://console.cloud.google.com/projectselector2/home/dashboard
query_project_id (str): Optional.
Which project the table lives. You can change this you want to query different projects.
limit (int): Optional
Number of rows.
from_file (boolean): Optional.
Uses the credentials from file, located in `~/.basedosdados/credentials/
reauth (boolean): Optional.
Re-authorize Google Cloud Project in case you need to change user or reset configurations.
compression (str): Optional.
Compression type. Only `GZIP` is available for now.
Raises:
Exception: If either table_id, dataset_id or query are empty.
"""
billing_project_id, from_file = _set_config_variables(
billing_project_id=billing_project_id, from_file=from_file
)
if (query is None) and ((table_id is None) or (dataset_id is None)):
raise BaseDosDadosException(
"Either table_id, dataset_id or query should be filled."
)
client = google_client(billing_project_id, from_file, reauth)
# makes sure that savepath is a filepath and not a folder
savepath = _sets_savepath(savepath)
# if query is not defined (so it won't be overwritten) and if
# table is a view or external or if limit is specified,
# convert it to a query.
if not query and (
not _is_table(client, dataset_id, table_id, query_project_id) or limit
):
query = f"""
SELECT *
FROM {query_project_id}.{dataset_id}.{table_id}
"""
if limit is not None:
query += f" limit {limit}"
if query:
# sql queries produces anonymous tables, whose names
# can be found within `job._properties`
job = client["bigquery"].query(query)
# views may take longer: wait for job to finish.
_wait_for(job)
dest_table = job._properties["configuration"]["query"]["destinationTable"]
project_id = dest_table["projectId"]
dataset_id = dest_table["datasetId"]
table_id = dest_table["tableId"]
_direct_download(client, dataset_id, table_id, savepath, project_id, compression)
read_sql(query, billing_project_id=None, from_file=False, reauth=False, use_bqstorage_api=False)
Load data from BigQuery using a query. Just a wrapper around pandas.read_gbq
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query |
sql |
Valid SQL Standard Query to basedosdados |
required |
billing_project_id |
str |
Optional. Project that will be billed. Find your Project ID here https://console.cloud.google.com/projectselector2/home/dashboard |
None |
from_file |
boolean |
Optional. Uses the credentials from file, located in `~/.basedosdados/credentials/ |
False |
reauth |
boolean |
Optional. Re-authorize Google Cloud Project in case you need to change user or reset configurations. |
False |
use_bqstorage_api |
boolean |
Optional. Use the BigQuery Storage API to download query results quickly, but at an increased cost(https://cloud.google.com/bigquery/docs/reference/storage/). To use this API, first enable it in the Cloud Console(https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com). You must also have the bigquery.readsessions.create permission on the project you are billing queries to. |
False |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
Query result |
Source code in basedosdados/download/download.py
def read_sql(
query,
billing_project_id=None,
from_file=False,
reauth=False,
use_bqstorage_api=False,
):
"""Load data from BigQuery using a query. Just a wrapper around pandas.read_gbq
Args:
query (sql):
Valid SQL Standard Query to basedosdados
billing_project_id (str): Optional.
Project that will be billed. Find your Project ID here https://console.cloud.google.com/projectselector2/home/dashboard
from_file (boolean): Optional.
Uses the credentials from file, located in `~/.basedosdados/credentials/
reauth (boolean): Optional.
Re-authorize Google Cloud Project in case you need to change user or reset configurations.
use_bqstorage_api (boolean): Optional.
Use the BigQuery Storage API to download query results quickly, but at an increased cost(https://cloud.google.com/bigquery/docs/reference/storage/).
To use this API, first enable it in the Cloud Console(https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com).
You must also have the bigquery.readsessions.create permission on the project you are billing queries to.
Returns:
pd.DataFrame:
Query result
"""
billing_project_id, from_file = _set_config_variables(
billing_project_id=billing_project_id, from_file=from_file
)
try:
# Set a two hours timeout
bigquery_storage_v1.client.BigQueryReadClient.read_rows = partialmethod(
bigquery_storage_v1.client.BigQueryReadClient.read_rows,
timeout=3600 * 2,
)
return pandas_gbq.read_gbq(
query,
credentials=credentials(from_file=from_file, reauth=reauth),
project_id=billing_project_id,
use_bqstorage_api=use_bqstorage_api,
)
except GenericGBQException as e:
if "Reason: 403" in str(e):
raise BaseDosDadosAccessDeniedException from e
if re.match("Reason: 400 POST .* [Pp]roject[ ]*I[Dd]", str(e)):
raise BaseDosDadosInvalidProjectIDException from e
raise
except PyDataCredentialsError as e:
raise BaseDosDadosAuthorizationException from e
except (OSError, ValueError) as e:
no_billing_id = "Could not determine project ID" in str(e)
no_billing_id |= "reading from stdin while output is captured" in str(e)
if no_billing_id:
raise BaseDosDadosNoBillingProjectIDException from e
raise
read_table(dataset_id, table_id, billing_project_id=None, query_project_id='basedosdados', limit=None, from_file=False, reauth=False, use_bqstorage_api=False)
Load data from BigQuery using dataset_id and table_id.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset_id |
str |
Optional. Dataset id available in basedosdados. It should always come with table_id. |
required |
table_id |
str |
Optional. Table id available in basedosdados.dataset_id. It should always come with dataset_id. |
required |
billing_project_id |
str |
Optional. Project that will be billed. Find your Project ID here https://console.cloud.google.com/projectselector2/home/dashboard |
None |
query_project_id |
str |
Optional. Which project the table lives. You can change this you want to query different projects. |
'basedosdados' |
limit |
int |
Optional. Number of rows to read from table. |
None |
from_file |
boolean |
Optional. Uses the credentials from file, located in `~/.basedosdados/credentials/ |
False |
reauth |
boolean |
Optional. Re-authorize Google Cloud Project in case you need to change user or reset configurations. |
False |
use_bqstorage_api |
boolean |
Optional. Use the BigQuery Storage API to download query results quickly, but at an increased cost(https://cloud.google.com/bigquery/docs/reference/storage/). To use this API, first enable it in the Cloud Console(https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com). You must also have the bigquery.readsessions.create permission on the project you are billing queries to. |
False |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
Query result |
Source code in basedosdados/download/download.py
def read_table(
dataset_id,
table_id,
billing_project_id=None,
query_project_id="basedosdados",
limit=None,
from_file=False,
reauth=False,
use_bqstorage_api=False,
):
"""Load data from BigQuery using dataset_id and table_id.
Args:
dataset_id (str): Optional.
Dataset id available in basedosdados. It should always come with table_id.
table_id (str): Optional.
Table id available in basedosdados.dataset_id.
It should always come with dataset_id.
billing_project_id (str): Optional.
Project that will be billed. Find your Project ID here https://console.cloud.google.com/projectselector2/home/dashboard
query_project_id (str): Optional.
Which project the table lives. You can change this you want to query different projects.
limit (int): Optional.
Number of rows to read from table.
from_file (boolean): Optional.
Uses the credentials from file, located in `~/.basedosdados/credentials/
reauth (boolean): Optional.
Re-authorize Google Cloud Project in case you need to change user or reset configurations.
use_bqstorage_api (boolean): Optional.
Use the BigQuery Storage API to download query results quickly, but at an increased cost(https://cloud.google.com/bigquery/docs/reference/storage/).
To use this API, first enable it in the Cloud Console(https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com).
You must also have the bigquery.readsessions.create permission on the project you are billing queries to.
Returns:
pd.DataFrame:
Query result
"""
billing_project_id, from_file = _set_config_variables(
billing_project_id=billing_project_id, from_file=from_file
)
if (dataset_id is not None) and (table_id is not None):
query = f"""
SELECT *
FROM `{query_project_id}.{dataset_id}.{table_id}`"""
if limit is not None:
query += f" LIMIT {limit}"
else:
raise BaseDosDadosException("Both table_id and dataset_id should be filled.")
return read_sql(
query,
billing_project_id=billing_project_id,
from_file=from_file,
reauth=reauth,
use_bqstorage_api=use_bqstorage_api,
)
Functions to get metadata from BD's API
get_dataset_description(dataset_id, verbose=True)
Prints the full dataset description.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset_id |
str |
Required. Dataset id available in list_datasets. |
required |
verbose |
bool |
Optional.
If set to True, information is printed to the screen. If set to False, data is returned as a |
True |
Returns:
| Type | Description |
|---|---|
stdout | str |
Source code in basedosdados/download/metadata.py
def get_dataset_description(
dataset_id,
verbose=True,
):
"""
Prints the full dataset description.
Args:
dataset_id (str): Required.
Dataset id available in list_datasets.
verbose (bool): Optional.
If set to True, information is printed to the screen. If set to False, data is returned as a `str`.
Returns:
stdout | str
"""
url = f"https://basedosdados.org/api/3/action/bd_bdm_dataset_show?dataset_id={dataset_id}"
response = _safe_fetch(url)
json_response = response.json()
description = json_response["result"]["notes"]
if verbose:
return print(description)
return description
get_table_columns(dataset_id, table_id, verbose=True)
Fetch the names, types and descriptions for the columns in the specified table. Prints
information on screen.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset_id |
str |
Required. Dataset id available in list_datasets. |
required |
table_id |
str |
Required. Table id available in list_dataset_tables |
required |
verbose |
bool |
Optional.
If set to True, information is printed to the screen. If set to False, data is returned as a |
True |
Returns:
| Type | Description |
|---|---|
stdout | list |
Source code in basedosdados/download/metadata.py
def get_table_columns(
dataset_id,
table_id,
verbose=True,
):
"""
Fetch the names, types and descriptions for the columns in the specified table. Prints
information on screen.
Args:
dataset_id (str): Required.
Dataset id available in list_datasets.
table_id (str): Required.
Table id available in list_dataset_tables
verbose (bool): Optional.
If set to True, information is printed to the screen. If set to False, data is returned as a `list` of `dict`s.
Returns:
stdout | list
"""
url = f"https://basedosdados.org/api/3/action/bd_bdm_table_show?dataset_id={dataset_id}&table_id={table_id}"
response = _safe_fetch(url)
json_response = response.json()
columns = json_response["result"]["columns"]
if verbose:
return _print_output(pd.DataFrame(columns))
return columns
get_table_description(dataset_id, table_id, verbose=True)
Prints the full table description.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset_id |
str |
Required. Dataset id available in list_datasets. |
required |
table_id |
str |
Required. Table id available in list_dataset_tables |
required |
verbose |
bool |
Optional.
If set to True, information is printed to the screen. If set to False, data is returned as a |
True |
Returns:
| Type | Description |
|---|---|
stdout | str |
Source code in basedosdados/download/metadata.py
def get_table_description(
dataset_id,
table_id,
verbose=True,
):
"""
Prints the full table description.
Args:
dataset_id (str): Required.
Dataset id available in list_datasets.
table_id (str): Required.
Table id available in list_dataset_tables
verbose (bool): Optional.
If set to True, information is printed to the screen. If set to False, data is returned as a `str`.
Returns:
stdout | str
"""
url = f"https://basedosdados.org/api/3/action/bd_bdm_table_show?dataset_id={dataset_id}&table_id={table_id}"
response = _safe_fetch(url)
json_response = response.json()
description = json_response["result"]["description"]
if verbose:
return print(description)
return description
get_table_size(dataset_id, table_id, verbose=True)
Use a query to get the number of rows and size (in Mb) of a table.
WARNING: this query may cost a lot depending on the table.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset_id |
str |
Optional. Dataset id available in basedosdados. It should always come with table_id. |
required |
table_id |
str |
Optional. Table id available in basedosdados.dataset_id. It should always come with dataset_id. |
required |
verbose |
bool |
Optional.
If set to True, information is printed to the screen. If set to False, data is returned as a |
True |
Source code in basedosdados/download/metadata.py
def get_table_size(
dataset_id,
table_id,
verbose=True,
):
"""Use a query to get the number of rows and size (in Mb) of a table.
WARNING: this query may cost a lot depending on the table.
Args:
dataset_id (str): Optional.
Dataset id available in basedosdados. It should always come with table_id.
table_id (str): Optional.
Table id available in basedosdados.dataset_id.
It should always come with dataset_id.
verbose (bool): Optional.
If set to True, information is printed to the screen. If set to False, data is returned as a `list` of `dict`s.
"""
url = f"https://basedosdados.org/api/3/action/bd_bdm_table_show?dataset_id={dataset_id}&table_id={table_id}"
response = _safe_fetch(url)
json_response = response.json()
size = json_response["result"]["size"]
if size is None:
return print("Size not available")
if verbose:
return _print_output(pd.DataFrame(size))
return size
list_dataset_tables(dataset_id, with_description=False, verbose=True)
Fetch table_id for tables available at the specified dataset_id. Prints the information on screen or returns it as a list.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset_id |
str |
Optional. Dataset id returned by list_datasets function |
required |
limit |
int |
Field to limit the number of results |
required |
with_description |
bool |
Optional If True, fetch short table descriptions for each table that match the search criteria. |
False |
verbose |
bool |
Optional. If set to True, information is printed to the screen. If set to False, a list object is returned. |
True |
Returns:
| Type | Description |
|---|---|
stdout | list |
Source code in basedosdados/download/metadata.py
def list_dataset_tables(
dataset_id,
with_description=False,
verbose=True,
):
"""
Fetch table_id for tables available at the specified dataset_id. Prints the information on screen or returns it as a list.
Args:
dataset_id (str): Optional.
Dataset id returned by list_datasets function
limit (int):
Field to limit the number of results
with_description (bool): Optional
If True, fetch short table descriptions for each table that match the search criteria.
verbose (bool): Optional.
If set to True, information is printed to the screen. If set to False, a list object is returned.
Returns:
stdout | list
"""
dataset_id = dataset_id.replace(
"-", "_"
) # The dataset_id pattern in the bd_dataset_search endpoint response uses a hyphen as a separator, while in the endpoint urls that specify the dataset_id parameter the separator used is an underscore. See issue #1079
url = f"https://basedosdados.org/api/3/action/bd_bdm_dataset_show?dataset_id={dataset_id}"
response = _safe_fetch(url)
json_response = response.json()
dataset = json_response["result"]
# this dict has all information need to output the function
table_dict = {
"table_id": [
dataset["resources"][k]["name"]
for k in range(len(dataset["resources"]))
if dataset["resources"][k]["resource_type"] == "bdm_table"
],
"description": [
dataset["resources"][k]["description"]
for k in range(len(dataset["resources"]))
if dataset["resources"][k]["resource_type"] == "bdm_table"
],
}
# select desired output using table_id info. Note that the output is either a standardized string or a list
if verbose & (with_description is False):
return _print_output(pd.DataFrame.from_dict(table_dict)[["table_id"]])
if verbose & with_description:
return _print_output(
pd.DataFrame.from_dict(table_dict)[["table_id", "description"]]
)
if (verbose is False) & (with_description is False):
return table_dict["table_id"]
if (verbose is False) & with_description:
return [
{
"table_id": table_dict["table_id"][k],
"description": table_dict["description"][k],
}
for k in range(len(table_dict["table_id"]))
]
raise ValueError("`verbose` and `with_description` argument must be of `bool` type.")
list_datasets(with_description=False, verbose=True)
This function uses bd_dataset_search website API
enpoint to retrieve a list of available datasets.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
with_description |
bool |
Optional If True, fetch short dataset description for each dataset. |
False |
verbose |
bool |
Optional. If set to True, information is printed to the screen. If set to False, a list object is returned. |
True |
Returns:
| Type | Description |
|---|---|
list | stdout |
Source code in basedosdados/download/metadata.py
def list_datasets(with_description=False, verbose=True):
"""
This function uses `bd_dataset_search` website API
enpoint to retrieve a list of available datasets.
Args:
with_description (bool): Optional
If True, fetch short dataset description for each dataset.
verbose (bool): Optional.
If set to True, information is printed to the screen. If set to False, a list object is returned.
Returns:
list | stdout
"""
# first request is made separately since we need to now the number of pages before the iteration
page_size = 100 # this function will only made more than one requisition if there are more than 100 datasets in the API response #pylint: disable=C0301
url = f"https://basedosdados.org/api/3/action/bd_dataset_search?q=&resource_type=bdm_table&page=1&page_size={page_size}" # pylint: disable=C0301
response = _safe_fetch(url)
json_response = response.json()
n_datasets = json_response["result"]["count"]
n_pages = math.ceil(n_datasets / page_size)
temp_dict = _dict_from_page(json_response)
temp_dicts = [temp_dict]
for page in range(2, n_pages + 1):
url = f"https://basedosdados.org/api/3/action/bd_dataset_search?q=&resource_type=bdm_table&page={page}&page_size={page_size}" # pylint: disable=C0301
response = _safe_fetch(url)
json_response = response.json()
temp_dict = _dict_from_page(json_response)
temp_dicts.append(temp_dict)
dataset_dict = defaultdict(list)
for d in temp_dicts: # pylint: disable=C0103
for key, value in d.items():
dataset_dict[key].append(value)
# flat inner lists
dataset_dict["dataset_id"] = [
item for sublist in dataset_dict["dataset_id"] for item in sublist
] # pylint: disable=C0301
dataset_dict["description"] = [
item for sublist in dataset_dict["description"] for item in sublist
] # pylint: disable=C0301
# select desired output using dataset_id info. Note that the output is either a standardized string or a list #pylint: disable=C0301
if verbose & (with_description is False):
return _print_output(pd.DataFrame.from_dict(dataset_dict)[["dataset_id"]])
if verbose & with_description:
return _print_output(
pd.DataFrame.from_dict(dataset_dict)[["dataset_id", "description"]]
)
if (verbose is False) & (with_description is False):
return dataset_dict["dataset_id"]
if (verbose is False) & with_description:
return [
{
"dataset_id": dataset_dict["dataset_id"][k],
"description": dataset_dict["description"][k],
}
for k in range(len(dataset_dict["dataset_id"]))
]
raise ValueError("`verbose` and `with_description` argument must be of `bool` type.")
search(query, order_by)
This function works as a wrapper to the bd_dataset_search website API
enpoint.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query |
str |
String to search in datasets and tables' metadata. |
required |
order_by |
str |
score|popular|recent Field by which the results will be ordered. |
required |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
Response from the API presented as a pandas DataFrame. Each row is a table. Each column is a field identifying the table. |
Source code in basedosdados/download/metadata.py
def search(query, order_by):
"""This function works as a wrapper to the `bd_dataset_search` website API
enpoint.
Args:
query (str):
String to search in datasets and tables' metadata.
order_by (str): score|popular|recent
Field by which the results will be ordered.
Returns:
pd.DataFrame:
Response from the API presented as a pandas DataFrame. Each row is
a table. Each column is a field identifying the table.
"""
# validate order_by input
if order_by not in ["score", "popular", "recent"]:
raise ValueError(
f'order_by must be score, popular or recent. Received "{order_by}"'
)
url = f"https://basedosdados.org/api/3/action/bd_dataset_search?q={query}&order_by={order_by}&resource_type=bdm_table"
response = _safe_fetch(url)
json_response = response.json()
dataset_dfs = []
# first loop identify the number of the tables in each datasets
for dataset in json_response["result"]["datasets"]:
tables_dfs = []
len(dataset["resources"])
# second loop extracts tables' information for each dataset
for table in dataset["resources"]:
data_table = pd.DataFrame(
{k: str(table[k]) for k in list(table.keys())}, index=[0]
)
tables_dfs.append(data_table)
# append tables' dataframes for each dataset
data_ds = tables_dfs[0].append(tables_dfs[1:]).reset_index(drop=True)
dataset_dfs.append(data_ds)
# append datasets' dataframes
df = dataset_dfs[0].append(dataset_dfs[1:]).reset_index(drop=True)
return df
Classes (Gerenciamento de dados)
Class for managing the files in cloud storage.
Storage (Base)
Manage files on Google Cloud Storage.
Source code in basedosdados/upload/storage.py
class Storage(Base):
"""
Manage files on Google Cloud Storage.
"""
def __init__(self, dataset_id=None, table_id=None, **kwargs):
super().__init__(**kwargs)
self.bucket = self.client["storage_staging"].bucket(self.bucket_name)
self.dataset_id = dataset_id.replace("-", "_")
self.table_id = table_id.replace("-", "_")
@staticmethod
def _resolve_partitions(partitions):
if isinstance(partitions, dict):
return "/".join(f"{k}={v}" for k, v in partitions.items()) + "/"
if isinstance(partitions, str):
if partitions.endswith("/"):
partitions = partitions[:-1]
# If there is no partition
if len(partitions) == 0:
return ""
# It should fail if there is folder which is not a partition
try:
# check if it fits rule
{b.split("=")[0]: b.split("=")[1] for b in partitions.split("/")}
except IndexError as e:
raise Exception(f"The path {partitions} is not a valid partition") from e
return partitions + "/"
raise Exception(f"Partitions format or type not accepted: {partitions}")
def _build_blob_name(self, filename, mode, partitions=None):
'''
Builds the blob name.
'''
# table folder
blob_name = f"{mode}/{self.dataset_id}/{self.table_id}/"
# add partition folder
if partitions is not None:
blob_name += self._resolve_partitions(partitions)
# add file name
blob_name += filename
return blob_name
def init(self, replace=False, very_sure=False):
"""Initializes bucket and folders.
Folder should be:
* `raw` : that contains really raw data
* `staging` : preprocessed data ready to upload to BigQuery
Args:
replace (bool): Optional.
Whether to replace if bucket already exists
very_sure (bool): Optional.
Are you aware that everything is going to be erased if you
replace the bucket?
Raises:
Warning: very_sure argument is still False.
"""
if replace:
if not very_sure:
raise Warning(
"\n********************************************************"
"\nYou are trying to replace all the data that you have "
f"in bucket {self.bucket_name}.\nAre you sure?\n"
"If yes, add the flag --very_sure\n"
"********************************************************"
)
self.bucket.delete(force=True)
self.client["storage_staging"].create_bucket(self.bucket)
for folder in ["staging/", "raw/"]:
self.bucket.blob(folder).upload_from_string("")
def upload(
self,
path,
mode="all",
partitions=None,
if_exists="raise",
chunk_size=None,
**upload_args,
):
"""Upload to storage at `<bucket_name>/<mode>/<dataset_id>/<table_id>`. You can:
* Add a single **file** setting `path = <file_path>`.
* Add a **folder** with multiple files setting `path =
<folder_path>`. *The folder should just contain the files and
no folders.*
* Add **partitioned files** setting `path = <folder_path>`.
This folder must follow the hive partitioning scheme i.e.
`<table_id>/<key>=<value>/<key2>=<value2>/<partition>.csv`
(ex: `mytable/country=brasil/year=2020/mypart.csv`).
*Remember all files must follow a single schema.* Otherwise, things
might fail in the future.
There are 6 modes:
* `raw` : should contain raw files from datasource
* `staging` : should contain pre-treated files ready to upload to BiqQuery
* `header`: should contain the header of the tables
* `auxiliary_files`: should contain auxiliary files from eache table
* `architecture`: should contain the architecture sheet of the tables
* `all`: if no treatment is needed, use `all`.
Args:
path (str or pathlib.PosixPath): Where to find the file or
folder that you want to upload to storage
mode (str): Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture|all]
partitions (str, pathlib.PosixPath, or dict): Optional.
*If adding a single file*, use this to add it to a specific partition.
* str : `<key>=<value>/<key2>=<value2>`
* dict: `dict(key=value, key2=value2)`
if_exists (str): Optional.
What to do if data exists
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
chunk_size (int): Optional
The size of a chunk of data whenever iterating (in bytes).
This must be a multiple of 256 KB per the API specification.
If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used.
upload_args ():
Extra arguments accepted by [`google.cloud.storage.blob.Blob.upload_from_file`](https://googleapis.dev/python/storage/latest/blobs.html?highlight=upload_from_filename#google.cloud.storage.blob.Blob.upload_from_filename)
"""
if (self.dataset_id is None) or (self.table_id is None):
raise Exception("You need to pass dataset_id and table_id")
path = Path(path)
if path.is_dir():
paths = [
f
for f in path.glob("**/*")
if f.is_file() and f.suffix in [".csv", ".parquet", "parquet.gzip"]
]
parts = [
(
filepath.as_posix()
.replace(path.as_posix() + "/", "")
.replace(str(filepath.name), "")
)
for filepath in paths
]
else:
paths = [path]
parts = [partitions or None]
self._check_mode(mode)
mode = (
["raw", "staging", "header", "auxiliary_files", "architecture"]
if mode == "all"
else [mode]
)
for m in mode:
for filepath, part in tqdm(list(zip(paths, parts)), desc="Uploading files"):
blob_name = self._build_blob_name(filepath.name, m, part)
blob = self.bucket.blob(blob_name, chunk_size=chunk_size)
if not blob.exists() or if_exists == "replace":
upload_args["timeout"] = upload_args.get("timeout", None)
blob.upload_from_filename(str(filepath), **upload_args)
elif if_exists == "pass":
pass
else:
raise BaseDosDadosException(
f"Data already exists at {self.bucket_name}/{blob_name}. "
"If you are using Storage.upload then set if_exists to "
"'replace' to overwrite data \n"
"If you are using Table.create then set if_storage_data_exists "
"to 'replace' to overwrite data."
)
logger.success(
" {object} {filename}_{mode} was {action}!",
filename=filepath.name,
mode=mode,
object="File",
action="uploaded",
)
def download(
self,
filename="*",
savepath="",
partitions=None,
mode="raw",
if_not_exists="raise",
):
"""Download files from Google Storage from path `mode`/`dataset_id`/`table_id`/`partitions`/`filename` and replicate folder hierarchy
on save,
There are 5 modes:
* `raw` : should contain raw files from datasource
* `staging` : should contain pre-treated files ready to upload to BiqQuery
* `header`: should contain the header of the tables
* `auxiliary_files`: should contain auxiliary files from eache table
* `architecture`: should contain the architecture sheet of the tables
You can also use the `partitions` argument to choose files from a partition
Args:
filename (str): Optional
Specify which file to download. If "*" , downloads all files within the bucket folder. Defaults to "*".
savepath (str):
Where you want to save the data on your computer. Must be a path to a directory.
partitions (str, dict): Optional
If downloading a single file, use this to specify the partition path from which to download.
* str : `<key>=<value>/<key2>=<value2>`
* dict: `dict(key=value, key2=value2)`
mode (str): Optional
Folder of which dataset to update.[raw|staging|header|auxiliary_files|architecture]
if_not_exists (str): Optional.
What to do if data not found.
* 'raise' : Raises FileNotFoundError.
* 'pass' : Do nothing and exit the function
Raises:
FileNotFoundError: If the given path `<mode>/<dataset_id>/<table_id>/<partitions>/<filename>` could not be found or there are no files to download.
"""
# Prefix to locate files within the bucket
prefix = f"{mode}/{self.dataset_id}/{self.table_id}/"
# Add specific partition to search prefix
if partitions:
prefix += self._resolve_partitions(partitions)
# if no filename is passed, list all blobs within a given table
if filename != "*":
prefix += filename
blob_list = list(self.bucket.list_blobs(prefix=prefix))
# if there are no blobs matching the search raise FileNotFoundError or return
if not blob_list:
if if_not_exists == "raise":
raise FileNotFoundError(f"Could not locate files at {prefix}")
return
# download all blobs matching the search to given savepath
for blob in tqdm(blob_list, desc="Download Blob"):
# parse blob.name and get the csv file name
csv_name = blob.name.split("/")[-1]
# build folder path replicating storage hierarchy
blob_folder = blob.name.replace(csv_name, "")
# replicate folder hierarchy
(Path(savepath) / blob_folder).mkdir(parents=True, exist_ok=True)
# download blob to savepath
blob.download_to_filename(filename=f"{savepath}/{blob.name}")
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="File",
action="downloaded",
)
def delete_file(self, filename, mode, partitions=None, not_found_ok=False):
"""Deletes file from path `<bucket_name>/<mode>/<dataset_id>/<table_id>/<partitions>/<filename>`.
Args:
filename (str): Name of the file to be deleted
mode (str): Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture|all]
partitions (str, pathlib.PosixPath, or dict): Optional.
Hive structured partition as a string or dict
* str : `<key>=<value>/<key2>=<value2>`
* dict: `dict(key=value, key2=value2)`
not_found_ok (bool): Optional.
What to do if file not found
"""
self._check_mode(mode)
mode = (
["raw", "staging", "header", "auxiliary_files", "architecture"]
if mode == "all"
else [mode]
)
for m in mode:
blob = self.bucket.blob(self._build_blob_name(filename, m, partitions))
if blob.exists() or not blob.exists() and not not_found_ok:
blob.delete()
else:
return
logger.success(
" {object} {filename}_{mode} was {action}!",
filename=filename,
mode=mode,
object="File",
action="deleted",
)
def delete_table(self, mode="staging", bucket_name=None, not_found_ok=False):
"""Deletes a table from storage, sends request in batches.
Args:
mode (str): Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture]
Folder of which dataset to update. Defaults to "staging".
bucket_name (str):
The bucket name from which to delete the table. If None, defaults to the bucket initialized when instantiating the Storage object.
(You can check it with the Storage().bucket property)
not_found_ok (bool): Optional.
What to do if table not found
"""
prefix = f"{mode}/{self.dataset_id}/{self.table_id}/"
if bucket_name is not None:
table_blobs = list(
self.client["storage_staging"]
.bucket(f"{bucket_name}")
.list_blobs(prefix=prefix)
)
else:
table_blobs = list(self.bucket.list_blobs(prefix=prefix))
if not table_blobs:
if not_found_ok:
return
raise FileNotFoundError(
f"Could not find the requested table {self.dataset_id}.{self.table_id}"
)
# Divides table_blobs list for maximum batch request size
table_blobs_chunks = [
table_blobs[i : i + 999] for i in range(0, len(table_blobs), 999)
]
for i, source_table in enumerate(
tqdm(table_blobs_chunks, desc="Delete Table Chunk")
):
counter = 0
while counter < 10:
try:
with self.client["storage_staging"].batch():
for blob in source_table:
blob.delete()
break
except Exception:
print(
f"Delete Table Chunk {i} | Attempt {counter}: delete operation starts again in 5 seconds...",
)
time.sleep(5)
counter += 1
traceback.print_exc(file=sys.stderr)
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.table_id,
mode=mode,
object="Table",
action="deleted",
)
def copy_table(
self,
source_bucket_name="basedosdados",
destination_bucket_name=None,
mode="staging",
):
"""Copies table from a source bucket to your bucket, sends request in batches.
Args:
source_bucket_name (str):
The bucket name from which to copy data. You can change it
to copy from other external bucket.
destination_bucket_name (str): Optional
The bucket name where data will be copied to.
If None, defaults to the bucket initialized when instantiating the Storage object (You can check it with the
Storage().bucket property)
mode (str): Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture]
Folder of which dataset to update. Defaults to "staging".
"""
source_table_ref = list(
self.client["storage_staging"]
.bucket(source_bucket_name)
.list_blobs(prefix=f"{mode}/{self.dataset_id}/{self.table_id}/")
)
if not source_table_ref:
raise FileNotFoundError(
f"Could not find the requested table {self.dataset_id}.{self.table_id}"
)
if destination_bucket_name is None:
destination_bucket = self.bucket
else:
destination_bucket = self.client["storage_staging"].bucket(
destination_bucket_name
)
# Divides source_table_ref list for maximum batch request size
source_table_ref_chunks = [
source_table_ref[i : i + 999] for i in range(0, len(source_table_ref), 999)
]
for i, source_table in enumerate(
tqdm(source_table_ref_chunks, desc="Copy Table Chunk")
):
counter = 0
while counter < 10:
try:
with self.client["storage_staging"].batch():
for blob in source_table:
self.bucket.copy_blob(
blob,
destination_bucket=destination_bucket,
)
break
except Exception:
print(
f"Copy Table Chunk {i} | Attempt {counter}: copy operation starts again in 5 seconds...",
)
counter += 1
time.sleep(5)
traceback.print_exc(file=sys.stderr)
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.table_id,
mode=mode,
object="Table",
action="copied",
)
copy_table(self, source_bucket_name='basedosdados', destination_bucket_name=None, mode='staging')
Copies table from a source bucket to your bucket, sends request in batches.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source_bucket_name |
str |
The bucket name from which to copy data. You can change it to copy from other external bucket. |
'basedosdados' |
destination_bucket_name |
str |
Optional The bucket name where data will be copied to. If None, defaults to the bucket initialized when instantiating the Storage object (You can check it with the Storage().bucket property) |
None |
mode |
str |
Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture] Folder of which dataset to update. Defaults to "staging". |
'staging' |
Source code in basedosdados/upload/storage.py
def copy_table(
self,
source_bucket_name="basedosdados",
destination_bucket_name=None,
mode="staging",
):
"""Copies table from a source bucket to your bucket, sends request in batches.
Args:
source_bucket_name (str):
The bucket name from which to copy data. You can change it
to copy from other external bucket.
destination_bucket_name (str): Optional
The bucket name where data will be copied to.
If None, defaults to the bucket initialized when instantiating the Storage object (You can check it with the
Storage().bucket property)
mode (str): Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture]
Folder of which dataset to update. Defaults to "staging".
"""
source_table_ref = list(
self.client["storage_staging"]
.bucket(source_bucket_name)
.list_blobs(prefix=f"{mode}/{self.dataset_id}/{self.table_id}/")
)
if not source_table_ref:
raise FileNotFoundError(
f"Could not find the requested table {self.dataset_id}.{self.table_id}"
)
if destination_bucket_name is None:
destination_bucket = self.bucket
else:
destination_bucket = self.client["storage_staging"].bucket(
destination_bucket_name
)
# Divides source_table_ref list for maximum batch request size
source_table_ref_chunks = [
source_table_ref[i : i + 999] for i in range(0, len(source_table_ref), 999)
]
for i, source_table in enumerate(
tqdm(source_table_ref_chunks, desc="Copy Table Chunk")
):
counter = 0
while counter < 10:
try:
with self.client["storage_staging"].batch():
for blob in source_table:
self.bucket.copy_blob(
blob,
destination_bucket=destination_bucket,
)
break
except Exception:
print(
f"Copy Table Chunk {i} | Attempt {counter}: copy operation starts again in 5 seconds...",
)
counter += 1
time.sleep(5)
traceback.print_exc(file=sys.stderr)
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.table_id,
mode=mode,
object="Table",
action="copied",
)
delete_file(self, filename, mode, partitions=None, not_found_ok=False)
Deletes file from path <bucket_name>/<mode>/<dataset_id>/<table_id>/<partitions>/<filename>.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
Name of the file to be deleted |
required |
mode |
str |
Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture|all] |
required |
partitions |
str, pathlib.PosixPath, or dict |
Optional. Hive structured partition as a string or dict
|
None |
not_found_ok |
bool |
Optional. What to do if file not found |
False |
Source code in basedosdados/upload/storage.py
def delete_file(self, filename, mode, partitions=None, not_found_ok=False):
"""Deletes file from path `<bucket_name>/<mode>/<dataset_id>/<table_id>/<partitions>/<filename>`.
Args:
filename (str): Name of the file to be deleted
mode (str): Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture|all]
partitions (str, pathlib.PosixPath, or dict): Optional.
Hive structured partition as a string or dict
* str : `<key>=<value>/<key2>=<value2>`
* dict: `dict(key=value, key2=value2)`
not_found_ok (bool): Optional.
What to do if file not found
"""
self._check_mode(mode)
mode = (
["raw", "staging", "header", "auxiliary_files", "architecture"]
if mode == "all"
else [mode]
)
for m in mode:
blob = self.bucket.blob(self._build_blob_name(filename, m, partitions))
if blob.exists() or not blob.exists() and not not_found_ok:
blob.delete()
else:
return
logger.success(
" {object} {filename}_{mode} was {action}!",
filename=filename,
mode=mode,
object="File",
action="deleted",
)
delete_table(self, mode='staging', bucket_name=None, not_found_ok=False)
Deletes a table from storage, sends request in batches.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mode |
str |
Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture] Folder of which dataset to update. Defaults to "staging". |
'staging' |
bucket_name |
str |
The bucket name from which to delete the table. If None, defaults to the bucket initialized when instantiating the Storage object. (You can check it with the Storage().bucket property) |
None |
not_found_ok |
bool |
Optional. What to do if table not found |
False |
Source code in basedosdados/upload/storage.py
def delete_table(self, mode="staging", bucket_name=None, not_found_ok=False):
"""Deletes a table from storage, sends request in batches.
Args:
mode (str): Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture]
Folder of which dataset to update. Defaults to "staging".
bucket_name (str):
The bucket name from which to delete the table. If None, defaults to the bucket initialized when instantiating the Storage object.
(You can check it with the Storage().bucket property)
not_found_ok (bool): Optional.
What to do if table not found
"""
prefix = f"{mode}/{self.dataset_id}/{self.table_id}/"
if bucket_name is not None:
table_blobs = list(
self.client["storage_staging"]
.bucket(f"{bucket_name}")
.list_blobs(prefix=prefix)
)
else:
table_blobs = list(self.bucket.list_blobs(prefix=prefix))
if not table_blobs:
if not_found_ok:
return
raise FileNotFoundError(
f"Could not find the requested table {self.dataset_id}.{self.table_id}"
)
# Divides table_blobs list for maximum batch request size
table_blobs_chunks = [
table_blobs[i : i + 999] for i in range(0, len(table_blobs), 999)
]
for i, source_table in enumerate(
tqdm(table_blobs_chunks, desc="Delete Table Chunk")
):
counter = 0
while counter < 10:
try:
with self.client["storage_staging"].batch():
for blob in source_table:
blob.delete()
break
except Exception:
print(
f"Delete Table Chunk {i} | Attempt {counter}: delete operation starts again in 5 seconds...",
)
time.sleep(5)
counter += 1
traceback.print_exc(file=sys.stderr)
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.table_id,
mode=mode,
object="Table",
action="deleted",
)
download(self, filename='*', savepath='', partitions=None, mode='raw', if_not_exists='raise')
Download files from Google Storage from path mode/dataset_id/table_id/partitions/filename and replicate folder hierarchy
on save,
There are 5 modes:
* raw : should contain raw files from datasource
* staging : should contain pre-treated files ready to upload to BiqQuery
* header: should contain the header of the tables
* auxiliary_files: should contain auxiliary files from eache table
* architecture: should contain the architecture sheet of the tables
You can also use the partitions argument to choose files from a partition
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
Optional Specify which file to download. If "" , downloads all files within the bucket folder. Defaults to "". |
'*' |
savepath |
str |
Where you want to save the data on your computer. Must be a path to a directory. |
'' |
partitions |
str, dict |
Optional If downloading a single file, use this to specify the partition path from which to download.
|
None |
mode |
str |
Optional Folder of which dataset to update.[raw|staging|header|auxiliary_files|architecture] |
'raw' |
if_not_exists |
str |
Optional. What to do if data not found.
|
'raise' |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the given path |
Source code in basedosdados/upload/storage.py
def download(
self,
filename="*",
savepath="",
partitions=None,
mode="raw",
if_not_exists="raise",
):
"""Download files from Google Storage from path `mode`/`dataset_id`/`table_id`/`partitions`/`filename` and replicate folder hierarchy
on save,
There are 5 modes:
* `raw` : should contain raw files from datasource
* `staging` : should contain pre-treated files ready to upload to BiqQuery
* `header`: should contain the header of the tables
* `auxiliary_files`: should contain auxiliary files from eache table
* `architecture`: should contain the architecture sheet of the tables
You can also use the `partitions` argument to choose files from a partition
Args:
filename (str): Optional
Specify which file to download. If "*" , downloads all files within the bucket folder. Defaults to "*".
savepath (str):
Where you want to save the data on your computer. Must be a path to a directory.
partitions (str, dict): Optional
If downloading a single file, use this to specify the partition path from which to download.
* str : `<key>=<value>/<key2>=<value2>`
* dict: `dict(key=value, key2=value2)`
mode (str): Optional
Folder of which dataset to update.[raw|staging|header|auxiliary_files|architecture]
if_not_exists (str): Optional.
What to do if data not found.
* 'raise' : Raises FileNotFoundError.
* 'pass' : Do nothing and exit the function
Raises:
FileNotFoundError: If the given path `<mode>/<dataset_id>/<table_id>/<partitions>/<filename>` could not be found or there are no files to download.
"""
# Prefix to locate files within the bucket
prefix = f"{mode}/{self.dataset_id}/{self.table_id}/"
# Add specific partition to search prefix
if partitions:
prefix += self._resolve_partitions(partitions)
# if no filename is passed, list all blobs within a given table
if filename != "*":
prefix += filename
blob_list = list(self.bucket.list_blobs(prefix=prefix))
# if there are no blobs matching the search raise FileNotFoundError or return
if not blob_list:
if if_not_exists == "raise":
raise FileNotFoundError(f"Could not locate files at {prefix}")
return
# download all blobs matching the search to given savepath
for blob in tqdm(blob_list, desc="Download Blob"):
# parse blob.name and get the csv file name
csv_name = blob.name.split("/")[-1]
# build folder path replicating storage hierarchy
blob_folder = blob.name.replace(csv_name, "")
# replicate folder hierarchy
(Path(savepath) / blob_folder).mkdir(parents=True, exist_ok=True)
# download blob to savepath
blob.download_to_filename(filename=f"{savepath}/{blob.name}")
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="File",
action="downloaded",
)
init(self, replace=False, very_sure=False)
Initializes bucket and folders.
Folder should be:
raw: that contains really raw datastaging: preprocessed data ready to upload to BigQuery
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
replace |
bool |
Optional. Whether to replace if bucket already exists |
False |
very_sure |
bool |
Optional. Are you aware that everything is going to be erased if you replace the bucket? |
False |
Exceptions:
| Type | Description |
|---|---|
Warning |
very_sure argument is still False. |
Source code in basedosdados/upload/storage.py
def init(self, replace=False, very_sure=False):
"""Initializes bucket and folders.
Folder should be:
* `raw` : that contains really raw data
* `staging` : preprocessed data ready to upload to BigQuery
Args:
replace (bool): Optional.
Whether to replace if bucket already exists
very_sure (bool): Optional.
Are you aware that everything is going to be erased if you
replace the bucket?
Raises:
Warning: very_sure argument is still False.
"""
if replace:
if not very_sure:
raise Warning(
"\n********************************************************"
"\nYou are trying to replace all the data that you have "
f"in bucket {self.bucket_name}.\nAre you sure?\n"
"If yes, add the flag --very_sure\n"
"********************************************************"
)
self.bucket.delete(force=True)
self.client["storage_staging"].create_bucket(self.bucket)
for folder in ["staging/", "raw/"]:
self.bucket.blob(folder).upload_from_string("")
upload(self, path, mode='all', partitions=None, if_exists='raise', chunk_size=None, **upload_args)
Upload to storage at <bucket_name>/<mode>/<dataset_id>/<table_id>. You can:
-
Add a single file setting
path = <file_path>. -
Add a folder with multiple files setting
path = <folder_path>. The folder should just contain the files and no folders. -
Add partitioned files setting
path = <folder_path>. This folder must follow the hive partitioning scheme i.e.<table_id>/<key>=<value>/<key2>=<value2>/<partition>.csv(ex:mytable/country=brasil/year=2020/mypart.csv).
Remember all files must follow a single schema. Otherwise, things might fail in the future.
There are 6 modes:
raw: should contain raw files from datasourcestaging: should contain pre-treated files ready to upload to BiqQueryheader: should contain the header of the tablesauxiliary_files: should contain auxiliary files from eache tablearchitecture: should contain the architecture sheet of the tablesall: if no treatment is needed, useall.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
str or pathlib.PosixPath |
Where to find the file or folder that you want to upload to storage |
required |
mode |
str |
Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture|all] |
'all' |
partitions |
str, pathlib.PosixPath, or dict |
Optional. If adding a single file, use this to add it to a specific partition.
|
None |
if_exists |
str |
Optional. What to do if data exists
|
'raise' |
chunk_size |
int |
Optional The size of a chunk of data whenever iterating (in bytes). This must be a multiple of 256 KB per the API specification. If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used. |
None |
upload_args |
Extra arguments accepted by |
{} |
Source code in basedosdados/upload/storage.py
def upload(
self,
path,
mode="all",
partitions=None,
if_exists="raise",
chunk_size=None,
**upload_args,
):
"""Upload to storage at `<bucket_name>/<mode>/<dataset_id>/<table_id>`. You can:
* Add a single **file** setting `path = <file_path>`.
* Add a **folder** with multiple files setting `path =
<folder_path>`. *The folder should just contain the files and
no folders.*
* Add **partitioned files** setting `path = <folder_path>`.
This folder must follow the hive partitioning scheme i.e.
`<table_id>/<key>=<value>/<key2>=<value2>/<partition>.csv`
(ex: `mytable/country=brasil/year=2020/mypart.csv`).
*Remember all files must follow a single schema.* Otherwise, things
might fail in the future.
There are 6 modes:
* `raw` : should contain raw files from datasource
* `staging` : should contain pre-treated files ready to upload to BiqQuery
* `header`: should contain the header of the tables
* `auxiliary_files`: should contain auxiliary files from eache table
* `architecture`: should contain the architecture sheet of the tables
* `all`: if no treatment is needed, use `all`.
Args:
path (str or pathlib.PosixPath): Where to find the file or
folder that you want to upload to storage
mode (str): Folder of which dataset to update [raw|staging|header|auxiliary_files|architecture|all]
partitions (str, pathlib.PosixPath, or dict): Optional.
*If adding a single file*, use this to add it to a specific partition.
* str : `<key>=<value>/<key2>=<value2>`
* dict: `dict(key=value, key2=value2)`
if_exists (str): Optional.
What to do if data exists
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
chunk_size (int): Optional
The size of a chunk of data whenever iterating (in bytes).
This must be a multiple of 256 KB per the API specification.
If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used.
upload_args ():
Extra arguments accepted by [`google.cloud.storage.blob.Blob.upload_from_file`](https://googleapis.dev/python/storage/latest/blobs.html?highlight=upload_from_filename#google.cloud.storage.blob.Blob.upload_from_filename)
"""
if (self.dataset_id is None) or (self.table_id is None):
raise Exception("You need to pass dataset_id and table_id")
path = Path(path)
if path.is_dir():
paths = [
f
for f in path.glob("**/*")
if f.is_file() and f.suffix in [".csv", ".parquet", "parquet.gzip"]
]
parts = [
(
filepath.as_posix()
.replace(path.as_posix() + "/", "")
.replace(str(filepath.name), "")
)
for filepath in paths
]
else:
paths = [path]
parts = [partitions or None]
self._check_mode(mode)
mode = (
["raw", "staging", "header", "auxiliary_files", "architecture"]
if mode == "all"
else [mode]
)
for m in mode:
for filepath, part in tqdm(list(zip(paths, parts)), desc="Uploading files"):
blob_name = self._build_blob_name(filepath.name, m, part)
blob = self.bucket.blob(blob_name, chunk_size=chunk_size)
if not blob.exists() or if_exists == "replace":
upload_args["timeout"] = upload_args.get("timeout", None)
blob.upload_from_filename(str(filepath), **upload_args)
elif if_exists == "pass":
pass
else:
raise BaseDosDadosException(
f"Data already exists at {self.bucket_name}/{blob_name}. "
"If you are using Storage.upload then set if_exists to "
"'replace' to overwrite data \n"
"If you are using Table.create then set if_storage_data_exists "
"to 'replace' to overwrite data."
)
logger.success(
" {object} {filename}_{mode} was {action}!",
filename=filepath.name,
mode=mode,
object="File",
action="uploaded",
)
Module for manage dataset to the server.
Dataset (Base)
Manage datasets in BigQuery.
Source code in basedosdados/upload/dataset.py
class Dataset(Base):
"""
Manage datasets in BigQuery.
"""
def __init__(self, dataset_id, **kwargs):
super().__init__(**kwargs)
self.dataset_id = dataset_id.replace("-", "_")
self.dataset_folder = Path(self.metadata_path / self.dataset_id)
self.metadata = Metadata(self.dataset_id, **kwargs)
@property
def dataset_config(self):
"""
Dataset config file.
"""
return self._load_yaml(
self.metadata_path / self.dataset_id / "dataset_config.yaml"
)
def _loop_modes(self, mode="all"):
"""
Loop modes.
"""
mode = ["prod", "staging"] if mode == "all" else [mode]
dataset_tag = lambda m: f"_{m}" if m == "staging" else ""
return (
{
"client": self.client[f"bigquery_{m}"],
"id": f"{self.client[f'bigquery_{m}'].project}.{self.dataset_id}{dataset_tag(m)}",
}
for m in mode
)
@staticmethod
def _setup_dataset_object(dataset_id, location=None):
"""
Setup dataset object.
"""
dataset = bigquery.Dataset(dataset_id)
## TODO: not being used since 1.6.0 - need to redo the description tha goes to bigquery
dataset.description = "Para saber mais acesse https://basedosdados.org/"
# dataset.description = self._render_template(
# Path("dataset/dataset_description.txt"), self.dataset_config
# )
dataset.location = location
return dataset
def _write_readme_file(self):
"""
Write README.md file.
"""
readme_content = (
f"Como capturar os dados de {self.dataset_id}?\n\nPara cap"
f"turar esses dados, basta verificar o link dos dados orig"
f"inais indicado em dataset_config.yaml no item website.\n"
f"\nCaso tenha sido utilizado algum código de captura ou t"
f"ratamento, estes estarão contidos em code/. Se o dado pu"
f"blicado for em sua versão bruta, não existirá a pasta co"
f"de/.\n\nOs dados publicados estão disponíveis em: https:"
f"//basedosdados.org/dataset/{self.dataset_id.replace('_','-')}"
)
readme_path = Path(self.metadata_path / self.dataset_id / "README.md")
with open(readme_path, "w", encoding="utf-8") as readmefile:
readmefile.write(readme_content)
def init(self, replace=False):
"""Initialize dataset folder at metadata_path at `metadata_path/<dataset_id>`.
The folder should contain:
* `dataset_config.yaml`
* `README.md`
Args:
replace (str): Optional. Whether to replace existing folder.
Raises:
FileExistsError: If dataset folder already exists and replace is False
"""
# Create dataset folder
try:
self.dataset_folder.mkdir(exist_ok=replace, parents=True)
except FileExistsError as e:
raise FileExistsError(
f"Dataset {str(self.dataset_folder.stem)} folder does not exists. "
"Set replace=True to replace current files."
) from e
# create dataset_config.yaml with metadata
self.metadata.create(if_exists="replace")
# create README.md file
self._write_readme_file()
# Add code folder
(self.dataset_folder / "code").mkdir(exist_ok=replace, parents=True)
return self
def publicize(self, mode="all", dataset_is_public=True):
"""Changes IAM configuration to turn BigQuery dataset public.
Args:
mode (bool): Which dataset to create [prod|staging|all].
dataset_is_public (bool): Control if prod dataset is public or not. By default staging datasets like `dataset_id_staging` are not public.
"""
for m in self._loop_modes(mode):
dataset = m["client"].get_dataset(m["id"])
entries = dataset.access_entries
# TODO https://github.com/basedosdados/mais/pull/1020
# TODO if staging dataset is private, the prod view can't acess it: if dataset_is_public and "staging" not in dataset.dataset_id:
if dataset_is_public:
if "staging" not in dataset.dataset_id:
entries.extend(
[
bigquery.AccessEntry(
role="roles/bigquery.dataViewer",
entity_type="iamMember",
entity_id="allUsers",
),
bigquery.AccessEntry(
role="roles/bigquery.metadataViewer",
entity_type="iamMember",
entity_id="allUsers",
),
bigquery.AccessEntry(
role="roles/bigquery.user",
entity_type="iamMember",
entity_id="allUsers",
),
]
)
else:
entries.extend(
[
bigquery.AccessEntry(
role="roles/bigquery.dataViewer",
entity_type="iamMember",
entity_id="allUsers",
),
]
)
dataset.access_entries = entries
m["client"].update_dataset(dataset, ["access_entries"])
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="Dataset",
action="publicized",
)
def create(
self, mode="all", if_exists="raise", dataset_is_public=True, location=None
):
"""Creates BigQuery datasets given `dataset_id`.
It can create two datasets:
* `<dataset_id>` (mode = 'prod')
* `<dataset_id>_staging` (mode = 'staging')
If `mode` is all, it creates both.
Args:
mode (str): Optional. Which dataset to create [prod|staging|all].
if_exists (str): Optional. What to do if dataset exists
* raise : Raises Conflict exception
* replace : Drop all tables and replace dataset
* update : Update dataset description
* pass : Do nothing
dataset_is_public (bool): Control if prod dataset is public or not. By default staging datasets like `dataset_id_staging` are not public.
location (str): Optional. Location of dataset data.
List of possible region names locations: https://cloud.google.com/bigquery/docs/locations
Raises:
Warning: Dataset already exists and if_exists is set to `raise`
"""
if if_exists == "replace":
self.delete(mode)
elif if_exists == "update":
self.update()
return
# Set dataset_id to the ID of the dataset to create.
for m in self._loop_modes(mode):
# Construct a full Dataset object to send to the API.
dataset_obj = self._setup_dataset_object(m["id"], location=location)
# Send the dataset to the API for creation, with an explicit timeout.
# Raises google.api_core.exceptions.Conflict if the Dataset already
# exists within the project.
try:
m["client"].create_dataset(dataset_obj) # Make an API request.
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="Dataset",
action="created",
)
except Conflict as e:
if if_exists == "pass":
return
raise Conflict(f"Dataset {self.dataset_id} already exists") from e
# Make prod dataset public
self.publicize(dataset_is_public=dataset_is_public)
def delete(self, mode="all"):
"""Deletes dataset in BigQuery. Toogle mode to choose which dataset to delete.
Args:
mode (str): Optional. Which dataset to delete [prod|staging|all]
"""
for m in self._loop_modes(mode):
m["client"].delete_dataset(m["id"], delete_contents=True, not_found_ok=True)
logger.info(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="Dataset",
action="deleted",
)
def update(self, mode="all", location=None):
"""Update dataset description. Toogle mode to choose which dataset to update.
Args:
mode (str): Optional. Which dataset to update [prod|staging|all]
location (str): Optional. Location of dataset data.
List of possible region names locations: https://cloud.google.com/bigquery/docs/locations
"""
for m in self._loop_modes(mode):
# Send the dataset to the API to update, with an explicit timeout.
# Raises google.api_core.exceptions.Conflict if the Dataset already
# exists within the project.
m["client"].update_dataset(
self._setup_dataset_object(
m["id"],
location=location,
),
fields=["description"],
) # Make an API request.
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="Dataset",
action="updated",
)
dataset_config
property
readonly
Dataset config file.
create(self, mode='all', if_exists='raise', dataset_is_public=True, location=None)
Creates BigQuery datasets given dataset_id.
It can create two datasets:
<dataset_id>(mode = 'prod')<dataset_id>_staging(mode = 'staging')
If mode is all, it creates both.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mode |
str |
Optional. Which dataset to create [prod|staging|all]. |
'all' |
if_exists |
str |
Optional. What to do if dataset exists
|
'raise' |
dataset_is_public |
bool |
Control if prod dataset is public or not. By default staging datasets like |
True |
location |
str |
Optional. Location of dataset data. List of possible region names locations: https://cloud.google.com/bigquery/docs/locations |
None |
Exceptions:
| Type | Description |
|---|---|
Warning |
Dataset already exists and if_exists is set to |
Source code in basedosdados/upload/dataset.py
def create(
self, mode="all", if_exists="raise", dataset_is_public=True, location=None
):
"""Creates BigQuery datasets given `dataset_id`.
It can create two datasets:
* `<dataset_id>` (mode = 'prod')
* `<dataset_id>_staging` (mode = 'staging')
If `mode` is all, it creates both.
Args:
mode (str): Optional. Which dataset to create [prod|staging|all].
if_exists (str): Optional. What to do if dataset exists
* raise : Raises Conflict exception
* replace : Drop all tables and replace dataset
* update : Update dataset description
* pass : Do nothing
dataset_is_public (bool): Control if prod dataset is public or not. By default staging datasets like `dataset_id_staging` are not public.
location (str): Optional. Location of dataset data.
List of possible region names locations: https://cloud.google.com/bigquery/docs/locations
Raises:
Warning: Dataset already exists and if_exists is set to `raise`
"""
if if_exists == "replace":
self.delete(mode)
elif if_exists == "update":
self.update()
return
# Set dataset_id to the ID of the dataset to create.
for m in self._loop_modes(mode):
# Construct a full Dataset object to send to the API.
dataset_obj = self._setup_dataset_object(m["id"], location=location)
# Send the dataset to the API for creation, with an explicit timeout.
# Raises google.api_core.exceptions.Conflict if the Dataset already
# exists within the project.
try:
m["client"].create_dataset(dataset_obj) # Make an API request.
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="Dataset",
action="created",
)
except Conflict as e:
if if_exists == "pass":
return
raise Conflict(f"Dataset {self.dataset_id} already exists") from e
# Make prod dataset public
self.publicize(dataset_is_public=dataset_is_public)
delete(self, mode='all')
Deletes dataset in BigQuery. Toogle mode to choose which dataset to delete.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mode |
str |
Optional. Which dataset to delete [prod|staging|all] |
'all' |
Source code in basedosdados/upload/dataset.py
def delete(self, mode="all"):
"""Deletes dataset in BigQuery. Toogle mode to choose which dataset to delete.
Args:
mode (str): Optional. Which dataset to delete [prod|staging|all]
"""
for m in self._loop_modes(mode):
m["client"].delete_dataset(m["id"], delete_contents=True, not_found_ok=True)
logger.info(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="Dataset",
action="deleted",
)
init(self, replace=False)
Initialize dataset folder at metadata_path at metadata_path/<dataset_id>.
The folder should contain:
dataset_config.yamlREADME.md
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
replace |
str |
Optional. Whether to replace existing folder. |
False |
Exceptions:
| Type | Description |
|---|---|
FileExistsError |
If dataset folder already exists and replace is False |
Source code in basedosdados/upload/dataset.py
def init(self, replace=False):
"""Initialize dataset folder at metadata_path at `metadata_path/<dataset_id>`.
The folder should contain:
* `dataset_config.yaml`
* `README.md`
Args:
replace (str): Optional. Whether to replace existing folder.
Raises:
FileExistsError: If dataset folder already exists and replace is False
"""
# Create dataset folder
try:
self.dataset_folder.mkdir(exist_ok=replace, parents=True)
except FileExistsError as e:
raise FileExistsError(
f"Dataset {str(self.dataset_folder.stem)} folder does not exists. "
"Set replace=True to replace current files."
) from e
# create dataset_config.yaml with metadata
self.metadata.create(if_exists="replace")
# create README.md file
self._write_readme_file()
# Add code folder
(self.dataset_folder / "code").mkdir(exist_ok=replace, parents=True)
return self
publicize(self, mode='all', dataset_is_public=True)
Changes IAM configuration to turn BigQuery dataset public.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mode |
bool |
Which dataset to create [prod|staging|all]. |
'all' |
dataset_is_public |
bool |
Control if prod dataset is public or not. By default staging datasets like |
True |
Source code in basedosdados/upload/dataset.py
def publicize(self, mode="all", dataset_is_public=True):
"""Changes IAM configuration to turn BigQuery dataset public.
Args:
mode (bool): Which dataset to create [prod|staging|all].
dataset_is_public (bool): Control if prod dataset is public or not. By default staging datasets like `dataset_id_staging` are not public.
"""
for m in self._loop_modes(mode):
dataset = m["client"].get_dataset(m["id"])
entries = dataset.access_entries
# TODO https://github.com/basedosdados/mais/pull/1020
# TODO if staging dataset is private, the prod view can't acess it: if dataset_is_public and "staging" not in dataset.dataset_id:
if dataset_is_public:
if "staging" not in dataset.dataset_id:
entries.extend(
[
bigquery.AccessEntry(
role="roles/bigquery.dataViewer",
entity_type="iamMember",
entity_id="allUsers",
),
bigquery.AccessEntry(
role="roles/bigquery.metadataViewer",
entity_type="iamMember",
entity_id="allUsers",
),
bigquery.AccessEntry(
role="roles/bigquery.user",
entity_type="iamMember",
entity_id="allUsers",
),
]
)
else:
entries.extend(
[
bigquery.AccessEntry(
role="roles/bigquery.dataViewer",
entity_type="iamMember",
entity_id="allUsers",
),
]
)
dataset.access_entries = entries
m["client"].update_dataset(dataset, ["access_entries"])
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="Dataset",
action="publicized",
)
update(self, mode='all', location=None)
Update dataset description. Toogle mode to choose which dataset to update.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mode |
str |
Optional. Which dataset to update [prod|staging|all] |
'all' |
location |
str |
Optional. Location of dataset data. List of possible region names locations: https://cloud.google.com/bigquery/docs/locations |
None |
Source code in basedosdados/upload/dataset.py
def update(self, mode="all", location=None):
"""Update dataset description. Toogle mode to choose which dataset to update.
Args:
mode (str): Optional. Which dataset to update [prod|staging|all]
location (str): Optional. Location of dataset data.
List of possible region names locations: https://cloud.google.com/bigquery/docs/locations
"""
for m in self._loop_modes(mode):
# Send the dataset to the API to update, with an explicit timeout.
# Raises google.api_core.exceptions.Conflict if the Dataset already
# exists within the project.
m["client"].update_dataset(
self._setup_dataset_object(
m["id"],
location=location,
),
fields=["description"],
) # Make an API request.
logger.success(
" {object} {object_id}_{mode} was {action}!",
object_id=self.dataset_id,
mode=mode,
object="Dataset",
action="updated",
)
Class for manage tables in Storage and Big Query
Table (Base)
Manage tables in Google Cloud Storage and BigQuery.
Source code in basedosdados/upload/table.py
class Table(Base):
"""
Manage tables in Google Cloud Storage and BigQuery.
"""
def __init__(self, dataset_id, table_id, **kwargs):
super().__init__(**kwargs)
self.table_id = table_id.replace("-", "_")
self.dataset_id = dataset_id.replace("-", "_")
self.dataset_folder = Path(self.metadata_path / self.dataset_id)
self.table_folder = self.dataset_folder / table_id
self.table_full_name = dict(
prod=f"{self.client['bigquery_prod'].project}.{self.dataset_id}.{self.table_id}",
staging=f"{self.client['bigquery_staging'].project}.{self.dataset_id}_staging.{self.table_id}",
)
self.table_full_name.update(dict(all=deepcopy(self.table_full_name)))
self.metadata = Metadata(self.dataset_id, self.table_id, **kwargs)
@property
def table_config(self):
"""
Load table_config.yaml
"""
return self._load_yaml(self.table_folder / "table_config.yaml")
def _get_table_obj(self, mode):
"""
Get table object from BigQuery
"""
return self.client[f"bigquery_{mode}"].get_table(self.table_full_name[mode])
def _is_partitioned(self):
"""
Check if table is partitioned
"""
## check if the table are partitioned, need the split because of a change in the type of partitions in pydantic
partitions = self.table_config["partitions"]
if partitions is None or len(partitions) == 0:
return False
if isinstance(partitions, list):
# check if any None inside list.
# False if it is the case Ex: [None, 'partition']
# True otherwise Ex: ['partition1', 'partition2']
return all(item is not None for item in partitions)
raise ValueError("Partitions must be a list or None")
def _load_schema(self, mode="staging"):
"""Load schema from table_config.yaml
Args:
mode (bool): Which dataset to create [prod|staging].
"""
self._check_mode(mode)
json_path = self.table_folder / f"schema-{mode}.json"
columns = self.table_config["columns"]
if mode == "staging":
new_columns = []
for c in columns:
# case is_in_staging are None then must be True
is_in_staging = (
True if c.get("is_in_staging") is None else c["is_in_staging"]
)
# append columns declared in table_config.yaml to schema only if is_in_staging: True
if is_in_staging and not c.get("is_partition"):
c["type"] = "STRING"
new_columns.append(c)
del columns
columns = new_columns
elif mode == "prod":
schema = self._get_table_obj(mode).schema
# get field names for fields at schema and at table_config.yaml
column_names = [c["name"] for c in columns]
schema_names = [s.name for s in schema]
# check if there are mismatched fields
not_in_columns = [name for name in schema_names if name not in column_names]
not_in_schema = [name for name in column_names if name not in schema_names]
# raise if field is not in table_config
if not_in_columns:
raise BaseDosDadosException(
"Column {error_columns} was not found in table_config.yaml. Are you sure that "
"all your column names between table_config.yaml, publish.sql and "
"{project_id}.{dataset_id}.{table_id} are the same?".format(
error_columns=not_in_columns,
project_id=self.table_config["project_id_prod"],
dataset_id=self.table_config["dataset_id"],
table_id=self.table_config["table_id"],
)
)
# raise if field is not in schema
if not_in_schema:
raise BaseDosDadosException(
"Column {error_columns} was not found in publish.sql. Are you sure that "
"all your column names between table_config.yaml, publish.sql and "
"{project_id}.{dataset_id}.{table_id} are the same?".format(
error_columns=not_in_schema,
project_id=self.table_config["project_id_prod"],
dataset_id=self.table_config["dataset_id"],
table_id=self.table_config["table_id"],
)
)
# if field is in schema, get field_type and field_mode
for c in columns:
for s in schema:
if c["name"] == s.name:
c["type"] = s.field_type
c["mode"] = s.mode
break
## force utf-8, write schema_{mode}.json
json.dump(columns, (json_path).open("w", encoding="utf-8"))
# load new created schema
return self.client[f"bigquery_{mode}"].schema_from_json(str(json_path))
def _make_publish_sql(self):
"""Create publish.sql with columns and bigquery_type"""
### publish.sql header and instructions
publish_txt = """
/*
Query para publicar a tabela.
Esse é o lugar para:
- modificar nomes, ordem e tipos de colunas
- dar join com outras tabelas
- criar colunas extras (e.g. logs, proporções, etc.)
Qualquer coluna definida aqui deve também existir em `table_config.yaml`.
# Além disso, sinta-se à vontade para alterar alguns nomes obscuros
# para algo um pouco mais explícito.
TIPOS:
- Para modificar tipos de colunas, basta substituir STRING por outro tipo válido.
- Exemplo: `SAFE_CAST(column_name AS NUMERIC) column_name`
- Mais detalhes: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
*/
"""
# remove triple quotes extra space
publish_txt = inspect.cleandoc(publish_txt)
publish_txt = textwrap.dedent(publish_txt)
# add create table statement
project_id_prod = self.client["bigquery_prod"].project
publish_txt += f"\n\nCREATE VIEW {project_id_prod}.{self.dataset_id}.{self.table_id} AS\nSELECT \n"
# sort columns by is_partition, partitions_columns come first
if self._is_partitioned():
columns = sorted(
self.table_config["columns"],
key=lambda k: (k["is_partition"] is not None, k["is_partition"]),
reverse=True,
)
else:
columns = self.table_config["columns"]
# add columns in publish.sql
for col in columns:
name = col["name"]
bigquery_type = (
"STRING"
if col["bigquery_type"] is None
else col["bigquery_type"].upper()
)
publish_txt += f"SAFE_CAST({name} AS {bigquery_type}) {name},\n"
## remove last comma
publish_txt = publish_txt[:-2] + "\n"
# add from statement
project_id_staging = self.client["bigquery_staging"].project
publish_txt += (
f"FROM {project_id_staging}.{self.dataset_id}_staging.{self.table_id} AS t"
)
# save publish.sql in table_folder
(self.table_folder / "publish.sql").open("w", encoding="utf-8").write(
publish_txt
)
def _make_template(self, columns, partition_columns, if_table_config_exists, force_columns):
# create table_config.yaml with metadata
self.metadata.create(
if_exists=if_table_config_exists,
columns=partition_columns + columns,
partition_columns=partition_columns,
force_columns=force_columns,
table_only=False,
)
self._make_publish_sql()
@staticmethod
def _sheet_to_df(columns_config_url_or_path):
"""
Convert sheet to dataframe
"""
url = columns_config_url_or_path.replace("edit#gid=", "export?format=csv&gid=")
try:
return pd.read_csv(StringIO(requests.get(url, timeout=10).content.decode("utf-8")))
except Exception as e:
raise BaseDosDadosException(
"Check if your google sheet Share are: Anyone on the internet with this link can view"
) from e
def table_exists(self, mode):
"""Check if table exists in BigQuery.
Args:
mode (str): Which dataset to check [prod|staging].
"""
try:
ref = self._get_table_obj(mode=mode)
except google.api_core.exceptions.NotFound:
ref = None
return bool(ref)
def update_columns(self, columns_config_url_or_path=None):
"""
Fills columns in table_config.yaml automatically using a public google sheets URL or a local file. Also regenerate
publish.sql and autofill type using bigquery_type.
The sheet must contain the columns:
- name: column name
- description: column description
- bigquery_type: column bigquery type
- measurement_unit: column mesurement unit
- covered_by_dictionary: column related dictionary
- directory_column: column related directory in the format <dataset_id>.<table_id>:<column_name>
- temporal_coverage: column temporal coverage
- has_sensitive_data: the column has sensitive data
- observations: column observations
Args:
columns_config_url_or_path (str): Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/<table_key>/edit#gid=<table_gid>.
"""
ruamel = ryaml.YAML()
ruamel.preserve_quotes = True
ruamel.indent(mapping=4, sequence=6, offset=4)
table_config_yaml = ruamel.load(
(self.table_folder / "table_config.yaml").open(encoding="utf-8")
)
if "https://docs.google.com/spreadsheets/d/" in columns_config_url_or_path:
if (
"edit#gid=" not in columns_config_url_or_path
or "https://docs.google.com/spreadsheets/d/"
not in columns_config_url_or_path
or not columns_config_url_or_path.split("=")[1].isdigit()
):
raise BaseDosDadosException(
"The Google sheet url not in correct format."
"The url must be in the format https://docs.google.com/spreadsheets/d/<table_key>/edit#gid=<table_gid>"
)
df = self._sheet_to_df(columns_config_url_or_path)
else:
file_type = columns_config_url_or_path.split(".")[-1]
if file_type == "csv":
df = pd.read_csv(columns_config_url_or_path, encoding="utf-8")
elif file_type in ["xls", "xlsx", "xlsm", "xlsb", "odf", "ods", "odt"]:
df = pd.read_excel(columns_config_url_or_path)
else:
raise BaseDosDadosException(
"File not suported. Only csv, xls, xlsx, xlsm, xlsb, odf, ods, odt are supported."
)
df = df.fillna("NULL")
required_columns = [
"name",
"bigquery_type",
"description",
"temporal_coverage",
"covered_by_dictionary",
"directory_column",
"measurement_unit",
"has_sensitive_data",
"observations",
]
not_found_columns = required_columns.copy()
for sheet_column in df.columns.tolist():
for required_column in required_columns:
if sheet_column == required_column:
not_found_columns.remove(required_column)
if not_found_columns:
raise BaseDosDadosException(
f"The following required columns are not found: {', '.join(not_found_columns)}."
)
columns_parameters = zip(
*[df[required_column].tolist() for required_column in required_columns]
)
for (
name,
bigquery_type,
description,
temporal_coverage,
covered_by_dictionary,
directory_column,
measurement_unit,
has_sensitive_data,
observations,
) in columns_parameters:
for col in table_config_yaml["columns"]:
if col["name"] == name:
col["bigquery_type"] = (
col["bigquery_type"]
if bigquery_type == "NULL"
else bigquery_type.lower()
)
col["description"] = (
col["description"] if description == "NULL" else description
)
col["temporal_coverage"] = (
col["temporal_coverage"]
if temporal_coverage == "NULL"
else [temporal_coverage]
)
col["covered_by_dictionary"] = (
"no"
if covered_by_dictionary == "NULL"
else covered_by_dictionary
)
dataset = directory_column.split(".")[0]
col["directory_column"]["dataset_id"] = (
col["directory_column"]["dataset_id"]
if dataset == "NULL"
else dataset
)
table = directory_column.split(".")[-1].split(":")[0]
col["directory_column"]["table_id"] = (
col["directory_column"]["table_id"]
if table == "NULL"
else table
)
column = directory_column.split(".")[-1].split(":")[-1]
col["directory_column"]["column_name"] = (
col["directory_column"]["column_name"]
if column == "NULL"
else column
)
col["measurement_unit"] = (
col["measurement_unit"]
if measurement_unit == "NULL"
else measurement_unit
)
col["has_sensitive_data"] = (
"no" if has_sensitive_data == "NULL" else has_sensitive_data
)
col["observations"] = (
col["observations"] if observations == "NULL" else observations
)
with open(self.table_folder / "table_config.yaml", "w", encoding="utf-8") as f:
ruamel.dump(table_config_yaml, f)
# regenerate publish.sql
self._make_publish_sql()
def init(
self,
data_sample_path=None,
if_folder_exists="raise",
if_table_config_exists="raise",
source_format="csv",
force_columns = False,
columns_config_url_or_path=None,
): # sourcery skip: low-code-quality
"""Initialize table folder at metadata_path at `metadata_path/<dataset_id>/<table_id>`.
The folder should contain:
* `table_config.yaml`
* `publish.sql`
You can also point to a sample of the data to auto complete columns names.
Args:
data_sample_path (str, pathlib.PosixPath): Optional.
Data sample path to auto complete columns names
It supports Comma Delimited CSV, Apache Avro and
Apache Parquet.
if_folder_exists (str): Optional.
What to do if table folder exists
* 'raise' : Raises FileExistsError
* 'replace' : Replace folder
* 'pass' : Do nothing
if_table_config_exists (str): Optional
What to do if table_config.yaml and publish.sql exists
* 'raise' : Raises FileExistsError
* 'replace' : Replace files with blank template
* 'pass' : Do nothing
source_format (str): Optional
Data source format. Only 'csv', 'avro' and 'parquet'
are supported. Defaults to 'csv'.
force_columns (bool): Optional.
If set to `True`, overwrite CKAN's columns with the ones provi
ded.
If set to `False`, keep CKAN's columns instead of the ones pro
vided.
columns_config_url_or_path (str): Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/<table_key>/edit#gid=<table_gid>.
Raises:
FileExistsError: If folder exists and replace is False.
NotImplementedError: If data sample is not in supported type or format.
"""
if not self.dataset_folder.exists():
raise FileExistsError(
f"Dataset folder {self.dataset_folder} folder does not exists. "
"Create a dataset before adding tables."
)
try:
self.table_folder.mkdir(exist_ok=(if_folder_exists == "replace"))
except FileExistsError as e:
if if_folder_exists == "raise":
raise FileExistsError(
f"Table folder already exists for {self.table_id}. "
) from e
if if_folder_exists == "pass":
return self
if not data_sample_path and if_table_config_exists != "pass":
raise BaseDosDadosException(
"You must provide a path to correctly create config files"
)
partition_columns = []
if isinstance(
data_sample_path,
(
str,
Path,
),
):
# Check if partitioned and get data sample and partition columns
data_sample_path = Path(data_sample_path)
if data_sample_path.is_dir():
data_sample_path = [
f
for f in data_sample_path.glob("**/*")
if f.is_file() and f.suffix == f".{source_format}"
][0]
partition_columns = [
k.split("=")[0]
for k in data_sample_path.as_posix().split("/")
if "=" in k
]
columns = Datatype(self, source_format).header(data_sample_path)
else:
columns = ["column_name"]
if if_table_config_exists == "pass":
# Check if config files exists before passing
if (
Path(self.table_folder / "table_config.yaml").is_file()
and Path(self.table_folder / "publish.sql").is_file()
):
pass
# Raise if no sample to determine columns
elif not data_sample_path:
raise BaseDosDadosException(
"You must provide a path to correctly create config files"
)
else:
self._make_template(columns, partition_columns, if_table_config_exists, force_columns=force_columns)
elif if_table_config_exists == "raise":
# Check if config files already exist
if (
Path(self.table_folder / "table_config.yaml").is_file()
and Path(self.table_folder / "publish.sql").is_file()
):
raise FileExistsError(
f"table_config.yaml and publish.sql already exists at {self.table_folder}"
)
# if config files don't exist, create them
self._make_template(columns, partition_columns, if_table_config_exists, force_columns=force_columns)
else:
# Raise: without a path to data sample, should not replace config files with empty template
self._make_template(columns, partition_columns, if_table_config_exists, force_columns=force_columns)
if columns_config_url_or_path is not None:
self.update_columns(columns_config_url_or_path)
return self
def create(
self,
path=None,
force_dataset=True,
if_table_exists="raise",
if_storage_data_exists="raise",
if_table_config_exists="raise",
source_format="csv",
force_columns=False,
columns_config_url_or_path=None,
dataset_is_public=True,
location=None,
chunk_size=None,
):
"""Creates BigQuery table at staging dataset.
If you add a path, it automatically saves the data in the storage,
creates a datasets folder and BigQuery location, besides creating the
table and its configuration files.
The new table should be located at `<dataset_id>_staging.<table_id>` in BigQuery.
It looks for data saved in Storage at `<bucket_name>/staging/<dataset_id>/<table_id>/*`
and builds the table.
It currently supports the types:
- Comma Delimited CSV
- Apache Avro
- Apache Parquet
Data can also be partitioned following the hive partitioning scheme
`<key1>=<value1>/<key2>=<value2>` - for instance,
`year=2012/country=BR`. The partition is automatcally detected
by searching for `partitions` on the `table_config.yaml`.
Args:
path (str or pathlib.PosixPath): Where to find the file that you want to upload to create a table with
job_config_params (dict): Optional.
Job configuration params from bigquery
if_table_exists (str): Optional
What to do if table exists
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
force_dataset (bool): Creates `<dataset_id>` folder and BigQuery Dataset if it doesn't exists.
if_table_config_exists (str): Optional.
What to do if config files already exist
* 'raise': Raises FileExistError
* 'replace': Replace with blank template
* 'pass'; Do nothing
if_storage_data_exists (str): Optional.
What to do if data already exists on your bucket:
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
source_format (str): Optional
Data source format. Only 'csv', 'avro' and 'parquet'
are supported. Defaults to 'csv'.
force_columns (bool): Optional.
If set to `True`, overwrite CKAN's columns with the ones provi
ded.
If set to `False`, keep CKAN's columns instead of the ones pro
vided.
columns_config_url_or_path (str): Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/<table_key>/edit#gid=<table_gid>.
dataset_is_public (bool): Control if prod dataset is public or not. By default staging datasets like `dataset_id_staging` are not public.
location (str): Optional. Location of dataset data.
List of possible region names locations: https://cloud.google.com/bigquery/docs/locations
chunk_size (int): Optional
The size of a chunk of data whenever iterating (in bytes).
This must be a multiple of 256 KB per the API specification.
If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used.
"""
if path is None:
# Look if table data already exists at Storage
data = self.client["storage_staging"].list_blobs(
self.bucket_name, prefix=f"staging/{self.dataset_id}/{self.table_id}"
)
# Raise: Cannot create table without external data
if not data:
raise BaseDosDadosException(
"You must provide a path for uploading data"
)
# Add data to storage
if isinstance(
path,
(
str,
Path,
),
):
Storage(self.dataset_id, self.table_id, **self.main_vars).upload(
path,
mode="staging",
if_exists=if_storage_data_exists,
chunk_size=chunk_size,
)
# Create Dataset if it doesn't exist
if force_dataset:
dataset_obj = Dataset(self.dataset_id, **self.main_vars)
try:
dataset_obj.init()
except FileExistsError:
pass
dataset_obj.create(
if_exists="pass", location=location, dataset_is_public=dataset_is_public
)
self.init(
data_sample_path=path,
if_folder_exists="replace",
if_table_config_exists=if_table_config_exists,
columns_config_url_or_path=columns_config_url_or_path,
source_format=source_format,
force_columns=force_columns
)
table = bigquery.Table(self.table_full_name["staging"])
table.external_data_configuration = Datatype(
self, source_format, "staging", partitioned=self._is_partitioned()
).external_config
# Lookup if table alreay exists
table_ref = None
try:
table_ref = self.client["bigquery_staging"].get_table(
self.table_full_name["staging"]
)
except google.api_core.exceptions.NotFound:
pass
if isinstance(table_ref, google.cloud.bigquery.table.Table):
if if_table_exists == "pass":
return None
if if_table_exists == "raise":
raise FileExistsError(
"Table already exists, choose replace if you want to overwrite it"
)
if if_table_exists == "replace":
self.delete(mode="staging")
self.client["bigquery_staging"].create_table(table)
logger.success(
"{object} {object_id} was {action}!",
object_id=self.table_id,
object="Table",
action="created",
)
return None
def update(self, mode="all"):
"""Updates BigQuery schema and description.
Args:
mode (str): Optional.
Table of which table to update [prod|staging|all]
not_found_ok (bool): Optional.
What to do if table is not found
"""
self._check_mode(mode)
mode = ["prod", "staging"] if mode == "all" else [mode]
for m in mode:
try:
table = self._get_table_obj(m)
except google.api_core.exceptions.NotFound:
continue
# if m == "staging":
table.description = self._render_template(
Path("table/table_description.txt"), self.table_config
)
# save table description
with open(
self.metadata_path
/ self.dataset_id
/ self.table_id
/ "table_description.txt",
"w",
encoding="utf-8",
) as f:
f.write(table.description)
# when mode is staging the table schema already exists
table.schema = self._load_schema(m)
fields = ["description", "schema"] if m == "prod" else ["description"]
self.client[f"bigquery_{m}"].update_table(table, fields=fields)
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Table",
action="updated",
)
def publish(self, if_exists="raise"):
"""Creates BigQuery table at production dataset.
Table should be located at `<dataset_id>.<table_id>`.
It creates a view that uses the query from
`<metadata_path>/<dataset_id>/<table_id>/publish.sql`.
Make sure that all columns from the query also exists at
`<metadata_path>/<dataset_id>/<table_id>/table_config.sql`, including
the partitions.
Args:
if_exists (str): Optional.
What to do if table exists.
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
Todo:
* Check if all required fields are filled
"""
if if_exists == "replace":
self.delete(mode="prod")
self.client["bigquery_prod"].query(
(self.table_folder / "publish.sql").open("r", encoding="utf-8").read()
).result()
self.update()
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Table",
action="published",
)
def delete(self, mode):
"""Deletes table in BigQuery.
Args:
mode (str): Table of which table to delete [prod|staging]
"""
self._check_mode(mode)
if mode == "all":
for m, n in self.table_full_name[mode].items():
self.client[f"bigquery_{m}"].delete_table(n, not_found_ok=True)
logger.info(
" {object} {object_id}_{mode} was {action}!",
object_id=self.table_id,
mode=mode,
object="Table",
action="deleted",
)
else:
self.client[f"bigquery_{mode}"].delete_table(
self.table_full_name[mode], not_found_ok=True
)
logger.info(
" {object} {object_id}_{mode} was {action}!",
object_id=self.table_id,
mode=mode,
object="Table",
action="deleted",
)
def append(
self,
filepath,
partitions=None,
if_exists="replace",
chunk_size=None,
**upload_args,
):
"""Appends new data to existing BigQuery table.
As long as the data has the same schema. It appends the data in the
filepath to the existing table.
Args:
filepath (str or pathlib.PosixPath): Where to find the file that you want to upload to create a table with
partitions (str, pathlib.PosixPath, dict): Optional.
Hive structured partition as a string or dict
* str : `<key>=<value>/<key2>=<value2>`
* dict: `dict(key=value, key2=value2)`
if_exists (str): 0ptional.
What to do if data with same name exists in storage
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
chunk_size (int): Optional
The size of a chunk of data whenever iterating (in bytes).
This must be a multiple of 256 KB per the API specification.
If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used.
"""
if not self.table_exists("staging"):
raise BaseDosDadosException(
"You cannot append to a table that does not exist"
)
Storage(self.dataset_id, self.table_id, **self.main_vars).upload(
filepath,
mode="staging",
partitions=partitions,
if_exists=if_exists,
chunk_size=chunk_size,
**upload_args,
)
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Table",
action="appended",
)
table_config
property
readonly
Load table_config.yaml
append(self, filepath, partitions=None, if_exists='replace', chunk_size=None, **upload_args)
Appends new data to existing BigQuery table.
As long as the data has the same schema. It appends the data in the filepath to the existing table.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filepath |
str or pathlib.PosixPath |
Where to find the file that you want to upload to create a table with |
required |
partitions |
str, pathlib.PosixPath, dict |
Optional. Hive structured partition as a string or dict
|
None |
if_exists |
str |
0ptional. What to do if data with same name exists in storage
|
'replace' |
chunk_size |
int |
Optional The size of a chunk of data whenever iterating (in bytes). This must be a multiple of 256 KB per the API specification. If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used. |
None |
Source code in basedosdados/upload/table.py
def append(
self,
filepath,
partitions=None,
if_exists="replace",
chunk_size=None,
**upload_args,
):
"""Appends new data to existing BigQuery table.
As long as the data has the same schema. It appends the data in the
filepath to the existing table.
Args:
filepath (str or pathlib.PosixPath): Where to find the file that you want to upload to create a table with
partitions (str, pathlib.PosixPath, dict): Optional.
Hive structured partition as a string or dict
* str : `<key>=<value>/<key2>=<value2>`
* dict: `dict(key=value, key2=value2)`
if_exists (str): 0ptional.
What to do if data with same name exists in storage
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
chunk_size (int): Optional
The size of a chunk of data whenever iterating (in bytes).
This must be a multiple of 256 KB per the API specification.
If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used.
"""
if not self.table_exists("staging"):
raise BaseDosDadosException(
"You cannot append to a table that does not exist"
)
Storage(self.dataset_id, self.table_id, **self.main_vars).upload(
filepath,
mode="staging",
partitions=partitions,
if_exists=if_exists,
chunk_size=chunk_size,
**upload_args,
)
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Table",
action="appended",
)
create(self, path=None, force_dataset=True, if_table_exists='raise', if_storage_data_exists='raise', if_table_config_exists='raise', source_format='csv', force_columns=False, columns_config_url_or_path=None, dataset_is_public=True, location=None, chunk_size=None)
Creates BigQuery table at staging dataset.
If you add a path, it automatically saves the data in the storage, creates a datasets folder and BigQuery location, besides creating the table and its configuration files.
The new table should be located at <dataset_id>_staging.<table_id> in BigQuery.
It looks for data saved in Storage at <bucket_name>/staging/<dataset_id>/<table_id>/*
and builds the table.
It currently supports the types:
- Comma Delimited CSV
- Apache Avro
- Apache Parquet
Data can also be partitioned following the hive partitioning scheme
<key1>=<value1>/<key2>=<value2> - for instance,
year=2012/country=BR. The partition is automatcally detected
by searching for partitions on the table_config.yaml.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
str or pathlib.PosixPath |
Where to find the file that you want to upload to create a table with |
None |
job_config_params |
dict |
Optional. Job configuration params from bigquery |
required |
if_table_exists |
str |
Optional What to do if table exists
|
'raise' |
force_dataset |
bool |
Creates |
True |
if_table_config_exists |
str |
Optional. What to do if config files already exist
|
'raise' |
if_storage_data_exists |
str |
Optional. What to do if data already exists on your bucket:
|
'raise' |
source_format |
str |
Optional Data source format. Only 'csv', 'avro' and 'parquet' are supported. Defaults to 'csv'. |
'csv' |
force_columns |
bool |
Optional.
If set to |
False |
columns_config_url_or_path |
str |
Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/ |
None |
dataset_is_public |
bool |
Control if prod dataset is public or not. By default staging datasets like |
True |
location |
str |
Optional. Location of dataset data. List of possible region names locations: https://cloud.google.com/bigquery/docs/locations |
None |
chunk_size |
int |
Optional The size of a chunk of data whenever iterating (in bytes). This must be a multiple of 256 KB per the API specification. If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used. |
None |
Source code in basedosdados/upload/table.py
def create(
self,
path=None,
force_dataset=True,
if_table_exists="raise",
if_storage_data_exists="raise",
if_table_config_exists="raise",
source_format="csv",
force_columns=False,
columns_config_url_or_path=None,
dataset_is_public=True,
location=None,
chunk_size=None,
):
"""Creates BigQuery table at staging dataset.
If you add a path, it automatically saves the data in the storage,
creates a datasets folder and BigQuery location, besides creating the
table and its configuration files.
The new table should be located at `<dataset_id>_staging.<table_id>` in BigQuery.
It looks for data saved in Storage at `<bucket_name>/staging/<dataset_id>/<table_id>/*`
and builds the table.
It currently supports the types:
- Comma Delimited CSV
- Apache Avro
- Apache Parquet
Data can also be partitioned following the hive partitioning scheme
`<key1>=<value1>/<key2>=<value2>` - for instance,
`year=2012/country=BR`. The partition is automatcally detected
by searching for `partitions` on the `table_config.yaml`.
Args:
path (str or pathlib.PosixPath): Where to find the file that you want to upload to create a table with
job_config_params (dict): Optional.
Job configuration params from bigquery
if_table_exists (str): Optional
What to do if table exists
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
force_dataset (bool): Creates `<dataset_id>` folder and BigQuery Dataset if it doesn't exists.
if_table_config_exists (str): Optional.
What to do if config files already exist
* 'raise': Raises FileExistError
* 'replace': Replace with blank template
* 'pass'; Do nothing
if_storage_data_exists (str): Optional.
What to do if data already exists on your bucket:
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
source_format (str): Optional
Data source format. Only 'csv', 'avro' and 'parquet'
are supported. Defaults to 'csv'.
force_columns (bool): Optional.
If set to `True`, overwrite CKAN's columns with the ones provi
ded.
If set to `False`, keep CKAN's columns instead of the ones pro
vided.
columns_config_url_or_path (str): Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/<table_key>/edit#gid=<table_gid>.
dataset_is_public (bool): Control if prod dataset is public or not. By default staging datasets like `dataset_id_staging` are not public.
location (str): Optional. Location of dataset data.
List of possible region names locations: https://cloud.google.com/bigquery/docs/locations
chunk_size (int): Optional
The size of a chunk of data whenever iterating (in bytes).
This must be a multiple of 256 KB per the API specification.
If not specified, the chunk_size of the blob itself is used. If that is not specified, a default value of 40 MB is used.
"""
if path is None:
# Look if table data already exists at Storage
data = self.client["storage_staging"].list_blobs(
self.bucket_name, prefix=f"staging/{self.dataset_id}/{self.table_id}"
)
# Raise: Cannot create table without external data
if not data:
raise BaseDosDadosException(
"You must provide a path for uploading data"
)
# Add data to storage
if isinstance(
path,
(
str,
Path,
),
):
Storage(self.dataset_id, self.table_id, **self.main_vars).upload(
path,
mode="staging",
if_exists=if_storage_data_exists,
chunk_size=chunk_size,
)
# Create Dataset if it doesn't exist
if force_dataset:
dataset_obj = Dataset(self.dataset_id, **self.main_vars)
try:
dataset_obj.init()
except FileExistsError:
pass
dataset_obj.create(
if_exists="pass", location=location, dataset_is_public=dataset_is_public
)
self.init(
data_sample_path=path,
if_folder_exists="replace",
if_table_config_exists=if_table_config_exists,
columns_config_url_or_path=columns_config_url_or_path,
source_format=source_format,
force_columns=force_columns
)
table = bigquery.Table(self.table_full_name["staging"])
table.external_data_configuration = Datatype(
self, source_format, "staging", partitioned=self._is_partitioned()
).external_config
# Lookup if table alreay exists
table_ref = None
try:
table_ref = self.client["bigquery_staging"].get_table(
self.table_full_name["staging"]
)
except google.api_core.exceptions.NotFound:
pass
if isinstance(table_ref, google.cloud.bigquery.table.Table):
if if_table_exists == "pass":
return None
if if_table_exists == "raise":
raise FileExistsError(
"Table already exists, choose replace if you want to overwrite it"
)
if if_table_exists == "replace":
self.delete(mode="staging")
self.client["bigquery_staging"].create_table(table)
logger.success(
"{object} {object_id} was {action}!",
object_id=self.table_id,
object="Table",
action="created",
)
return None
delete(self, mode)
Deletes table in BigQuery.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mode |
str |
Table of which table to delete [prod|staging] |
required |
Source code in basedosdados/upload/table.py
def delete(self, mode):
"""Deletes table in BigQuery.
Args:
mode (str): Table of which table to delete [prod|staging]
"""
self._check_mode(mode)
if mode == "all":
for m, n in self.table_full_name[mode].items():
self.client[f"bigquery_{m}"].delete_table(n, not_found_ok=True)
logger.info(
" {object} {object_id}_{mode} was {action}!",
object_id=self.table_id,
mode=mode,
object="Table",
action="deleted",
)
else:
self.client[f"bigquery_{mode}"].delete_table(
self.table_full_name[mode], not_found_ok=True
)
logger.info(
" {object} {object_id}_{mode} was {action}!",
object_id=self.table_id,
mode=mode,
object="Table",
action="deleted",
)
init(self, data_sample_path=None, if_folder_exists='raise', if_table_config_exists='raise', source_format='csv', force_columns=False, columns_config_url_or_path=None)
Initialize table folder at metadata_path at metadata_path/<dataset_id>/<table_id>.
The folder should contain:
table_config.yamlpublish.sql
You can also point to a sample of the data to auto complete columns names.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data_sample_path |
str, pathlib.PosixPath |
Optional. Data sample path to auto complete columns names It supports Comma Delimited CSV, Apache Avro and Apache Parquet. |
None |
if_folder_exists |
str |
Optional. What to do if table folder exists
|
'raise' |
if_table_config_exists |
str |
Optional What to do if table_config.yaml and publish.sql exists
|
'raise' |
source_format |
str |
Optional Data source format. Only 'csv', 'avro' and 'parquet' are supported. Defaults to 'csv'. |
'csv' |
force_columns |
bool |
Optional.
If set to |
False |
columns_config_url_or_path |
str |
Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/ |
None |
Exceptions:
| Type | Description |
|---|---|
FileExistsError |
If folder exists and replace is False. |
NotImplementedError |
If data sample is not in supported type or format. |
Source code in basedosdados/upload/table.py
def init(
self,
data_sample_path=None,
if_folder_exists="raise",
if_table_config_exists="raise",
source_format="csv",
force_columns = False,
columns_config_url_or_path=None,
): # sourcery skip: low-code-quality
"""Initialize table folder at metadata_path at `metadata_path/<dataset_id>/<table_id>`.
The folder should contain:
* `table_config.yaml`
* `publish.sql`
You can also point to a sample of the data to auto complete columns names.
Args:
data_sample_path (str, pathlib.PosixPath): Optional.
Data sample path to auto complete columns names
It supports Comma Delimited CSV, Apache Avro and
Apache Parquet.
if_folder_exists (str): Optional.
What to do if table folder exists
* 'raise' : Raises FileExistsError
* 'replace' : Replace folder
* 'pass' : Do nothing
if_table_config_exists (str): Optional
What to do if table_config.yaml and publish.sql exists
* 'raise' : Raises FileExistsError
* 'replace' : Replace files with blank template
* 'pass' : Do nothing
source_format (str): Optional
Data source format. Only 'csv', 'avro' and 'parquet'
are supported. Defaults to 'csv'.
force_columns (bool): Optional.
If set to `True`, overwrite CKAN's columns with the ones provi
ded.
If set to `False`, keep CKAN's columns instead of the ones pro
vided.
columns_config_url_or_path (str): Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/<table_key>/edit#gid=<table_gid>.
Raises:
FileExistsError: If folder exists and replace is False.
NotImplementedError: If data sample is not in supported type or format.
"""
if not self.dataset_folder.exists():
raise FileExistsError(
f"Dataset folder {self.dataset_folder} folder does not exists. "
"Create a dataset before adding tables."
)
try:
self.table_folder.mkdir(exist_ok=(if_folder_exists == "replace"))
except FileExistsError as e:
if if_folder_exists == "raise":
raise FileExistsError(
f"Table folder already exists for {self.table_id}. "
) from e
if if_folder_exists == "pass":
return self
if not data_sample_path and if_table_config_exists != "pass":
raise BaseDosDadosException(
"You must provide a path to correctly create config files"
)
partition_columns = []
if isinstance(
data_sample_path,
(
str,
Path,
),
):
# Check if partitioned and get data sample and partition columns
data_sample_path = Path(data_sample_path)
if data_sample_path.is_dir():
data_sample_path = [
f
for f in data_sample_path.glob("**/*")
if f.is_file() and f.suffix == f".{source_format}"
][0]
partition_columns = [
k.split("=")[0]
for k in data_sample_path.as_posix().split("/")
if "=" in k
]
columns = Datatype(self, source_format).header(data_sample_path)
else:
columns = ["column_name"]
if if_table_config_exists == "pass":
# Check if config files exists before passing
if (
Path(self.table_folder / "table_config.yaml").is_file()
and Path(self.table_folder / "publish.sql").is_file()
):
pass
# Raise if no sample to determine columns
elif not data_sample_path:
raise BaseDosDadosException(
"You must provide a path to correctly create config files"
)
else:
self._make_template(columns, partition_columns, if_table_config_exists, force_columns=force_columns)
elif if_table_config_exists == "raise":
# Check if config files already exist
if (
Path(self.table_folder / "table_config.yaml").is_file()
and Path(self.table_folder / "publish.sql").is_file()
):
raise FileExistsError(
f"table_config.yaml and publish.sql already exists at {self.table_folder}"
)
# if config files don't exist, create them
self._make_template(columns, partition_columns, if_table_config_exists, force_columns=force_columns)
else:
# Raise: without a path to data sample, should not replace config files with empty template
self._make_template(columns, partition_columns, if_table_config_exists, force_columns=force_columns)
if columns_config_url_or_path is not None:
self.update_columns(columns_config_url_or_path)
return self
publish(self, if_exists='raise')
Creates BigQuery table at production dataset.
Table should be located at <dataset_id>.<table_id>.
It creates a view that uses the query from
<metadata_path>/<dataset_id>/<table_id>/publish.sql.
Make sure that all columns from the query also exists at
<metadata_path>/<dataset_id>/<table_id>/table_config.sql, including
the partitions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
if_exists |
str |
Optional. What to do if table exists.
|
'raise' |
Todo:
* Check if all required fields are filled
Source code in basedosdados/upload/table.py
def publish(self, if_exists="raise"):
"""Creates BigQuery table at production dataset.
Table should be located at `<dataset_id>.<table_id>`.
It creates a view that uses the query from
`<metadata_path>/<dataset_id>/<table_id>/publish.sql`.
Make sure that all columns from the query also exists at
`<metadata_path>/<dataset_id>/<table_id>/table_config.sql`, including
the partitions.
Args:
if_exists (str): Optional.
What to do if table exists.
* 'raise' : Raises Conflict exception
* 'replace' : Replace table
* 'pass' : Do nothing
Todo:
* Check if all required fields are filled
"""
if if_exists == "replace":
self.delete(mode="prod")
self.client["bigquery_prod"].query(
(self.table_folder / "publish.sql").open("r", encoding="utf-8").read()
).result()
self.update()
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Table",
action="published",
)
table_exists(self, mode)
Check if table exists in BigQuery.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mode |
str |
Which dataset to check [prod|staging]. |
required |
Source code in basedosdados/upload/table.py
def table_exists(self, mode):
"""Check if table exists in BigQuery.
Args:
mode (str): Which dataset to check [prod|staging].
"""
try:
ref = self._get_table_obj(mode=mode)
except google.api_core.exceptions.NotFound:
ref = None
return bool(ref)
update(self, mode='all')
Updates BigQuery schema and description.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mode |
str |
Optional. Table of which table to update [prod|staging|all] |
'all' |
not_found_ok |
bool |
Optional. What to do if table is not found |
required |
Source code in basedosdados/upload/table.py
def update(self, mode="all"):
"""Updates BigQuery schema and description.
Args:
mode (str): Optional.
Table of which table to update [prod|staging|all]
not_found_ok (bool): Optional.
What to do if table is not found
"""
self._check_mode(mode)
mode = ["prod", "staging"] if mode == "all" else [mode]
for m in mode:
try:
table = self._get_table_obj(m)
except google.api_core.exceptions.NotFound:
continue
# if m == "staging":
table.description = self._render_template(
Path("table/table_description.txt"), self.table_config
)
# save table description
with open(
self.metadata_path
/ self.dataset_id
/ self.table_id
/ "table_description.txt",
"w",
encoding="utf-8",
) as f:
f.write(table.description)
# when mode is staging the table schema already exists
table.schema = self._load_schema(m)
fields = ["description", "schema"] if m == "prod" else ["description"]
self.client[f"bigquery_{m}"].update_table(table, fields=fields)
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Table",
action="updated",
)
update_columns(self, columns_config_url_or_path=None)
Fills columns in table_config.yaml automatically using a public google sheets URL or a local file. Also regenerate publish.sql and autofill type using bigquery_type.
The sheet must contain the columns:
- name: column name
- description: column description
- bigquery_type: column bigquery type
- measurement_unit: column mesurement unit
- covered_by_dictionary: column related dictionary
- directory_column: column related directory in the format
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
columns_config_url_or_path |
str |
Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/ |
None |
Source code in basedosdados/upload/table.py
def update_columns(self, columns_config_url_or_path=None):
"""
Fills columns in table_config.yaml automatically using a public google sheets URL or a local file. Also regenerate
publish.sql and autofill type using bigquery_type.
The sheet must contain the columns:
- name: column name
- description: column description
- bigquery_type: column bigquery type
- measurement_unit: column mesurement unit
- covered_by_dictionary: column related dictionary
- directory_column: column related directory in the format <dataset_id>.<table_id>:<column_name>
- temporal_coverage: column temporal coverage
- has_sensitive_data: the column has sensitive data
- observations: column observations
Args:
columns_config_url_or_path (str): Path to the local architeture file or a public google sheets URL.
Path only suports csv, xls, xlsx, xlsm, xlsb, odf, ods, odt formats.
Google sheets URL must be in the format https://docs.google.com/spreadsheets/d/<table_key>/edit#gid=<table_gid>.
"""
ruamel = ryaml.YAML()
ruamel.preserve_quotes = True
ruamel.indent(mapping=4, sequence=6, offset=4)
table_config_yaml = ruamel.load(
(self.table_folder / "table_config.yaml").open(encoding="utf-8")
)
if "https://docs.google.com/spreadsheets/d/" in columns_config_url_or_path:
if (
"edit#gid=" not in columns_config_url_or_path
or "https://docs.google.com/spreadsheets/d/"
not in columns_config_url_or_path
or not columns_config_url_or_path.split("=")[1].isdigit()
):
raise BaseDosDadosException(
"The Google sheet url not in correct format."
"The url must be in the format https://docs.google.com/spreadsheets/d/<table_key>/edit#gid=<table_gid>"
)
df = self._sheet_to_df(columns_config_url_or_path)
else:
file_type = columns_config_url_or_path.split(".")[-1]
if file_type == "csv":
df = pd.read_csv(columns_config_url_or_path, encoding="utf-8")
elif file_type in ["xls", "xlsx", "xlsm", "xlsb", "odf", "ods", "odt"]:
df = pd.read_excel(columns_config_url_or_path)
else:
raise BaseDosDadosException(
"File not suported. Only csv, xls, xlsx, xlsm, xlsb, odf, ods, odt are supported."
)
df = df.fillna("NULL")
required_columns = [
"name",
"bigquery_type",
"description",
"temporal_coverage",
"covered_by_dictionary",
"directory_column",
"measurement_unit",
"has_sensitive_data",
"observations",
]
not_found_columns = required_columns.copy()
for sheet_column in df.columns.tolist():
for required_column in required_columns:
if sheet_column == required_column:
not_found_columns.remove(required_column)
if not_found_columns:
raise BaseDosDadosException(
f"The following required columns are not found: {', '.join(not_found_columns)}."
)
columns_parameters = zip(
*[df[required_column].tolist() for required_column in required_columns]
)
for (
name,
bigquery_type,
description,
temporal_coverage,
covered_by_dictionary,
directory_column,
measurement_unit,
has_sensitive_data,
observations,
) in columns_parameters:
for col in table_config_yaml["columns"]:
if col["name"] == name:
col["bigquery_type"] = (
col["bigquery_type"]
if bigquery_type == "NULL"
else bigquery_type.lower()
)
col["description"] = (
col["description"] if description == "NULL" else description
)
col["temporal_coverage"] = (
col["temporal_coverage"]
if temporal_coverage == "NULL"
else [temporal_coverage]
)
col["covered_by_dictionary"] = (
"no"
if covered_by_dictionary == "NULL"
else covered_by_dictionary
)
dataset = directory_column.split(".")[0]
col["directory_column"]["dataset_id"] = (
col["directory_column"]["dataset_id"]
if dataset == "NULL"
else dataset
)
table = directory_column.split(".")[-1].split(":")[0]
col["directory_column"]["table_id"] = (
col["directory_column"]["table_id"]
if table == "NULL"
else table
)
column = directory_column.split(".")[-1].split(":")[-1]
col["directory_column"]["column_name"] = (
col["directory_column"]["column_name"]
if column == "NULL"
else column
)
col["measurement_unit"] = (
col["measurement_unit"]
if measurement_unit == "NULL"
else measurement_unit
)
col["has_sensitive_data"] = (
"no" if has_sensitive_data == "NULL" else has_sensitive_data
)
col["observations"] = (
col["observations"] if observations == "NULL" else observations
)
with open(self.table_folder / "table_config.yaml", "w", encoding="utf-8") as f:
ruamel.dump(table_config_yaml, f)
# regenerate publish.sql
self._make_publish_sql()
Class to manage the metadata of datasets and tables
Metadata (Base)
Manage metadata in CKAN backend.
Source code in basedosdados/upload/metadata.py
class Metadata(Base):
"""
Manage metadata in CKAN backend.
"""
def __init__(self, dataset_id, table_id=None, **kwargs):
super().__init__(**kwargs)
self.table_id = table_id
self.dataset_id = dataset_id
if self.table_id:
self.dataset_metadata_obj = Metadata(self.dataset_id, **kwargs)
url = "https://basedosdados.org"
self.CKAN_API_KEY = self.config.get("ckan", {}).get("api_key")
self.CKAN_URL = self.config.get("ckan", {}).get("url", "") or url
@property
def filepath(self) -> str:
"""Build the dataset or table filepath"""
filename = "dataset_config.yaml"
if self.table_id:
filename = f"{self.table_id}/table_config.yaml"
return self.metadata_path / self.dataset_id / filename
@property
def local_metadata(self) -> dict:
"""Load dataset or table local metadata"""
if self.filepath.exists():
with open(self.filepath, "r", encoding="utf-8") as file:
return ryaml.safe_load(file.read())
return {}
@property
def ckan_metadata(self) -> dict:
"""Load dataset or table metadata from Base dos Dados CKAN"""
ckan_dataset, ckan_table = self.ckan_metadata_extended
return ckan_table or ckan_dataset
@property
def ckan_metadata_extended(self) -> dict:
"""Load dataset and table metadata from Base dos Dados CKAN"""
dataset_id = self.dataset_id.replace("_", "-")
url = f"{self.CKAN_URL}/api/3/action/package_show?id={dataset_id}"
ckan_response = requests.get(url, timeout=10).json()
dataset = ckan_response.get("result")
if not ckan_response.get("success"):
return {}, {}
if self.table_id:
for resource in dataset["resources"]:
if resource["name"] == self.table_id:
return dataset, resource
return dataset, {}
@property
def owner_org(self):
"""
Build `owner_org` field for each use case: table, dataset, new
or existing.
"""
# in case `self` refers to a CKAN table's metadata
if self.table_id and self.exists_in_ckan():
return self.dataset_metadata_obj.ckan_metadata.get("owner_org")
# in case `self` refers to a new table's metadata
if self.table_id and not self.exists_in_ckan():
if self.dataset_metadata_obj.exists_in_ckan():
return self.dataset_metadata_obj.ckan_metadata.get("owner_org")
# mock `owner_org` for validation
return "3626e93d-165f-42b8-bde1-2e0972079694"
# for datasets, `owner_org` must come from the YAML file
organization_id = "".join(self.local_metadata.get("organization") or [])
url = f"{self.CKAN_URL}/api/3/action/organization_show?id={organization_id}"
response = requests.get(url, timeout=10).json()
if not response.get("success"):
raise BaseDosDadosException("Organization not found")
owner_org = response.get("result", {}).get("id")
return owner_org
@property
def ckan_data_dict(self) -> dict:
"""Helper function that structures local metadata for validation"""
ckan_dataset, ckan_table = self.ckan_metadata_extended
metadata = {
"id": ckan_dataset.get("id"),
"name": ckan_dataset.get("name") or self.dataset_id.replace("_", "-"),
"type": ckan_dataset.get("type") or "dataset",
"title": self.local_metadata.get("title"),
"private": ckan_dataset.get("private") or False,
"owner_org": self.owner_org,
"resources": ckan_dataset.get("resources", [])
or [{"resource_type": "external_link", "name": ""}]
or [{"resource_type": "information_request", "name": ""}],
"groups": [
{"name": group} for group in self.local_metadata.get("groups", []) or []
],
"tags": [
{"name": tag} for tag in self.local_metadata.get("tags", []) or []
],
"organization": {"name": self.local_metadata.get("organization")},
"extras": [
{
"key": "dataset_args",
"value": {
"short_description": self.local_metadata.get(
"short_description"
),
"description": self.local_metadata.get("description"),
"ckan_url": self.local_metadata.get("ckan_url"),
"github_url": self.local_metadata.get("github_url"),
},
}
],
}
if self.table_id:
metadata["resources"] = [
{
"id": ckan_table.get("id"),
"description": self.local_metadata.get("description"),
"name": self.local_metadata.get("table_id"),
"resource_type": ckan_table.get("resource_type") or "bdm_table",
"version": self.local_metadata.get("version"),
"dataset_id": self.local_metadata.get("dataset_id"),
"table_id": self.local_metadata.get("table_id"),
"spatial_coverage": self.local_metadata.get("spatial_coverage"),
"temporal_coverage": self.local_metadata.get("temporal_coverage"),
"update_frequency": self.local_metadata.get("update_frequency"),
"observation_level": self.local_metadata.get("observation_level"),
"last_updated": self.local_metadata.get("last_updated"),
"published_by": self.local_metadata.get("published_by"),
"data_cleaned_by": self.local_metadata.get("data_cleaned_by"),
"data_cleaning_description": self.local_metadata.get(
"data_cleaning_description"
),
"data_cleaning_code_url": self.local_metadata.get(
"data_cleaning_code_url"
),
"partner_organization": self.local_metadata.get(
"partner_organization"
),
"raw_files_url": self.local_metadata.get("raw_files_url"),
"auxiliary_files_url": self.local_metadata.get(
"auxiliary_files_url"
),
"architecture_url": self.local_metadata.get("architecture_url"),
"source_bucket_name": self.local_metadata.get("source_bucket_name"),
"project_id_prod": self.local_metadata.get("project_id_prod"),
"project_id_staging": self.local_metadata.get("project_id_staging"),
"partitions": self.local_metadata.get("partitions"),
"uncompressed_file_size": self.local_metadata.get(
"uncompressed_file_size"
),
"compressed_file_size": self.local_metadata.get(
"compressed_file_size"
),
"columns": self.local_metadata.get("columns"),
"metadata_modified": self.local_metadata.get("metadata_modified"),
"package_id": ckan_dataset.get("id"),
}
]
return metadata
@property
@lru_cache(256)
def columns_schema(self) -> dict:
"""Returns a dictionary with the schema of the columns"""
url = f"{self.CKAN_URL}/api/3/action/bd_bdm_columns_schema"
return requests.get(url, timeout=10).json().get("result")
@property
@lru_cache(256)
def metadata_schema(self) -> dict:
"""Get metadata schema from CKAN API endpoint"""
if self.table_id:
table_url = f"{self.CKAN_URL}/api/3/action/bd_bdm_table_schema"
return requests.get(table_url, timeout=10).json().get("result")
dataset_url = f"{self.CKAN_URL}/api/3/action/bd_dataset_schema"
return requests.get(dataset_url, timeout=10).json().get("result")
def exists_in_ckan(self) -> bool:
"""Check if Metadata object refers to an existing CKAN package or reso
urce.
Returns:
bool: The existence condition of the metadata in CKAN. `True` if i
t exists, `False` otherwise.
"""
if self.table_id:
url = f"{self.CKAN_URL}/api/3/action/bd_bdm_table_show?"
url += f"dataset_id={self.dataset_id}&table_id={self.table_id}"
else:
id = self.dataset_id.replace("_", "-")
# TODO: use `bd_bdm_dataset_show` when it's available for empty packages
url = f"{self.CKAN_URL}/api/3/action/package_show?id={id}"
exists_in_ckan = requests.get(url, timeout=10).json().get("success")
return exists_in_ckan
def is_updated(self) -> bool:
"""Check if a dataset or table is updated
Returns:
bool: The update condition of local metadata. `True` if it corresp
onds to the most recent version of the given table or dataset's me
tadata in CKAN, `False` otherwise.
"""
if not self.local_metadata.get("metadata_modified"):
return bool(not self.exists_in_ckan())
ckan_modified = self.ckan_metadata.get("metadata_modified")
local_modified = self.local_metadata.get("metadata_modified")
return ckan_modified == local_modified
def create(
self,
if_exists: str = "raise",
columns: list = None,
partition_columns: list = None,
force_columns: bool = False,
table_only: bool = True,
) -> Metadata:
"""Create metadata file based on the current version saved to CKAN database
Args:
if_exists (str): Optional. What to do if config exists
* raise : Raises Conflict exception
* replace : Replaces config file with most recent
* pass : Do nothing
columns (list): Optional.
A `list` with the table columns' names.
partition_columns(list): Optional.
A `list` with the name of the table columns that partition the
data.
force_columns (bool): Optional.
If set to `True`, overwrite CKAN's columns with the ones provi
ded.
If set to `False`, keep CKAN's columns instead of the ones pro
vided.
table_only (bool): Optional. If set to `True`, only `table_config.
yaml` is created, even if there is no `dataset_config.yaml` fo
r the correspondent dataset metadata. If set to `False`, both
files are created if `dataset_config.yaml` doesn't exist yet.
Defaults to `True`.
Returns:
Metadata: An instance of the `Metadata` class.
Raises:
FileExistsError: If the correspodent YAML configuration file alrea
dy exists and `if_exists` is set to `"raise"`.
"""
# see: https://docs.python.org/3/reference/compound_stmts.html#function-definitions
columns = [] if columns is None else columns
partition_columns = [] if partition_columns is None else partition_columns
if self.filepath.exists() and if_exists == "raise":
raise FileExistsError(
f"{self.filepath} already exists."
+ " Set the arg `if_exists` to `replace` to replace it."
)
if if_exists != "pass":
ckan_metadata = self.ckan_metadata
# Add local columns if
# 1. columns is empty and
# 2. force_columns is True
# TODO: Is this sufficient to add columns?
if self.table_id and (force_columns or not ckan_metadata.get("columns")):
ckan_metadata["columns"] = [{"name": c} for c in columns]
yaml_obj = build_yaml_object(
dataset_id=self.dataset_id,
table_id=self.table_id,
config=self.config,
schema=self.metadata_schema,
metadata=ckan_metadata,
columns_schema=self.columns_schema,
partition_columns=partition_columns,
)
self.filepath.parent.mkdir(parents=True, exist_ok=True)
with open(self.filepath, "w", encoding="utf-8") as file:
ruamel = ryaml.YAML()
ruamel.preserve_quotes = True
ruamel.indent(mapping=4, sequence=6, offset=4)
ruamel.dump(yaml_obj, file)
# if `dataset_config.yaml` doesn't exist but user wants to create
# it alongside `table_config.yaml`
dataset_config_exists = (
self.metadata_path / self.dataset_id / "dataset_config.yaml"
).exists()
if self.table_id and not table_only and not dataset_config_exists:
self.dataset_metadata_obj.create(if_exists=if_exists)
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Metadata",
action="created",
)
return self
def validate(self) -> bool:
"""Validate dataset_config.yaml or table_config.yaml files.
The yaml file should be located at
metadata_path/dataset_id[/table_id/],
as defined in your config.toml
Returns:
bool:
True if the metadata is valid. False if it is invalid.
Raises:
BaseDosDadosException:
when the file has validation errors.
"""
ckan = RemoteCKAN(self.CKAN_URL, user_agent="", apikey=None)
response = ckan.action.bd_dataset_validate(**self.ckan_data_dict)
if response.get("errors"):
error = {self.ckan_data_dict.get("name"): response["errors"]}
message = f"{self.filepath} has validation errors: {error}"
raise BaseDosDadosException(message)
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Metadata",
action="validated",
)
return True
def publish(
self,
all: bool = False,
if_exists: str = "raise",
update_locally: bool = False,
) -> dict:
"""Publish local metadata modifications.
`Metadata.validate` is used to make sure no local invalid metadata is
published to CKAN. The `config.toml` `api_key` variable must be set
at the `[ckan]` section for this method to work.
Args:
all (bool): Optional. If set to `True`, both `dataset_config.yaml`
and `table_config.yaml` are published for the given dataset_id
and table_id.
if_exists (str): Optional. What to do if config exists
* raise : Raises BaseDosDadosException if metadata already exi
sts in CKAN
* replace : Overwrite metadata in CKAN if it exists
* pass : Do nothing
update_locally (bool): Optional. If set to `True`, update the local
metadata with the one published to CKAN.
Returns:
dict:
In case of success, a `dict` with the modified data
is returned.
Raises:
BaseDosDadosException:
In case of CKAN's ValidationError or
NotAuthorized exceptions.
"""
# alert user if they don't have an api_key set up yet
if not self.CKAN_API_KEY:
raise BaseDosDadosException(
"You can't use `Metadata.publish` without setting an `api_key`"
"in your ~/.basedosdados/config.toml. Please set it like this:"
'\n\n```\n[ckan]\nurl="<CKAN_URL>"\napi_key="<API_KEY>"\n```'
)
# check if metadata exists in CKAN and handle if_exists options
if self.exists_in_ckan():
if if_exists == "raise":
raise BaseDosDadosException(
f"{self.dataset_id or self.table_id} already exists in CKAN."
f" Set the arg `if_exists` to `replace` to replace it."
)
if if_exists == "pass":
return {}
ckan = RemoteCKAN(self.CKAN_URL, user_agent="", apikey=self.CKAN_API_KEY)
try:
self.validate()
assert self.is_updated(), (
f"Could not publish metadata due to out-of-date config file. "
f"Please run `basedosdados metadata create {self.dataset_id} "
f"{self.table_id or ''}` to get the most recently updated met"
f"adata and apply your changes to it."
)
data_dict = self.ckan_data_dict.copy()
if self.table_id:
# publish dataset metadata first if user wants to publish both
if all:
self.dataset_metadata_obj.publish(if_exists=if_exists)
data_dict = data_dict["resources"][0]
published = ckan.call_action(
action="resource_patch"
if self.exists_in_ckan()
else "resource_create",
data_dict=data_dict,
)
else:
data_dict["resources"] = []
published = ckan.call_action(
action="package_patch"
if self.exists_in_ckan()
else "package_create",
data_dict=data_dict,
)
# recreate local metadata YAML file with the published data
if published and update_locally:
self.create(if_exists="replace")
self.dataset_metadata_obj.create(if_exists="replace")
logger.success(
" {object} {object_id} was {action}!",
object_id=data_dict,
object="Metadata",
action="published",
)
return published
except (BaseDosDadosException, ValidationError) as e:
message = (
f"Could not publish metadata due to a validation error. Pleas"
f"e see the traceback below to get information on how to corr"
f"ect it.\n\n{repr(e)}"
)
raise BaseDosDadosException(message) from e
except NotAuthorized as e:
message = (
"Could not publish metadata due to an authorization error. Pl"
"ease check if you set the `api_key` at the `[ckan]` section "
"of your ~/.basedosdados/config.toml correctly. You must be a"
"n authorized user to publish modifications to a dataset or t"
"able's metadata."
)
raise BaseDosDadosException(message) from e
ckan_data_dict: dict
property
readonly
Helper function that structures local metadata for validation
ckan_metadata: dict
property
readonly
Load dataset or table metadata from Base dos Dados CKAN
ckan_metadata_extended: dict
property
readonly
Load dataset and table metadata from Base dos Dados CKAN
columns_schema: dict
property
readonly
Returns a dictionary with the schema of the columns
filepath: str
property
readonly
Build the dataset or table filepath
local_metadata: dict
property
readonly
Load dataset or table local metadata
metadata_schema: dict
property
readonly
Get metadata schema from CKAN API endpoint
owner_org
property
readonly
Build owner_org field for each use case: table, dataset, new
or existing.
create(self, if_exists='raise', columns=None, partition_columns=None, force_columns=False, table_only=True)
Create metadata file based on the current version saved to CKAN database
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
if_exists |
str |
Optional. What to do if config exists * raise : Raises Conflict exception * replace : Replaces config file with most recent * pass : Do nothing |
'raise' |
columns |
list |
Optional.
A |
None |
partition_columns(list) |
Optional.
A |
required | |
force_columns |
bool |
Optional.
If set to |
False |
table_only |
bool |
Optional. If set to |
True |
Returns:
| Type | Description |
|---|---|
Metadata |
An instance of the |
Exceptions:
| Type | Description |
|---|---|
FileExistsError |
If the correspodent YAML configuration file alrea |
Source code in basedosdados/upload/metadata.py
def create(
self,
if_exists: str = "raise",
columns: list = None,
partition_columns: list = None,
force_columns: bool = False,
table_only: bool = True,
) -> Metadata:
"""Create metadata file based on the current version saved to CKAN database
Args:
if_exists (str): Optional. What to do if config exists
* raise : Raises Conflict exception
* replace : Replaces config file with most recent
* pass : Do nothing
columns (list): Optional.
A `list` with the table columns' names.
partition_columns(list): Optional.
A `list` with the name of the table columns that partition the
data.
force_columns (bool): Optional.
If set to `True`, overwrite CKAN's columns with the ones provi
ded.
If set to `False`, keep CKAN's columns instead of the ones pro
vided.
table_only (bool): Optional. If set to `True`, only `table_config.
yaml` is created, even if there is no `dataset_config.yaml` fo
r the correspondent dataset metadata. If set to `False`, both
files are created if `dataset_config.yaml` doesn't exist yet.
Defaults to `True`.
Returns:
Metadata: An instance of the `Metadata` class.
Raises:
FileExistsError: If the correspodent YAML configuration file alrea
dy exists and `if_exists` is set to `"raise"`.
"""
# see: https://docs.python.org/3/reference/compound_stmts.html#function-definitions
columns = [] if columns is None else columns
partition_columns = [] if partition_columns is None else partition_columns
if self.filepath.exists() and if_exists == "raise":
raise FileExistsError(
f"{self.filepath} already exists."
+ " Set the arg `if_exists` to `replace` to replace it."
)
if if_exists != "pass":
ckan_metadata = self.ckan_metadata
# Add local columns if
# 1. columns is empty and
# 2. force_columns is True
# TODO: Is this sufficient to add columns?
if self.table_id and (force_columns or not ckan_metadata.get("columns")):
ckan_metadata["columns"] = [{"name": c} for c in columns]
yaml_obj = build_yaml_object(
dataset_id=self.dataset_id,
table_id=self.table_id,
config=self.config,
schema=self.metadata_schema,
metadata=ckan_metadata,
columns_schema=self.columns_schema,
partition_columns=partition_columns,
)
self.filepath.parent.mkdir(parents=True, exist_ok=True)
with open(self.filepath, "w", encoding="utf-8") as file:
ruamel = ryaml.YAML()
ruamel.preserve_quotes = True
ruamel.indent(mapping=4, sequence=6, offset=4)
ruamel.dump(yaml_obj, file)
# if `dataset_config.yaml` doesn't exist but user wants to create
# it alongside `table_config.yaml`
dataset_config_exists = (
self.metadata_path / self.dataset_id / "dataset_config.yaml"
).exists()
if self.table_id and not table_only and not dataset_config_exists:
self.dataset_metadata_obj.create(if_exists=if_exists)
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Metadata",
action="created",
)
return self
exists_in_ckan(self)
Check if Metadata object refers to an existing CKAN package or reso urce.
Returns:
| Type | Description |
|---|---|
bool |
The existence condition of the metadata in CKAN. |
Source code in basedosdados/upload/metadata.py
def exists_in_ckan(self) -> bool:
"""Check if Metadata object refers to an existing CKAN package or reso
urce.
Returns:
bool: The existence condition of the metadata in CKAN. `True` if i
t exists, `False` otherwise.
"""
if self.table_id:
url = f"{self.CKAN_URL}/api/3/action/bd_bdm_table_show?"
url += f"dataset_id={self.dataset_id}&table_id={self.table_id}"
else:
id = self.dataset_id.replace("_", "-")
# TODO: use `bd_bdm_dataset_show` when it's available for empty packages
url = f"{self.CKAN_URL}/api/3/action/package_show?id={id}"
exists_in_ckan = requests.get(url, timeout=10).json().get("success")
return exists_in_ckan
is_updated(self)
Check if a dataset or table is updated
Returns:
| Type | Description |
|---|---|
bool |
The update condition of local metadata. |
Source code in basedosdados/upload/metadata.py
def is_updated(self) -> bool:
"""Check if a dataset or table is updated
Returns:
bool: The update condition of local metadata. `True` if it corresp
onds to the most recent version of the given table or dataset's me
tadata in CKAN, `False` otherwise.
"""
if not self.local_metadata.get("metadata_modified"):
return bool(not self.exists_in_ckan())
ckan_modified = self.ckan_metadata.get("metadata_modified")
local_modified = self.local_metadata.get("metadata_modified")
return ckan_modified == local_modified
publish(self, all=False, if_exists='raise', update_locally=False)
Publish local metadata modifications.
Metadata.validate is used to make sure no local invalid metadata is
published to CKAN. The config.toml api_key variable must be set
at the [ckan] section for this method to work.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
all |
bool |
Optional. If set to |
False |
if_exists |
str |
Optional. What to do if config exists * raise : Raises BaseDosDadosException if metadata already exi sts in CKAN * replace : Overwrite metadata in CKAN if it exists * pass : Do nothing |
'raise' |
update_locally |
bool |
Optional. If set to |
False |
Returns:
| Type | Description |
|---|---|
dict |
In case of success, a |
Source code in basedosdados/upload/metadata.py
def publish(
self,
all: bool = False,
if_exists: str = "raise",
update_locally: bool = False,
) -> dict:
"""Publish local metadata modifications.
`Metadata.validate` is used to make sure no local invalid metadata is
published to CKAN. The `config.toml` `api_key` variable must be set
at the `[ckan]` section for this method to work.
Args:
all (bool): Optional. If set to `True`, both `dataset_config.yaml`
and `table_config.yaml` are published for the given dataset_id
and table_id.
if_exists (str): Optional. What to do if config exists
* raise : Raises BaseDosDadosException if metadata already exi
sts in CKAN
* replace : Overwrite metadata in CKAN if it exists
* pass : Do nothing
update_locally (bool): Optional. If set to `True`, update the local
metadata with the one published to CKAN.
Returns:
dict:
In case of success, a `dict` with the modified data
is returned.
Raises:
BaseDosDadosException:
In case of CKAN's ValidationError or
NotAuthorized exceptions.
"""
# alert user if they don't have an api_key set up yet
if not self.CKAN_API_KEY:
raise BaseDosDadosException(
"You can't use `Metadata.publish` without setting an `api_key`"
"in your ~/.basedosdados/config.toml. Please set it like this:"
'\n\n```\n[ckan]\nurl="<CKAN_URL>"\napi_key="<API_KEY>"\n```'
)
# check if metadata exists in CKAN and handle if_exists options
if self.exists_in_ckan():
if if_exists == "raise":
raise BaseDosDadosException(
f"{self.dataset_id or self.table_id} already exists in CKAN."
f" Set the arg `if_exists` to `replace` to replace it."
)
if if_exists == "pass":
return {}
ckan = RemoteCKAN(self.CKAN_URL, user_agent="", apikey=self.CKAN_API_KEY)
try:
self.validate()
assert self.is_updated(), (
f"Could not publish metadata due to out-of-date config file. "
f"Please run `basedosdados metadata create {self.dataset_id} "
f"{self.table_id or ''}` to get the most recently updated met"
f"adata and apply your changes to it."
)
data_dict = self.ckan_data_dict.copy()
if self.table_id:
# publish dataset metadata first if user wants to publish both
if all:
self.dataset_metadata_obj.publish(if_exists=if_exists)
data_dict = data_dict["resources"][0]
published = ckan.call_action(
action="resource_patch"
if self.exists_in_ckan()
else "resource_create",
data_dict=data_dict,
)
else:
data_dict["resources"] = []
published = ckan.call_action(
action="package_patch"
if self.exists_in_ckan()
else "package_create",
data_dict=data_dict,
)
# recreate local metadata YAML file with the published data
if published and update_locally:
self.create(if_exists="replace")
self.dataset_metadata_obj.create(if_exists="replace")
logger.success(
" {object} {object_id} was {action}!",
object_id=data_dict,
object="Metadata",
action="published",
)
return published
except (BaseDosDadosException, ValidationError) as e:
message = (
f"Could not publish metadata due to a validation error. Pleas"
f"e see the traceback below to get information on how to corr"
f"ect it.\n\n{repr(e)}"
)
raise BaseDosDadosException(message) from e
except NotAuthorized as e:
message = (
"Could not publish metadata due to an authorization error. Pl"
"ease check if you set the `api_key` at the `[ckan]` section "
"of your ~/.basedosdados/config.toml correctly. You must be a"
"n authorized user to publish modifications to a dataset or t"
"able's metadata."
)
raise BaseDosDadosException(message) from e
validate(self)
Validate dataset_config.yaml or table_config.yaml files. The yaml file should be located at metadata_path/dataset_id[/table_id/], as defined in your config.toml
Returns:
| Type | Description |
|---|---|
bool |
True if the metadata is valid. False if it is invalid. |
Source code in basedosdados/upload/metadata.py
def validate(self) -> bool:
"""Validate dataset_config.yaml or table_config.yaml files.
The yaml file should be located at
metadata_path/dataset_id[/table_id/],
as defined in your config.toml
Returns:
bool:
True if the metadata is valid. False if it is invalid.
Raises:
BaseDosDadosException:
when the file has validation errors.
"""
ckan = RemoteCKAN(self.CKAN_URL, user_agent="", apikey=None)
response = ckan.action.bd_dataset_validate(**self.ckan_data_dict)
if response.get("errors"):
error = {self.ckan_data_dict.get("name"): response["errors"]}
message = f"{self.filepath} has validation errors: {error}"
raise BaseDosDadosException(message)
logger.success(
" {object} {object_id} was {action}!",
object_id=self.table_id,
object="Metadata",
action="validated",
)
return True
add_yaml_property(yaml, properties=None, definitions=None, metadata=None, goal=None, has_column=False)
Recursivelly adds properties to yaml to maintain order.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
yaml |
CommentedMap |
A YAML object with complex fields. |
required |
properties |
dict |
A dictionary that contains the description of the c omplex field. |
None |
definitions |
dict |
A dictionary with the schemas of each complex fiel d. |
None |
metadata |
dict |
A dictionary with the metadata to fill the YAML. |
None |
goal |
str |
The next key to be added to the YAML. |
None |
has_column |
bool |
If the goal is a column, no comments are written. |
False |
Source code in basedosdados/upload/metadata.py
def add_yaml_property(
yaml: CommentedMap,
properties: dict = None,
definitions: dict = None,
metadata: dict = None,
goal=None,
has_column=False,
):
"""Recursivelly adds properties to yaml to maintain order.
Args:
yaml (CommentedMap): A YAML object with complex fields.
properties (dict): A dictionary that contains the description of the c
omplex field.
definitions (dict): A dictionary with the schemas of each complex fiel
d.
metadata (dict): A dictionary with the metadata to fill the YAML.
goal (str): The next key to be added to the YAML.
has_column (bool): If the goal is a column, no comments are written.
"""
# see: https://docs.python.org/3/reference/compound_stmts.html#function-definitions
properties = {} if properties is None else properties
definitions = {} if definitions is None else definitions
metadata = {} if metadata is None else metadata
# Looks for the key
# If goal is none has to look for id_before == None
for key, property in properties.items():
goal_was_reached = key == goal
goal_was_reached |= property["yaml_order"]["id_before"] is None
if goal_was_reached:
if "allOf" in property:
yaml = handle_complex_fields(
yaml_obj=yaml,
k=key,
properties=properties,
definitions=definitions,
data=metadata,
)
if yaml[key] == ordereddict():
yaml[key] = handle_data(k=key, data=metadata)
else:
yaml[key] = handle_data(k=key, data=metadata)
# Add comments
comment = None
if not has_column:
description = properties[key].get("description", [])
comment = "\n" + "".join(description)
yaml.yaml_set_comment_before_after_key(key, before=comment)
break
# Return a ruaml object when property doesn't point to any other property
id_after = properties[key]["yaml_order"]["id_after"]
if id_after is None:
return yaml
if id_after not in properties.keys():
raise BaseDosDadosException(
f"Inconsistent YAML ordering: {id_after} is pointed to by {key}"
f" but doesn't have itself a `yaml_order` field in the JSON S"
f"chema."
)
updated_props = deepcopy(properties)
updated_props.pop(key)
return add_yaml_property(
yaml=yaml,
properties=updated_props,
definitions=definitions,
metadata=metadata,
goal=id_after,
has_column=has_column,
)
build_yaml_object(dataset_id, table_id, config, schema, metadata=None, columns_schema=None, partition_columns=None)
Build a dataset_config.yaml or table_config.yaml
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset_id |
str |
The dataset id. |
required |
table_id |
str |
The table id. |
required |
config |
dict |
A dict with the |
required |
schema |
dict |
A dict with the JSON Schema of the dataset or table. |
required |
metadata |
dict |
A dict with the metadata of the dataset or table. |
None |
columns_schema |
dict |
A dict with the JSON Schema of the columns of the table. |
None |
partition_columns |
list |
A list with the partition columns of the table. |
None |
Returns:
| Type | Description |
|---|---|
CommentedMap |
A YAML object with the dataset or table metadata. |
Source code in basedosdados/upload/metadata.py
def build_yaml_object(
dataset_id: str,
table_id: str,
config: dict,
schema: dict,
metadata: dict = None,
columns_schema: dict = None,
partition_columns: list = None,
):
"""Build a dataset_config.yaml or table_config.yaml
Args:
dataset_id (str): The dataset id.
table_id (str): The table id.
config (dict): A dict with the `basedosdados` client configurations.
schema (dict): A dict with the JSON Schema of the dataset or table.
metadata (dict): A dict with the metadata of the dataset or table.
columns_schema (dict): A dict with the JSON Schema of the columns of
the table.
partition_columns (list): A list with the partition columns of the
table.
Returns:
CommentedMap: A YAML object with the dataset or table metadata.
"""
# see: https://docs.python.org/3/reference/compound_stmts.html#function-definitions
metadata = {} if metadata is None else metadata
columns_schema = {} if columns_schema is None else columns_schema
partition_columns = [] if partition_columns is None else partition_columns
properties: dict = schema["properties"]
definitions: dict = schema["definitions"]
# Drop all properties without yaml_order
properties = {
key: value for key, value in properties.items() if value.get("yaml_order")
}
# Add properties
yaml = add_yaml_property(
yaml=ryaml.CommentedMap(),
properties=properties,
definitions=definitions,
metadata=metadata,
)
# Add columns
if metadata.get("columns"):
yaml["columns"] = []
for metadatum in metadata.get("columns"):
properties = add_yaml_property(
yaml=ryaml.CommentedMap(),
properties=columns_schema["properties"],
definitions=columns_schema["definitions"],
metadata=metadatum,
has_column=True,
)
yaml["columns"].append(properties)
# Add partitions in case of new dataset/talbe or local overwriting
if partition_columns and partition_columns != ["[]"]:
yaml["partitions"] = ""
for local_column in partition_columns:
for remote_column in yaml["columns"]:
if remote_column["name"] == local_column:
remote_column["is_partition"] = True
yaml["partitions"] = partition_columns
# Nullify `partitions` field in case of other-than-None empty values
if yaml.get("partitions") == "":
yaml["partitions"] = None
if table_id:
# Add dataset_id and table_id
yaml["dataset_id"] = dataset_id
yaml["table_id"] = table_id
# Add gcloud config variables
yaml["source_bucket_name"] = str(config.get("bucket_name"))
yaml["project_id_prod"] = str(
config.get("gcloud-projects", {}).get("prod", {}).get("name")
)
yaml["project_id_staging"] = str(
config.get("gcloud-projects", {}).get("staging", {}).get("name")
)
return yaml
handle_complex_fields(yaml_obj, k, properties, definitions, data)
Parse complex fields and send each part of them to handle_data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
yaml_obj |
ruamel.yaml.CommentedMap |
A YAML object with complex fields . |
required |
k |
str |
The name of the key of the complex field. |
required |
properties |
dict |
A dictionary that contains the description of the c omplex field. |
required |
definitions |
dict |
A dictionary with the schemas of the each component of the complex field. |
required |
data |
dict |
A dictionary with the metadata of the complex field. |
required |
Returns:
| Type | Description |
|---|---|
CommentedMap |
A YAML object augmented with the complex field. |
Source code in basedosdados/upload/metadata.py
def handle_complex_fields(yaml_obj, k, properties, definitions, data):
"""Parse complex fields and send each part of them to `handle_data`.
Args:
yaml_obj (ruamel.yaml.CommentedMap): A YAML object with complex fields
.
k (str): The name of the key of the complex field.
properties (dict): A dictionary that contains the description of the c
omplex field.
definitions (dict): A dictionary with the schemas of the each component
of the complex field.
data (dict): A dictionary with the metadata of the complex field.
Returns:
CommentedMap: A YAML object augmented with the complex field.
"""
yaml_obj[k] = ryaml.CommentedMap()
# Parsing 'allOf': [{'$ref': '#/definitions/PublishedBy'}]
# To get PublishedBy
d = properties[k]["allOf"][0]["$ref"].split("/")[-1]
if "properties" in definitions[d].keys():
for dk, _ in definitions[d]["properties"].items():
yaml_obj[k][dk] = handle_data(
k=dk,
data=data.get(k, {}),
)
return yaml_obj
handle_data(k, data, local_default=None)
Parse API's response data so that it is used in the YAML configuration files.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
k |
str |
a key of the CKAN API's response metadata dictionary. |
required |
data |
dict |
a dictionary of metadata generated from the API. |
required |
local_default |
Any |
the default value of the given key in ca
se its value is set to |
None |
Returns:
| Type | Description |
|---|---|
list |
a list of metadata values |
Source code in basedosdados/upload/metadata.py
def handle_data(k, data, local_default=None):
"""Parse API's response data so that it is used in the YAML configuration
files.
Args:
k (str): a key of the CKAN API's response metadata dictionary.
data (dict): a dictionary of metadata generated from the API.
local_default (Any): the default value of the given key in ca
se its value is set to `None` in CKAN.
Returns:
list: a list of metadata values
"""
# If no data is None then return a empty dict
data = data if data is not None else {}
# If no data is found for that key, uses local default
selected = data.get(k, local_default)
# In some cases like `tags`, `groups`, `organization`
# the API default is to return a dict or list[dict] with all info.
# But, we just use `name` to build the yaml
_selected = deepcopy(selected)
if _selected == []:
return _selected
if not isinstance(_selected, list):
_selected = [_selected]
if isinstance(_selected[0], dict):
if _selected[0].get("id") is not None:
return [s.get("name") for s in _selected]
return selected