.. note::
For the latest source, discussion, etc, please visit the
GitHub repository <https://github.com/totvslabs/pyCarol>_
.. image:: https://badge.buildkite.com/b92ca1611add8d61063f61c92b9798fe81e859d468aae36463.svg :target: https://buildkite.com/totvslabs/pycarol
.. contents::
Run pip install pycarol to install the latest stable version from PyPI <https://pypi.python.org/pypi/pycarol>_. Documentation for the latest release <http://pycarol.readthedocs.io/>__ is hosted on readthedocs.
This will install the minimal dependencies. To install pyCarol with the dataframes dependencies use
pip install pycarol[dataframe], or to install with dask+pipeline dependencies use pip install pycarol[pipeline,dask]
The options we have are: complete, dataframe, onlineapp, dask, pipeline
To install from source:
pip install -r requirements.txt to install the minimal requirements;pip install -e . ".[dev]" to install the minimal requirements + dev libs;pip install -e . ".[pipeline]" to install the minimal requirements + pipelines dependencies;pip install -e . ".[complete]" to install all dependencies;Carol is the main object to access pyCarol and all Carol's APIs.
.. code:: python
from pycarol import PwdAuth, Carol
carol = Carol(domain=TENANT_NAME, app_name=APP_NAME,
auth=PwdAuth(USERNAME, PASSWORD), organization=ORGANIZATION)
where domain is the tenant name, app_name is the Carol's app name, if any, auth
is the authentication method to be used (using user/password in this case) and organization is the organization
one wants to connect. Carols's URL is build as www.ORGANIZATION.carol.ai/TENANT_NAME
It is also possible to initialize the object with a token generated via user/password. This is useful when creating an online app that interacts with Carol
.. code:: python
from pycarol import PwdKeyAuth, Carol
carol = Carol(domain=TENANT_NAME, app_name=APP_NAME,
auth=PwdKeyAuth(pwd_auth_token), organization=ORGANIZATION)
To use API keys instead of username and password:
.. code:: python
from pycarol import ApiKeyAuth, Carol
carol = Carol(domain=DOMAIN,
app_name=APP_NAME,
auth=ApiKeyAuth(api_key=X_AUTH_KEY),
connector_id=CONNECTOR, organization=ORGANIZATION)
In this case one changes the authentication method to ApiKeyAuth. Noticed that one needs to pass the connector_id
too. An API key is always associated to a connector ID.
It is possible to use pyCarol to generate an API key
.. code:: python
from pycarol import PwdAuth, ApiKeyAuth, Carol
carol = Carol(domain=TENANT_NAME, app_name=APP_NAME, organization=ORGANIZATION,
auth=PwdAuth(USERNAME, PASSWORD), connector_id=CONNECTOR)
api_key = carol.issue_api_key()
print(f"This is a API key {api_key['X-Auth-Key']}")
print(f"This is the connector Id {api_key['X-Auth-ConnectorId']}")
To get the details of the API key you can do:
.. code:: python
details = carol.api_key_details(APIKEY, CONNECTORID)
Finally, to revoke an API key:
.. code:: python
carol.api_key_revoke(CONNECTORID)
Never write in plain text your password/API token in your application. Use environment variables. pyCarol can use environment variables automatically. When none parameter is passed to the Carol constructor pycarol will look for:
CAROLTENANT for domainCAROLAPPNAME for app_nameCAROL_DOMAIN for environmentCAROLORGANIZATION for organizationCAROLAPPOAUTH for authCAROLCONNECTORID for connector_idCAROLUSER for carol user emailCAROLPWD for user password.e.g., one can create a .env file like this:
.. code:: python
CAROLAPPNAME=myApp
CAROLTENANT=myTenant
CAROLORGANIZATION=myOrganization
CAROLAPPOAUTH=myAPIKey
CAROLCONNECTORID=myConnector
and then
.. code:: python
from pycarol import Carol
from dotenv import load_dotenv
load_dotenv(".env") #this will import these env variables to your execution.
carol = Carol()
From both Staging Tables and Data Models (BQ Layer) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Use this method when you need to read only a subset of records and columns or when data transformation is needed.
.. code:: python
from pycarol import BQ, Carol
bq = BQ(Carol())
query_str = "SELECT * FROM stg_connectorname_table_name"
results = bq.query(query_str)
In case one needs a service account with access to BigQuery, the following code can be used:
.. code:: python
from pycarol import Carol
from pycarol.bigquery import TokenManager
tm = TokenManager(Carol())
service_account = tm.get_token().service_account
After each execution of BQ.query, the BQ object will have an attribute called
job. This attribute is of type bigquery.job.query.QueryJob and may be useful for
monitoring/debug jobs.
PyCarol provides access to BigQuery Storage API also. It allows for much faster reading times, but with limited querying capabilities. For instance, only tables are readable, so 'ingestion_stg_model_deep_audit' is ok, but 'stg_model_deep_audit' is not (it is a view).
.. code:: python
from pycarol import BQStorage, Carol
bq = BQStorage(Carol())
table_name = "ingestion_stg_model_deep_audit"
col_names = ["request_id", "version"]
restriction = "branch = '01'"
sample_size = 1000
df = bq.query(
table_name,
col_names,
row_restriction=restriction,
sample_percentage=sample_size,
return_dataframe=True
)
From Data Models (RT Layer): Filter queries ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Use this when you need low latency (only if RT layer is enabled).
.. code:: python
from pycarol.filter import TYPE_FILTER, TERM_FILTER, Filter
from pycarol import Query
json_query = Filter.Builder() \
.must(TYPE_FILTER(value='ratings' + "Golden")) \
.must(TERM_FILTER(key='mdmGoldenFieldAndValues.userid.raw',value='123'))\
.build().to_json()
FIELDS_ITEMS = ['mdmGoldenFieldAndValues.mdmaddress.coordinates']
query = Query(carol, page_size=10, print_status=True, only_hits=True,
fields=FIELDS_ITEMS, max_hits=200).query(json_query).go()
query.results
The result will be 200 hits of the query json_query above, the pagination will be 10, that means in each response
there will be 10 records. The query will return only the fields set in FIELDS_ITEMS.
The parameter only_hits = True will make sure that only records into the path $hits.mdmGoldenFieldAndValues will return.
If one wants all the response use only_hits = False. Also, if your filter has an aggregation, one should use
only_hits = False and get_aggs=True, e.g.,
.. code:: python
from pycarol import Query
from pycarol.filter import TYPE_FILTER, Filter, CARDINALITY
json_query = Filter.Builder() \
.must(TYPE_FILTER(value='datamodelname' + "Golden")) \
.aggregation(CARDINALITY(name='cardinality', params = ["mdmGoldenFieldAndValues.taxid.raw"], size=40))\
.build().to_json()
query = Query(carol, get_aggs=True, only_hits=False)
query.query(json_query).go()
query.results
From Data Models (RT Layer): Named queries ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code:: python
from pycarol import Query
named_query = 'revenueHist' # named query name
params = {"bin":"1d","cnpj":"24386434000130"} #query parameters to send.
results = Query(carol).named(named_query, params=params).go().results
It is possible to use all the parameters used in the filter query, i.e., only_hits ,
save_results, etc. For more information for the possible input parameters check the docstring.
What if one does not remember the parameters for a given named query?
.. code:: python
named_query = 'revenueHist' # named query name
Query(carol).named_query_params(named_query)
> {'revenueHist': ['*cnpj', 'dateFrom', 'dateTo', '*bin']} #Parameters starting by * are mandatory.
The first step to send data to Carol is to create a connector.
.. code:: python
from pycarol import Connectors
connector_id = Connectors(carol).create(name='my_connector', label="connector_label", group_name="GroupName")
print(f"This is the connector id: {connector_id}")
With the connector Id on hands we can create the staging schema and then create the staging table. Assuming we have a sample of the data we want to send.
.. code:: python
from pycarol import Staging
json_ex = {"name":'Rafael',"email": {"type": "email", "email": 'rafael@totvs.com.br'} }
staging = Staging(carol)
staging.create_schema(staging_name='my_stag', data = json_ex,
crosswalk_name= 'my_crosswalk' ,crosswalk_list=['name'],
connector_name='my_connector')
The json schema will be in the variable schema.schema. The code above will create the following schema:
.. code:: python
{
'mdmCrosswalkTemplate': {
'mdmCrossreference': {
'my_crosswalk': [
'name'
]
}
},
'mdmFlexible': 'false',
'mdmStagingMapping': {
'properties': {
'email': {
'properties': {
'email': {
'type': 'string'
},
'type': {
'type': 'string'
}
},
'type': 'nested'
},
'name': {
'type': 'string'
}
}
},
'mdmStagingType': 'my_stag'
}
To send the data (assuming we have a json with the data we want to send).
.. code:: python
from pycarol import Staging
json_ex = [{"name":'Rafael',"email": {"type": "email", "email": 'rafael@totvs.com.br'} },
{"name":'Leandro',"email": {"type": "email", "email": 'Leandro@totvs.com.br'} },
{"name":'Joao',"email": {"type": "email", "email": 'joao@rolima.com.br'} },
{"name":'Marcelo',"email": {"type": "email", "email": 'marcelo@totvs.com.br'} }]
staging = Staging(carol)
staging.send_data(staging_name = 'my_stag', data = json_ex, step_size = 2,
connector_id=connectorId, print_stats = True)
The parameter step_size says how many registers will be sent each time. Remember the the max size per payload is
5MB. The parameter data can be a pandas DataFrame.
OBS: It is not possible to create a mapping using pycarol. The Mapping has to be done via the UI
To log messages to Carol:
.. code:: python
from pycarol import Carol, CarolHandler
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
carol = CarolHandler(Carol())
carol.setLevel(logging.INFO)
logger.addHandler(carol)
logger.debug('This is a debug message') #This will not be logged in Carol. Level is set to INFO
logger.info('This is an info message')
logger.warning('This is a warning message')
logger.error('This is an error message')
logger.critical('This is a critical message')
These methods will use the current long task id provided by Carol when running your application. For local environments you need to set that manually first on the beginning of your code:
.. code:: python
import os
os.environ['LONGTASKID'] = task_id
We recommend to log only INFO+ information in Carol. If no TASK ID is passed it works as a Console Handler.
We can use pyCarol to access the settings of your Carol App.
.. code:: python
from pycarol.apps import Apps
app = Apps(carol)
settings = app.get_settings(app_name='my_app')
print(settings)
The settings will be returned as a dictionary where the keys are the parameter names and the values are the value for that parameter. Please note that your app must be created in Carol.
track_tasks: Track a list of tasks... code:: python
from pycarol import Carol
from pycarol.functions import track_tasks
carol = Carol()
def callback(task_list):
print(task_list)
track_tasks(carol=carol, task_list=['task_id_1', 'task_id_2'], callback=callback)
master branch;master;FAQs
Carol Python API and Tools
We found that pycarol demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.