This library provides a Python wrapper over the Keboola Common Interface. It simplifies all tasks related to the communication of the Docker component with the Keboola Connection that is defined by the Common Interface. Such tasks are config manipulation, validation, component state, I/O handling, I/O metadata and manifest files, logging, etc.
It is being developed by the Keboola Data Services team and officially supported by Keboola. It aims to simplify the Keboola Component creation process, by removing the necessity of writing boilerplate code to manipulate with the Common Interface.
Another useful use-case is within the Keboola Python Transformations to simplify the I/O handling.
- API Documentation: API docs
- Source code: https://github.com/keboola/python-component
- PYPI project code: https://test.pypi.org/project/keboola.component-kds/
Documentation: https://developers.keboola.com/extend/component/python-component-library
- Python Component Cookiecutter template project: https://bitbucket.org/kds_consulting_team/cookiecutter-python-component
The package may be installed via PIP:
pip install keboola.component
The package contains two core modules:
keboola.component.interface- Core methods and class to initialize and handle the Keboola Common Interface taskskeboola.component.dao- Data classes and containers for objects defined by the Common Interface such as manifest files, metadata, environment variables, etc.keboola.component.base- Base classes to build the Keboola Component applications from.
Core class that serves to initialize the docker environment. It handles the following tasks:
- Environment initialisation
- Loading all environment variables
- Loading the configuration file and initialization of the data folder
- State file processing.
- Logging
- Data folder manipulation
- Manifest file processing
- Config validation
- Metadata manipulation
- OAuth configuration handling.
The core class is keboola.component.interface.CommonInterface, upon it's initialization the environment is created.
e.g.
- data folder initialized (either from the Environment Variable or manually)
- config.json is loaded
- All Environment variables are loaded
The optional parameter data_folder_path of the constructor is the path to the data directory. If not
provided it will be determined in this order:
KBC_DATADIRenvironment variable if present- -d / --data argument from the command line if present
- data folder inside the current working directory if present
- data folder inside the parent directory of the current working directory if present
The class can be either extended or just instantiated and manipulated like object. The CommonInterface class is
exposed in the keboola.component namespace:
from keboola.component import CommonInterface
# init the interface
# A ValueError error is raised if the KBC_DATADIR does not exist or contains non-existent path.
ci = CommonInterface()To specify the data folder path manually use this code:
from keboola.component import CommonInterface
# init the interface
# A ValueError error is raised if the data folder path does not exist.
ci = CommonInterface(data_folder_path='/data')The below example loads initializes the common interface class and automatically loading config.json from the
data folder which is defined by an environment
variable KBC_DATADIR, if the variable is not present, and error is raised. To override the data folder location
provide the data_folder_path parameter into constructor.
NOTE: The configuration object is initialized upon access and a ValueError is thrown if the config.json does not
exist in the data folder. e.g. cfg = ci.configuration may throw a ValueError even though the data folder exists and
ci (CommonInterface)
is properly initialized.
from keboola.component import CommonInterface
# Logger is automatically set up based on the component setup (GELF or STDOUT)
import logging
SOME_PARAMETER = 'some_user_parameter'
REQUIRED_PARAMETERS = [SOME_PARAMETER]
# init the interface
# A ValueError error is raised if the KBC_DATADIR does not exist or contains non-existent path.
ci = CommonInterface()
# A ValueError error is raised if the config.json file does not exists in the data dir.
# Checks for required parameters and throws ValueError if any is missing.
ci.validate_configuration(REQUIRED_PARAMETERS)
# print KBC Project ID from the environment variable if present:
logging.info(ci.environment_variables.project_id)
# load particular configuration parameter
logging.info(ci.configuration.parameters[SOME_PARAMETER])Input and output tables specified by user are listed in the configuration file. Apart from that, all input tables provided by user also include manifest file with additional metadata.
Tables and their manifest files are represented by the keboola.component.dao.TableDefinition object and may be loaded
using the convenience method get_input_tables_definitions(). The result object contains all metadata about the table,
such as manifest file representations, system path and name.
from keboola.component import CommonInterface
import logging
# init the interface
ci = CommonInterface()
input_tables = ci.get_input_tables_definitions()
# print path of the first table (random order)
first_table = input_tables[0]
logging.info(f'The first table named: "{first_table.name}" is at path: {first_table.full_path}')
# get information from table manifest
logging.info(f'The first table has following columns defined in the manifest {first_table.column_names}')import csv
from keboola.component import CommonInterface
# initialize the library
ci = CommonInterface()
# get list of input tables from the input mapping ()
tables = ci.configuration.tables_input_mapping
j = 0
for table in tables:
# get csv file name
inName = table.destination
# read input table manifest and get it's physical representation
table_def = ci.get_input_table_definition_by_name(table.destination)
# get csv file name with full path from output mapping
outName = ci.configuration.tables_output_mapping[j].full_path
# get file name from output mapping
outDestination = ci.configuration.tables_output_mapping[j]['destination']The component may define
output manifest files
that define options on storing the results back to the Keboola Connection Storage. This library provides methods that
simplifies the manifest file creation and allows defining the export options and metadata of the result table using
helper objects TableDefinition
and TableMetadata.
TableDefinition object serves as a result container containing all the information needed to store the Table into the
Storage. It contains the manifest file representation and initializes all attributes available in the manifest.
This object represents both Input and Output manifests. All output manifest attributes are exposed in the class.
There are convenience methods for result processing and manifest creation CommonInterface.write_manifest. Also it is
possible to create the container for the output table using the CommonInterface.create_out_table_definition().
Table schema example:
from keboola.component import CommonInterface
from keboola.component.dao import ColumnDefinition, DataType, SupportedDataTypes, BaseType
# init the interface
ci = CommonInterface(data_folder_path='data')
# create container for the result
out = ci.create_out_table_definition("testDef",
schema=['foo', 'bar'],
destination='some-destination',
primary_key=['foo'],
incremental=True,
delete_where={'column': 'lilly',
'values': ['a', 'b'],
'operator': 'eq'})
# update column
out.update_column('foo',
ColumnDefinition(data_types=BaseType(dtype=SupportedDataTypes.INTEGER, length='20')))
# add new columns
out.add_column('note', ColumnDefinition(nullable=False))
out.add_column('test1')
out.add_columns(['test2', 'test3', 'test4'])
# add new typed column
out.add_column('id', ColumnDefinition(primary_key=True,
data_types={'snowflake': DataType(dtype="INTEGER", length='200')})
)
out.add_columns({
'phone': ColumnDefinition(primary_key=True,
data_types={'snowflake': DataType(dtype="INTEGER", length='200'),
'bigquery': DataType(dtype="BIGINT")}),
'new2': ColumnDefinition(data_types={'snowflake': DataType(dtype="INTEGER", length='200')}),
})
# delete columns
out.delete_column('bar')
out.delete_columns(['test2', 'test3'])
# write some content
with open(out.full_path, 'w') as result:
result.write('line')
# write manifest
ci.write_manifest(out)Example:
from keboola.component import CommonInterface
from keboola.component import dao
# init the interface
ci = CommonInterface()
# create container for the result
result_table = ci.create_out_table_definition('my_new_result_table', primary_key=['id'], incremental=True)
# write some content
with open(result_table.full_path, 'w') as result:
result.write('line')
# add some metadata
result_table.table_metadata.add_table_description('My new table description')
# add column datatype
result_table.table_metadata.add_column_data_type('id', dao.SupportedDataTypes.STRING,
source_data_type='VARCHAR(100)',
nullable=True,
length=100)
# write manifest
ci.write_manifest(result_table)from keboola.component import CommonInterface
# init the interface
ci = CommonInterface()
table_def = ci.get_input_table_definition_by_name('input.csv')from keboola.component import dao
table_def = dao.TableDefinition.build_from_manifest('data/in/tables/table.csv.manifest')
# print table.csv full-path if present:
print(table_def.full_path)
# rows count
print(table_def.rows_count)To retrieve the manifest file representation that is compliant with Keboola Connection Common Interface use
the table_def.get_manifest_dictionary() method.
from keboola.component import dao
table_def = dao.TableDefinition.build_from_manifest('data/in/tables/table.csv.manifest')
# get the manifest file representation
manifest_dict = table_def.get_manifest_dictionary()Similarly as tables, files and their manifest files are represented by the keboola.component.dao.FileDefinition object
and may be loaded using the convenience method get_input_files_definitions(). The result object contains all metadata
about the file, such as manifest file representations, system path and name.
The get_input_files_definitions() supports filter parameters to filter only files with a specific tag or retrieve only
the latest file of each. This is especially useful because the KBC input mapping will by default include all versions of
files matching specific tag. By default, the method returns only the latest file of each.
from keboola.component import CommonInterface
import logging
# init the interface
ci = CommonInterface()
input_files = ci.get_input_files_definitions(tags=['my_tag'], only_latest_files=True)
# print path of the first file (random order) matching the criteria
first_file = input_files[0]
logging.info(f'The first file named: "{first_file.name}" is at path: {first_file.full_path}')
When working with files it may be useful to retrieve them in a dictionary structure grouped either by name or a tag
group. For this there are convenience methods get_input_file_definitions_grouped_by_tag_group()
and get_input_file_definitions_grouped_by_name()
from keboola.component import CommonInterface
import logging
# init the interface
ci = CommonInterface()
# group by tag
input_files_by_tag = ci.get_input_file_definitions_grouped_by_tag_group(only_latest_files=True)
# print list of files matching specific tag
logging.info(input_files_by_tag['my_tag'])
# group by name
input_files_by_name = ci.get_input_file_definitions_grouped_by_name(only_latest_files=True)
# print list of files matching specific name
logging.info(input_files_by_name['image.jpg'])State files can be easily written and
loaded using the get_state_file() and write_state_file() methods:
from keboola.component import CommonInterface
from datetime import datetime
import logging
# init the interface
ci = CommonInterface()
last_state = ci.get_state_file()
# print last_updated if exists
logging.info(f'Previous job stored following last_updated value: {last_state.get("last_updated", "")})')
# store new state file
ci.write_state_file({"last_updated": datetime.now().isoformat()})The library automatically initializes STDOUT or GELF logger based on the presence of the KBC_LOGGER_PORT/HOST
environment variable upon the CommonInterface initialization. To use the GELF logger just enable the logger for your
appplication in the Developer Portal. More info in
the dedicated article.
Once it is enabled, you may just log your messages using the logging library:
from keboola.component import CommonInterface
from datetime import datetime
import logging
# init the interface
ci = CommonInterface()
logging.info("Info message")TIP: When the logger verbosity is set to verbose you may leverage extra fields to log the detailed message in
the detail of the log event by adding extra fields to you messages:
logging.error(f'{error}. See log detail for full query. ',
extra={"failed_query": json.dumps(query)})You may also choose to override the settings by enabling the GELF or STDOUT explicitly and specifying the host/port parameters:
from keboola.component import CommonInterface
import os
import logging
# init the interface
ci = CommonInterface()
os.environ['KBC_LOGGER_ADDR'] = 'localhost'
os.environ['KBC_LOGGER_PORT'] = 12201
ci.set_gelf_logger(log_level=logging.INFO, transport_layer='UDP')
logging.info("Info message")Base class for general Python components. Base your components on this class for simpler debugging.
It performs following tasks by default:
- Initializes the CommonInterface.
- For easier debugging the data folder is picked up by default from
../datapath, relative to working directory when theKBC_DATADIRenv variable is not specified. - If
debugparameter is present in theconfig.json, the default logger is set to verbose DEBUG mode. - Executes sync actions ->
runby default. See the sync actions section.
Constructor arguments:
- data_path_override: optional path to data folder that overrides the default behaviour
(
KBC_DATADIRenvironment variable). May be also specified by-dor--datacommandline argument
Raises: UserException - on config validation errors.
Example usage:
import csv
import logging
from datetime import datetime
from keboola.component.base import ComponentBase, sync_action
from keboola.component import UserException
# configuration variables
KEY_PRINT_HELLO = 'print_hello'
# list of mandatory parameters => if some is missing,
# component will fail with readable message on initialization.
REQUIRED_PARAMETERS = [KEY_PRINT_HELLO]
REQUIRED_IMAGE_PARS = []
class Component(ComponentBase):
def run(self):
'''
Main execution code
'''
# ####### EXAMPLE TO REMOVE
# check for missing configuration parameters
self.validate_configuration_parameters(REQUIRED_PARAMETERS)
self.validate_image_parameters(REQUIRED_IMAGE_PARS)
params = self.configuration.parameters
# Access parameters in data/config.json
if params.get(KEY_PRINT_HELLO):
logging.info("Hello World")
# get last state data/in/state.json from previous run
previous_state = self.get_state_file()
logging.info(previous_state.get('some_state_parameter'))
# Create output table (Tabledefinition - just metadata)
table = self.create_out_table_definition('output.csv', incremental=True, primary_key=['timestamp'])
# get file path of the table (data/out/tables/Features.csv)
out_table_path = table.full_path
logging.info(out_table_path)
# DO whatever and save into out_table_path
with open(table.full_path, mode='wt', encoding='utf-8', newline='') as out_file:
writer = csv.DictWriter(out_file, fieldnames=['timestamp'])
writer.writeheader()
writer.writerow({"timestamp": datetime.now().isoformat()})
# Save table manifest (output.csv.manifest) from the tabledefinition
self.write_manifest(table)
# Write new state - will be available next run
self.write_state_file({"some_state_parameter": "value"})
# ####### EXAMPLE TO REMOVE END
# sync action that is executed when configuration.json "action":"testConnection" parameter is present.
@sync_action('testConnection')
def test_connection(self):
connection = self.configuration.parameters.get('test_connection')
if connection == "fail":
raise UserException("failed")
elif connection == "succeed":
# this is ignored when run as sync action.
logging.info("succeed")
"""
Main entrypoint
"""
if __name__ == "__main__":
try:
comp = Component()
# this triggers the run method by default and is controlled by the configuration.action paramter
comp.execute_action()
except UserException as exc:
logging.exception(exc)
exit(1)
except Exception as exc:
logging.exception(exc)
exit(2)In cases of a static schemas of output/input tables, the schemas can be defined using a JSON Table Schema. For output mapping these json schemas can be automatically turned into out table definitions.
{
"name": "product",
"description": "this table holds data on products",
"parent_tables": [],
"primary_keys": [
"id"
],
"fields": [
{
"name": "id",
"base_type": "string",
"description": "ID of the product",
"length": "100",
"nullable": false
},
{
"name": "name",
"base_type": "string",
"description": "Plain-text name of the product",
"length": "1000",
"default": "Default Name"
}
]
}The example below shows how a table definition can be created from a json schema using the ComponentBase. The schema is located in the 'src/schemas' directory.
import csv
from keboola.component.base import ComponentBase
DUMMY_PRODUCT_DATA = [{"id": "P0001",
"name": "juice"},
{"id": "P0002",
"name": "chocolate bar"},
{"id": "P0003",
"name": "Stylish Pants"},
]
class Component(ComponentBase):
def __init__(self):
super().__init__()
def run(self):
product_schema = self.get_table_schema_by_name('product')
product_table = self.create_out_table_definition_from_schema(product_schema)
with open(product_table.full_path, 'w') as outfile:
writer = csv.DictWriter(outfile, fieldnames=product_table.column_names)
writer.writerows(DUMMY_PRODUCT_DATA)
self.write_manifest(product_table)From the documentation Sync actions:
Action provide a way to execute very quick tasks in a single Component, using a single code base. The default
component’s action (run) executes as a background, asynchronous job. It is queued, has plenty of execution time, and
there are cases when you might not want to wait for it. Apart from the default run, there can be synchronous actions
with limited execution time and you must wait for them. When we refer to actions, we mean synchronous actions. Using
actions is fully optional.
For example, in our database extractor, the main task (run action) is the data extraction itself. But we also want to
be able to test the database credentials and list tables available in the database. These tasks would be very helpful in
the UI. It is not possible to do these things directly in the browser. Setting up a separate component would bring an
overhead of maintaining both the extractor’s Docker image and the new component.
Data is exchanged via stdout or stderr.
- Sync actions need to be registered in the Developer Portal first.
Following are handled by the decorator automatically
- All success responses have to output valid JSON string. Meaning nothing can log into the stdout during the action execution
- For success action the output needs to be always
{"status":"success"}in stdout.
Decorator sync_action was added. It takes one parameter action_name that will create mapping between the actual
method and the sync action name registered in the Developer Portal.
- Decorated methods can also be called from within the program and return values.
- They can log normally -> when run as sync action all logging within the method is muted.
- When a return value is produced, it is expected to be
dictorlistobject. These will be printed to stdout at the end. - Exceptions can be thrown normally and the message will be propagated to the platform.
Each action has to have specific output based on type of the UI element that the action is triggered with. It can either have no return value (success / fail type of actions) or UI element specific output.
For convenience each output variant is represented by classes specified in keboola.component.sync_actions module.
Result expected by validation button element.
from keboola.component.base import ComponentBase, sync_action
from keboola.component.sync_actions import ValidationResult, MessageType
class Component(ComponentBase):
def run(self):
pass
@sync_action('validate_example')
def validate_message(self) -> ValidationResult:
return ValidationResult('Some warning **markdown** message', MessageType.WARNING)Element of a dynamic (multi)select UI element. The UI objects expects list of such elements.
from keboola.component.base import ComponentBase, sync_action
from keboola.component.sync_actions import ValidationResult, MessageType, SelectElement
from typing import List
class Component(ComponentBase):
def run(self):
pass
@sync_action('validate_example')
def validate_message(self) -> List[SelectElement]:
return [SelectElement(value="value1", label="Value 1 label"),
SelectElement(value="value2", label="Value 2 label")]Some actions like test connection button expect only success / failure type of result with no return value.
from keboola.component.base import ComponentBase, sync_action
from keboola.component import UserException
import logging
class Component(ComponentBase):
def __init__(self):
super().__init__()
@sync_action('testConnection')
def test_connection(self):
# this is ignored when run as sync action.
logging.info("Testing Connection")
print("test print")
params = self.configuration.parameters
connection = params.get('test_connection')
if connection == "fail":
raise UserException("failed")
elif connection == "succeed":
# this is ignored when run as sync action.
logging.info("succeed")MIT licensed, see LICENSE file.
