ploomber.tasks.SQLScript¶
- class ploomber.tasks.SQLScript(source, product, dag, name=None, client=None, params=None)¶
Execute a script in a SQL database to create a relation or view
- Parameters:
source (str or pathlib.Path) – SQL script source, if str, the content is interpreted as the actual script, if pathlib.Path, the content of the file is loaded
product (ploomber.products.product) – Product generated upon successful execution
dag (ploomber.DAG) – A DAG to add this task to
name (str) – A str to indentify this task. Should not already exist in the dag
client (ploomber.clients.{SQLAlchemyClient, DBAPIClient}, optional) – The client used to connect to the database. Only required if no dag-level client has been declared using dag.clients[class]
params (dict, optional) – Parameters to pass to the script, by default, the callable will be executed with a “product” (which will contain the product object). It will also include a “upstream” parameter if the task has upstream dependencies along with any parameters declared here. The source code is converted to a jinja2.Template for passing parameters, refer to jinja2 documentation for details
Examples
Spec API:
clients: SQLScript: clients.get SQLiteRelation: clients.get tasks: - source: script.sql product: [subset, table]
Python API (SQLite):
>>> import sqlite3 >>> import pandas as pd >>> from ploomber import DAG >>> from ploomber.products import SQLiteRelation >>> from ploomber.tasks import SQLScript >>> from ploomber.clients import DBAPIClient >>> con_raw = sqlite3.connect(database='my_db.db') >>> df = pd.DataFrame({'a': range(100), 'b': range(100)}) >>> _ = df.to_sql('numbers', con_raw, index=False) >>> dag = DAG() >>> client = DBAPIClient(sqlite3.connect, dict(database='my_db.db'), ... split_source=';') >>> dag.clients[SQLScript] = client >>> dag.clients[SQLiteRelation] = client >>> script = ('DROP TABLE IF EXISTS {{product}};' ... 'CREATE TABLE {{product}} AS ' ... 'SELECT * FROM numbers LIMIT 3') >>> _ = SQLScript(script, SQLiteRelation(('subset', 'table')), ... dag=dag, name='create-subset') >>> _ = dag.build() >>> df = pd.read_sql('SELECT * FROM subset', con_raw) >>> con_raw.close() >>> df.head(3) a b 0 0 0 1 1 1 2 2 2
See also
ploomber.clients.SQLDump
A task to execute a
SELECT
statement and dump the output into a file
Methods
build
([force, catch_exceptions])Build a single task
debug
()Debug task, only implemented in certain tasks
load
([limit])Load this task's product in a pandas.DataFrame
render
([force, outdated_by_code, remote])Renders code and product, all upstream tasks must have been rendered first, for that reason, this method will usually not be called directly but via DAG.render(), which renders in the right order.
run
()This is the only required method Task subclasses must implement
set_upstream
(other[, group_name])status
([return_code_diff, sections])Prints the current task status
- build(force=False, catch_exceptions=True)¶
Build a single task
Although Tasks are primarily designed to execute via DAG.build(), it is possible to do so in isolation. However, this only works if the task does not have any unrendered upstream dependencies, if that’s the case, you should call DAG.render() before calling Task.build()
- Returns:
A dictionary with keys ‘run’ and ‘elapsed’
- Return type:
dict
- Raises:
TaskBuildError – If the error failed to build because it has upstream dependencies, the build itself failed or build succeded but on_finish hook failed
DAGBuildEarlyStop – If any task or on_finish hook raises a DAGBuildEarlyStop error
- debug()¶
Debug task, only implemented in certain tasks
- load(limit=10)¶
Load this task’s product in a pandas.DataFrame
- Parameters:
limit (int, default=10) – How many records to load, defaults to 10
- render(force=False, outdated_by_code=True, remote=False)¶
Renders code and product, all upstream tasks must have been rendered first, for that reason, this method will usually not be called directly but via DAG.render(), which renders in the right order.
Render fully determines whether a task should run or not.
- Parameters:
force (bool, default=False) – If True, mark status as WaitingExecution/WaitingUpstream even if the task is up-to-date (if there are any File(s) with clients, this also ignores the status of the remote copy), otherwise, the normal process follows and only up-to-date tasks are marked as Skipped.
outdated_by_code (bool, default=True) – Factors to determine if Task.product is marked outdated when source code changes. Otherwise just the upstream timestamps are used.
remote (bool, default=False) – Use remote metadata to determine status
Notes
This method tries to avoid calls to check for product status whenever possible, since checking product’s metadata can be a slow operation (e.g. if metadata is stored in a remote database)
When passing force=True, product’s status checking is skipped altogether, this can be useful when we only want to quickly get a rendered DAG object to interact with it
- run()¶
This is the only required method Task subclasses must implement
- set_upstream(other, group_name=None)¶
- status(return_code_diff=False, sections=None)¶
Prints the current task status
- Parameters:
sections (list, optional) – Sections to include. Defaults to “name”, “last_run”, “oudated”, “product”, “doc”, “location”
Attributes
PRODUCT_CLASSES_ALLOWED
client
exec_status
name
A str that represents the name of the task, you can access tasks in a dag using dag['some_name']
on_failure
Callable to be executed if task fails (passes Task as first parameter and the exception as second parameter)
on_finish
Callable to be executed after this task is built successfully (passes Task as first parameter)
on_render
params
dict that holds the parameter that will be passed to the task upon execution.
product
The product this task will create upon execution
source
Source is used by the task to compute its output, for most cases this is source code, for example PythonCallable takes a function as source and SQLScript takes a string with SQL code as source.
upstream
A mapping for upstream dependencies {task name} -> [task object]