bauplan package
Submodules
- bauplan.exceptions module
AccessDeniedError
ApiMethodError
ApiRouteError
BauplanError
BauplanInternalError
InvalidDataError
InvalidPlanError
JobError
MismatchedPythonVersionsError
MissingMagicCellError
MissingPandasError
NoResultsFoundError
ObjectCannotBeSerializedError
ObjectTooBigError
ResourceNotFoundError
TooManyRequestsError
UnauthorizedError
UnhandledRuntimeError
UpdateConflictError
UserObjectKeyNotExistsError
UserObjectWithKeyExistsError
- bauplan.helpers module
- bauplan.schema module
APIBranch
APIMetadata
APIResponse
Entry
Namespace
Ref
RefMetadata
RefMetadata.author_time
RefMetadata.authors
RefMetadata.commit_time
RefMetadata.committer
RefMetadata.common_ancestor_hash
RefMetadata.from_dict()
RefMetadata.message
RefMetadata.model_computed_fields
RefMetadata.model_config
RefMetadata.model_fields
RefMetadata.num_commits_ahead
RefMetadata.num_commits_behind
RefMetadata.num_total_commits
RefMetadata.parent_commit_hashes
Table
TableField
TableMetadata
TableWithMetadata
TableWithMetadata.fields
TableWithMetadata.id
TableWithMetadata.last_updated_ms
TableWithMetadata.metadata_location
TableWithMetadata.model_computed_fields
TableWithMetadata.model_config
TableWithMetadata.model_fields
TableWithMetadata.name
TableWithMetadata.raw
TableWithMetadata.records
TableWithMetadata.size
TableWithMetadata.snapshots
- bauplan.standard_expectations module
expect_column_accepted_values()
expect_column_all_null()
expect_column_all_unique()
expect_column_equal_concatenation()
expect_column_mean_greater_or_equal_than()
expect_column_mean_greater_than()
expect_column_mean_smaller_or_equal_than()
expect_column_mean_smaller_than()
expect_column_no_nulls()
expect_column_not_unique()
expect_column_some_null()
- bauplan.state module
- bauplan.store module
Module contents
- class bauplan.Client(api_key: str | None = None, profile: str | None = None, namespace: str | None = None, **kwargs)
Bases:
object
A consistent interface to access Bauplan operations.
Using the client
import bauplan client = bauplan.Client() # query the table and return result set as an arrow Table my_table = client.query('SELECT sum(trips) trips FROM travel_table', branch_name='main') # efficiently cast the table to a pandas DataFrame df = my_table.to_pandas()
Notes on authentication
# by default, authenticate from BAUPLAN_API_KEY >> BAUPLAN_PROFILE >> ~/.bauplan/config.yml client = bauplan.Client() # client used ~/.bauplan/config.yml profile 'default' os.environ['BAUPLAN_PROFILE'] = "someprofile" client = bauplan.Client() # >> client now uses profile 'someprofile' os.environ['BAUPLAN_API_KEY'] = "mykey" client = bauplan.Client() # >> client now authenticates with api_key value "mykey", because api key > profile # specify authentication directly - this supercedes BAUPLAN_API_KEY in the environment client = bauplan.Client(api_key='MY_KEY') # specify a profile from ~/.bauplan/config.yml - this supercedes BAUPLAN_PROFILE in the environment client = bauplan.Client(profile='default')
Handling Exceptions
Catalog operations (branch/table methods) raise a subclass of
bauplan.exceptions.BauplanError
that mirror HTTP status codes.400: InvalidDataError
401: UnauthorizedError
403: AccessDeniedError
404: ResourceNotFoundError e.g .ID doesn’t match any records
404: ApiRouteError e.g. the given route doesn’t exist
405: ApiMethodError e.g. POST on a route with only GET defined
409: UpdateConflictError e.g. creating a record with a name that already exists
429: TooManyRequestsError
Run/Query/Scan/Import operations raise a subclass of
bauplan.exceptions.BauplanError
that represents, and also return aRunState
object containing details and logs:JobError
e.g. something went wrong in a run/query/import/scan; includes error details
Run/import operations also return a state object that includes a
job_status
and other details. There are two ways to check status for run/import operations:try/except the JobError exception
check the
state.job_status
attribute
Examples:
try: state = client.run(...) state = client.scan(...) state = client.plan_import(...) state = client.apply_import(...) state = client.query(...) except bauplan.exceptions.JobError as e: ... state = client.run(...) if state.job_status != "success": ...
- Parameters:
api_key – (optional) Your unique Bauplan API key; mutually exclusive with
profile
. If not provided, fetch precedence is 1) environment BAUPLAN_API_KEY 2) .bauplan/config.ymlprofile – (optional) The Bauplan config profile name to use to determine api_key; mutually exclusive with
api_key
namespace – (optional) The default namespace to use for queries and runs.
- apply_import(plan: Dict, onto_branch: str, args: Dict | None = None, client_timeout: int | float | None = None) ApplyPlanState
Apply a Bauplan table import plan for a given branch and table.
An import is an operation to create a table in Bauplan from a file in the cloud. This is the equivalent of running through the CLI the
bauplan import apply
command.import bauplan # get the object representing the table import plan s3_path = 's3://path/to/my/files/*.parquet' plan_state = client.plan_import( from_ref='main', table_name='newtablename', search_string=s3_path ) if plan_state.error: plan_error_action(...) # apply the table import plan to create/replace a table on this branch apply_state = client.apply_import( plan=plan_state.plan, onto_branch='myname.mybranch', ) if apply_state.error: apply_error_action(...)
- Parameters:
plan – dict representation of an import plan, generated by client.plan_import
onto_branch – name of the branch on which to apply the plan
args – dict of arbitrary args to pass to the backend
client_timeout – seconds to timeout; this also cancels the remote job execution.
- create_branch(branch_name: str, from_ref: str) APIBranch
Create a new branch at a given ref.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() assert client.create_branch( branch_name='myzone.newbranch', from_ref='main' )
- Parameters:
branch_name – The name of the new branch
ref – The name of the base branch; either a branch like “main” or ref like “main@[sha]”
- Returns:
a boolean for whether the new branch was created
- create_namespace(branch_name: str, namespace_name: str) Namespace
Create a new namespace at a given branch.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() assert client.create_namespace( branch_name='myzone.newbranch', namespace_name='main' )
- Parameters:
branch_name – The name of the branch to create the namespace on.
ref – The namespace_name of the namespace.
- Returns:
a boolean for whether the new namespace was created
- delete_branch(branch_name: str) bool
Delete a branch.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() assert client.delete_branch(branch_name='mybranch')
- Parameters:
branch_name – The name of the branch to delete.
- Returns:
A boolean for if the branch was deleted
- delete_namespace(branch_name: str, namespace_name: str) bool
Delete a namespace.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() assert client.delete_namespace( branch_name='mybranch', namespace_name='mynamespace', )
- Parameters:
branch_name – The name of the branch to delete the namespace from.
namespace_name – The name of the namespace to delete.
- Returns:
A boolean for if the namespace was deleted
- drop_table(table_name: str, branch_name: str) bool
Drop a table.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() assert client.drop_table(table_name='mytable', branch_name='mybranch')
- Parameters:
table_name – The name of the table to delete
branch_name – The name of the branch on which the table is stored
- Returns:
A boolean for if the table was deleted
- get_branch(branch_name: str, limit: int | None = None, itersize: int | None = None) Generator[Table, None, None]
Get the tables and views in the target branch.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() # retrieve only the tables as tuples of (name, kind) tables = [(b.name, b.kind) for b in client.get_branch('main')]
- Parameters:
branch_name – The name of the branch to retrieve.
- Returns:
A list of Table objects, each having “name”, “kind” (e.g. TABLE)
- get_branch_metadata(branch_name: str) Ref
Get the data and metadata for a branch.
import bauplan client = bauplan.Client() data = get_branch_metadata('main') # print the number of total commits on the branch print(data.num_total_commits)
- Parameters:
branch_name – The name of the branch to retrieve.
- Returns:
A dictionary of metadata of type RefMetadata
- get_branches(itersize: int | None = None, limit: int | None = None, name: str | None = None, user: str | None = None) Generator[APIBranch, None, None]
Get the available data branches in the Bauplan catalog.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() for branch in client.get_branches(): print(branch.name, branch.hash)
- Parameters:
itersize – int 1-500
limit – int > 0
- Returns:
a list of Ref objects, each having attributes: “name”, “hash”
- get_namespaces(branch_name: str, itersize: int | None = None, limit: int | None = None, in_namespace: str | None = None) Generator[Namespace, None, None]
Get the available data namespaces in the Bauplan catalog branch.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() for namespace in client.get_namespaces(): print(namespace.name)
- Parameters:
itersize – int 1-500
limit – int > 0
in_namespace – The namespace to filter by.
- Returns:
a list of Namespace objects, each having attributes: “name”
- get_table(branch_name: str, table_name: str) List[TableField]
Get the fields metadata for a table in the target branch.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() # get the fields and metadata for the taxi_zones table in the main branch fields = get_table(branch_name='main', table_name='taxi_zones') # loop through the fields and print their name, required, and type for c in fields: print(c.name, c.required, c.type)
- Parameters:
branch_name – The name of the branch to get the table from.
table_name – The name of the table to retrieve.
- Returns:
a list of fields, each having “name”, “required”, “type”
- get_table_with_metadata(branch_name: str, table_name: str, include_raw: bool = False) TableWithMetadata
Get the table data and metadata for a table in the target branch.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() # get the fields and metadata for the taxi_zones table in the main branch table = client.get_table_with_metadata(branch_name='main', table_name='taxi_zones') # loop through the fields and print their name, required, and type for c in table.fields: print(c.name, c.required, c.type) # show the number of records in the table print(table.records)
- Parameters:
branch_name – The name of the branch to get the table from.
table_name – The name of the table to retrieve.
include_raw – Whether or not to include the raw metadata.json object as a nested dict
- Returns:
a TableWithMetadata object, optionally including the raw metadata.json object
- get_tables(branch_name: str, limit: int | None = None, itersize: int | None = None) Generator[Table, None, None]
Get the tables and views in the target branch.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() # retrieve only the tables as tuples of (name, kind) tables = client.get_tables('main') for table in tables: print(table.name, table.kind)
- Parameters:
branch_name – The name of the branch to retrieve.
- Returns:
A list of tables, each having “name”, “kind” (e.g. TABLE)
- merge_branch(onto_branch: str, from_ref: str) bool
Merge one branch into another.
Upon failure, raises
bauplan.exceptions.BauplanError
import bauplan client = bauplan.Client() assert merge_branch( onto_branch='myzone.somebranch', from_ref='myzone.oldbranch' )
- Parameters:
onto_branch – The name of the merge target
from_ref – The name of the merge source; either a branch like “main” or ref like “main@[sha]”
- Returns:
a boolean for whether the merge worked
- plan_import(table_name: str, search_string: str, from_ref: str = 'main', append: bool = False, replace: bool = False, args: Dict | None = None, client_timeout: int | float | None = None) PlanImportState
Create a table import plan from an S3 location.
An import is an operation to create a table in Bauplan from a file in the cloud. This is the equivalent of running through the CLI the
bauplan import plan
command.import bauplan client = bauplan.Client() s3_path = 's3://path/to/my/files/*.parquet' plan_state = client.plan_import( from_ref='main', # optional table_name='newtablename', search_string=s3_path, ) if plan_state.error: plan_error_action(...) success_action(plan_state.plan)
If you want to save the plan object output for record-keeping or future processing, you can use the plan object attribute to do something like:
plan_state = client.plan_import(...) import yaml plan_dict = plan_state.plan yaml.safe_dump(plan_dict, open('path/to/file.yaml','w'))
- Parameters:
search_string – The filepath of the plan to import.
table_name – The name of the table to import into.
append – Append the data to an existing table. Mutually exclusive with replace.
replace – Replace the data in an existing table. Mutually exclusive with append.
table_name – The name of the table to import into.
from_ref – The name of the branch to import from.
args – dict of arbitrary args to pass to the backend
client_timeout – seconds to timeout; this also cancels the remote job execution.
- query(query: str, branch_name: str = 'main', max_rows: int | None = None, no_cache: bool | None = False, connector: str | None = None, connector_config_key: str | None = None, connector_config_uri: str | None = None, namespace: str | None = None, args: Dict[str, Any] | None = None, client_timeout: int | float | None = None) Table
Execute a SQL query and return the results as a pyarrow.Table. Note that this function uses Arrow also internally, resulting in a fast data transfer.
If you prefer to return the results as a pandas DataFrame, use the
to_pandas
function of pyarrow.Table.import bauplan client = bauplan.Client() # query the table and return result set as an arrow Table my_table = client.query('SELECT c1 FROM my_table', branch_name='main') # efficiently cast the table to a pandas DataFrame df = mytable.to_pandas()
- Parameters:
query – The Bauplan query to execute.
branch_name – The branch to read from and write to (default: your local active branch, else ‘main’).
max_rows – The maximum number of rows to return; default:
None
(no limit).no_cache – Whether to disable caching for the query (default:
False
).connector – The connector type for the model (defaults to Bauplan). Allowed values are ‘snowflake’ and ‘dremio’.
connector_config_key – The key name if the SSM key is custom with the pattern bauplan/connectors/<connector_type>/<key>.
connector_config_uri – Full SSM uri if completely custom path, e.g. ssm://us-west-2/123456789012/baubau/dremio.
namespace – The Namespace to run the query in. If not set, the query will be run in the default namespace for your account.
args – Additional arguments to pass to the query (default: None).
client_timeout – seconds to timeout; this also cancels the remote job execution.
- Returns:
The query results as a
pyarrow.Table
.
- query_to_file(filename: str, query: str, branch_name: str = 'main', max_rows: int | None = None, no_cache: bool | None = False, connector: str | None = None, connector_config_key: str | None = None, connector_config_uri: str | None = None, namespace: str | None = None, args: Dict[str, Any] | None = None, client_timeout: int | float | None = None) None
Execute a SQL query and write the results to a file.
import bauplan client = bauplan.Client() # query the table and iterate through the results one row at a time for row in client.query_to_generator('SELECT c1 FROM my_table', branch_name='main'): # do logic
- Parameters:
filename – The name of the file to write the results to.
query – The Bauplan query to execute.
max_rows – The maximum number of rows to return; default:
None
(no limit).no_cache – Whether to disable caching for the query (default:
False
).branch_name – The branch to read from and write to (default: your local active branch, else ‘main’).
connector – The connector type for the model (defaults to Bauplan). Allowed values are ‘snowflake’ and ‘dremio’.
connector_config_key – The key name if the SSM key is custom with the pattern bauplan/connectors/<connector_type>/<key>.
connector_config_uri – Full SSM uri if completely custom path, e.g. ssm://us-west-2/123456789012/baubau/dremio.
namespace – The Namespace to run the query in. If not set, the query will be run in the default namespace for your account.
args – Additional arguments to pass to the query (default: None).
client_timeout – seconds to timeout; this also cancels the remote job execution.
- query_to_generator(query: str, branch_name: str | None = None, max_rows: int | None = None, no_cache: bool | None = False, connector: str | None = None, connector_config_key: str | None = None, connector_config_uri: str | None = None, namespace: str | None = None, args: Dict[str, Any] | None = None, client_timeout: int | float | None = None) Generator[Dict[str, Any], None, None]
Execute a SQL query and return the results as a generator, where each row is a Python dictionary.
import bauplan client = bauplan.Client() # query the table and iterate through the results one row at a time for row in client.query_to_generator('SELECT c1 FROM my_table', branch_name='main'): # do logic
- Parameters:
query – The Bauplan query to execute.
branch_name – The branch to read from and write to (default: your local active branch, else ‘main’).
max_rows – The maximum number of rows to return; default:
None
(no limit).no_cache – Whether to disable caching for the query (default:
False
).connector – The connector type for the model (defaults to Bauplan). Allowed values are ‘snowflake’ and ‘dremio’.
connector_config_key – The key name if the SSM key is custom with the pattern bauplan/connectors/<connector_type>/<key>.
connector_config_uri – Full SSM uri if completely custom path, e.g. ssm://us-west-2/123456789012/baubau/dremio.
namespace – The Namespace to run the query in. If not set, the query will be run in the default namespace for your account.
args – Additional arguments to pass to the query (default:
None
).client_timeout – seconds to timeout; this also cancels the remote job execution.
- Yield:
A dictionary representing a row of query results.
- run(project_dir: str = '.', branch_name: str | None = None, id: str | None = None, parameters: Dict[str, str | int | float | bool] | None = None, namespace: str | None = None, args: Dict[str, Any] | None = None, client_timeout: int | float | None = None) RunState
Run a Bauplan project and return the state of the run. This is the equivalent of running through the CLI the
bauplan run
command.- Parameters:
project_dir – The directory of the project (where the
bauplan_project.yml
file is located).branch_name – The branch to read from and write to (default: your local active branch, else ‘main’).
id – The ID of the run (optional). This can be used to re-run a previous run, e.g., on a different branch.
parameters – Parameters for templating into SQL or Python models.
namespace – The Namespace to run the job in. If not set, the job will be run in the default namespace for the project.
args – Additional arguments (optional).
client_timeout – seconds to timeout; this also cancels the remote job execution.
- Returns:
The state of the run.
- scan(table_name: str, branch_name: str | None = None, columns: list | None = None, filters: str | None = None, limit: int | None = None, connector: str | None = None, connector_config_key: str | None = None, connector_config_uri: str | None = None, namespace: str | None = None, args: Dict[str, str] | None = None, client_timeout: int | float | None = None, **kwargs: Any) Table
Execute a table scan (with optional filters) and return the results as an arrow Table.
Note that this function uses SQLGlot to compose a safe SQL query, and then internally defer to the query_to_arrow function for the actual scan.
import bauplan client = bauplan.Client() # run a table scan over the data lake # filters are passed as a string my_table = client.scan( table_name='my_table', columns=['c1'], filter='c2 > 10' branch_name='main' )
- Parameters:
table_name – The table to scan.
branch_name – The branch to read from and write to (default: your local active branch, else ‘main’).
columns – The columns to return (default:
None
).filters – The filters to apply (default:
None
).namespace – The Namespace to run the scan in. If not set, the scan will be run in the default namespace for your account.
args – dict of arbitrary args to pass to the backend.
client_timeout – seconds to timeout; this also cancels the remote job execution.
- Returns:
The scan results as a
pyarrow.Table
.
- table_create_plan(table_name: str, search_uri: str, branch: str, namespace: str | None = None, replace: bool = False, args: Dict | None = None, client_timeout: int | float | None = None) TableCreatePlanState
Create a table import plan from an S3 location.
This operation will attempt to create a table based of schemas of N parquet files found by a given search uri. A YAML file containing the schema and plan is returns and if there are no conflicts, it is automatically applied.
import bauplan client = bauplan.Client() s3_path = 's3://path/to/my/files/*.parquet' plan_state = client.table_create_plan( branch='main' table_name='newtablename', search_string=s3_path, ) if plan_state.error: plan_error_action(...) success_action(plan_state.plan)
- Parameters:
search_string – The filepath of the plan to import.
table_name – The name of the table which will be created
branch – The branch in which to create the table in
namespace – Optional argument specifying the namespace. If not specified, it will be inferred based on table location or the default namespace
- table_create_plan_apply(plan: Dict, args: Dict | None = None, client_timeout: int | float | None = None) TableCreatePlanState
Apply a plan for creating a table. It is done automaticaly during the table plan creation if no schema conflicts exist. Otherwise, if schema conflicts exist, then this function is used to apply them after the schema conflicts are resolved. Most common schema conflict is a two parquet files with the same column name but different datatype
- Parameters:
search_string – The filepath of the plan to import.
plan – The name of the table to import into.
append – Append the data to an existing table. Mutually exclusive with replace.
replace – Replace the data in an existing table. Mutually exclusive with append.
table_name – The name of the table to import into.
from_ref – The name of the branch to import from.
args – dict of arbitrary args to pass to the backend
client_timeout – seconds to timeout; this also cancels the remote job execution.
- table_data_import(table_name: str, branch: str, search_uri: str, namespace: str | None = None, continue_on_error: bool = False, import_duplicate_files: bool = False, best_effort: bool = False, transformation_query: str | None = None, args: Dict | None = None, client_timeout: int | float | None = None) TableDataImportState
Imports data into an already existing table.
import bauplan client = bauplan.Client() s3_path = 's3://path/to/my/files/*.parquet' plan_state = client.table_data_import( table_name='newtablename', search_uri=s3_path, branch_name='main' ) if plan_state.error: plan_error_action(...) success_action(plan_state.plan)
- Parameters:
table_name – Previously created table in into which data will be imported
branch – Branch in which to import the table
search_uri – Uri which to scan for files to import
namespace – Namespace of the table. If not specified, namespace will be infered from table name or default settings
continue_on_error – Do not fail the import even if 1 data import fails
import_duplicate_files – Ignore prevention of importing s3 files that were already imported
best_effort – Don’t fail if schema of table does not match.
transformation_query – Optional duckdb compliant query applied on each parquet file. Use original_table as the table in the query
args – dict of arbitrary args to pass to the backend
client_timeout – seconds to timeout; this also cancels the remote job execution.
- class bauplan.JobStatus(canceled: 'str' = 'CANCELLED', cancelled: 'str' = 'CANCELLED', failed: 'str' = 'FAILED', rejected: 'str' = 'REJECTED', success: 'str' = 'SUCCESS', timeout: 'str' = 'TIMEOUT', unknown: 'str' = 'UNKNOWN')
Bases:
object
- canceled: str = 'CANCELLED'
- cancelled: str = 'CANCELLED'
- failed: str = 'FAILED'
- rejected: str = 'REJECTED'
- success: str = 'SUCCESS'
- timeout: str = 'TIMEOUT'
- unknown: str = 'UNKNOWN'
- class bauplan.Model(name: str, columns: List[str] | None = None, filter: str | None = None, ref: str | None = None, connector: str | None = None, connector_config_key: str | None = None, connector_config_uri: str | None = None, **kwargs: Any)
Bases:
object
Represents a model (dataframe/table representing a DAG step) as an input to a function.
e.g.
@bauplan.model() def some_parent_model(): return pyarrow.Table.from_pydict({'bar': [1, 2, 3]}) @bauplan.model() def your_cool_model( # parent models are passed as inputs, using bauplan.Model # class parent_0=bauplan.Model( 'some_parent_model', columns=['bar'], filter='bar > 1', ) ): # Can return a pandas dataframe or a pyarrow table return pyarrow.Table.from_pandas( pd.DataFrame({ 'foo': parent_0['bar'] * 2, }) )
Bauplan can wrap other engines for the processing of some models, exposing a common interface and unified API for the user while dispatching the relevant operations to the underlying engine.
The authentication and authorization happens securely and transparently through ssm; the user is asked to specify a connector type and the credentials through the relevant keywords:
@bauplan.model() def your_cool_model( parent_0=bauplan.Model( 'some_parent_model', columns=['bar'], filter='bar > 1', connector='dremio', connector_config_key='bauplan', ) ): # parent_0 inside the function body # will still be an Arrow table: the user code # should still be the same as the data is moved # transparently by Bauplan from an engine to the function. return pyarrow.Table.from_pandas( pd.DataFrame({ 'foo': parent_0['bar'] * 2, }) )
- Parameters:
name – The name of the model.
columns – The list of columns in the model. If the arg is not provided, the model will load all columns.
filter – The optional filter for the model. Defaults to None.
ref – The optional reference to the model. Defaults to None.
connector – The connector type for the model (defaults to Bauplan SQL). Allowed values are ‘snowflake’ and ‘dremio’.
connector_config_key – The key name if the SSM key is custom with the pattern bauplan/connectors/<connector_type>/<key>.
connector_config_uri – Full SSM uri if completely custom path, e.g. ssm://us-west-2/123456789012/baubau/dremio.
- bauplan.expectation(**kwargs: Any) Callable
Decorator that defines a Bauplan expectation.
An expectation is a function from one (or more) dataframe-like object(s) to a boolean: it is commonly used to perform data validation and data quality checks when running a pipeline. Expectations takes as input the table(s) they are validating and return a boolean indicating whether the expectation is met or not. A Python expectation needs a Python environment to run, which is defined using the python decorator, e.g.:
@bauplan.expectation() @bauplan.python('3.10') def test_joined_dataset( data=bauplan.Model( 'join_dataset', columns=['anomaly'] ) ): # your data validation code here return expect_column_no_nulls(data, 'anomaly')
- Parameters:
f – The function to decorate.
- bauplan.model(name: str | None = None, columns: List[str] | None = None, materialize: bool | None = None, internet_access: bool | None = None, partitioned_by: str | List[str] | Tuple[str, ...] | None = None, materialization_strategy: Literal['NONE', 'REPLACE', 'APPEND'] | None = None, cache_strategy: Literal['NONE', 'DEFAULT'] | None = None, **kwargs: Any) Callable
Decorator that specifies a Bauplan model.
A model is a function from one (or more) dataframe-like object(s) to another dataframe-like object: it is used to define a transformation in a pipeline. Models are chained together implicitly by using them as inputs to their children. A Python model needs a Python environment to run, which is defined using the python decorator, e.g.:
@bauplan.model( columns=['*'], materialize=False ) @bauplan.python('3.11') def source_scan( data=bauplan.Model( 'iot_kaggle', columns=['*'], filter="motion='false'" ) ): # your code here return data
- Parameters:
name – the name of the model (e.g. ‘users’); if missing the function name is used.
columns – the columns of the output dataframe after the model runs (e.g. [‘id’, ‘name’, ‘email’]). Use [‘*’] as a wildcard.
materialize – whether the model should be materialized.
internet_access – whether the model requires internet access.
partitioned_by – the columns to partition the data by.
materialization_strategy – the materialization strategy to use.
cache_strategy – the cache strategy to use.
- bauplan.pyspark(version: str | None = None, conf: Dict[str, str] | None = None, **kwargs: Any) Callable
Decorator that makes a pyspark session available to a Bauplan function (a model or an expectation). Add a spark=None parameter to the function model args
- Parameters:
version – the version string of pyspark
conf – A dict containing the pyspark config
- bauplan.python(version: str | None = None, pip: Dict[str, str] | None = None, **kwargs: Any) Callable
Decorator that defines a Python environment for a Bauplan function (e.g. a model or expectation). It is used to specify directly in code the configuration of the Python environment required to run the function, i.e. the Python version and the Python packages required.
- Parameters:
version – The python version for the interpreter (e.g.
'3.11'
).pip – A dictionary of dependencies (and versions) required by the function (e.g.
{'requests': '2.26.0'}
).
- bauplan.resources(cpus: int | float | None = None, memory: int | str | None = None, memory_swap: int | str | None = None, timeout: int | None = None, **kwargs: Any) Callable
Decorator that defines the resources required by a Bauplan function (e.g. a model or expectation). It is used to specify directly in code the configuration of the resources required to run the function.
- Parameters:
cpus – The number of CPUs required by the function (e.g:
`0.5`
)memory – The amount of memory required by the function (e.g:
`1G`
,`1000`
)memory_swap – The amount of swap memory required by the function (e.g:
`1G`
,`1000`
)timeout – The maximum time the function is allowed to run (e.g:
`60`
)