Specification driven datagen by anupkalburgi · Pull Request #372 · databrickslabs/dbldatagen

anupkalburgi · 2025-11-04T14:28:33Z

Changes

Linked issues

Resolves #..

Requirements

manually tested
updated documentation
updated demos
updated tests

codecov · 2025-11-06T16:14:20Z

Codecov Report

❌ Patch coverage is 95.01558% with 16 lines in your changes missing coverage. Please review.
✅ Project coverage is 92.28%. Comparing base (4687b8c) to head (d527300).
⚠️ Report is 1 commits behind head on master.

Files with missing lines	Patch %	Lines
dbldatagen/spec/generator_spec_impl.py	91.89%	3 Missing and 6 partials ⚠️
dbldatagen/spec/generator_spec.py	95.41%	3 Missing and 2 partials ⚠️
dbldatagen/spec/compat.py	60.00%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #372      +/-   ##
==========================================
+ Coverage   92.12%   92.28%   +0.16%     
==========================================
  Files          47       55       +8     
  Lines        4217     4538     +321     
  Branches      766      836      +70     
==========================================
+ Hits         3885     4188     +303     
- Misses        186      195       +9     
- Partials      146      155       +9

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dbldatagen/spec/generator_spec.py

dbldatagen/spec/generator_spec_impl.py

Copilot

Pull request overview

This PR introduces a new Pydantic-based specification API for dbldatagen, providing a declarative, type-safe approach to synthetic data generation. The changes add comprehensive validation, test coverage, and example specifications while updating documentation and build configuration to support both Pydantic V1 and V2.

Key Changes:

New spec-based API with Pydantic models for defining data generation configurations
Comprehensive validation framework with error collection and reporting
Pydantic V1/V2 compatibility layer for broad environment support

Reviewed changes

Copilot reviewed 17 out of 17 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
tests/test_specs.py	Comprehensive test suite for ValidationResult, ColumnDefinition, DatagenSpec validation, and target configurations
tests/test_datasets_with_specs.py	Tests for Pydantic model validation with BasicUser and BasicStockTicker specifications
tests/test_datagen_specs.py	Tests for DatagenSpec creation, validation, and generator options
pyproject.toml	Added ipython dependency, test matrix for Pydantic versions, and disabled warn_unused_ignores
makefile	Updated to use Pydantic version-specific test environments and removed .venv target
examples/datagen_from_specs/basic_user_datagen_spec.py	Example DatagenSpec factory for generating basic user data with pre-configured specs
examples/datagen_from_specs/basic_stock_ticker_datagen_spec.py	Complex example with OHLC stock data generation including time-series and volatility modeling
examples/datagen_from_specs/README.md	Documentation for Pydantic-based dataset specifications with usage examples
dbldatagen/spec/validation.py	ValidationResult class for collecting and reporting validation errors and warnings
dbldatagen/spec/output_targets.py	Pydantic models for UCSchemaTarget and FilePathTarget output destinations
dbldatagen/spec/generator_spec_impl.py	Generator class implementing the spec-to-DataFrame conversion logic
dbldatagen/spec/generator_spec.py	Core DatagenSpec and TableDefinition models with comprehensive validation
dbldatagen/spec/compat.py	Pydantic V1/V2 compatibility layer enabling cross-version support
dbldatagen/spec/column_spec.py	ColumnDefinition model with validation for primary keys and constraints
dbldatagen/spec/init.py	Module initialization with lazy imports to avoid heavy dependencies
README.md	Updated feature list and formatting to mention new Pydantic-based API
CHANGELOG.md	Added entry for Pydantic-based specification API feature

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tests/test_specs.py

examples/datagen_from_specs/basic_stock_ticker_datagen_spec.py

dbldatagen/spec/generator_spec_impl.py

makefile

dbldatagen/spec/__init__.py

dbldatagen/spec/column_spec.py

dbldatagen/spec/generator_spec_impl.py

ghanse · 2025-12-04T19:58:35Z

dbldatagen/spec/generator_spec_impl.py

+                # Write data based on destination type
+                if isinstance(output_destination, FilePathTarget):
+                    output_path = posixpath.join(output_destination.base_path, table_name)
+                    df.write.format(output_destination.output_format).mode("overwrite").save(output_path)
+                    logger.info(f"Wrote table '{table_name}' to file path: {output_path}")
+
+                elif isinstance(output_destination, UCSchemaTarget):
+                    output_table = f"{output_destination.catalog}.{output_destination.schema_}.{table_name}"
+                    df.write.mode("overwrite").saveAsTable(output_table)
+                    logger.info(f"Wrote table '{table_name}' to Unity Catalog: {output_table}")


We should use utils.write_data_to_output for this.

The method in utils relies on OutputDataset. See the other comments above.

ghanse · 2025-12-04T20:00:33Z

dbldatagen/spec/output_targets.py

We have OutputDataset in config.py. I think we can reuse it here instead of creating new classes?

imo, The output target in spec would be better suited for the spec as it does validations for UC and file paths volumes. This config.OutputDataset is generic, and getting specific errors would mean essientially copying this over to the new spec folder or other way round. Is there something I am missing here ?

OutputDataset could extend BaseModel and perform the same validations.

* added use of ABC to mark TextGenerator as abstract * Lint text generators module * Add persistence methods * Add tests and docs; Update PR template * Update hatch installation for push action * Refactor * Update method names and signatures --------- Co-authored-by: ronanstokes-db <[email protected]> Co-authored-by: Ronan Stokes <[email protected]>

Copilot

Pull request overview

Copilot reviewed 24 out of 24 changed files in this pull request and generated 6 comments.

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tests/test_output.py

pyproject.toml

Copilot · 2025-12-23T13:56:32Z

dbldatagen/spec/generator_spec.py

+            known_options = ["random", "randomSeed", "randomSeedMethod", "verbose", "debug", "seedColumnName"]
+            for key in self.generator_options:
+                if key not in known_options:
+                    result.add_warning(f"Unknown generator option: '{key}'. " "This may be ignored during generation.")


The warning message contains two separate string literals that should be combined into a single string for better readability.

Suggested change

result.add_warning(f"Unknown generator option: '{key}'. " "This may be ignored during generation.")

result.add_warning(f"Unknown generator option: '{key}'. This may be ignored during generation.")

dbldatagen/spec/__init__.py

alexott · 2025-12-23T13:59:46Z

@anupkalburgi how hard it will be to extend this implementation to load spec from YAML files? I.e., with pydantic-yaml.

I'm looking into PRs like #376 and thinking that it could be easier to provide these generators as a "standard" generation notebook that will receive a file name as a parameter and generate data...

anupkalburgi · 2026-01-08T17:51:31Z

@anupkalburgi how hard it will be to extend this implementation to load spec from YAML files? I.e., with pydantic-yaml.

I'm looking into PRs like #376 and thinking that it could be easier to provide these generators as a "standard" generation notebook that will receive a file name as a parameter and generate data...

That is the idea with core idea behind this PR as far as we can get input format (yaml/json/py dict) to pydantic model, we would be able to pass that as the config to the generator that takes the pydantic object. Will be adding examples and helper methods in subsequent prs

Copilot

Pull request overview

Copilot reviewed 24 out of 24 changed files in this pull request and generated 20 comments.

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pyproject.toml

dbldatagen/spec/generator_spec_impl.py

dbldatagen/spec/__init__.py

tests/test_output.py

dbldatagen/spec/generator_spec.py

tests/test_spec_init.py

Co-authored-by: Copilot <[email protected]>

Copilot

Pull request overview

Copilot reviewed 24 out of 24 changed files in this pull request and generated 4 comments.

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dbldatagen/spec/generator_spec_impl.py

dbldatagen/spec/__init__.py

ghanse

Left a few comments. We also need to add documentation.

ghanse · 2026-01-21T15:38:41Z

dbldatagen/spec/generator_spec.py

+    :param number_of_rows: Total number of data rows to generate for this table.
+                          Must be a positive integer
+    :param partitions: Number of Spark partitions to use when generating data.
+                      If None, defaults to Spark's default parallelism setting.
+                      More partitions can improve generation speed for large datasets
+    :param columns: List of ColumnDefinition objects specifying the columns to generate
+                   in this table. At least one column must be specified


Would a user need to set the same property for every columnspec? For example for random seed value?

dbldatagen/spec/generator_spec.py

ghanse · 2026-01-21T15:58:35Z

dbldatagen/spec/generator_spec.py

+            known_options = ["random", "randomSeed", "randomSeedMethod", "verbose", "debug", "seedColumnName"]
+            for key in self.generator_options:
+                if key not in known_options:
+                    result.add_warning(f"Unknown generator option: '{key}'. " "This may be ignored during generation.")


ghanse · 2026-01-21T16:03:26Z

dbldatagen/spec/generator_spec.py

+                if key not in known_options:
+                    result.add_warning(f"Unknown generator option: '{key}'. " "This may be ignored during generation.")
+
+    def validate(self, strict: bool = True) -> ValidationResult:  # type: ignore[override]


Can we modify this slightly to return something usable when validation passes? Maybe add an is_valid field to ValidationResult?

ValidationResult has is_valid method

Why should we need a method for this? Is it not a property of the validation result? Is a single validation result intended to be re-used?

dbldatagen/spec/generator_spec.py

ghanse · 2026-01-21T16:13:16Z

dbldatagen/spec/generator_spec.py

+                )
+                display(HTML(message))
+
+            df = pd.DataFrame([col.dict() for col in table_def.columns])


Can we use Spark here to avoid the Pandas dependency?

This is used for displaying the columns using display()
This is done before we invoke the generator, and access to the Spark object is not verified.

There are ways to display DataFrames with Spark, right?

dbldatagen/spec/generator_spec_impl.py

examples/datagen_from_specs/README.md

ghanse · 2026-01-21T16:22:45Z

dbldatagen/spec/output_targets.py

OutputDataset could extend BaseModel and perform the same validations.

Co-authored-by: Copilot <[email protected]>

Copilot

Pull request overview

Copilot reviewed 26 out of 26 changed files in this pull request and generated 9 comments.

Comments suppressed due to low confidence (1)

tests/test_output.py:110

This test unconditionally sleeps for the full time_limit (40s) before stopping the streaming query, so it will always add ~40s per parametrized case even if data is available earlier. Consider polling for output/progress and breaking early (with a max timeout) to keep CI runtime bounded while still being robust.

        start_time = time.time()
        elapsed_time = 0
        time_limit = 40.0

        while elapsed_time < time_limit:
            time.sleep(1)
            elapsed_time = time.time() - start_time

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Copilot · 2026-02-10T10:20:52Z

dbldatagen/spec/output_targets.py

+        if not v.strip():
+            raise ValueError("Identifier must be non-empty.")
+        if not v.isidentifier():
+            logger.warning(f"'{v}' is not a basic Python identifier. Ensure validity for Unity Catalog.")
+        return v.strip()


validate_identifiers checks v.isidentifier() before stripping whitespace, which can generate spurious warnings for values like ' main '. Strip first (and ideally validate/log using the stripped value) so warnings reflect the actual persisted identifier.

Suggested change

if not v.strip():

raise ValueError("Identifier must be non-empty.")

if not v.isidentifier():

logger.warning(f"'{v}' is not a basic Python identifier. Ensure validity for Unity Catalog.")

return v.strip()

value = v.strip()

if not value:

raise ValueError("Identifier must be non-empty.")

if not value.isidentifier():

logger.warning(

f"'{value}' is not a basic Python identifier. Ensure validity for Unity Catalog."

)

return value

Copilot · 2026-02-10T10:20:53Z

dbldatagen/spec/column_spec.py

+        if is_primary:
+            if "min" in options or "max" in options:
+                raise ValueError(f"Primary key column '{name}' cannot have min/max options.")
+


Primary-key validation only rejects option keys min/max, but the rest of the codebase (and dbldatagen APIs) predominantly use minValue/maxValue (see existing dataset providers). As a result, primary keys can still be configured with minValue/maxValue without error, contradicting the docstring constraint. Consider rejecting minValue/maxValue as well (and/or normalizing legacy keys).

Copilot · 2026-02-10T10:20:53Z

dbldatagen/spec/generator_spec_impl.py

+                    logger.warning("No output destination specified, skipping data write")
+                    return


In _writePreparedData, the else branch logs a warning and returns when output_destination is None or an unexpected type. This exits the method on the first table, so later tables are never even built, which conflicts with the docstring (“data is generated but not persisted anywhere”) and also with the documented ValueError behavior for unknown destination types. Consider: (1) if destination is None, still build all tables and just skip write (use continue), and (2) if destination is an unrecognized type, raise ValueError instead of silently returning.

Suggested change

logger.warning("No output destination specified, skipping data write")

return

if output_destination is None:

logger.warning(

f"No output destination specified for table '{table_name}', skipping data write"

)

continue

raise ValueError(

f"Unrecognized output destination type: {type(output_destination).__name__}"

)

Copilot · 2026-02-10T10:20:53Z

dbldatagen/spec/generator_spec.py

+            current = col.name
+
+            while current:
+                if current in visited:
+                    # Found a cycle
+                    cycle_path = " -> ".join([*list(visited), current])
+                    errors.append(
+                        f"Table '{table_name}': Circular dependency detected in column '{col.name}': {cycle_path}"
+                    )
+                    break
+
+                visited.add(current)


Circular dependency error messages build cycle_path from a set (visited), which produces non-deterministic ordering in the reported path. Using an ordered structure (e.g., list for the traversal path plus a separate set for membership checks) would produce stable, readable cycle reports.

Suggested change

current = col.name

while current:

if current in visited:

# Found a cycle

cycle_path = " -> ".join([*list(visited), current])

errors.append(

f"Table '{table_name}': Circular dependency detected in column '{col.name}': {cycle_path}"

)

break

visited.add(current)

path: list[str] = []

current = col.name

while current:

if current in visited:

# Found a cycle; build a deterministic, ordered cycle path

if current in path:

start_index = path.index(current)

cycle_nodes = path[start_index:] + [current]

else:

cycle_nodes = path + [current]

cycle_path = " -> ".join(cycle_nodes)

errors.append(

f"Table '{table_name}': Circular dependency detected in column '{col.name}': {cycle_path}"

)

break

visited.add(current)

path.append(current)

Copilot · 2026-02-10T10:20:53Z

examples/datagen_from_specs/README.md

+
+## Requirements
+
+- Python 3.8+


The README states a Python 3.8+ requirement, but the project metadata requires Python >=3.10 (pyproject.toml requires-python). Please align this requirement text with the actual supported minimum version to avoid confusing users.

Suggested change

- Python 3.8+

- Python 3.10+

Copilot · 2026-02-10T10:20:54Z

dbldatagen/spec/generator_spec.py

+import pandas as pd
+from IPython.display import HTML, display
+


generator_spec.py unconditionally imports pandas and IPython.display at module import time, but display_all_tables()’s docstring claims it will fall back in non-notebook environments. With the current imports, environments without IPython/pandas will fail at import-time before any fallback can occur. Consider moving these imports inside display_all_tables() (or guarding them with try/except) so validation/serialization features can be used without notebook-only deps.

Suggested change

import pandas as pd

from IPython.display import HTML, display

try:

import pandas as pd

except ImportError: # Optional dependency; used only for display utilities.

pd = None # type: ignore[assignment]

try:

from IPython.display import HTML, display

except ImportError: # Optional dependency; used only in notebook environments.

HTML = None # type: ignore[assignment]

display = None # type: ignore[assignment]

Copilot · 2026-02-10T10:20:54Z

dbldatagen/spec/generator_spec.py

+    :param result: ValidationResult to collect errors/warnings
+    """
+    column_names = [col.name for col in table_def.columns]
+    duplicates = [name for name in set(column_names) if column_names.count(name) > 1]


Duplicate column detection builds duplicates by iterating a set(column_names), which makes the resulting error message order non-deterministic across runs. Sorting the duplicates (or preserving first-seen order) will make validation output stable and easier to test/debug.

Suggested change

duplicates = [name for name in set(column_names) if column_names.count(name) > 1]

seen: set[str] = set()

duplicates: list[str] = []

duplicate_seen: set[str] = set()

for name in column_names:

if name in seen and name not in duplicate_seen:

duplicates.append(name)

duplicate_seen.add(name)

else:

seen.add(name)

Copilot · 2026-02-10T10:20:54Z

dbldatagen/spec/generator_spec_impl.py

+    else:
+        kwargs = col_def.options.copy() if col_def.options is not None else {}
+
+        if col_type:
+            kwargs["colType"] = col_type
+        if col_def.baseColumn:
+            kwargs["baseColumn"] = col_def.baseColumn
+        if col_def.baseColumnType:
+            kwargs["baseColumnType"] = col_def.baseColumnType
+        if col_def.omit is not None:
+            kwargs["omit"] = col_def.omit
+


ColumnDefinition.nullable is defined and documented, but _columnSpecToDatagenColumnSpec never forwards it to DataGenerator.withColumn(...) (which supports a nullable kwarg). This means nullable settings in specs are silently ignored. Consider adding nullable=col_def.nullable (or omitting it when None) in the non-primary (and possibly primary) branch to match the spec model.

Copilot · 2026-02-10T10:20:54Z

tests/test_spec_compat.py

+            max_val: int
+
+            @root_validator()
+            def validate_range(cls, values):


Normal methods should have 'self', rather than 'cls', as their first parameter.

Suggested change

def validate_range(cls, values):

def validate_range(self, values):

anupkalburgi · 2026-03-05T17:27:10Z

This is now part of the new PR #381

anupkalburgi · 2026-03-05T17:28:04Z

Moved all the changes to PR 381

anupkalburgi added 4 commits September 23, 2025 12:16

working test wireup

20db964

Initial code, spec and test, pushing for review

4e9cba5

fixing tests

d37de68

changes to make file

f5214ca

anupkalburgi force-pushed the ak/spec branch from 47e80f1 to f5214ca Compare November 6, 2025 15:55

updating docs

0a8fa2a

anupkalburgi commented Nov 10, 2025

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anupkalburgi added 3 commits November 17, 2025 13:56

fixing tests, removing the solved todos, targets to a diff module

61f676d

converting to camelCase

e139c8b

validation into a diff module

52a4283

anupkalburgi marked this pull request as ready for review December 2, 2025 14:31

anupkalburgi requested review from a team as code owners December 2, 2025 14:31

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removing compat/scratch notes

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anupkalburgi force-pushed the ak/spec branch from 1b90b6a to a0ce13b Compare December 2, 2025 14:56

marking the spec module experimental

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Format modules (#367)

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add more imp tests

b1ac085

adding missed test files

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anupkalburgi changed the title ~~Ak/spec~~ Specification driven datagneration Dec 18, 2025

anupkalburgi changed the title ~~Specification driven datagneration~~ Specification driven datagen Dec 18, 2025

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Update dbldatagen/spec/generator_spec.py

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Update README.md

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	result.add_warning(f"Unknown generator option: '{key}'. " "This may be ignored during generation.")
	result.add_warning(f"Unknown generator option: '{key}'. This may be ignored during generation.")

		logger.warning("No output destination specified, skipping data write")
		return

-                    logger.warning("No output destination specified, skipping data write")
-                    return
+                    if output_destination is None:
+                        logger.warning(
+                            f"No output destination specified for table '{table_name}', skipping data write"
+                        )
+                        continue
+                    raise ValueError(
+                        f"Unrecognized output destination type: {type(output_destination).__name__}"
+                    )

		import pandas as pd
		from IPython.display import HTML, display

-import pandas as pd
-from IPython.display import HTML, display
+try:
+    import pandas as pd
+except ImportError:  # Optional dependency; used only for display utilities.
+    pd = None  # type: ignore[assignment]
+try:
+    from IPython.display import HTML, display
+except ImportError:  # Optional dependency; used only in notebook environments.
+    HTML = None  # type: ignore[assignment]
+    display = None  # type: ignore[assignment]

-    duplicates = [name for name in set(column_names) if column_names.count(name) > 1]
+    seen: set[str] = set()
+    duplicates: list[str] = []
+    duplicate_seen: set[str] = set()
+    for name in column_names:
+        if name in seen and name not in duplicate_seen:
+            duplicates.append(name)
+            duplicate_seen.add(name)
+        else:
+            seen.add(name)

	def validate_range(cls, values):
	def validate_range(self, values):

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codecov bot commented Nov 6, 2025 •

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ghanse Feb 4, 2026 •

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anupkalburgi Jan 21, 2026 •

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