2026-01-28 13:47:34 +00:00
|
|
|
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
2025-10-27 18:29:12 +00:00
|
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
|
|
2026-02-13 22:28:35 +00:00
|
|
|
from unittest.mock import patch
|
|
|
|
|
|
|
|
|
|
import pytest
|
|
|
|
|
|
2025-10-27 18:29:12 +00:00
|
|
|
from data_designer.config.utils.code_lang import CodeLang
|
|
|
|
|
from data_designer.config.validator_params import CodeValidatorParams
|
|
|
|
|
from data_designer.engine.validators.sql import SQLValidator
|
|
|
|
|
|
|
|
|
|
|
2026-02-13 22:28:35 +00:00
|
|
|
def test_valid_ansi_sql_code() -> None:
|
2025-10-27 18:29:12 +00:00
|
|
|
sql_validator = SQLValidator(CodeValidatorParams(code_lang=CodeLang.SQL_ANSI))
|
|
|
|
|
code = "SELECT category, COUNT(*) as total_incidents FROM security_incidents_2 GROUP BY category;"
|
|
|
|
|
result = sql_validator.run_validation([{"sql": code}])
|
|
|
|
|
assert result.data[0].is_valid
|
|
|
|
|
assert result.data[0].error_messages == ""
|
|
|
|
|
|
|
|
|
|
|
2026-02-13 22:28:35 +00:00
|
|
|
def test_invalid_ansi_sql_code() -> None:
|
2025-10-27 18:29:12 +00:00
|
|
|
sql_validator = SQLValidator(CodeValidatorParams(code_lang=CodeLang.SQL_ANSI))
|
|
|
|
|
code = "NOT SQL"
|
|
|
|
|
result = sql_validator.run_validation([{"sql": code}])
|
|
|
|
|
assert not result.data[0].is_valid
|
|
|
|
|
assert result.data[0].error_messages == "PRS: Line 1, Position 1: Found unparsable section: 'NOT SQL'"
|
2026-02-13 22:28:35 +00:00
|
|
|
|
|
|
|
|
|
|
|
|
|
def test_sql_validator_multi_column_input_raises() -> None:
|
|
|
|
|
sql_validator = SQLValidator(CodeValidatorParams(code_lang=CodeLang.SQL_ANSI))
|
|
|
|
|
with pytest.raises(ValueError, match="single column input"):
|
|
|
|
|
sql_validator.run_validation([{"sql": "SELECT 1", "extra": "ignored"}])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def test_sql_validator_decimal_without_scale_fails() -> None:
|
|
|
|
|
sql_validator = SQLValidator(CodeValidatorParams(code_lang=CodeLang.SQL_ANSI))
|
|
|
|
|
code = "CREATE TABLE example (amount DECIMAL(10));"
|
|
|
|
|
result = sql_validator.run_validation([{"sql": code}])
|
|
|
|
|
assert not result.data[0].is_valid
|
|
|
|
|
assert "DECIMAL definitions without a scale" in result.data[0].error_messages
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def test_sql_validator_handles_lint_exception() -> None:
|
|
|
|
|
sql_validator = SQLValidator(CodeValidatorParams(code_lang=CodeLang.SQL_ANSI))
|
chore: Improve CLI startup with lazy heavy import cleanup (#330)
* perf: defer heavy imports to improve CLI startup time
Move expensive imports (engine, models, controllers) out of the module-level import path so that data-designer --help and other non-generation commands no longer pay the full startup cost.
Key changes:
- Defer controller imports to inside command functions
- Remove eager re-export chains from CLI package __init__ files
- Move default-settings bootstrap into load_config_builder() and DataDesigner.__init__() instead of running at import time
- Add lazy __getattr__ exports in interface/__init__.py
- Replace module-level tokenizer init with cached lazy getter
- Fix ModelProvider import to use config layer instead of engine
- Update test mock paths to match new import locations
Reduces CLI import-time from ~1.67s to ~0.46s.
* perf: defer pandas/numpy in io_helpers and add config_list benchmark
- Replace eager `from lazy_heavy_imports import pd, np` in io_helpers
with module-level __getattr__ (for backwards-compatible external
access / test mocks) and function-level imports in the 3 functions
that actually use them (read_parquet_dataset, smart_load_dataframe,
_convert_to_serializable). Importing io_helpers no longer triggers
pandas/numpy loading.
- Defer heavy imports in list and reset CLI commands into function
bodies to avoid loading repositories, Rich, and prompt_toolkit at
module import time.
- Add `config_list` (data-designer config list) measurement to the
CLI startup benchmark with isolated cold measurement in a separate
venv and a --skip-config-list-check flag.
- Update test mock paths to match new import locations.
* Refine lazy import usage and TYPE_CHECKING cleanup
* Run license header updater on PR-touched files
* fix: update sqlfluff mock target for lazy imports in test_sql
* perf: cache globals() in lazy __getattr__ to avoid repeated lookups
Add globals() caching and explanatory comment to all three lazy
__getattr__ implementations (lazy_heavy_imports, config/__init__,
interface/__init__) so subsequent attribute accesses bypass __getattr__.
* perf: lazy CLI command loading and deferred heavy import evaluations
- Add LazyTyperGroup to defer command module loading until invocation, allowing module-level imports in all CLI command files
- Split DataFrameSeedSource into seed_source_dataframe.py to isolate pandas dependency from other seed source classes
- Move TypeVar/TypeAlias definitions (DataT, NumpyArray1dT, RadomStateT, EngineT) to TYPE_CHECKING blocks with runtime fallbacks
- Wrap module-level constants in lru_cache (phone_number parquet data, jsonschema validator) to defer I/O and heavy imports to first use
- Update test mock targets to patch at usage-site for module-level imports
* refactor: use direct pandas import in seed_source_dataframe
Drop lazy-loading for pandas in DataFrameSeedSource; use direct import
for simplicity.
* update lazy import pattern
* update tests to use lazy import namespace
Switch test modules to import data_designer.lazy_heavy_imports as lazy
and reference heavy libraries through that namespace. This keeps heavy
imports deferred during module import and aligns tests with the new
lazy-import usage pattern.
* tighten import perf test thresholds
Document recent baseline timings and lower the allowed average
import time and timeout so regressions are detected sooner.
* document pandas import requirement
Clarify that Pydantic needs DataFrame resolved at module load and
that keeping the direct import preserves IDE typing support.
* increase timeout time
* use lazy pandas imports in visualization tests
- replace direct pandas usage with lazy.pd in visualization tests to avoid eager imports
- add TYPE_CHECKING pandas import and keep CLI controller imports sorted
* fix lazy pandas runtime usage and preview mocks
Switch sample-record handling to lazy pandas types so runtime paths no longer
depend on TYPE_CHECKING imports. Align preview controller tests to patch the
module-local DataDesigner symbol, preventing real engine invocation in save
results scenarios.
2026-02-18 21:24:15 +00:00
|
|
|
with patch("data_designer.lazy_heavy_imports.sqlfluff.lint", side_effect=RuntimeError("boom")):
|
2026-02-13 22:28:35 +00:00
|
|
|
result = sql_validator.run_validation([{"sql": "SELECT 1"}])
|
|
|
|
|
assert not result.data[0].is_valid
|
|
|
|
|
assert "Exception during SQL parsing" in result.data[0].error_messages
|