TestRunner

Execute data quality tests against tables in Collate using the TestRunner API

Test Runner

The TestRunner class provides a fluent API for executing data quality tests against tables cataloged in Collate. It automatically fetches table metadata and service connections, letting you run tests with minimal configuration.

The TestRunner lets you:

• Execute tests defined in code against cataloged tables.
• Run tests previously configured in the Collate UI.
• Load test definitions from YAML workflow files.
• Validate data at the table and column levels.
• Get detailed test results for programmatic handling.

Note: If you’re using Collate Cloud, TestRunner needs access to Collate’s SaaS-managed secrets manager to decrypt your connection credentials. For more information about how to set up secret credentials and troubleshoot issues, see Using External Secrets Managers.

Basic Usage

The following sections walk through each step of running a test, from creating a runner to processing results.

Creating a TestRunner

Create a runner for a specific table using its fully qualified name (FQN):

from metadata.sdk import configure
from metadata.sdk.data_quality import TestRunner

# Configure SDK first
configure(
    host="http://localhost:8585/api",
    jwt_token="your-jwt-token"
)

# Create runner for a table
runner = TestRunner.for_table("MySQL.ecommerce.public.customers")

The table FQN format is: {service}.{database}.{schema}.{table}.

Adding Tests

Add test definitions to the runner:

from metadata.sdk.data_quality import (
    TableRowCountToBeBetween,
    ColumnValuesToBeNotNull,
    ColumnValuesToBeUnique
)

# Add a table-level test
runner.add_test(
    TableRowCountToBeBetween(min_count=1000, max_count=100000)
)

# Add column-level tests
runner.add_test(
    ColumnValuesToBeNotNull(column="email")
)

runner.add_test(
    ColumnValuesToBeUnique(column="customer_id")
)

Adding Multiple Tests

Use add_tests() to add several tests at once:

runner.add_tests(
    TableRowCountToBeBetween(min_count=1000),
    ColumnValuesToBeNotNull(column="email"),
    ColumnValuesToBeUnique(column="customer_id"),
    ColumnValuesToMatchRegex(
        column="email",
        regex=r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
    )
)

Running Tests

Execute all configured tests:

results = runner.run()

# Process results
for result in results:
    test_case = result.testCase
    test_result = result.testCaseResult

    print(f"Test: {test_case.name.root}")
    print(f"Status: {test_result.testCaseStatus}")
    print(f"Result: {test_result.result}")

Complete Example

Here’s a complete example of testing a customer table:

from metadata.sdk import configure
from metadata.sdk.data_quality import (
    TestRunner,
    TableRowCountToBeBetween,
    ColumnValuesToBeNotNull,
    ColumnValuesToBeUnique,
    ColumnValuesToMatchRegex,
    ColumnValuesToBeBetween
)

# Configure SDK
configure(
    host="http://localhost:8585/api",
    jwt_token="your-jwt-token"
)

# Create runner
runner = TestRunner.for_table("MySQL.ecommerce.public.customers")

# Define data quality tests
runner.add_tests(
    # Table should have between 1000 and 1M customers
    TableRowCountToBeBetween(min_count=1000, max_count=1000000),

    # Required fields must not be null
    ColumnValuesToBeNotNull(column="customer_id"),
    ColumnValuesToBeNotNull(column="email"),
    ColumnValuesToBeNotNull(column="created_at"),

    # Customer ID must be unique
    ColumnValuesToBeUnique(column="customer_id"),

    # Email must match valid pattern
    ColumnValuesToMatchRegex(
        column="email",
        regex=r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
    ),

    # Age should be reasonable
    ColumnValuesToBeBetween(column="age", min_value=18, max_value=120)
)

# Run tests
results = runner.run()

# Handle results
all_passed = all(
    result.testCaseResult.testCaseStatus == "Success"
    for result in results
)

if all_passed:
    print("✓ All data quality tests passed!")
else:
    print("✗ Some tests failed:")
    for result in results:
        if result.testCaseResult.testCaseStatus != "Success":
            print(f"  - {result.testCase.name.root}: {result.testCaseResult.result}")

Running Tests from Collate UI

Instead of defining tests in code, run tests that data stewards have configured in the Collate UI. This enables a collaborative workflow where:

• Data stewards define and maintain test criteria in the UI.
• Engineers execute those tests automatically in pipelines.

from metadata.sdk.data_quality import TestRunner

# Create runner
runner = TestRunner.for_table("BigQuery.analytics.customer_360")

# Run all tests defined in Collate for this table
results = runner.run()

# The runner automatically executes all tests configured in the UI
for result in results:
    print(f"{result.testCase.name.root}: {result.testCaseResult.testCaseStatus}")

This approach ensures:

• Test definitions stay synchronized with business requirements.
• Engineers don’t need to modify code when test criteria change.
• All stakeholders own data quality.

Customizing Test Metadata

Customize test names, display names, and descriptions:

from metadata.sdk.data_quality import ColumnValuesToBeNotNull

# Use fluent API for customization
test = ColumnValuesToBeNotNull(column="email") \
    .with_name("email_not_null_check") \
    .with_display_name("Email Not Null Validation") \
    .with_description("Ensures all customer records have valid email addresses")

runner.add_test(test)

Or pass values directly to the constructor:

test = ColumnValuesToBeNotNull(
    column="email",
    name="email_not_null_check",
    display_name="Email Not Null Validation",
    description="Ensures all customer records have valid email addresses"
)

Configuring Row Count Computation

Some tests support computing the number and percentage of rows that passed or failed:

from metadata.sdk.data_quality import ColumnValuesToBeBetween

test = ColumnValuesToBeBetween(
    column="age",
    min_value=18,
    max_value=120
).with_compute_row_count(True)

runner.add_test(test)

This provides detailed metrics about test failures, useful for:

• Identifying the scope of data quality issues.
• Prioritizing remediation efforts.
• Tracking data quality trends over time.

Test Runner Configuration

Customize the test runner behavior using the setup() method:

from metadata.generated.schema.metadataIngestion.workflow import LogLevels

runner = TestRunner.for_table("MySQL.ecommerce.public.orders")

# Configure runner behavior
runner.setup(
    force_test_update=True,        # Update existing tests
    log_level=LogLevels.DEBUG,     # Set logging level
    raise_on_error=False,          # Don't raise exceptions on errors
    success_threshold=90,          # Pass if 90% of tests succeed
    enable_streamable_logs=True    # Enable streamable log output
)

# Add and run tests
runner.add_test(TableRowCountToBeBetween(min_count=100))
results = runner.run()

Configuration Parameters

The table below lists all parameters accepted by the setup() method.

Parameter	Type	Default	Description
force_test_update	bool	False	Force update even if tests already exist
log_level	LogLevels	INFO	Logging level (DEBUG, INFO, WARN, ERROR)
raise_on_error	bool	False	Raise exceptions if test data already exists
success_threshold	int	90	Percentage threshold for overall success
enable_streamable_logs	bool	False	Enable streamable log output

Understanding Test Results

Test results contain detailed information about test execution:

results = runner.run()

for result in results:
    # Test case information
    test_case = result.testCase
    print(f"Test Name: {test_case.name.root}")
    print(f"Test Type: {test_case.testDefinition.name}")

    # Test result information
    test_result = result.testCaseResult
    print(f"Status: {test_result.testCaseStatus}")  # Success, Failed, Aborted
    print(f"Result: {test_result.result}")           # Detailed result message
    print(f"Timestamp: {test_result.timestamp}")

    # Row-level statistics (if enabled)
    if test_result.passedRows is not None:
        print(f"Passed Rows: {test_result.passedRows}")
        print(f"Failed Rows: {test_result.failedRows}")
        print(f"Passed %: {test_result.passedRowsPercentage}")

Test Status Values

Each test result includes one of the following status values.

• Success: Test passed all validation criteria.
• Failed: Test did not meet validation criteria.
• Aborted: Test execution was interrupted or could not complete.

Integration with ETL Workflows

Integrate TestRunner into your extract, transform, load (ETL) pipelines:

import pandas as pd
from sqlalchemy import create_engine
from metadata.sdk import configure
from metadata.sdk.data_quality import TestRunner, TableRowCountToBeBetween

# Configure SDK
configure(host="http://localhost:8585/api", jwt_token="token")

# ETL: Extract
source_df = pd.read_csv("data/customers.csv")

# ETL: Transform
transformed_df = transform_data(source_df)

# ETL: Load
engine = create_engine("postgresql://user:pass@localhost/db")
transformed_df.to_sql("customers", engine, if_exists="replace")

# Validate: Run data quality tests
runner = TestRunner.for_table("Postgres.analytics.public.customers")
runner.add_test(
    TableRowCountToBeBetween(min_count=len(transformed_df))
)
results = runner.run()

# Check results
if all(r.testCaseResult.testCaseStatus == "Success" for r in results):
    print("✓ Data quality validation passed")
else:
    print("✗ Data quality validation failed")
    # Optionally rollback or alert
    rollback_transaction()

Error Handling

Handle potential errors gracefully:

from metadata.sdk.data_quality import TestRunner

try:
    runner = TestRunner.for_table("MySQL.ecommerce.public.customers")
    runner.add_test(TableRowCountToBeBetween(min_count=100))
    results = runner.run()

except ValueError as e:
    print(f"Configuration error: {e}")
    # Table not found or service not configured

except Exception as e:
    print(f"Test execution failed: {e}")
    # Network issues, authentication problems, etc.

Best Practices

Follow these guidelines to get the most out of the TestRunner API.

• Use descriptive test names: Make test failures easy to understand.

  test = TableRowCountToBeBetween(min_count=1000) \
      .with_name("daily_customer_count_check") \
      .with_description("Validates daily customer ingestion meets minimum threshold")
  

• Use UI-defined tests: Let data stewards define test criteria.

  # Just run - no need to define tests in code
  runner = TestRunner.for_table("Table.FQN")
  results = runner.run()
  

• Handle results programmatically: Don’t just print—take action.

  if any(r.testCaseResult.testCaseStatus == "Failed" for r in results):
      rollback_transaction()
  

• Use appropriate thresholds: Set realistic min/max values based on data patterns.

• Combine table and column tests: Ensure both structural and content quality.

Secrets Managers

If your organization uses an external secrets manager for credential storage, TestRunner needs it configured before it can decrypt connection credentials. For more information about how to set up secret credentials and troubleshoot issues, see Using External Secrets Managers.

Next Steps

Once you have TestRunner working, explore these related guides.

• Learn about DataFrame Validation for validating transformations.
• Review the Test Definitions Reference for all available tests.
• Explore Advanced Usage including YAML workflows.