sql-test-generator

name	sql-test-generator
description	ALWAYS use this skill when users ask to create, generate, or write UNIT TESTS for BigQuery SQL queries. Invoke proactively whenever the request includes "test" or "tests" with a query/table name. This skill is for unit testing ONLY (not data quality checks - use bigconfig-generator for Bigeye monitoring). Works with bigquery-etl-core skill to understand query patterns.

SQL Test Generator (Unit Tests Only)

Composable: Works with bigquery-etl-core (for patterns), metadata-manager (for test updates), and query-writer (for query understanding) When to use: Creating/updating unit test fixtures for BigQuery SQL queries, preventing production queries in tests

NOT for data quality checks: Use bigconfig-generator skill for Bigeye monitoring. The checks.sql file approach is DEPRECATED.

Generate unit test fixtures for BigQuery SQL queries following bigquery-etl conventions. These are development-time tests that validate query logic on small, synthetic data.

Official Documentation: https://mozilla.github.io/bigquery-etl/cookbooks/testing/

Unit Tests vs Data Quality Checks

CRITICAL DISTINCTION:

This skill creates UNIT TESTS:

• Test query logic during development
• Run on small, synthetic fixtures (not production data)
• Validate transformations, joins, aggregations work correctly
• Part of CI/CD pipeline

For data quality monitoring (NOT this skill):

• Use bigconfig-generator skill to create Bigeye monitoring configurations
• Monitors production data for quality issues (anomalies, nulls, freshness)
• checks.sql files are DEPRECATED - do NOT create or update them
• All production data quality checks should be done via Bigeye

🚨 REQUIRED READING - Start Here

BEFORE creating any test fixtures, you MUST read these files:

1. CRITICAL FOR SAFETY: Read references/preventing_production_queries.md

   • Prevents accidentally querying production data
   • Explains how to verify fixtures are working correctly
   • Required reading before ANY test creation

2. UNDERSTAND TEST PATTERNS: Read references/test_strategy_patterns.md

   • Learn which test scenarios are needed for different query types
   • Understand how many tests to create
   • See examples of test coverage strategies

📋 Templates - Copy These Structures

When creating test fixtures, READ and COPY the structure from these template files:

For Input Fixtures:

• Glean events? → READ assets/glean_events_fixture.yaml and copy its structure

  • Shows proper client_info, events, and extra formatting
  • Use this for any *_stable.events_v1 or Glean ping tables

• Legacy telemetry with arrays? → READ assets/legacy_array_fixture.yaml

  • Shows how to structure array fields that get UNNESTed
  • Use for legacy telemetry tables with array columns

• Simple table? → READ assets/simple_test_input.yaml

  • Basic fixture structure for flat tables

• UNION ALL query? → READ both assets/union_all_fixture1.yaml AND assets/union_all_fixture2.yaml

  • Shows how to create fixtures for multiple data sources
  • Critical: ALL sources need fixtures in the SAME test directory

For Query Parameters:

• READ assets/query_params_example.yaml - Must be array format, not key-value pairs

For Expected Output:

• READ assets/simple_test_expect.yaml - Shows array format and NULL handling
• READ assets/timestamp_example.yaml - Shows correct TIMESTAMP format (ISO 8601 with timezone)

Reference Documentation (Read as needed)

• references/common_test_failures.md - READ THIS when tests fail - Real-world failures and solutions (NULL handling, ordering, timestamps)
• references/yaml_format_guide.md - YAML syntax, type inference, nested structures (read if you encounter YAML errors)
• references/external_documentation.md - Links to official docs and related resources

DataHub Usage (CRITICAL for Token Efficiency)

IMPORTANT: DataHub is for lineage discovery ONLY, NOT for schema lookups

For schema discovery, use this priority order:

1. 🥇 Local schema.yaml files in sql/ directory
2. 🥈 Glean Dictionary for _live and _stable tables
3. 🥉 BigQuery ETL docs (https://mozilla.github.io/bigquery-etl/)
4. ❌ NEVER use DataHub for schemas - use it only for lineage

When DataHub IS useful:

• ✅ Discovering upstream/downstream table dependencies
• ✅ Finding which tables are related
• ✅ Understanding table usage patterns

See these guides:

• ../metadata-manager/references/schema_discovery_guide.md - Complete schema discovery workflow
• ../metadata-manager/references/glean_dictionary_patterns.md - Token-efficient Glean Dictionary usage
• ../metadata-manager/scripts/datahub_lineage.py - Efficient lineage queries

Test Structure

Tests live in: tests/sql/<project>/<dataset>/<table>/<test_name>/

Required files:

• Input fixtures: <full_table_reference>.yaml (e.g., moz-fx-data-shared-prod.telemetry.events.yaml)
• Expected output: expect.yaml
• Query parameters (if needed): query_params.yaml

Critical Requirements

⚠️ PREVENTING PRODUCTION QUERIES - READ THIS FIRST ⚠️

The #1 way tests accidentally query production data:

• Missing fixture files for ANY table referenced in FROM/JOIN/UNION clauses
• When a fixture is missing, BigQuery falls back to querying the production table

How to prevent this:

1. Use grep -E "FROM|JOIN" query.sql to identify ALL source tables
2. Create a fixture file for EVERY table found, even if it contributes minimal data
3. If test results show thousands of rows or production-like data, STOP - you're querying production
4. Check the "Initialized" lines in pytest output - every source table should appear

1. File Naming - MOST COMMON FAILURE

Input fixtures must match how the table is referenced in the query:

• Query uses moz-fx-data-shared-prod.dataset.table → file must be moz-fx-data-shared-prod.dataset.table.yaml
• Query uses dataset.table → file must be dataset.table.yaml
• Query uses table → file must be table.yaml

Wrong naming causes tests to query production BigQuery instead of your fixtures.

2. YAML Format

All fixtures must use array syntax (starts with -). See references/yaml_format_guide.md for details.

# Correct - array syntax with dashes
- field1: value1
  field2: value2

3. Required Fields

• Input fixtures: Include ALL fields the query references, even if NULL: loaded: null
• expect.yaml: COMPLETELY OMIT fields that will be NULL - do NOT include them with field: null
  • ❌ Wrong: avg_time_seconds: null
  • ✅ Correct: omit the field entirely
  • BigQuery does not return NULL fields in results, so including them in expect.yaml will cause test failures
• CRITICAL for UNION/UNION ALL queries: You MUST create fixtures for ALL source tables with at least one row of test data. If you want a source to contribute no data, either:
  • Add a fixture with data that gets filtered out by WHERE clauses, OR
  • Create a minimal fixture that passes filters but contributes to the test
  • DO NOT create empty array fixtures ([]) - they cause "Schema has no fields" errors
  • DO NOT omit fixture files - missing fixtures cause the query to hit production BigQuery
• Omit generated_time columns from expect.yaml

4. Query Parameters Format

Must be an array (starts with -), not key-value pairs. See assets/query_params_example.yaml for template.

- name: submission_date
  type: DATE
  value: "2024-12-01"

5. TIMESTAMP Format ⚠️ VERY COMMON FAILURE

CRITICAL: BigQuery returns TIMESTAMP and DATETIME fields in ISO 8601 format with timezone.

In expect.yaml, ALWAYS use ISO 8601 format:

# ✅ CORRECT - ISO 8601 with timezone
- campaign_created_at: "2025-06-01T10:00:00+00:00"
  campaign_updated_at: "2025-07-01T12:00:00+00:00"

# ❌ WRONG - Missing 'T' and timezone
- campaign_created_at: "2025-06-01 10:00:00"
  campaign_updated_at: "2025-07-01 12:00:00"

Format rules:

• TIMESTAMP fields: 2025-06-01T10:00:00+00:00 (ISO 8601)
• DATE fields: 2025-06-01 (YYYY-MM-DD)
• With microseconds: 2025-06-01T10:00:00.123456+00:00

Input fixtures can use either format, but expect.yaml MUST use ISO 8601.

See references/common_test_failures.md section 3 for details and examples.

Test Generation Strategy

See references/test_strategy_patterns.md for comprehensive patterns.

Quick analysis checklist:

• Join types (FULL OUTER, LEFT, INNER) → test each branch
• Aggregations/GROUP BY → test single and multiple rows
• CASE/IF logic → test each branch
• UDFs and map operations → test empty, existing, new values
• Incremental logic → test first run vs subsequent runs
• Date-based filtering → test different date ranges
• Version filtering → test both sides of thresholds (use "120.0.0" format)
• UNION/UNION ALL → create ONE comprehensive test with fixtures for ALL data sources

Number of tests:

• Simple queries: 1 test
• Moderate complexity: 1-2 tests
• Complex (FULL OUTER JOIN, maps, multiple CTEs): 3-5 tests
• UNION ALL queries: 1 comprehensive test per date/filter branch

Test naming: Use descriptive snake_case: test_new_clients_only, test_union_all_sources

Environment Setup

Before running tests, configure Google Cloud authentication:

export GOOGLE_PROJECT_ID=bigquery-etl-integration-test
gcloud config set project $GOOGLE_PROJECT_ID
gcloud auth application-default login

See https://mozilla.github.io/bigquery-etl/cookbooks/testing/ for more details.

Workflow

Step-by-Step Process

1. Read the required reference files (from "REQUIRED READING" section above)

   • READ references/preventing_production_queries.md
   • READ references/test_strategy_patterns.md

2. Read the query.sql file

3. Gather schema information efficiently (use priority order):

   • FIRST: Check local schema.yaml files in sql/ directory for derived tables
   • SECOND: Check Glean Dictionary for _live and _stable tables
     • See ../metadata-manager/references/glean_dictionary_patterns.md for token-efficient extraction
     • Extract only needed fields (don't read entire large files)
   • THIRD: Check https://mozilla.github.io/bigquery-etl/ dataset browser
   • DataHub MCP: For lineage ONLY, NOT for schemas
     • Use DataHub to discover upstream/downstream tables
     • Then use priority order above to get schemas for those tables
     • See ../metadata-manager/references/schema_discovery_guide.md for complete workflow
     • See ../metadata-manager/scripts/datahub_lineage.py for efficient lineage queries

4. Identify ALL source tables - use this command:

   grep -E "FROM|JOIN" query.sql
   

   Write down EVERY table you find. This is your checklist.

5. Determine test scenarios needed based on query complexity

6. Read the appropriate template files for your data sources:

   • For Glean events tables → READ assets/glean_events_fixture.yaml
   • For legacy telemetry with arrays → READ assets/legacy_array_fixture.yaml
   • For simple tables → READ assets/simple_test_input.yaml
   • For query parameters → READ assets/query_params_example.yaml
   • For UNION ALL queries → READ assets/union_all_fixture1.yaml and assets/union_all_fixture2.yaml

• ✅ Check pytest output for "Initialized" lines - count should match your table checklist from step 2
• ✅ Test should complete in <30 seconds
• ✅ Results should be small (< 10 rows typically)
• ❌ If you see thousands of rows, real production IDs, or slow execution → you're querying production!
• ❌ Missing "Initialized" line for a table → add that fixture file

11. Ask about data quality monitoring:

• After tests pass successfully, check if monitoring exists:
  • Check for bigconfig.yml file: sql/<project>/<dataset>/<table>/bigconfig.yml
• If bigconfig.yml does NOT exist, proactively ask the user:
  • "Would you like to add Bigeye monitoring/data quality checks for this table?"
• Suggest relevant checks based on table type:
  • Aggregation tables (DAU, MAU, etc.) → freshness, volume/anomaly detection, null checks
  • Event tables → freshness, volume checks
  • Derived tables → freshness, schema validation
• If user agrees, use bigconfig-generator skill to create monitoring configurations
• Note: Only ask if bigconfig.yml doesn't exist - don't ask if monitoring is already configured

Production Query Checklist

Before finalizing tests, verify:

• I ran grep -E "FROM|JOIN" query.sql to find all source tables
• I created a fixture file for each source table found
• I checked pytest output for "Initialized" messages matching each source table
• Test results are small and match my expect.yaml
• Test runs quickly (<30 seconds)

Note: After running tests, if you need to use metadata-manager or query production tables, switch back to the main project:

export GOOGLE_PROJECT_ID=mozdata
gcloud config set project $GOOGLE_PROJECT_ID

Example Tests

Example 1: Simple Query with Join

For a query at sql/moz-fx-data-shared-prod/dataset/table_v1/query.sql that joins telemetry.clients_daily with the target table:

Test directory: tests/sql/moz-fx-data-shared-prod/dataset/table_v1/test_new_clients/

Input fixture: moz-fx-data-shared-prod.telemetry.clients_daily.yaml

- client_id: "abc123"
  submission_date: "2025-01-01"
  search_count: 5

Query params: query_params.yaml

- name: submission_date
  type: DATE
  value: "2025-01-01"

Expected output: expect.yaml

- client_id: "abc123"
  total_searches: 5
  first_seen_date: "2025-01-01"

Example 2: UNION ALL Query with Multiple Sources

For a query that unions three data sources (legacy, glean, and ads):

Test directory: tests/sql/moz-fx-data-shared-prod/dataset/table_v1/test_union_all_sources/

IMPORTANT: Create fixtures for ALL three sources in ONE test directory:

Fixture 1: moz-fx-data-shared-prod.legacy_source.table.yaml

- submission_timestamp: "2024-12-15 10:00:00"
  document_id: "doc1"
  version: "120.0.0"
  event_count: 5

Fixture 2: moz-fx-data-shared-prod.glean_source.table.yaml

- submission_timestamp: "2024-12-15 14:30:00"
  document_id: "doc2"
  client_info:
    app_display_version: "121.0.0"
  events:
    - category: "interaction"
      name: "click"

Fixture 3: moz-fx-data-shared-prod.ads_source.table.yaml

- submission_hour: "2024-12-15 10:00:00"
  ad_id: 12345
  interaction_type: "impression"
  interaction_count: 100

Query params: query_params.yaml

- name: submission_date
  type: DATE
  value: "2024-12-15"

Expected output: expect.yaml

- submission_date: "2024-12-15"
  source: "legacy"
  event_count: 5
- submission_date: "2024-12-15"
  source: "glean"
  event_count: 1
- submission_date: "2024-12-15"
  source: "ads"
  event_count: 100

Mozilla-Specific Telemetry Patterns

See example assets for templates:

• assets/glean_events_fixture.yaml - Glean events with extras structure
• assets/legacy_array_fixture.yaml - Legacy telemetry with array fields

Glean Events with Extras

See assets/glean_events_fixture.yaml for complete example. Events use nested structure:

• Access via events.category, events.name
• Extra fields via mozfun.map.get_key(events.extra, 'field_name')

Legacy Telemetry Arrays

See assets/legacy_array_fixture.yaml. Arrays get unnested with CROSS JOIN UNNEST(tiles).

Version Numbers

Always use three-part versions ("121.0.0") to avoid YAML float parsing. See references/yaml_format_guide.md.

Common Errors

See references/common_test_failures.md for real-world examples and solutions. See references/yaml_format_guide.md and references/preventing_production_queries.md for detailed guides.

1. NULL Fields in expect.yaml ⚠️ COMMON

Including field: null in expect.yaml causes test failures because BigQuery omits NULL fields from results. Solution: Completely omit NULL fields from expect.yaml - don't include them at all. See references/common_test_failures.md section 1.

2. Non-Deterministic ORDER BY

When ORDER BY fields have duplicate values, row order becomes non-deterministic and tests fail. Solution: Either create test data with different ORDER BY values, or run test to see actual order and update expect.yaml. See references/common_test_failures.md section 2.

3. Type Inference Issues

Version numbers like "120.0" parsed as FLOAT64 instead of STRING. Solution: Use three-part versions: "120.0.0" or simple integers: "120"

4. Empty Fixture Arrays

Creating [] causes "Schema has no fields" errors. Solution: Always include at least one row. To filter out data, use WHERE clause filtering.

5. Missing Fixtures Query Production ⚠️ CRITICAL

If ANY source table lacks a fixture, query hits production BigQuery. Symptoms: Thousands of rows, real production values, slow execution (>10 seconds) Solution: Create fixtures for ALL source tables. See references/preventing_production_queries.md

6. NULL Field Handling (Duplicate - see #1 above)

Omit NULL fields from expect.yaml - BigQuery doesn't return them in results.

Full troubleshooting: https://mozilla.github.io/bigquery-etl/cookbooks/testing/

Integration with Other Skills

sql-test-generator is invoked by other skills when test fixtures need to be created or updated:

Works with bigquery-etl-core

• References core skill for test structure, naming conventions, and query patterns
• Uses common table patterns for test creation
• Invocation: Automatically available as foundation skill

Invoked by metadata-manager

• When queries are modified: metadata-manager detects when test fixtures need updating
• metadata-manager delegates to sql-test-generator for:
  • New source tables added to query (JOINs, new FROM clauses)
  • Tests querying production (thousands of rows in output)
  • Creating new test scenarios for new logic
• metadata-manager handles simple updates (expect.yaml changes) directly
• Invocation pattern: metadata-manager explicitly invokes sql-test-generator with "Use sql-test-generator skill" instructions

Works with query-writer

• After query-writer creates queries: Use sql-test-generator to create test fixtures
• Analyzes query structure to identify all source tables
• Creates comprehensive test coverage
• Invocation: After query.sql is complete, invoke sql-test-generator to create tests

Coordinates with bigconfig-generator

• After tests pass: Check if bigconfig.yml exists for the table
• If bigconfig.yml does NOT exist, proactively ask user if they want data quality monitoring
• Suggests relevant monitoring checks based on table type (aggregation, event, derived)
• If user agrees, delegate to bigconfig-generator skill to create Bigeye configurations
• Workflow: sql-test-generator → check for bigconfig.yml → ask user → bigconfig-generator (if approved)

Typical Invocation Scenarios

Direct invocation by user:

• User asks: "Generate tests for query X"
• User asks: "Create test fixtures for table Y"

Invoked by metadata-manager:

• Query modified with new JOINs → metadata-manager invokes sql-test-generator
• Tests showing production data → metadata-manager invokes sql-test-generator
• New test scenarios needed → metadata-manager invokes sql-test-generator

After query-writer:

• New query.sql created → invoke sql-test-generator to create initial tests

Key Capabilities

Prevents production queries:

• Ensures ALL source tables have fixtures (critical for UNION/UNION ALL queries)
• Detects missing fixtures via "Initialized" lines in pytest output
• Provides clear symptoms of production query issues

Handles complex queries:

• UNION ALL with multiple sources
• Nested structures and complex types
• YAML type inference issues (version numbers, etc.)

Provides best practices:

• Fixture naming conventions
• Test organization (one comprehensive test vs multiple)
• Production query detection and prevention