Skip to content

README.md

Latest commit

 

History

History
115 lines (83 loc) · 3.15 KB

README.md

File metadata and controls

115 lines (83 loc) · 3.15 KB

Integration Tests

This directory contains integration tests for the Databricks MCP Server tools. These tests run against a real Databricks workspace.

Prerequisites

  1. Databricks Authentication: Configure your Databricks credentials via environment variables or ~/.databrickscfg
  2. Test Catalog: Set TEST_CATALOG in tests/test_config.py or use the default
  3. Python Dependencies: Install test dependencies with pip install -e ".[dev]"

Running Tests

Quick Start: Run All Tests

# Run all tests (excluding slow tests)
python tests/integration/run_tests.py

# Run all tests including slow tests (cluster lifecycle, etc.)
python tests/integration/run_tests.py --all

View Test Reports

# Show report from the latest test run
python tests/integration/run_tests.py --report

# Show report from a specific run (by timestamp)
python tests/integration/run_tests.py --report 20260331_112315

Check Status of Running Tests

# Show status of ongoing and recently completed runs
python tests/integration/run_tests.py --status

Advanced Options

# Run with fewer parallel workers (default: 8)
python tests/integration/run_tests.py -j 4

# Combine options
python tests/integration/run_tests.py --all -j 4

# Clean up old test results (keeps last 5 runs)
python tests/integration/run_tests.py --cleanup-results

Run Individual Test Folders

# Run a specific test folder
python -m pytest tests/integration/sql -m integration -v

# Run a specific test
python -m pytest tests/integration/sql/test_sql.py::TestExecuteSql::test_simple_query -v

Test Output

Test results are saved to .test-results/<timestamp>/:

.test-results/
└── 20260331_112315/
    ├── results.json      # Machine-readable results
    ├── sql.txt           # Logs for sql tests
    ├── workspace_files.txt
    ├── dashboards.txt
    └── ...

Test Markers

  • @pytest.mark.integration - Standard integration tests
  • @pytest.mark.slow - Tests that take a long time (cluster creation, etc.)

Test Folders

Folder Description
sql/ SQL execution and query tests
workspace_files/ Workspace file upload/download tests
volume_files/ Unity Catalog volume file operations
dashboards/ AI/BI dashboard management
genie/ Genie (AI assistant) spaces
agent_bricks/ Agent Bricks tool tests
compute/ Cluster and serverless compute
jobs/ Job creation and execution
pipelines/ DLT pipeline management
vector_search/ Vector search endpoints and indexes
serving/ Model serving endpoints
apps/ Databricks Apps
lakebase/ Lakebase database operations
pdf/ PDF processing tests

Re-running Failed Tests

After a test run, you can re-run specific failed tests:

# View the failure details
cat .test-results/<timestamp>/jobs.txt

# Re-run with more verbose output
python -m pytest tests/integration/jobs -v --tb=long

Cleanup

Test resources are automatically cleaned up after tests. If cleanup fails, resources are prefixed with ai_dev_kit_test_ for easy identification.