Principle:Apache Spark Parallel Test Execution
| Field | Value |
|---|---|
| Sources | https://github.com/apache/spark |
| Domains | Testing, Python |
| Last Updated | 2026-02-08 14:00 GMT |
Overview
A test execution strategy that distributes test modules across multiple worker processes using priority queues to minimize total execution time.
Description
When a test suite contains modules of varying execution duration, naive sequential execution leads to suboptimal resource utilization. Parallel test execution addresses this by using a priority queue that schedules long-running (heavy) test modules first, then fills remaining worker capacity with shorter tests. This bin-packing approach minimizes the critical path of the test pipeline. The strategy also supports testing across multiple Python interpreters simultaneously.
The key elements of this strategy are:
- Test classification -- categorizing tests as heavy (long-running) or light (fast) based on historical execution times
- Priority scheduling -- assigning lower priority numbers (higher priority) to heavy tests so they are dequeued first
- Worker pool -- spawning a fixed number of worker threads that pull tests from the shared priority queue
- Multi-interpreter support -- running the same tests against different Python executables (CPython, PyPy) in parallel
The benefit of scheduling heavy tests first is that it avoids the scenario where a single long-running test is left executing while all other workers are idle. By frontloading the longest tests, the overall makespan (wall-clock time) is minimized.
Usage
Use this when running a large test suite where individual test modules have significantly different execution times and multi-core hardware is available. This is particularly applicable to:
- PySpark test suites with mixed-duration modules
- CI/CD environments with multiple available CPU cores
- Situations requiring cross-interpreter compatibility testing
Theoretical Basis
This is a variant of the Longest Processing Time (LPT) scheduling algorithm for minimizing makespan on parallel machines. The algorithm can be expressed in pseudocode:
# Classify tests by expected duration
heavy_tests, light_tests = classify_tests(all_tests)
# Build priority queue (lower number = higher priority)
priority_queue = PriorityQueue()
for test in heavy_tests:
priority_queue.add(test, priority=0)
for test in light_tests:
priority_queue.add(test, priority=100)
# Spawn worker pool
workers = spawn_workers(N)
# Each worker pulls from the shared queue
for worker in workers:
while not priority_queue.empty():
test = priority_queue.get()
result = run(test)
report(result)
The LPT algorithm is a well-known approximation algorithm for the parallel machine scheduling problem (P||C_max). It guarantees a makespan no worse than (4/3 - 1/(3m)) times the optimal, where m is the number of machines (workers). In practice, the approximation ratio is much better when there are many more tasks than workers.