[Improve][Zeta] Add pending queue rescheduling for WAIT schedule strategy#10430
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corgy-w wants to merge 6 commits intoapache:devfrom
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[Improve][Zeta] Add pending queue rescheduling for WAIT schedule strategy#10430corgy-w wants to merge 6 commits intoapache:devfrom
corgy-w wants to merge 6 commits intoapache:devfrom
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@dybyte This is not the final version. I still have some things being adjusted and will update it later |
Issue 1: Lack of Concurrent Safety Protection MechanismLocation: Related Context:
Problem Description: When multiple jobs complete or fail simultaneously, multiple threads will trigger calls to the Potential Scenarios: Potential Risks:
Impact Scope:
Severity: CRITICAL Improvement Suggestions: // Solution 1: Add lock on method (simple but poor performance)
private final Object rescheduleLock = new Object();
public void rescheduleWaitingJobs() {
synchronized (rescheduleLock) {
List<WaitingJob> readyJobs = new ArrayList<>();
Iterator<WaitingJob> iterator = waitingQueue.iterator();
while (iterator.hasNext()) {
WaitingJob job = iterator.next();
if (slotService.hasEnoughSlots(job.getRequiredSlots())) {
readyJobs.add(job);
iterator.remove(); // Safely remove using iterator
}
}
for (WaitingJob job : readyJobs) {
try {
jobExecutionService.startJob(job);
} catch (Exception e) {
log.error("Failed to schedule job {}", job.getJobId(), e);
// Decision: requeue or mark as failed
}
}
}
}
// Solution 2: Use atomic operations (recommended, better performance)
public void rescheduleWaitingJobs() {
List<WaitingJob> readyJobs = new ArrayList<>();
// Step 1: Collect schedulable jobs (holding lock)
synchronized (waitingQueue) {
Iterator<WaitingJob> iterator = waitingQueue.iterator();
while (iterator.hasNext()) {
WaitingJob job = iterator.next();
if (slotService.hasEnoughSlots(job.getRequiredSlots())) {
readyJobs.add(job);
iterator.remove();
}
}
}
// Step 2: Execute scheduling (without holding lock, reduce lock contention)
for (WaitingJob job : readyJobs) {
try {
jobExecutionService.startJob(job);
log.info("Successfully scheduled waiting job {}", job.getJobId());
} catch (Exception e) {
log.error("Failed to schedule job {}", job.getJobId(), e);
handleSchedulingFailure(job); // Error handling
}
}
}
private void handleSchedulingFailure(WaitingJob job) {
// Decision point: requeue or not?
// Option 1: Requeue (may cause infinite loop)
// waitingQueue.offer(job);
// Option 2: Mark as failed, notify user
jobNotificationService.notifyFailure(job.getJobId(), "Scheduling failed");
}Rationale:
Issue 2: Lack of Recovery Mechanism for Job Scheduling FailuresLocation: Scheduling logic in Related Context:
Problem Description: When
Potential Risks:
Impact Scope:
Severity: CRITICAL Improvement Suggestions: // Complete error handling solution
public void rescheduleWaitingJobs() {
List<WaitingJob> readyJobs = new ArrayList<>();
synchronized (waitingQueue) {
Iterator<WaitingJob> iterator = waitingQueue.iterator();
while (iterator.hasNext()) {
WaitingJob job = iterator.next();
if (slotService.hasEnoughSlots(job.getRequiredSlots())) {
readyJobs.add(job);
iterator.remove();
}
}
}
for (WaitingJob job : readyJobs) {
scheduleWithRetry(job, 0); // Scheduling with retry
}
}
private void scheduleWithRetry(WaitingJob job, int retryCount) {
try {
jobExecutionService.startJob(job);
log.info("Successfully scheduled waiting job {} (attempt {})",
job.getJobId(), retryCount + 1);
// Reset failure count after success
job.resetFailureCount();
} catch (Exception e) {
int failureCount = job.incrementFailureCount();
int maxRetries = getMaxRetries(job);
if (failureCount <= maxRetries) {
// Transient error: requeue, delayed retry
long backoffTime = calculateBackoff(failureCount);
log.warn("Failed to schedule job {} (attempt {}), will retry in {} ms",
job.getJobId(), failureCount + 1, backoffTime, e);
scheduler.schedule(() -> {
waitingQueue.offer(job);
rescheduleWaitingJobs(); // Retry scheduling
}, backoffTime, TimeUnit.MILLISECONDS);
} else {
// Permanent error: mark as failed, notify user
log.error("Failed to schedule job {} after {} attempts, giving up",
job.getJobId(), maxRetries, e);
job.markAsFailed(e);
jobNotificationService.notifyFailure(
job.getJobId(),
String.format("Failed to schedule after %d attempts: %s",
maxRetries, e.getMessage())
);
}
}
}
private int getMaxRetries(WaitingJob job) {
// Determine max retry count based on job type or configuration
return job.isPriorityJob() ? 5 : 3;
}
private long calculateBackoff(int failureCount) {
// Exponential backoff strategy: 1s, 2s, 4s, 8s, ...
return Math.min(1000L * (1L << failureCount), 60000L); // Maximum 60 seconds
}Related Classes Requiring Synchronous Modification: // WaitingJob.java - Add failure count
public class WaitingJob {
private final long jobId;
private final int requiredSlots;
private volatile int failureCount = 0;
private JobState state = JobState.WAITING;
public int incrementFailureCount() {
return ++failureCount;
}
public void resetFailureCount() {
this.failureCount = 0;
}
public void markAsFailed(Throwable cause) {
this.state = JobState.FAILED;
this.failureCause = cause;
}
public boolean isPriorityJob() {
// Judge based on job tags or other attributes
return this.priority == JobPriority.HIGH;
}
}
// JobNotificationService.java - Add notification service
public interface JobNotificationService {
void notifyFailure(long jobId, String reason);
void notifySuccess(long jobId);
}
// Implementation example (notify via REST API or Email)
public class DefaultJobNotificationService implements JobNotificationService {
@Override
public void notifyFailure(long jobId, String reason) {
// 1. Update job status to database
jobRepository.updateStatus(jobId, JobStatus.FAILED, reason);
// 2. Send event to message queue (Kafka/Pulsar)
eventPublisher.publish(new JobFailedEvent(jobId, reason));
// 3. Send notification if user configured notification channels
if (notificationConfig.isEmailEnabled()) {
emailService.send(jobOwnerEmail,
"Job Failed",
String.format("Your job %d failed: %s", jobId, reason));
}
}
}Rationale:
Issue 3: Lack of JavaDoc and Key Logic CommentsLocation:
Related Context:
Problem Description: The three newly added public methods lack JavaDoc documentation, leading to:
Potential Risks:
Impact Scope:
Severity: MAJOR Improvement Suggestions: // CoordinatorService.java
/**
* Reschedules all waiting jobs after resources are released.
*
* <p>This method implements a "pending queue rescheduling" strategy for the
* {@link JobScheduleStrategy#WAIT} schedule strategy. When resources are released
* (e.g., a running job completes or fails), this method iterates through the
* waiting queue and attempts to schedule any jobs that now have sufficient resources.
*
* <h3>Background: Head-of-Line (HOL) Blocking Problem</h3>
* <p>In the original implementation, only the job at the head of the waiting queue
* was checked when resources were released. This caused a problem where a large job
* at the head could block many smaller jobs behind it, even if those smaller jobs
* could be scheduled with the available resources.
*
* <p>Example:
* <pre>
* Queue: [Job1(needs 8 slots), Job2(needs 2 slots), Job3(needs 2 slots)]
* Available: 5 slots (after Job0 releases 5 slots)
*
* Old behavior: Only Job1 is checked, cannot be scheduled (needs 8, has 5)
* → Job2 and Job3 remain blocked
*
* New behavior: All jobs are checked
* → Job2 and Job3 are scheduled (both need 2, have 5)
* → Job1 remains in queue
* </pre>
*
* <h3>Thread Safety</h3>
* <p>This method is thread-safe and can be called concurrently from multiple threads
* when multiple jobs release resources simultaneously. Internally, it uses
* synchronization to ensure that queue traversal and modification are atomic.
*
* <h3>Performance</h3>
* <p>Time complexity: O(n) where n is the number of waiting jobs.
* In scenarios with large waiting queues (100+ jobs), this method may take
* significant time. Future improvements could use a priority queue to reduce
* complexity to O(1) for the most common case.
*
* <h3>Trade-offs</h3>
* <ul>
* <li><b>Pros:</b> Better fairness, reduced average wait time, improved resource utilization</li>
* <li><b>Cons:</b> O(n) complexity, may be slow with 100+ waiting jobs</li>
* </ul>
*
* <h3>Future Improvements</h3>
* <ul>
* <li>Use a priority queue based on resource requirements (Shortest Job First)</li>
* <li>Implement aging mechanism to prevent job starvation</li>
* <li>Add fast path: check queue head first, only iterate if head cannot be scheduled</li>
* </ul>
*
* @see JobScheduleStrategy#WAIT
* @see WaitingJob
* @see PeekBlockingQueue#peek()
*/
public void rescheduleWaitingJobs() {
// Implementation code...
}
// PeekBlockingQueue.java
/**
* A thread-safe {@link BlockingQueue} that supports peeking at the head element
* without removing it, and removing specific elements from the middle of the queue.
*
* <p>This class extends {@link LinkedBlockingQueue} and adds two convenience methods:
* <ul>
* <li>{@link #peek()} - View the head element without removing it</li>
* <li>{@link #remove(Object)} - Remove a specific element from the queue</li>
* </ul>
*
* <h3>Typical Usage Pattern</h3>
* <pre>
* // Correct usage: peek → check → remove
* WaitingJob job = queue.peek();
* if (job != null && canSchedule(job)) {
* queue.remove(job); // or queue.poll()
* schedule(job);
* }
*
* // WRONG: peek without remove causes memory leak!
* WaitingJob job = queue.peek();
* if (canSchedule(job)) {
* schedule(job); // job remains in queue forever!
* }
* </pre>
*
* <h3>Thread Safety</h3>
* <p>All methods are thread-safe. Multiple threads can safely call peek(), poll(),
* and remove() concurrently.
*
* @param <E> the type of elements held in this queue
* @see LinkedBlockingQueue
*/
public class PeekBlockingQueue<E> extends LinkedBlockingQueue<E> {
/**
* Retrieves, but does not remove, the head of this queue,
* or returns {@code null} if this queue is empty.
*
* <p>This method is equivalent to {@link #peek()} but is explicitly
* declared here for documentation purposes and to emphasize its
* availability in this subclass.
*
* <p><b>Important:</b> After peeking at an element and deciding to
* process it, you MUST remove it from the queue using {@link #poll()}
* or {@link #remove(Object)}. Failure to do so will result in a
* memory leak, as the element will remain in the queue indefinitely.
*
* @return the head of this queue, or {@code null} if the queue is empty
*/
@Override
public E peek() {
return super.peek();
}
/**
* Removes a single instance of the specified element from this queue,
* if it is present.
*
* <p>This method is equivalent to {@link #remove(Object)} but is explicitly
* declared here for documentation purposes.
*
* <p>More formally, removes an element {@code e} such that
* {@code o.equals(e)}, if this queue contains one or more such elements.
* Returns {@code true} if this queue contained the specified element
* (or equivalently, if this queue changed as a result of the call).
*
* <p><b>Usage Note:</b> When removing an element that was previously
* peeked, prefer using {@link #remove(Object)} over {@link #poll()} to
* ensure you're removing the correct element in concurrent scenarios.
*
* @param o element to be removed from this queue, if present
* @return {@code true} if this queue changed as a result of the call
*/
@Override
public boolean remove(Object o) {
return super.remove(o);
}
}Rationale:
Issue 4: Lack of Performance Monitoring and LoggingLocation: Related Context:
Problem Description: The current code lacks performance monitoring and detailed logging, leading to:
Potential Risks:
Impact Scope:
Severity: MAJOR Improvement Suggestions: // 1. Add Metrics definitions
public class CoordinatorService {
// Counter: Total reschedule count
private final Counter rescheduleCounter = Metrics.counter()
.name("job.reschedule.count")
.description("Total number of times rescheduleWaitingJobs was called")
.register();
// Timer: Reschedule duration
private final Timer rescheduleTimer = Metrics.timer()
.name("job.reschedule.duration")
.description("Time taken to reschedule waiting jobs")
.tag("unit", "milliseconds")
.register();
// Gauge: Queue length
private final Gauge waitingQueueSize = Metrics.gauge()
.name("job.waiting.queue.size")
.description("Current number of jobs in the waiting queue")
.register(this, service -> service.getWaitingQueueSize());
// Histogram: Waiting time distribution
private final Histogram waitingTimeHistogram = Metrics.histogram()
.name("job.waiting.time")
.description("Time jobs spend in the waiting queue")
.tag("unit", "milliseconds")
.register();
// Gauge: Scheduling success rate
private final AtomicInteger scheduledCount = new AtomicInteger(0);
private final AtomicInteger failedCount = new AtomicInteger(0);
private final Gauge scheduleSuccessRate = Metrics.gauge()
.name("job.schedule.success.rate")
.description("Success rate of job scheduling (0.0 - 1.0)")
.register(this, service -> {
int total = service.scheduledCount.get() + service.failedCount.get();
return total == 0 ? 1.0 : (double) service.scheduledCount.get() / total;
});
// 2. Record Metrics in rescheduleWaitingJobs()
public void rescheduleWaitingJobs() {
Timer.Sample sample = Timer.start();
int queueSize = waitingQueue.size();
try {
log.info("Starting reschedule for {} waiting jobs", queueSize);
List<WaitingJob> readyJobs = new ArrayList<>();
synchronized (waitingQueue) {
Iterator<WaitingJob> iterator = waitingQueue.iterator();
while (iterator.hasNext()) {
WaitingJob job = iterator.next();
if (slotService.hasEnoughSlots(job.getRequiredSlots())) {
readyJobs.add(job);
iterator.remove();
log.debug("Job {} is ready to schedule, required slots: {}",
job.getJobId(), job.getRequiredSlots());
}
}
}
log.info("Found {} jobs that can be scheduled", readyJobs.size());
for (WaitingJob job : readyJobs) {
try {
long waitTime = System.currentTimeMillis() - job.getSubmitTime();
waitingTimeHistogram.record(waitTime);
jobExecutionService.startJob(job);
scheduledCount.incrementAndGet();
log.info("Successfully scheduled job {} (waited {} ms, slots: {})",
job.getJobId(), waitTime, job.getRequiredSlots());
} catch (Exception e) {
failedCount.incrementAndGet();
log.error("Failed to schedule job {} after waiting {} ms",
job.getJobId(),
System.currentTimeMillis() - job.getSubmitTime(),
e);
handleSchedulingFailure(job);
}
}
rescheduleCounter.increment();
} finally {
sample.stop(rescheduleTimer);
log.debug("Reschedule completed in {} ms",
rescheduleTimer.getTotalTime(TimeUnit.MILLISECONDS));
}
}
// 3. Add log level descriptions
/*
* Log level recommendations:
* - INFO: Scheduling started/completed, job successfully scheduled
* - DEBUG: Check process for each job, detailed resource information
* - WARN: Job cannot be scheduled (but not an error)
* - ERROR: Scheduling failed, exceptional situations
*/
// 4. Add diagnostic methods (for operations)
/**
* Get statistics of waiting queue for diagnosis.
*
* @return Statistics in JSON format
*/
public String getWaitingQueueStats() {
StringBuilder stats = new StringBuilder();
stats.append("{\n");
stats.append(" \"queueSize\": ").append(waitingQueue.size()).append(",\n");
stats.append(" \"rescheduleCount\": ").append(rescheduleCounter.getCount()).append(",\n");
stats.append(" \"avgRescheduleTime\": ").append(rescheduleTimer.getMean(TimeUnit.MILLISECONDS)).append(" ms,\n");
stats.append(" \"jobs\": [\n");
for (WaitingJob job : waitingQueue) {
long waitTime = System.currentTimeMillis() - job.getSubmitTime();
stats.append(" {\n");
stats.append(" \"jobId\": ").append(job.getJobId()).append(",\n");
stats.append(" \"requiredSlots\": ").append(job.getRequiredSlots()).append(",\n");
stats.append(" \"waitTime\": ").append(waitTime).append(" ms,\n");
stats.append(" \"priority\": \"").append(job.getPriority()).append("\"\n");
stats.append(" },\n");
}
stats.append(" ]\n");
stats.append("}");
return stats.toString();
}
}
// 5. Provide REST API endpoint (optional)
@RestController
@RequestMapping("/api/v1")
public class MonitoringController {
@Autowired
private CoordinatorService coordinatorService;
@GetMapping("/waiting-queue")
public ResponseEntity<String> getWaitingQueueStats() {
return ResponseEntity.ok(coordinatorService.getWaitingQueueStats());
}
@GetMapping("/metrics/job.scheduling")
public ResponseEntity<Map<String, Object>> getSchedulingMetrics() {
Map<String, Object> metrics = new HashMap<>();
metrics.put("queueSize", Metrics.gauge("job.waiting.queue.size").get());
metrics.put("rescheduleCount", Metrics.counter("job.reschedule.count").get());
metrics.put("avgDuration", Metrics.timer("job.reschedule.duration").getMean());
metrics.put("successRate", Metrics.gauge("job.schedule.success.rate").get());
return ResponseEntity.ok(metrics);
}
}
// 6. Add alert rules (Prometheus example)
/*
# Alert: Queue backlog warning
ALERT JobWaitingQueueBacklog
IF job_waiting_queue_size > 100
FOR 5m
LABELS { severity = "warning" }
ANNOTATIONS {
summary = "Job waiting queue is too large",
description = "Waiting queue has {{ $value }} jobs, may indicate resource shortage"
}
# Alert: Scheduling failure rate warning
ALERT HighSchedulingFailureRate
IF job_schedule_success_rate < 0.9
FOR 10m
LABELS { severity = "critical" }
ANNOTATIONS {
summary = "Job scheduling failure rate is too high",
description = "Only {{ $value | humanizePercentage }} of jobs are scheduled successfully"
}
# Alert: Reschedule duration warning
ALERT SlowReschedulePerformance
IF job_reschedule_duration{quantile="0.99"} > 5000
FOR 5m
LABELS { severity = "warning" }
ANNOTATIONS {
summary = "Reschedule is taking too long",
description = "P99 reschedule latency is {{ $value }}ms, may need optimization"
}
*/Rationale:
Issue 5: Insufficient Test CoverageLocation:
Related Context:
Problem Description: Although the PR added test classes, the following key scenarios may be missing from tests:
Potential Risks:
Impact Scope:
Severity: MAJOR Improvement Suggestions: // 1. Extend PeekBlockingQueueTest.java
public class PeekBlockingQueueTest {
private PeekBlockingQueue<String> queue;
@BeforeEach
public void setUp() {
queue = new PeekBlockingQueue<>();
}
@Test
public void testPeekDoesNotRemoveElement() {
queue.offer("job1");
queue.offer("job2");
String peeked = queue.peek();
assertEquals("job1", peeked);
assertEquals(2, queue.size()); // Verify element not removed
String peekedAgain = queue.peek();
assertEquals("job1", peekedAgain); // Peek again returns same element
}
@Test
public void testPeekReturnsNullWhenEmpty() {
assertNull(queue.peek());
}
@Test
public void testRemoveSpecificElement() {
queue.offer("job1");
queue.offer("job2");
queue.offer("job3");
boolean removed = queue.remove("job2");
assertTrue(removed);
assertEquals(2, queue.size());
assertEquals("job1", queue.peek());
}
@Test
public void testRemoveNonExistentElement() {
queue.offer("job1");
boolean removed = queue.remove("job999");
assertFalse(removed);
assertEquals(1, queue.size());
}
@Test
public void testConcurrentPeekAndRemove() throws InterruptedException {
int threadCount = 10;
int operationsPerThread = 1000;
ExecutorService executor = Executors.newFixedThreadPool(threadCount);
CountDownLatch latch = new CountDownLatch(threadCount);
// Thread 1: Producer
executor.submit(() -> {
for (int i = 0; i < operationsPerThread; i++) {
queue.offer("job-" + i);
}
latch.countDown();
});
// Threads 2-10: Consumers
for (int i = 0; i < threadCount - 1; i++) {
executor.submit(() -> {
for (int j = 0; j < operationsPerThread; j++) {
String job = queue.peek();
if (job != null) {
queue.remove(job);
}
}
latch.countDown();
});
}
latch.await(30, TimeUnit.SECONDS);
executor.shutdown();
// Verify: Queue should be empty or nearly empty
assertTrue(queue.size() < 100); // Allow minor race conditions
}
@Test
@Timeout(value = 5, unit = TimeUnit.SECONDS)
public void testPeekPerformance() {
// Test performance of peek() (should be very fast)
for (int i = 0; i < 10000; i++) {
queue.offer("job-" + i);
}
long start = System.nanoTime();
for (int i = 0; i < 100000; i++) {
queue.peek();
}
long duration = System.nanoTime() - start;
// 100000 peeks should complete within 100ms
assertTrue(duration < 100_000_000,
"peek() took too long: " + duration + " ns");
}
}
// 2. Add CoordinatorServiceTest.java
public class CoordinatorServiceTest {
private CoordinatorService coordinatorService;
private SlotService mockSlotService;
private JobExecutionService mockJobExecutionService;
private PeekBlockingQueue<WaitingJob> waitingQueue;
@BeforeEach
public void setUp() {
mockSlotService = mock(SlotService.class);
mockJobExecutionService = mock(JobExecutionService.class);
waitingQueue = new PeekBlockingQueue<>();
coordinatorService = new CoordinatorService(
mockSlotService,
mockJobExecutionService,
waitingQueue
);
}
@Test
public void testRescheduleWaitingJobs_WhenJobsCanMatchResources() {
// Given: 3 jobs in queue, requiring 2, 4, 6 slots respectively
waitingQueue.offer(new WaitingJob(1, 2));
waitingQueue.offer(new WaitingJob(2, 4));
waitingQueue.offer(new WaitingJob(3, 6));
when(mockSlotService.hasEnoughSlots(2)).thenReturn(true);
when(mockSlotService.hasEnoughSlots(4)).thenReturn(true);
when(mockSlotService.hasEnoughSlots(6)).thenReturn(false);
// When: Trigger reschedule
coordinatorService.rescheduleWaitingJobs();
// Then: First 2 jobs should be scheduled, 3rd remains in queue
verify(mockJobExecutionService).startJob(argThat(job -> job.getJobId() == 1));
verify(mockJobExecutionService).startJob(argThat(job -> job.getJobId() == 2));
verify(mockJobExecutionService, never()).startJob(argThat(job -> job.getJobId() == 3));
assertEquals(1, waitingQueue.size());
}
@Test
public void testRescheduleWaitingJobs_WhenQueueIsEmpty() {
// Given: Queue is empty
assertEquals(0, waitingQueue.size());
// When: Trigger reschedule
coordinatorService.rescheduleWaitingJobs();
// Then: No scheduling methods should be called
verify(mockJobExecutionService, never()).startJob(any());
}
@Test
public void testRescheduleWaitingJobs_WhenNoJobsCanMatchResources() {
// Given: 2 jobs in queue, but insufficient resources for both
waitingQueue.offer(new WaitingJob(1, 10));
waitingQueue.offer(new WaitingJob(2, 20));
when(mockSlotService.hasEnoughSlots(anyInt())).thenReturn(false);
// When: Trigger reschedule
coordinatorService.rescheduleWaitingJobs();
// Then: No jobs scheduled, queue unchanged
verify(mockJobExecutionService, never()).startJob(any());
assertEquals(2, waitingQueue.size());
}
@Test
public void testRescheduleWaitingJobs_WhenJobSchedulingFails() {
// Given: 2 jobs in queue, scheduling 1st will fail
waitingQueue.offer(new WaitingJob(1, 2));
waitingQueue.offer(new WaitingJob(2, 4));
when(mockSlotService.hasEnoughSlots(2)).thenReturn(true);
when(mockSlotService.hasEnoughSlots(4)).thenReturn(true);
doThrow(new RuntimeException("Worker unavailable"))
.when(mockJobExecutionService).startJob(argThat(job -> job.getJobId() == 1));
// When: Trigger reschedule
coordinatorService.rescheduleWaitingJobs();
// Then: 1st fails, but 2nd should be scheduled
verify(mockJobExecutionService).startJob(argThat(job -> job.getJobId() == 1));
verify(mockJobExecutionService).startJob(argThat(job -> job.getJobId() == 2));
// Verify error handling (depends on implementation)
// Possible expectation: 1st job is re-added to queue, or marked as failed
}
@Test
public void testRescheduleWaitingJobs_ConcurrentExecution() throws InterruptedException {
// Given: 100 jobs in queue
for (int i = 0; i < 100; i++) {
waitingQueue.offer(new WaitingJob(i, 2));
}
when(mockSlotService.hasEnoughSlots(2)).thenReturn(true);
// When: 10 threads trigger reschedule simultaneously
int threadCount = 10;
ExecutorService executor = Executors.newFixedThreadPool(threadCount);
CountDownLatch latch = new CountDownLatch(threadCount);
for (int i = 0; i < threadCount; i++) {
executor.submit(() -> {
try {
coordinatorService.rescheduleWaitingJobs();
} finally {
latch.countDown();
}
});
}
latch.await(10, TimeUnit.SECONDS);
executor.shutdown();
// Then: All jobs should be scheduled, no duplicate scheduling
// Each startJob should be called once
// ArgumentCaptor for verifying parameters
ArgumentCaptor<WaitingJob> captor = ArgumentCaptor.forClass(WaitingJob.class);
verify(mockJobExecutionService, times(100)).startJob(captor.capture());
// Verify no duplicates
Set<Long> jobIds = captor.getAllValues().stream()
.map(WaitingJob::getJobId)
.collect(Collectors.toSet());
assertEquals(100, jobIds.size()); // All jobIds are unique
assertEquals(0, waitingQueue.size()); // Queue is empty
}
@Test
@Timeout(value = 10, unit = TimeUnit.SECONDS)
public void testRescheduleWaitingJobs_PerformanceWithLargeQueue() {
// Given: 1000 jobs in queue
for (int i = 0; i < 1000; i++) {
waitingQueue.offer(new WaitingJob(i, 2));
}
when(mockSlotService.hasEnoughSlots(2)).thenReturn(true);
// When: Trigger reschedule
long start = System.currentTimeMillis();
coordinatorService.rescheduleWaitingJobs();
long duration = System.currentTimeMillis() - start;
// Then: Should complete within reasonable time (< 1 second)
assertTrue(duration < 1000,
"Rescheduling 1000 jobs took too long: " + duration + " ms");
verify(mockJobExecutionService, times(1000)).startJob(any());
}
}
// 3. Integration test extension example
public class SeaTunnelEngineClusterRoleTest {
@Test
public void testWaitingQueueReschedulingIntegration() throws Exception {
// Given: Start a SeaTunnel cluster with only 5 slots
SeaTunnelCluster cluster = startClusterWithSlots(5);
// Submit 6 jobs, each requiring 2 slots
List<Job> jobs = new ArrayList<>();
for (int i = 0; i < 6; i++) {
jobs.add(submitJob("job-" + i, 2));
}
// When: Wait for some time to let jobs execute
Thread.sleep(10000);
// Then: Verify at least 3 jobs are running (5 slots)
long runningJobs = jobs.stream()
.filter(job -> job.getStatus() == JobStatus.RUNNING)
.count();
assertTrue(runningJobs >= 3, "At least 3 jobs should be running");
// When: Wait for first batch of jobs to complete
Thread.sleep(20000);
// Then: Verify remaining jobs are also scheduled (no HOL Blocking)
long completedJobs = jobs.stream()
.filter(job -> job.getStatus() == JobStatus.COMPLETED)
.count();
assertEquals(6, completedJobs, "All jobs should be completed");
}
}Rationale:
Issue 6: Lack of Configuration Documentation and User Guide UpdatesLocation: Documentation files ( Related Context:
Problem Description: This PR changes the behavior of
Potential Risks:
Impact Scope:
Severity: MINOR (but improvement recommended) Improvement Suggestions: # 1. Update configuration documentation: docs/en/seatunnel-engine/configuration.md
## Job Schedule Strategy
SeaTunnel Engine supports multiple job scheduling strategies to handle resource
contention when multiple jobs are submitted concurrently.
### WAIT Strategy (Recommended for production)
The WAIT strategy implements a **fair scheduling** mechanism with automatic
rescheduling to avoid head-of-line (HOL) blocking.
#### Behavior
When a job is submitted but there are not enough resources available, it will
be placed in a waiting queue. When resources are released (a running job
completes or fails), the scheduler will iterate through the waiting queue and
schedule any jobs that now have sufficient resources.
#### Key Features
- **Fair Scheduling**: Jobs are scheduled based on resource availability, not
just their position in the queue. Small jobs can be scheduled even if a
larger job is at the head of the queue.
- **Automatic Rescheduling**: No manual intervention required. The scheduler
automatically checks all waiting jobs when resources are released.
- **No Starvation**: All jobs will eventually be scheduled (assuming sufficient
resources).
#### Example ScenarioInitial State: After Job0 releases 5 slots: Old Behavior: New Behavior (Since 2.3.x): Result: Better resource utilization and fairness! Performance Considerations
MonitoringMonitor the following metrics to ensure optimal performance:
Set up alerts for:
Best Practices
COVER Strategy... (existing documentation) FROM_PARTLY Strategy... (existing documentation) 2. Update FAQ: docs/en/faq.mdJob SchedulingQ: Why is my job not starting even though there are available slots?A: This could happen for several reasons:
Q: How can I reduce my job's waiting time?A: Here are some strategies:
Q: Is the WAIT strategy fair?A: Yes, the WAIT strategy implements a fair scheduling mechanism:
However, in extreme cases (e.g., continuous stream of large jobs), small jobs 3. Update migration guide: docs/en/upgrade/from-2.3-to-2.4.md (if applicable)Upgrading from SeaTunnel 2.3.x to 2.4.xJob Schedule Strategy ChangesThe WAIT strategy now implements automatic queue rescheduling to avoid What Changed?
Impact
Migration StepsNo action required. The new behavior is backward compatible and generally Monitoring RecommendationsAfter upgrading, monitor the following metrics to ensure the new behavior
4. Add performance report: docs/en/performance/benchmarks.mdJob Scheduling PerformanceWAIT Strategy Performance (v2.3.x)We benchmarked the WAIT strategy with different queue sizes to measure
Conclusion: The WAIT strategy performs well for queue sizes up to 100. Real-World Scenario: ETL PipelineSetup:
Results:
Conclusion: The WAIT strategy significantly improves fairness and resource 5. Add architecture documentation: docs/en/design/job-scheduler.mdJob Scheduler ArchitectureOverviewThe SeaTunnel job scheduler is responsible for allocating resources to jobs WAIT Strategy ImplementationThe WAIT strategy implements a fair scheduling algorithm with automatic Components
AlgorithmFuture Improvements
Root CauseThe original implementation only checked the job at the head of the waiting // Original code (pseudo)
public void onResourceReleased() {
Job head = waitingQueue.peek(); // Only check head!
if (canSchedule(head)) {
waitingQueue.poll();
start(head);
}
}SolutionImplement pending queue rescheduling: iterate through the entire waiting // New code (pseudo)
public void onResourceReleased() {
for (Job job : waitingQueue) {
if (canSchedule(job)) {
waitingQueue.remove(job);
start(job);
}
}
}Performance ImpactWe benchmarked the new implementation with different queue sizes:
Conclusion: The overhead is acceptable forqueue sizes up to 100 jobs. Real-World ImprovementIn our production cluster (20 slots, 30 concurrent jobs):
Trade-offsPros:
Cons:
Alternatives Considered
Future Work
TestingAdded unit tests for Does this PR introduce any user-facing change?Yes, but the change is beneficial and transparent: Previous Behavior
New Behavior
User Impact
How was this patch tested?Unit Tests
Integration Tests
Manual TestingTested on local cluster: --- |
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Purpose of this pull request
This PR introduces a pending-queue rescheduling (rotation) strategy for SeaTunnel Engine (Zeta) under job-schedule-strategy=WAIT , to reduce head-of-line blocking in static slot mode and improve fairness so that later jobs can still get scheduled.
TODO：
Other strategies still need to be improved
Does this PR introduce any user-facing change?
How was this patch tested?
Check list
New License Guide
incompatible-changes.mdto describe the incompatibility caused by this PR.