diff --git a/gobblin-yarn/src/main/java/org/apache/gobblin/yarn/YarnAutoScalingManager.java b/gobblin-yarn/src/main/java/org/apache/gobblin/yarn/YarnAutoScalingManager.java
index 4ed63b74440..5f5c872c67e 100644
--- a/gobblin-yarn/src/main/java/org/apache/gobblin/yarn/YarnAutoScalingManager.java
+++ b/gobblin-yarn/src/main/java/org/apache/gobblin/yarn/YarnAutoScalingManager.java
@@ -30,6 +30,8 @@
 import java.util.concurrent.TimeUnit;
 import java.util.stream.Collectors;
 
+
+import org.apache.commons.compress.utils.Sets;
 import org.apache.hadoop.yarn.api.records.Resource;
 import org.apache.helix.HelixDataAccessor;
 import org.apache.helix.HelixManager;
@@ -38,6 +40,7 @@
 import org.apache.helix.task.JobContext;
 import org.apache.helix.task.JobDag;
 import org.apache.helix.task.TaskDriver;
+import org.apache.helix.task.TaskPartitionState;
 import org.apache.helix.task.TaskState;
 import org.apache.helix.task.WorkflowConfig;
 import org.apache.helix.task.WorkflowContext;
@@ -68,6 +71,7 @@ public class YarnAutoScalingManager extends AbstractIdleService {
   private final String AUTO_SCALING_PREFIX = GobblinYarnConfigurationKeys.GOBBLIN_YARN_PREFIX + "autoScaling.";
   private final String AUTO_SCALING_POLLING_INTERVAL_SECS =
       AUTO_SCALING_PREFIX + "pollingIntervalSeconds";
+  private static final int THRESHOLD_NUMBER_OF_ATTEMPTS_FOR_LOGGING = 20;
   private final int DEFAULT_AUTO_SCALING_POLLING_INTERVAL_SECS = 60;
   // Only one container will be requested for each N partitions of work
   private final String AUTO_SCALING_PARTITIONS_PER_CONTAINER = AUTO_SCALING_PREFIX + "partitionsPerContainer";
@@ -94,6 +98,8 @@ public class YarnAutoScalingManager extends AbstractIdleService {
   private final double overProvisionFactor;
   private final SlidingWindowReservoir slidingFixedSizeWindow;
   private static int maxIdleTimeInMinutesBeforeScalingDown = DEFAULT_MAX_IDLE_TIME_BEFORE_SCALING_DOWN_MINUTES;
+  private static final HashSet<TaskPartitionState>
+      UNUSUAL_HELIX_TASK_STATES = Sets.newHashSet(TaskPartitionState.ERROR, TaskPartitionState.DROPPED);
 
   public YarnAutoScalingManager(GobblinApplicationMaster appMaster) {
     this.config = appMaster.getConfig();
@@ -189,6 +195,21 @@ private Set<String> getParticipants(String filterString) {
           .keySet().stream().filter(x -> filterString.isEmpty() || x.contains(filterString)).collect(Collectors.toSet());
     }
 
+    private String getInuseParticipantForHelixPartition(JobContext jobContext, int partition) {
+      if (jobContext.getPartitionNumAttempts(partition) > THRESHOLD_NUMBER_OF_ATTEMPTS_FOR_LOGGING) {
+        log.warn("Helix task {} has been retried for {} times, please check the config to see how we can handle this task better",
+            jobContext.getTaskIdForPartition(partition), jobContext.getPartitionNumAttempts(partition));
+      }
+      if (!UNUSUAL_HELIX_TASK_STATES.contains(jobContext.getPartitionState(partition))) {
+        return jobContext.getAssignedParticipant(partition);
+      }
+      // adding log here now for debugging
+      //todo: if this happens frequently, we should reset to status to retriable or at least report the error earlier
+      log.info("Helix task {} is in {} state which is unexpected, please watch out to see if this get recovered",
+          jobContext.getTaskIdForPartition(partition), jobContext.getPartitionState(partition));
+      return null;
+    }
+
     /**
      * Iterate through the workflows configured in Helix to figure out the number of required partitions
      * and request the {@link YarnService} to scale to the desired number of containers.
@@ -220,7 +241,10 @@ void runInternal() {
           int numPartitions = 0;
           String jobTag = defaultHelixInstanceTags;
           if (jobContext != null) {
-            inUseInstances.addAll(jobContext.getPartitionSet().stream().map(jobContext::getAssignedParticipant)
+            log.debug("JobContext {} num partitions {}", jobContext, jobContext.getPartitionSet().size());
+
+            inUseInstances.addAll(jobContext.getPartitionSet().stream()
+                .map(i -> getInuseParticipantForHelixPartition(jobContext, i))
                 .filter(Objects::nonNull).collect(Collectors.toSet()));
 
             numPartitions = jobContext.getPartitionSet().size();