Introduction
The SPARK003 – Job Execution Failed (bad Spark config) error occurs in Databricks when a job fails due to incorrect Spark configurations, resource limitations, or incompatible cluster settings.
🚨 Common symptoms of the SPARK003 error:
- Job fails immediately after submission.
- Cluster does not start or crashes mid-execution.
- Databricks logs show errors related to Spark configuration settings.
- High memory usage or executor failures.
This guide provides a step-by-step approach to troubleshooting and fixing SPARK003 job failures caused by bad Spark configurations in Databricks.
1. Check Job and Cluster Logs for Errors
Symptoms:
- Job fails instantly or after a few seconds.
- Logs indicate misconfigured Spark parameters.
- Error messages include memory allocation issues.
Fix:
✅ Check job logs for detailed error messages:
- Go to Databricks UI → Jobs → Select Failed Job → View Output.
- Check the “Spark UI” logs for executor failures or memory errors.
- Look for errors in the “Driver Logs” and “Executor Logs.”
✅ If logs indicate configuration issues, reset Spark parameters in cluster settings.
databricks jobs get --job-id <job-id>
2. Verify Spark Configuration in Cluster Settings
Symptoms:
- Error: “Invalid Spark configuration parameter.”
- Cluster fails to start with job execution.
- Job runs out of memory due to incorrect Spark tuning.
Fix:
✅ Check Spark configuration settings in your cluster:
- Go to Databricks UI → Clusters → Edit Cluster.
- Check the “Advanced Options” section under “Spark Config.”
- Remove or correct invalid Spark parameters.
✅ Ensure Spark memory settings are properly configured:
{
"spark.executor.memory": "8g",
"spark.driver.memory": "4g",
"spark.executor.cores": "4",
"spark.executor.instances": "5"
}
✅ Restart the cluster after making changes.
3. Fix Memory and Resource Allocation Issues
Symptoms:
- Error: “Job failed due to insufficient memory.”
- Executors are killed due to out-of-memory (OOM) errors.
- Databricks UI shows high memory usage.
Fix:
✅ Increase memory allocation for executors and driver:
{
"spark.driver.memory": "8g",
"spark.executor.memory": "16g",
"spark.memory.fraction": "0.8"
}
✅ Reduce the number of partitions for large jobs:
df = df.repartition(100) # Adjust partition count to avoid excessive memory usage
✅ Use coalesce()
to merge partitions and reduce memory load:
df = df.coalesce(10)
4. Ensure Cluster and Databricks Runtime Compatibility
Symptoms:
- Error: “Incompatible Spark configuration with Databricks runtime version.”
- Cluster fails to start with specific configurations.
Fix:
✅ Check the Databricks runtime version:
databricks clusters get --cluster-id <cluster-id>
✅ Ensure the Spark version matches your job requirements:
- Go to Clusters → Select Cluster → Configuration.
- Upgrade or downgrade to a compatible runtime version.
✅ If using ML libraries, ensure compatible ML runtime:
- Use Databricks ML Runtime instead of Standard Runtime.
5. Fix Executor and Driver Misconfigurations
Symptoms:
- Executors keep restarting.
- Job runs but performs poorly or stalls.
- Executor heartbeat timeouts in logs.
Fix:
✅ Increase executor core count for better parallelism:
{
"spark.executor.cores": "4",
"spark.executor.instances": "10"
}
✅ Ensure enough executor memory is allocated:
{
"spark.executor.memoryOverhead": "2g"
}
✅ Enable dynamic allocation to optimize resources:
{
"spark.dynamicAllocation.enabled": "true",
"spark.dynamicAllocation.minExecutors": "2",
"spark.dynamicAllocation.maxExecutors": "10"
}
6. Fix Storage and Data Source Configuration Issues
Symptoms:
- Error: “FileNotFoundException: Path does not exist.”
- Slow job execution due to storage latency.
- Databricks fails to access S3, ADLS, or GCS.
Fix:
✅ Check if data paths are correct and accessible:
dbutils.fs.ls("dbfs:/mnt/my-bucket/data/")
✅ Ensure the right authentication method is used for cloud storage:
- AWS S3: Use IAM roles with correct permissions.
- Azure ADLS: Use Azure AD authentication.
- Google Cloud Storage: Ensure service accounts are correctly assigned.
✅ For high-performance reads, enable optimized Parquet and Delta reads:
spark.conf.set("spark.sql.parquet.v2.enabled", "false")
7. Fix Job Scheduling and Execution Issues
Symptoms:
- Job gets stuck in “Pending” state.
- Job fails intermittently without logs.
Fix:
✅ Check job scheduling and execution logs:
databricks jobs list-runs --job-id <job-id>
✅ If multiple jobs are running, reduce concurrency:
- Limit maximum concurrent runs to avoid resource contention.
- Use job queueing for better execution order.
✅ Ensure the job cluster has enough workers:
{
"spark.databricks.cluster.scaleUpFactor": "2"
}
8. Reset Spark Configuration to Defaults
Symptoms:
- Persistent errors despite fixing configurations.
- Unknown configuration conflicts in the cluster.
Fix:
✅ Reset Spark configuration to defaults in Databricks UI:
- Go to Clusters → Edit Cluster → Remove Spark Configurations.
- Restart the cluster.
- Manually reapply essential configurations.
✅ Use Databricks CLI to remove Spark configurations:
databricks clusters edit --json '{"spark_conf": {}}'
Step-by-Step Troubleshooting Guide
Step 1: Check Job Logs for Specific Errors
databricks jobs get-output --job-id <job-id>
Step 2: Validate Cluster and Spark Configurations
databricks clusters get --cluster-id <cluster-id>
Step 3: Fix Memory and Resource Allocation
- Increase
spark.driver.memory
andspark.executor.memory
settings.
Step 4: Check Data Storage and Network Connectivity
- Verify if S3, ADLS, or GCS are accessible.
Step 5: Optimize Spark Execution Settings
- Enable dynamic allocation and coalesce() partitions for large jobs.
Best Practices to Prevent SPARK003 Job Failures
✅ Use Auto-Scaling for Large Workloads
- Enable dynamic allocation to scale up and down automatically.
✅ Monitor Spark Memory Usage Regularly
- Use Databricks Ganglia Metrics to track memory and CPU usage.
✅ Ensure Clusters Have the Right Spark Configuration
- Use standardized cluster configurations for all jobs.
✅ Cache Frequently Used Data to Reduce API Calls
df.cache()
df.show()
Conclusion
The SPARK003 – Job Execution Failed (bad Spark config) error occurs due to incorrect Spark settings, memory constraints, or cluster misconfigurations. By following this guide, you can:
✅ Check logs for root causes.
✅ Fix Spark memory and execution settings.
✅ Ensure storage and network connections are stable.
✅ Optimize cluster configurations for better performance.
By applying these best practices, you can ensure stable and efficient job execution in Databricks. 🚀