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SPARK003 – Job Execution Failed (Bad Spark Config) in Databricks

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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:

  1. Go to Databricks UI → Jobs → Select Failed Job → View Output.
  2. Check the “Spark UI” logs for executor failures or memory errors.
  3. 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:

  1. Go to Databricks UI → Clusters → Edit Cluster.
  2. Check the “Advanced Options” section under “Spark Config.”
  3. 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:

  1. Go to Clusters → Edit Cluster → Remove Spark Configurations.
  2. Restart the cluster.
  3. 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 and spark.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. 🚀

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