Here’s list of 20 common Databricks issues users face, with additional context and examples for clarity:

1. Cluster Launch Failures

• Causes: Cloud provider quotas (e.g., AWS/Azure instance limits), misconfigured network settings (VPC, subnets), or invalid Spark configurations.
• Fix: Check cloud provider quotas, validate network settings, and review cluster configurations.

2. Slow Query Performance

• Causes: Poorly optimized Spark SQL, lack of caching, or small file problems (e.g., too many tiny Parquet/Delta files).
• Example: A SELECT * query on a massive Delta table without predicate filters.

3. Data Skew in Spark Jobs

• Signs: A few tasks take significantly longer than others.
• Fix: Use salting, repartitioning, or adaptive query execution (AQE) in Spark 3.0+.

4. Out-of-Memory (OOM) Errors

• Common In: Executors or driver nodes due to insufficient spark.executor.memory or spark.driver.memory settings.
• Fix: Tune memory settings or increase instance sizes.

5. Cost Overruns

• Causes: Overprovisioned clusters (e.g., large instances for small workloads), idle clusters left running, or frequent autoscaling.
• Tip: Use job clusters instead of all-purpose clusters for scheduled workloads.

6. Delta Lake Merge/Update Failures

• Issues: Conflicts during MERGE operations, schema mismatches, or transactional log corruption.
• Fix: Use optimistic concurrency control and validate schemas before merging.

7. Notebook Dependency Conflicts

• Example: Mixing incompatible Python libraries (e.g., Pandas vs. PySpark versions).
• Fix: Use cluster-scoped init scripts or MLflow for environment isolation.

8. Metastore Connectivity Issues

• Errors: Hive metastore (AWS Glue/Azure SQL) timeouts or permission issues.
• Root Cause: Network ACLs, security group rules, or IAM roles misconfigured.

9. Autoscaling Inefficiency

• Problem: Clusters not scaling up/down as expected due to uneven workloads or misconfigured scaling policies.
• Fix: Adjust spark.databricks.autoscaling settings or use instance pools.

10. Timeout Errors in Streaming Jobs

• Example: Structured Streaming jobs failing due to TimeoutException in Kafka or Event Hubs.
• Fix: Increase timeout thresholds or optimize micro-batch processing.

11. Access Control Issues

• Scenarios: Users unable to access notebooks, data, or clusters due to misconfigured Databricks workspace permissions or Unity Catalog policies.
• Tip: Use granular access controls in Unity Catalog (for data governance).

12. DBFS Mount Failures

• Causes: Invalid credentials for cloud storage (S3, ADLS) or expired SAS tokens.
• Fix: Re-mount storage with updated credentials and validate IAM roles.

13. Job Failures with External Libraries

• Issue: Jobs failing due to missing JARs/Python wheels or version conflicts.
• Fix: Use cluster-level libraries or install dependencies via init scripts.

14. Schema Evolution Problems

• Example: Adding columns to Delta tables causing downstream job failures.
• Tip: Use mergeSchema=true or enable automatic schema evolution.

15. Driver Node Crashes

• Causes: Heavy operations (e.g., .collect()) on the driver, exceeding driver memory.
• Fix: Avoid collecting large datasets to the driver; offload to executors.

16. Cloud Storage Latency

• Problem: Slow reads/writes to S3, ADLS, or GCS due to eventual consistency or throttling.
• Fix: Use optimized connectors (e.g., s3a:// with AWS) or caching.

17. Inconsistent Time Zones

• Example: Timestamps in notebooks showing UTC vs. local time discrepancies.
• Fix: Set spark.sql.session.timeZone in cluster configurations.

18. Delta Table Vacuum Issues

• Error: Cannot vacuum, files in use due to retention period conflicts.
• Solution: Adjust delta.logRetentionDuration and delta.deletedFileRetentionDuration.

19. API Rate Limiting

• Scenario: Automation scripts hitting Databricks REST API rate limits.
• Fix: Implement retry logic with exponential backoff.

20. Secret Management Problems

• Example: Hardcoded credentials in notebooks or failed secret scoping via Databricks Secrets API.
• Best Practice: Use Databricks Secrets with Azure Key Vault/AWS Secrets Manager integration.

Mohammad Gufran Jahangir