Introduction

DELTA002 – Delta Table Version Mismatch is an error in Delta Lake that indicates a version conflict between the transaction log and the data being read or written. This error typically occurs when multiple concurrent operations modify the same Delta table, or when incorrect Delta log versions are accessed.

🚨 Common Symptoms:

• Error: “DELTA002: Delta table version mismatch.”
• Read or write operations fail after recent table updates.
• Time travel or rollback operations fail to locate the expected version.

What Causes the Version Mismatch?

1. Concurrent modifications to the same Delta table.
2. Incorrect Delta log version reference during read or write.
3. Delta log corruption or missing transaction files.
4. Time travel to a deleted or non-existent version.
5. File system issues (e.g., S3 eventual consistency or ADLS latency) causing version misalignment.

Common Scenarios and Fixes

1. Concurrent Writes Causing Version Conflict

Symptoms:

• The error appears during parallel writes to the same Delta table.
• Time travel queries fail to locate the correct version.

Causes:

• Multiple jobs or notebooks writing to the same Delta table simultaneously.
• Optimistic concurrency control fails due to conflicting updates.

Fix:
✅ Enable serial writes using spark.databricks.delta.concurrentWrites to prevent conflicts:

spark.conf.set("spark.databricks.delta.concurrentWrites", "false")

✅ Use transactional commands like MERGE or UPDATE with conflict detection:

MERGE INTO target_table AS t
USING source_table AS s
ON t.id = s.id
WHEN MATCHED THEN UPDATE SET t.value = s.value
WHEN NOT MATCHED THEN INSERT (id, value) VALUES (s.id, s.value);

2. Delta Log Corruption or Missing Transaction Files

Symptoms:

• Error during Delta table reads or updates.
• Delta log contains missing or corrupt transaction files.

Causes:

• S3 eventual consistency issues causing incomplete writes.
• File system errors or accidental deletion of Delta log files.

Fix:
✅ Check the Delta log for missing or corrupt files:

ls /dbfs/mnt/delta_table/_delta_log/

✅ Repair or restore the Delta log from backups if possible.

✅ Use Delta Lake’s RESTORE feature to revert to a consistent state:

RESTORE delta.`/mnt/delta_table/` TO VERSION AS OF 10;

3. Time Travel to a Non-Existent Version

Symptoms:

• Error: “Version not found in Delta log.”
• Time travel query fails to locate the requested version.

Causes:

• The requested version has been deleted due to VACUUM.
• Incorrect version reference in the query.

Fix:
✅ Check available versions using DESCRIBE HISTORY:

DESCRIBE HISTORY delta.`/mnt/delta_table/`;

✅ Ensure the requested version is within the retention period:

SELECT * FROM delta.`/mnt/delta_table/` VERSION AS OF 10;

✅ Avoid setting a short retention period when running VACUUM:

VACUUM delta.`/mnt/delta_table/` RETAIN 168 HOURS; -- Retain 7 days

4. File System Latency or Inconsistent Reads

Symptoms:

• Intermittent version mismatch errors on cloud storage (S3, ADLS, GCS).
• Table appears correct in some queries but fails in others.

Causes:

• S3 eventual consistency issues when listing or reading files.
• Slow synchronization between multiple data nodes.

Fix:
✅ Enable Delta Lake retries for consistency issues:

spark.conf.set("spark.databricks.delta.retentionDurationCheck.enabled", "false")

✅ For S3, use S3-specific consistency checks:

• Enable S3 Guard or AWS Glue Data Catalog for consistency management.

✅ For Azure, ensure proper configuration of ADLS Gen2 storage.

5. Incorrect Cluster State or Caching Issues

Symptoms:

• The table works on some clusters but fails on others.
• Cached table versions are outdated, causing conflicts.

Causes:

• Cluster state is out of sync with the Delta log.
• Cached data does not reflect recent changes.

Fix:
✅ Clear the cache before reading the Delta table:

spark.catalog.clearCache()

✅ Restart the cluster to ensure consistent state.

✅ Avoid caching Delta tables during frequent updates.

Step-by-Step Troubleshooting Guide

1. Check Delta Table History for Recent Changes: DESCRIBE HISTORY delta.`/mnt/delta_table/`;
2. Verify the Delta Log for Missing Files: ls /dbfs/mnt/delta_table/_delta_log/
3. Identify Concurrent Writes or Conflicting Updates:
   • Ensure no other jobs are modifying the table simultaneously.
4. If Using Time Travel, Verify the Requested Version Exists: SELECT * FROM delta.`/mnt/delta_table/` VERSION AS OF 10;
5. Check Cloud Storage Consistency (S3, ADLS, GCS):
   • Run storage connectivity and consistency checks.

Best Practices to Prevent Delta Table Version Mismatch

✅ Enable Serial Writes for Critical Tables

• Prevents concurrent modifications and conflicts.

✅ Use Delta Lake Transactions (MERGE, UPDATE, DELETE) with Conflict Detection

• Reduces the chances of version conflicts.

✅ Avoid Frequent VACUUM with Short Retention Periods

• Retain at least 7 days of history to prevent accidental data loss.

✅ Monitor Delta Table History and Changes

• Use DESCRIBE HISTORY to track modifications and detect conflicts early.

✅ Back Up Delta Logs Regularly

• Restore from a backup in case of corruption or accidental deletion.

Conclusion

The DELTA002 – Delta Table Version Mismatch error typically occurs due to concurrent writes, time travel to non-existent versions, or Delta log corruption. By following this guide, you can identify the root cause, troubleshoot effectively, and implement best practices to avoid version conflicts in Delta Lake.