Retry and Failure Handling Strategy for CDC Merge Pipeline from Kafka to Databricks and Hyperscale

@Janice Chi welcome to the Microsoft Q&A community.

Your CDC ingestion pipeline is quite robust, and ensuring reliable retries and failure handling is crucial for maintaining data integrity. Here are some insights based on best practices:

1. Common Reasons for MERGE INTO Failures in Kafka to Silver Delta Tables

Schema Evolution Issues: If new columns are introduced or data types change, the MERGE operation may fail.

Concurrency Conflicts: High ingestion rates can lead to concurrent updates, causing deadlocks or race conditions.

Cluster Resource Constraints: Large tables may cause memory or compute exhaustion, leading to timeouts.

Data Skew: Uneven distribution of data across partitions can lead to performance bottlenecks.

Idempotent Retry Patterns: Using offset-based tracking ensures that retries do not duplicate data. Implementing checkpointing at the offset level can help.

2. Reliable Reprocessing Without Data Corruption

Atomicity at Offset Level: Using Delta Lake transaction logs ensures that each offset range is processed atomically.

Partitioned Processing: Reprocessing should be done at the Kafka topic-partition-offset level to avoid duplication.

Schema Validation Before Merge: Running schema checks before ingestion can prevent failures due to unexpected changes.

3. Failure Points in Silver to Hyperscale Stage

JDBC Write Timeouts: Large batch writes may exceed timeout limits.

Merge Constraint Violations: Primary key conflicts or missing dependencies can cause failures.

• Safe Retry Strategies:

Implement batch-level retries with exponential backoff.

Use staging tables to validate data before merging into Hyperscale.

• Maintain run_id-based tracking to ensure failed batches can be retried safely.Your CDC ingestion pipeline is quite robust, and ensuring reliable retries and failure handling is crucial for maintaining data integrity. Here are some insights based on best practices:
  1. Common Reasons for MERGE INTO Failures in Kafka to Silver Delta Tables
  • Schema Evolution Issues: If new columns are introduced or data types change, the MERGE operation may fail.
  • Concurrency Conflicts: High ingestion rates can lead to concurrent updates, causing deadlocks or race conditions.
  • Cluster Resource Constraints: Large tables may cause memory or compute exhaustion, leading to timeouts.
  • Data Skew: Uneven distribution of data across partitions can lead to performance bottlenecks.
  • Idempotent Retry Patterns: Using offset-based tracking ensures that retries do not duplicate data. Implementing checkpointing at the offset level can help.
  2. Reliable Reprocessing Without Data Corruption
  • Atomicity at Offset Level: Using Delta Lake transaction logs ensures that each offset range is processed atomically.
  • Partitioned Processing: Reprocessing should be done at the Kafka topic-partition-offset level to avoid duplication.
  • Schema Validation Before Merge: Running schema checks before ingestion can prevent failures due to unexpected changes.
  3. Failure Points in Silver to Hyperscale Stage
  • JDBC Write Timeouts: Large batch writes may exceed timeout limits.
  • Merge Constraint Violations: Primary key conflicts or missing dependencies can cause failures.
  • Safe Retry Strategies:
    • Implement batch-level retries with exponential backoff.
    • Use staging tables to validate data before merging into Hyperscale.
    • Maintain run_id-based tracking to ensure failed batches can be retried safely.

4. Recommended Control Table Patterns

Status Model for Failures:

Transient Failures (e.g., network issues) → Auto-retry.

  **Logical Failures** (e.g., data validation errors) → Flag for manual review.
  
     **Schema-related Failures** → Require intervention before retry.
     
     **Metadata Enhancements**:
     
        Include **error category** (e.g., timeout, schema mismatch).
        
           Track **retry attempts** to prevent infinite loops.
           
              Maintain **audit logs** for debugging.
              

For more details, you can check this resource on retry and failure handling strategies. Let me know if you need further clarification.

I hope these helps. Let me know if you have any further questions or need additional assistance.

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