GCP Spanner Database Table Transcation Aborted Issue

[Onkar]

So we have a spanner database consisting of multiple tables. But the write operations like INSERT are failing on one of the tables with the below error. 

Gcloud command output:

ERROR: (gcloud.spanner.databases.execute-sql) ABORTED: Transaction was aborted. - '@type': type.googleapis.com/google.rpc.RetryInfo retryDelay: 0.011158286s

Spanner Studio Output: 

 We have checked all the metrics from the Instance and Database resource utilization. Also, there is no lock on the table at all.

Apart from these, we have also created a new table in the same DB and the same query is working on that table.

Would like to hear from you all if anyone has an idea regarding the issue. If faced in the past?

[ms4446 · Accepted solution]

Transaction aborts are a core mechanism Spanner uses to guarantee consistency and isolation in its distributed database environment. They commonly occur due to the following:

• Contention: Multiple transactions try to modify the same data concurrently.
• Transient Errors: Temporary internal Spanner conditions or network glitches.

Mitigation Strategies

1. Mitigating Contention

   • Schema Redesign: Distribute writes evenly across the database to minimize hotspots (e.g., consider alternative modeling strategies for high-contention entities).
   • Batching Writes: Combine multiple operations into single transactions, reducing the overall transaction rate.
   • Intelligent Retries: Implement exponential backoff retries. Cloud Spanner client libraries often handle this, but custom logic may be needed for complex cases.

2. Minimizing Transaction Duration

   • Reduce the time transactions are held open. Prepare data in advance and commit as soon as possible to decrease conflict likelihood.

3. Optimizing Secondary Indexes

   • Evaluate indexes carefully. While beneficial for reads, they can add overhead to writes. Ensure their design doesn't create new hotspots.

4. Investigating Operational Issues

   • Utilize Spanner Monitoring: Track metrics (transaction durations, retries, errors) to identify non-obvious problems.
   • Check for Hidden Factors: Consider if recent schema changes, hardware issues, or network problems might be contributing factors.

5. Google Cloud Resources

   • Consult Documentation: Refer to official resources for best practices and troubleshooting tips.
   • Engage Support: Seek direct assistance from Google Cloud Support for persistent issues.

6. Monitoring and Logging

   • Proactive Logging: Track aborted transactions, their patterns, and associated errors to pinpoint root causes.

Additional Considerations

• Client Libraries: Effectively leverage their built-in transaction management and retry features.
• Transaction Complexity: Simplify large transactions or break them into smaller units to reduce abort risk.
• Global Transactions: Use with caution, as their increased complexity and latency can make aborts more likely.

Successfully addressing "Transaction was aborted" errors in Cloud Spanner requires a holistic approach. This involves strategic schema design, efficient transaction management, proactive monitoring, and leveraging Google Cloud's resources. By understanding the causes of aborts and implementing these solutions, you can significantly improve the stability and performance of your Spanner database.

[Onkar]

I agree but still we are unclear about the resolution to the issue. 

[ms4446]

If you're still encountering the "Transaction was aborted" error in Spanner and the advice provided hasn't resolved the issue, it might be helpful to take a more targeted approach. Here are some steps you can take:

1. Deep Dive into Transaction Patterns

• Analyze Transaction Conflicts: Use Spanner's query execution statistics to pinpoint if specific transactions frequently conflict. This helps identify tables or rows with high contention.
• Review Access Patterns: Examine how your application accesses data. Are many transactions updating the same rows quickly? This is a likely cause of conflicts.

2. Optimize Application Logic

• Minimize Transaction Scope: Keep transactions as small and short as possible (affecting few rows, running quickly). This might mean restructuring data access.
• Implement Efficient Retries: Retries are necessary, but use exponential backoff and jitter to avoid overloading the system.

3. Schema and Index Optimization

• Reevaluate Schema Design: Could sharding keys distribute writes better? Schema changes can reduce contention.
• Optimize Indexes: Are secondary indexes causing issues? Removing or redesigning them might help.

4. Use Partitioned DML for Bulk Operations

• If doing bulk updates/deletes, Partitioned DML is designed to handle these operations across partitions, reducing conflicts.

5. Analyze Hotspots

• Identify Write Hotspots: Spanner's Key Visualizer tool helps find hotspots (high activity areas) in your database. Knowing these, you can adjust access patterns or schema to mitigate problems.

6. Consult Google Cloud Support

Resolving "Transaction was aborted" errors in Cloud Spanner often requires a combination of optimizing application logic, adjusting database schema and access patterns, and sometimes, engaging with Google Cloud Support for more in-depth analysis.

[Onkar]

We have connected with support but still no concrete answer from them. Just FYI we have also scaled down all the client's pods and cleaned up all sessions. We have monitored all the metrics and since there is no locking its showing.

So still we are unclear on the root cause & its still failing on same error on INSERT.

[ms4446]

Here are some recommendations that might help in isolating and resolving the problem:

1. Transaction Isolation and Serialization Review: Given Spanner's use of serializable isolation, it's crucial to ensure that your application's transactions are designed to avoid dependencies that could lead to serialization anomalies. Re-examine your transaction logic to ensure it's optimized for Spanner's concurrency control mechanisms.

2. Detailed Analysis of INSERT Statements: Focus on the specific INSERT statements that are failing. Analyze whether these operations target hot spots or involve secondary indexes or interleaved tables, which could increase contention. Adjusting these INSERT operations might alleviate the issues.

3. Schema-related Considerations: Verify that the data types and sizes of the values being inserted match the schema definitions exactly. For tables that are interleaved, assess whether the parent-child relationship might be contributing to the contention and, if so, consider schema adjustments to mitigate this.

4. Enhanced Logging: If not already implemented, enable detailed logging for both your Spanner instance and the client library. Analyzing these logs could provide insights into patterns or anomalies associated with the aborted transactions.

5. Experiment with Transaction Modes: If your use case involves bulk inserts, experimenting with Partitioned DML could prove beneficial. Additionally, ensure that the transaction modes (Read-Write vs. ReadOnly) are correctly applied to your operations.

6. Further Engagement with Google Cloud Support: If the issue persists, I recommend providing detailed information about your troubleshooting steps and the strategies you've attempted to Google Cloud Support. Don't hesitate to request an escalation if you feel the issue requires more in-depth technical analysis.

Persistent transaction aborts, especially in complex distributed systems like Cloud Spanner, often require a multifaceted approach to diagnose and resolve. Your proactive steps in scaling down pods, cleaning up sessions, and monitoring metrics are commendable and form a solid foundation for the additional strategies suggested above.