DATASTREAM MYSQL HIGH LATENCY

[ubitscakle]

Hello,

I have been implementing a flow from MySQL in RDS to BigQuery. Everything is well configured: connection profile, SSH tunnel, and the tests were done. It is capturing events with a delay of 17 seconds, which I find very good. The problem I have is that writing to BigQuery is extremely slow, taking up to 15 minutes, and I have no idea what could be causing it.

[ms4446]

The slow writing to BigQuery in your Datastream setup, despite good capture latency, can be attributed to several factors:

1. BigQuery Write API Throttling

• Check Quotas: BigQuery has specific quotas on data ingestion rates per table and per project. Exceeding these quotas can lead to throttling, slowing down writes. Check your quotas in the BigQuery console to ensure they are sufficient for your workload.

• Streaming vs. Batch Loads: While streaming inserts offer low latency, they may not be optimal for large volumes of data. Consider using batch loads if you can tolerate higher latency for improved performance.

• Concurrent Streams: Too many concurrent streams writing to the same table can cause contention and slow down writes. Try reducing the number of concurrent streams or distributing them across different tables to alleviate this issue.

2. Datastream Configuration

• Stream Buffer Size: Increasing the stream buffer size in Datastream can help manage bursts of data, reducing backpressure and improving write speeds. A larger buffer can absorb variations in data volume more effectively.

• Parallelism: If you’re handling high data volumes, increasing Datastream parallelism can speed up data processing by handling more data concurrently. Ensure this is balanced with resource availability to avoid network and system bottlenecks.

• Network Latency: High network latency between Datastream and BigQuery can slow down data writes. Ensure good network connectivity and consider deploying Datastream in a region closer to your BigQuery instance to minimize latency.

3. BigQuery Schema

• Nested/Repeated Fields: Writing to tables with complex nested or repeated fields can be slower than writing to tables with simpler schemas. Flattening your data before writing to BigQuery can improve performance.

• Partitioning/Clustering: For large tables, partitioning and/or clustering based on relevant columns can enhance query performance and improve data ingestion efficiency by organizing data more effectively.

4. Other Factors

• MySQL Server Performance: If the MySQL server on RDS is overloaded or under-resourced, it can slow down Datastream’s data capture, impacting write speeds to BigQuery. Monitor and optimize your MySQL server’s performance as needed.

• BigQuery Load: Heavy load on BigQuery from other processes can slow down writes from Datastream. Monitor BigQuery’s performance and optimize or balance other workloads to mitigate this.

Additional Considerations:

• BigQuery Streaming Buffer: Monitor the BigQuery streaming buffer for any delays. Data might be queued in the buffer before being committed to the table, affecting write speeds.

• Data Transformations: If Datastream performs complex transformations on the data before writing to BigQuery, it could introduce delays. Ensure that transformations are optimized for efficiency.

• Error Handling: Proper error handling and retry mechanisms can help avoid additional delays caused by processing errors. Make sure retries are configured correctly to maintain performance.

Troubleshooting Steps:

1. Monitor Logs: Check Datastream and BigQuery logs for errors or warnings that might indicate issues with slow writes. Logs can provide insights into the bottlenecks in the data flow.

2. Profile Writes: Use BigQuery’s query profiler to analyze and optimize the performance of your write operations.

3. Experiment: Try different combinations of the above suggestions to determine which adjustments most effectively improve your write speed.