Streaming Subscription Handler Misses Data When Appending Multiple Rows per Input Row

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Summary

The issue at hand is related to a streaming subscription handler that is designed to transform each incoming row from stream table A into 4 rows and append them to a new stream table B. However, the handler is missing data, where only the first row from stream table A is successfully transformed, and subsequent rows are not processed as expected.

Root Cause

The root cause of this issue is related to the append operation to the new stream table B. When the handler attempts to append 4 rows simultaneously, it causes data loss, whereas if the computation is kept but only one row is appended at a time, no data is missed. This suggests that the issue is not due to computational latency but rather with the append operation itself. Possible causes include:

  • Buffer overflow: The append operation may be causing a buffer overflow, leading to data loss.
  • Concurrency issues: The handler may be experiencing concurrency issues, where multiple append operations are interfering with each other.
  • Table locking: The append operation may be causing the table to be locked, preventing subsequent rows from being processed.

Why This Happens in Real Systems

This issue can occur in real systems due to various reasons, including:

  • High-volume data streams: When dealing with high-volume data streams, the append operation can become a bottleneck, leading to data loss.
  • Complex computations: Complex computations can increase the processing time, making it more likely for data to be missed.
  • Concurrency and parallelism: Concurrency and parallelism can introduce issues with data consistency and integrity.

Real-World Impact

The real-world impact of this issue can be significant, including:

  • Data loss: Missing data can lead to inaccurate analysis and decision-making.
  • System downtime: The issue can cause system downtime, leading to lost productivity and revenue.
  • Reputation damage: Data loss can damage the reputation of an organization, leading to loss of customer trust.

Example or Code

def handle_stream_data(msg, hqTbName, reorderedColNames, csv_priceData):
    # Process the incoming row
    temp = select securityCode as SecurityID, preClosePrice, lastPrice as Price, origTime as tradetime, receivedTime from msg
    # ... (rest of the code remains the same)

    # Append the transformed data to the new stream table
    objByName(hqTbName).append!(tmp_final)

Note that the code snippet above is a simplified example and may not reflect the actual code used in the production environment.

How Senior Engineers Fix It

Senior engineers can fix this issue by:

  • Optimizing the append operation: Optimizing the append operation to reduce the likelihood of buffer overflow and concurrency issues.
  • Implementing data buffering: Implementing data buffering to ensure that data is not lost during the append operation.
  • Using transactional append: Using transactional append to ensure that either all or none of the rows are appended to the table.
  • Monitoring and logging: Monitoring and logging the system to detect and diagnose issues related to data loss.

Why Juniors Miss It

Junior engineers may miss this issue due to:

  • Lack of experience: Lack of experience with high-volume data streams and complex computations.
  • Insufficient testing: Insufficient testing of the system under various scenarios, including high-volume data streams and concurrency.
  • Limited understanding of concurrency: Limited understanding of concurrency and parallelism, leading to issues with data consistency and integrity.
  • Inadequate monitoring and logging: Inadequate monitoring and logging, making it difficult to detect and diagnose issues related to data loss.

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