Query related to inserting and updating data in BigQuery using streaming.

[NishinThattil]

For a project, new records are being created in BQ and subsequently modified with relevant data. However, the existing logic in BQ requires a cooling period of 90 minutes between the insertion and modification of a record.

Is there any solution for this issue.

[ms4446]

Yes, there are some strategies to handle the issue of a required 90-minute cooling period between inserting and updating records in Google Cloud BigQuery. Below are some approaches that consider the nuances of BigQuery's capabilities:

1. Staging Table and Scheduled Query

• Approach:

  • Create a temporary BigQuery 'staging' table for new records.
  • Use a scheduled query (running every 90 minutes or as needed) to update the main table from the staging table via a MERGE statement.

• SQL Example:

MERGE `your_project.your_dataset.your_target_table` AS target 
USING `your_project.your_dataset.your_staging_table` AS source 
  ON target.record_id = source.record_id 
WHEN MATCHED THEN 
  UPDATE SET 
      target.column1 = source.column1, 
      target.column2 = source.column2 
      -- ... other columns to update 
WHERE source.inserted_timestamp <= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 90 MINUTE) 

• Explanation: Records are efficiently updated by matching on a unique ID; updates occur only after the cooling period.

2. Conditional Logic for Delayed Display

• Approach: Instead of modifying the table, use SQL's CASE statements to show updated values in queries, respecting the cooling period.

• SQL Example:

SELECT 
    record_id, 
    column1, 
    column2, 
    -- ... other columns 
    CASE 
        WHEN inserted_timestamp <= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 90 MINUTE) 
            THEN updated_column1 
        ELSE column1 
    END AS updated_column1, 
    -- ... Repeat CASE logic for other updated columns 
FROM `your_project.your_dataset.your_table` 

• Explanation: Provides a view with updated values after the cooling period, suitable for analytics where real-time data modification isn't a priority.

3. BigQuery Dataflow for Real-time Updates

• Approach: A Google Cloud Dataflow streaming pipeline processes and updates records, using delays to adhere to the cooling period.

• Explanation: Dataflow reads new records, processes them (potentially with stateful processing or timers to implement the delay), and writes updates back to BigQuery. Ideal for demanding real-time updates.

Choosing the Right Solution:

• Staging Table and Scheduled Query: Simple and effective for batch-style updates.
• Conditional Logic: When reflecting updates in queries is the goal, without modifying the base data.
• BigQuery Dataflow: Scalable, real-time updates with complex requirements.

Additional Considerations:

• Impact on Analytics: Each approach influences downstream analytics; understand those effects.
• Error Handling: Vital for data integrity, especially in real-time scenarios.

[NishinThattil]

Our analysis of the proposed approaches revealed some limitations:

Staging tables: Due to our need for real-time data availability for a high volume of streaming inserts into BigQuery, using a staging table isn't feasible. Scheduled queries wouldn't work in this scenario because of the inherent delay involved.
Conditional Logic for Delayed Display: After discussions with application users, we determined that implementing conditional logic for delayed display based on a cooling period would introduce the same real-time data latency issue mentioned above.
Cloud Dataflow streaming pipeline: While this approach seems promising for processing and updating records in real-time, we're concerned about potential cost implications associated with using a streaming platform.

[ms4446]

Since the staging table and conditional logic approaches introduce unwanted delays and Cloud Dataflow's cost is a concern, you might consider leveraging BigQuery's native capabilities more effectively, particularly looking into:

1. Optimized Streaming Inserts for Immediate Availability: Continue using BigQuery's streaming insert capabilities for real-time data ingestion. This ensures that your data is available for querying almost immediately after it's sent to BigQuery.

2. Materialized Views for Aggregated Updates: If your updates can be represented as aggregations or transformations of the raw data, consider using BigQuery materialized views. Materialized views can automatically refresh, providing near real-time data while optimizing query performance and potentially reducing costs by minimizing the amount of data scanned.

3. Partitioning and Clustering for Efficient Queries: Ensure your tables are appropriately partitioned and clustered. This can significantly reduce the cost and increase the performance of queries by limiting the amount of data scanned. For updates, consider how partition expiration can automatically manage data lifecycle without explicit deletions.

Revisiting Cloud Dataflow with Cost Management

If the Cloud Dataflow approach still seems like the best fit for your real-time processing needs, consider the following strategies to manage and potentially reduce costs:

1. Efficient Pipeline Design: Design your pipeline to be as efficient as possible. Use grouping, filtering, and aggregation to minimize the volume of data processed and stored. Efficient use of PCollection and windowing can also help manage costs.

2. Cost Control Measures: Implement cost control measures in Google Cloud Platform (GCP):

   • Set up budget alerts to monitor your Cloud Dataflow and BigQuery expenses.

   • Use the GCP Pricing Calculator to estimate costs more accurately.

   • Consider using FlexRS (Flexible Resource Scheduling) for Dataflow, which can run your jobs at a lower cost during off-peak hours if your workload can tolerate some delay.

3. Monitoring and Optimization: Regularly monitor your pipeline's performance and costs using Google Cloud's operations suite (formerly Stackdriver). Look for opportunities to optimize your pipeline, such as by adjusting the parallelism or using more cost-effective compute resources.

By carefully considering these strategies and focusing on optimization and cost management, you can achieve a balance between real-time data availability, performance, and cost-efficiency in BigQuery.