Stream data availability

[NishinThattil]

Data is available for real-time analysis using GoogleSQL queries immediately after BigQuery successfully acknowledges a tabledata.insertAll request.

Recently streamed rows to an ingestion time partitioned table temporarily have a NULL value for the _PARTITIONTIME pseudo column. For such rows, BigQuery assigns the final non-NULL value of the PARTITIONTIME column in the background, typically within a few minutes. In rare cases, this can take up to 90 minutes.

Some recently streamed rows might not be available for table copy typically for a few minutes. In rare cases, this can take up to 90 minutes. To see whether data is available for table copy, check the tables.get response for a section named streamingBuffer. If the streamingBuffer section is absent, your data is available for copy. You can also use the streamingBuffer.oldestEntryTime field to identify the age of records in the streaming buffer.

What is the workaround for this buffer time issue?

[ms4446]

The streaming buffer in BigQuery serves as a temporary holding area for newly inserted data before it's committed to the final table storage. This mechanism enables near-real-time querying of the latest data. Delays in data finalization, including the population of the _PARTITIONTIME column, can be attributed to various factors such as high volumes of data being streamed, complex data transformations, and the inherent processing time required by BigQuery. It's important to note that schema changes and exceeding BigQuery streaming quotas typically result in errors rather than just delays.

Workarounds and Best Practices for Minimizing Impact

Optimize How You Write Data

• Batch Inserts: Grouping data into larger batches before insertion can reduce the number of API calls and associated overhead, making the process more efficient.

• Optimize Data Ingestion Methods: While there was a mention of STORAGE_API_AT_LEAST_ONCE, it's more accurate to focus on choosing the right method of ingestion (e.g., streaming inserts vs. batch loads) and ensuring idempotency in your operations to optimize for faster data availability.

Query Strategies

• Accept Temporary NULLs: Design your queries to handle NULL values in the _PARTITIONTIME column gracefully. This approach ensures that your analysis can proceed without interruption, even when some data is still in the streaming buffer.

• Tolerate Slight Delays: Incorporating a short delay tolerance in your data analysis can allow most of the streamed data to become finalized and available for querying, balancing the need for real-time access with data completeness.

Monitoring and Adjustment

• Track Streaming Buffer: Regularly monitor the streamingBuffer.oldestEntryTime and the presence of the streamingBuffer section in tables.get responses to understand the age and availability of your data.

• Optimize Dataflow and Manage Quotas: Adjusting your data ingestion strategies, such as batch sizes and windowing in Dataflow, and keeping an eye on quota usage can help maintain efficient data pipelines.

Alternative Strategies for Ultra-Low Latency Requirements

For scenarios where ultra-low latency is critical, direct use of the BigQuery API for inserts may offer more granular control over the data ingestion process. However, this approach should be balanced against the increased complexity and potential costs involved.

Additional Considerations

• Balance Trade-Offs: It's essential to weigh the trade-offs between the immediacy of data availability, accuracy, and completeness based on your specific use case requirements.

• Leverage Materialized Views: Materialized views can pre-aggregate data, potentially reducing the need for real-time queries on freshly streamed data and alleviating some of the challenges associated with the streaming buffer.

Effectively managing the streaming buffer in BigQuery and minimizing its impact involves a combination of strategic data ingestion practices, thoughtful query design, and proactive monitoring. By understanding the nuances of how BigQuery processes streamed data and applying these best practices, you can ensure that your data workflows remain efficient and aligned with your analytical needs.