BigQuery Storage Write API and multiple tables

[diegobx]

Hi everybody,

I am implementing a service in GO that will stream incoming logs (from other services) into BigQuery, using the new Storage Write API.

My question is how to handle writing to different tables (the destination table is decided by the GO service based on some attributes of the incoming logs).

Should the GO service open a separate BigQuery write stream for each table, or it's possible to write to different tables within the same write stream? Thanks!

Diego

[ms4446 · Accepted solution]

When using the BigQuery Storage Write API, each write stream is associated with a specific table. Therefore, if you want to write to multiple tables, you'll need to manage multiple write streams, one for each table.

You cannot write to different tables within the same write stream. Each stream is bound to a specific table at the time of its creation.

For implementing this in Go, you'd use the BigQuery Storage Write API's Go client library. The actual methods and usage might differ from the standard BigQuery API. I recommend referring to the official Google Cloud documentation and the Go client library's reference for accurate methods and examples.

[diegobx]

Hi, I was reading the documentation here:
https://cloud.google.com/bigquery/docs/reference/storage/rpc/google.cloud.bigquery.storage.v1#google...

and I noticed that the `AppendRowsRequest` includes the field `write_stream` with the following description:

Required. The write_stream identifies the append operation. It must be provided in the following scenarios:
1. In the first request to an AppendRows connection.
2. In all subsequent requests to an AppendRows connection, if you use the same connection to write to multiple tables or change the input schema for default streams.

It seems like it's actually possible to use the same stream to write to different tables.

Could you provide more insight when this is a good option VS opening multiple streams?

Example, Is there a limit on the number of streams a service can maintain open?

Thanks!

[ms4446]

Yes, you are correct. It is possible to use the same stream to write to different tables with the BigQuery Storage Write API. However, there are considerations to keep in mind:

1. Buffering and Latency: When you write to a stream, the data is buffered before being committed. If you write to multiple tables using the same stream, you'll need to manage the logic of switching between tables within the same stream, which can add complexity to your code. Additionally, if there's a high volume of logs for one table, logs for other tables might experience latency.

2. Stream Limits: While the BigQuery Storage Write API is designed to handle large volumes of data efficiently, there are limits associated with streams. It's essential to refer to the official BigQuery documentation for the most up-to-date information on stream limits.

3. Best Practices: Given the potential complexities and performance considerations, it's generally recommended to open a separate stream for each table. This ensures that the data for each table is managed independently, reducing the risk of one table's logs affecting another.

4. Number of Streams: While there might not be a strict limit on the number of streams you can open, there are practical considerations based on resources, quotas, and other factors. Managing multiple streams can be resource-intensive, especially if there are many tables.

5. Client Library Implementation: The decision of whether to use a single stream or multiple streams may also depend on the specific implementation of the Go client library. Some libraries may make it easier to manage multiple streams, while others may make it easier to use a single stream.

6. Additional Considerations:

   • Data Size: If you're writing a small amount of data, a single stream might be more efficient. For larger datasets, multiple streams can provide better performance.
   • Write Speed: If data is being written rapidly, using multiple streams can help distribute the load and reduce potential buffering issues.
   • Service Performance: BigQuery is a robust service, but the client's ability to manage multiple streams and the associated overhead should be considered.

So, while it's technically possible to use a single stream for multiple tables, using separate streams for each table is often simpler and more efficient. It's essential to evaluate your specific use case, consider the factors mentioned above, and refer to the official BigQuery documentation to make an informed decision. Experimentation, as you rightly pointed out, is often the best way to determine the most efficient approach for a particular application.