Bigquery Storage Write API Connection Management

Google Cloud Data Analytics
1

I’m testing using the Bigquery Storage Write API via the Python client.

I want to compare

client.append_rows(iter([request])) 
# and
append_rows_stream.send(request)
append_rows_stream.close()

Does append_rows create one connection and close it immediately? Does it create multiple connections if there are multiple requests in the iterator?

I tried using append_rows and checked the quota for Concurrent connections per project for us region but it’s showing 100 concurrent connections when I had just sent 5 requests in total.

2

BigQuery Storage Write API provides two primary methods for ingesting data into BigQuery: client.append_rows() and append_rows_stream. While both methods serve the purpose of data ingestion, they differ in their approach to connection management and are suited for distinct use cases.

Client.append_rows(): Batch Loading with Efficient Connection Management

The client.append_rows() method is specifically designed for batch loading scenarios, where a finite set of data needs to be ingested into BigQuery. This method optimizes connection management by efficiently batching all requests in an iterator and sending them over a single connection. Once all requests have been processed, the connection is gracefully closed. This approach ensures efficient utilization of resources and avoids the overhead of creating and managing multiple connections for each request.

append_rows_stream: Streaming Data with Explicit Connection Control

In contrast, the append_rows_stream method is tailored for handling continuous data streams. It provides explicit control over the connection, allowing users to send multiple requests over a single persistent connection until it is explicitly closed with the close() method. This approach is particularly useful for real-time or near-real-time data ingestion scenarios, where data is continuously generated and needs to be streamed into BigQuery without interruption.

Choosing the Right Method: Batch vs. Streaming

The choice between client.append_rows() and append_rows_stream depends on the data ingestion pattern and the desired level of control over connection management. If you have a known set of data to ingest in a batch, client.append_rows() is the recommended choice, as it efficiently handles connection management without requiring user intervention. However, if you are dealing with continuous data streams, append_rows_stream provides the flexibility to manage the connection explicitly, ensuring that data is ingested seamlessly without interruption.

Connection Pooling: An Unnecessary Complexity

The BigQuery Storage Write API client library already employs sophisticated connection management techniques, eliminating the need for manual connection pooling. The client library efficiently reuses connections for multiple requests, ensuring optimal resource utilization. Hence, manual connection pooling is not a standard practice with the BigQuery Python client library.

BigQuery Storage Write API, along with its Python client library, provides a comprehensive solution for data ingestion, offering both batch loading and streaming capabilities. The choice between client.append_rows() and append_rows_stream depends on the data ingestion pattern, while the client library’s intelligent connection management eliminates the need for manual intervention, ensuring efficient data ingestion across various use cases.

3

Thank you for the answer.

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For some reason, when I test append_rows with 10 parallel processes appending once each 2 seconds; I see that 100 concurrent connections. When I increase the number of processes to 50 the concurrent connection goes up to 300. And its always numbers like 100, 200 and 300. Only when I tried append_rows_stream, I saw an accurate peak in the graph. Why might this happen? Is there any latency after append_rows before the connection is closed?

4

The behavior you’re observing with the client.append_rows() method in the BigQuery Storage Write API can be attributed to how individual processes manage their connections to BigQuery, rather than a traditional connection pooling mechanism.

  1. Independent Connections in Parallel Processes:
    When you run multiple parallel processes that each use client.append_rows(), these processes independently establish connections to BigQuery. This can lead to an increase in the number of concurrent connections, as each process handles its own connection to the server.

  2. Connection Management in client.append_rows():
    The client.append_rows() method is designed for efficient batch operations. It manages connections on a per-request basis, opening a connection to send the batch of data and then closing it after the operation is complete.

  3. Potential Latency in Connection Closure:
    There might be a slight delay in the closure of connections due to network and server-side processing. However, this is typically minimal and not a result of a deliberate keep-alive strategy implemented in the client library.

  4. No Traditional Connection Pooling:
    The BigQuery client library does not use a traditional connection pool. Therefore, the concept of a default pool size or connection keep-alive as seen in typical database applications does not apply here.

  5. Using append_rows_stream for Continuous Streams:
    For scenarios involving continuous data streams, append_rows_stream is a more suitable method. It allows for a single persistent connection over which multiple requests can be sent, providing more control over the connection and potentially reducing the number of concurrent connections.

  6. Round Numbers in Concurrent Connections:
    The observation of round numbers like 100, 200, and 300 concurrent connections might be coincidental or related to how BigQuery service manages and reports connection quotas. It’s not directly indicative of a connection pooling mechanism.