BigQuery external table limitations

[amlanroy1980]

I have e-commerce product details stored in CSV format in the GCS bucket. Each file contains the attributes of a single product. The number of such files is approximately 4 billion.

I am planning to use a BigQuery external table to query the data.

It worked when I tested with 10 million such files. My question is, will it work when the number of files is as high as 4 billion? Even if it takes time (15-30 mins), it is fine.

What are the other recommended options to query the data if we don't use the BigQuery external table? 

[Poala_Tenorio]

While BigQuery can handle large datasets, querying 4 billion individual files might cause performance bottlenecks. The overhead associated with opening, reading, and closing each file can be substantial. Listing and managing metadata for 4 billion files in Google Cloud Storage (GCS) can be very slow and inefficient. Even if BigQuery can technically handle the workload, query performance might degrade significantly due to the sheer number of files.

Alternative Approaches:

• Consolidate smaller files into larger ones. Tools like Apache Beam, Dataflow, or even a custom solution can batch process the files and create consolidated files (e.g., daily or hourly).
• Use efficient storage formats like Parquet or ORC which support columnar storage and compression, reducing both storage size and read times.
• Load data into native BigQuery tables. This way, you take advantage of BigQuery's internal storage optimizations.
• Use partitioned and clustered tables to improve query performance. Partitioning by date or another logical attribute and clustering on frequent query fields can optimize query performance.
• Combine BigQuery with other GCP services like Dataflow for ETL processes and Cloud Composer for orchestration.
• Use tools like Apache Kafka or Google Cloud Pub/Sub along with Dataflow to stream and process data in near real-time, aggregating smaller data points into larger, queryable chunks.