davidzollo opened a new issue, #10355:
URL: https://github.com/apache/seatunnel/issues/10355
## Background
BigQuery is Google Cloud's serverless, highly scalable, and cost-effective
multi-cloud data warehouse. It is widely used by enterprises globally for data
analytics, business intelligence, and machine learning workloads.
Currently, SeaTunnel lacks native support for BigQuery as a sink, which
limits its ability to integrate with the Google Cloud ecosystem efficiently.
## Motivation
- **High Market Demand**: BigQuery is a core service in Google Cloud
Platform (GCP) with a large enterprise customer base
- **Cloud-Native Architecture**: While JDBC drivers exist for BigQuery, they
provide poor performance and limited functionality compared to native SDK
- **Advanced Features**: Native connector can support:
- Streaming inserts for real-time data ingestion
- Table partitioning and clustering
- Nested and repeated fields (STRUCT, ARRAY)
- Integration with Cloud Storage for efficient bulk loading
- Schema auto-detection and evolution
## Proposed Solution
Implement a dedicated BigQuery Sink connector using the Google Cloud Java
SDK with the following capabilities:
### Core Features
1. **Multiple Write Modes**
- Batch loading via Cloud Storage (for high throughput)
- Streaming inserts (for low latency)
- Support for both append and overwrite modes
2. **Schema Management**
- Automatic schema creation and evolution
- Support for complex data types (STRUCT, ARRAY, TIMESTAMP, GEOGRAPHY)
- Schema validation and type mapping
3. **Performance Optimization**
- Configurable batch size and flush interval
- Parallel writes with configurable parallelism
- Retry mechanism with exponential backoff
4. **Data Quality**
- Row-level error handling
- Dead letter queue for failed records
- Data validation before insertion
### Configuration Example
```hocon
sink {
BigQuery {
project = "my-gcp-project"
dataset = "my_dataset"
table = "my_table"
# Authentication
credentials_file = "/path/to/service-account.json"
# Write configuration
write_mode = "streaming" # or "batch"
create_disposition = "CREATE_IF_NEEDED"
write_disposition = "WRITE_APPEND" # or "WRITE_TRUNCATE"
# Performance tuning
max_batch_size = 1000
max_batch_bytes = 10485760 # 10MB
flush_interval_ms = 5000
# Schema options
auto_create_table = true
schema_update_options = ["ALLOW_FIELD_ADDITION"]
}
}
```
## Expected Benefits
1. **Better Performance**: Native SDK provides 10-100x better performance
than JDBC for large-scale data ingestion
2. **Cost Efficiency**: Optimized bulk loading via Cloud Storage reduces
costs
3. **Feature Completeness**: Access to BigQuery-specific features like
streaming inserts and schema evolution
4. **Enterprise Adoption**: Enables SeaTunnel to compete in GCP-based data
integration scenarios
## Technical Considerations
- **Dependencies**: Add `google-cloud-bigquery` SDK
- **Authentication**: Support service account JSON, application default
credentials, and workload identity
- **Error Handling**: Implement robust retry logic and error reporting
- **Testing**: Require integration tests with BigQuery emulator or test
project
## References
- [BigQuery Java Client
Library](https://cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/overview)
- [BigQuery Storage Write
API](https://cloud.google.com/bigquery/docs/write-api)
- [Best Practices for Loading
Data](https://cloud.google.com/bigquery/docs/best-practices-performance-input)
## Community Impact
This connector will:
- Expand SeaTunnel's cloud ecosystem support
- Attract GCP users to the SeaTunnel community
- Enable enterprises to build modern data pipelines on Google Cloud
---
**Priority**: High
**Estimated Effort**: Medium
**Target Release**: 2.3.14 or 3.0.0
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