Vishesh-Tripathi commented on issue #33012:
URL: https://github.com/apache/beam/issues/33012#issuecomment-2575627136
hello sir i am updating the file by adding this section is it correct ?
## BigQuery Support
The enrichment transform supports integration with **BigQuery** to
dynamically enrich data using BigQuery datasets. By leveraging BigQuery as an
external data source, users can execute efficient lookups for data enrichment
directly in their Apache Beam pipelines.
To use BigQuery for enrichment:
- Configure your BigQuery table as the data source for the enrichment
process.
- Ensure your pipeline has the appropriate credentials and permissions to
access the BigQuery dataset.
- Specify the query to extract the data to be used for enrichment.
This integration is particularly beneficial for use cases that require
augmenting real-time streaming data with information stored in BigQuery.
---
## Batching
To optimize requests to external services, the enrichment transform uses
batching. Instead of performing a lookup for each individual element, the
transform groups multiple elements into a batch and performs a single lookup
for the entire batch.
### Advantages of Batching:
- **Improved Throughput**: Reduces the number of network calls.
- **Lower Latency**: Fewer round trips to the external service.
- **Cost Optimization**: Minimizes API call costs when working with paid
external services.
Users can configure the batch size by specifying parameters in their
pipeline setup. Adjusting the batch size can help fine-tune the balance between
throughput and latency.
---
## Caching with `with_redis_cache`
For frequently used enrichment data, caching can significantly improve
performance by reducing repeated calls to the remote service. Apache Beam's
`with_redis_cache` method allows you to integrate a Redis cache into the
enrichment pipeline.
### Benefits of Caching:
- **Reduced Latency**: Fetches enrichment data from the cache instead of
making network calls.
- **Improved Resilience**: Minimizes the impact of network outages or
service downtimes.
- **Scalability**: Handles large volumes of enrichment requests efficiently.
To enable caching:
1. Set up a Redis instance accessible by your pipeline.
2. Use the `with_redis_cache` method to configure the cache in your
enrichment transform.
3. Specify the time-to-live (TTL) for cache entries to ensure data freshness.
Example:
```python
from apache_beam.transforms.enrichment import with_redis_cache
# Enrichment pipeline with Redis cache
enriched_data = (input_data
| 'Enrich with Cache' >>
with_redis_cache(redis_config=redis_config,
enrichment_transform=my_enrichment_transform))
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