Use Bigtable to enrich data
|
In Apache Beam 2.54.0 and later versions, the enrichment transform includes a built-in enrichment handler for Bigtable.
The following example demonstrates how to create a pipeline that use the enrichment transform with the BigTableEnrichmentHandler
handler.
The data stored in the Bigtable cluster uses the following format:
Row key | product:product_id | product:product_name | product:product_stock |
---|---|---|---|
1 | 1 | pixel 5 | 2 |
2 | 2 | pixel 6 | 4 |
3 | 3 | pixel 7 | 20 |
4 | 4 | pixel 8 | 10 |
import apache_beam as beam
from apache_beam.transforms.enrichment import Enrichment
from apache_beam.transforms.enrichment_handlers.bigtable import BigTableEnrichmentHandler
project_id = 'apache-beam-testing'
instance_id = 'beam-test'
table_id = 'bigtable-enrichment-test'
row_key = 'product_id'
data = [
beam.Row(sale_id=1, customer_id=1, product_id=1, quantity=1),
beam.Row(sale_id=3, customer_id=3, product_id=2, quantity=3),
beam.Row(sale_id=5, customer_id=5, product_id=4, quantity=2)
]
bigtable_handler = BigTableEnrichmentHandler(
project_id=project_id,
instance_id=instance_id,
table_id=table_id,
row_key=row_key)
with beam.Pipeline() as p:
_ = (
p
| "Create" >> beam.Create(data)
| "Enrich W/ BigTable" >> Enrichment(bigtable_handler)
| "Print" >> beam.Map(print))
Output:
Row(sale_id=1, customer_id=1, product_id=1, quantity=1, product={'product_id': '1', 'product_name': 'pixel 5', 'product_stock': '2'})
Row(sale_id=3, customer_id=3, product_id=2, quantity=3, product={'product_id': '2', 'product_name': 'pixel 6', 'product_stock': '4'})
Row(sale_id=5, customer_id=5, product_id=4, quantity=2, product={'product_id': '4', 'product_name': 'pixel 8', 'product_stock': '10'})
Related transforms
Not applicable.
Pydoc |
Last updated on 2024/12/20
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