Aggregations & Grouping⏱️ ~10 mins
8. Global Pipeline Totalizer
Enterprise Architecture Context
In production stream-processing architectures (Google Cloud Dataflow / Flink), pipeline stages must handle parallel transformations without data loss, managing schema mutations and aggregations across distributed worker workers.
Problem Statement
### Business Context
Financial end-of-day settlement pipelines sum transaction records across thousands of branch ledgers into a single reconciliation balance.
### Problem Statement
Write a function `sum_pcollection(input_pcoll)` that totals all numeric elements in a PCollection into a single scalar sum using `beam.CombineGlobally(sum)`.
Key Learning Objectives
- Understand distributed Apache Beam execution DAG stages and pipeline lifecycle.
- Apply idiomatic functional Python transforms using the pipe operator
|. - Ensure data consistency and idempotency across distributed stream workers.
Sample Data Fixtures
Sample Example 1
Input Stream:
[10, 20, 30]
Expected Output:
60
Sample Example 2
Input Stream:
[-10, 5, 5]
Expected Output:
0
Topics:#CombineGlobally#Aggregations
solution.pyPython 3.11 (Apache Beam)
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Input PCollection3 elements
| # | Element / Payload |
|---|---|
| 1 | 10 |
| 2 | 20 |
| 3 | 30 |
Expected Output PCollection
60
Aggregations & Grouping⏱️ ~10 mins
8. Global Pipeline Totalizer
Enterprise Architecture Context
In production stream-processing architectures (Google Cloud Dataflow / Flink), pipeline stages must handle parallel transformations without data loss, managing schema mutations and aggregations across distributed worker workers.
Problem Statement
### Business Context
Financial end-of-day settlement pipelines sum transaction records across thousands of branch ledgers into a single reconciliation balance.
### Problem Statement
Write a function `sum_pcollection(input_pcoll)` that totals all numeric elements in a PCollection into a single scalar sum using `beam.CombineGlobally(sum)`.
Key Learning Objectives
- Understand distributed Apache Beam execution DAG stages and pipeline lifecycle.
- Apply idiomatic functional Python transforms using the pipe operator
|. - Ensure data consistency and idempotency across distributed stream workers.
Sample Data Fixtures
Sample Example 1
Input Stream:
[10, 20, 30]
Expected Output:
60
Sample Example 2
Input Stream:
[-10, 5, 5]
Expected Output:
0
Topics:#CombineGlobally#Aggregations