Orchestration library for GCP data pipelines
Project description
gcp-pipeline-orchestration
Control library - Airflow DAGs, sensors, operators.
Depends on: gcp-pipeline-core
NO Apache Beam dependency.
Architecture
GCP-PIPELINE-ORCHESTRATION
─────────────────────────
┌─────────────────────────────────────────────────────────────────┐
│ CONTROL LAYER │
│ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Sensors │ │
│ │ • BasePubSubPullSensor (detect .ok files) │ │
│ │ • Filter by extension (.ok, .csv) │ │
│ │ • Extract file metadata to XCom │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Operators │ │
│ │ • BatchDataflowOperator (start batch ingestion) │ │
│ │ • StreamingDataflowOperator (start streaming) │ │
│ │ • ReconciliationOperator (validate counts) │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Entity Dependency │ │
│ │ • EntityDependencyChecker (wait for all entities) │ │
│ │ • Query job_control table for entity status │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ DAG Factories │ │
│ │ • DAGFactory (generate DAGs from config) │ │
│ │ • Callbacks (on_failure, on_success) │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
│
▼
Uses: gcp-pipeline-core
Orchestration Flow
Pub/Sub Airflow External
─────── ─────── ────────
.ok file ┌─────────────────────────────────────────────────────┐
notification │ │
│ │ ┌──────────────┐ │
└───────►│ │ PubSub │ │
│ │ Pull Sensor │ │
│ │ │ │
│ │ • Filter .ok │ │
│ │ • Extract │ │
│ │ metadata │ │
│ └──────┬───────┘ │
│ │ │
│ ▼ (XCom: file_path, entity, date) │
│ ┌──────────────┐ │
│ │ File │ │
│ │ Discovery │ │
│ │ │ │
│ │ • Find all │ │
│ │ split files│ │
│ └──────┬───────┘ │
│ │ │
│ ▼ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Trigger │───►│ Dataflow │ │
│ │ Dataflow │ │ Job │ │
│ └──────────────┘ └──────┬───────┘ │
│ │ (Failure) │
│ ▼ │
│ ┌──────────────┐ │
│ │ Error Log │ │
│ │ (BigQuery) │ │
│ └──────┬───────┘ │
│ │ │
│ ┌───────────────────┘ (Success) │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ Dependency │ (Application1 only - waits for 3 entities) │
│ │ Checker │ │
│ └──────┬───────┘ │
│ │ │
│ ▼ (all ready) │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Trigger │───►│ dbt │ │
│ │ dbt │ │ Transform │ │
│ └──────────────┘ └──────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────┐
│ │ PERIODIC MONITORING │
│ │ │
│ │ ┌──────────────┐ ┌──────────────┐ │
│ │ │ Error │◄───────┤ Error Log │ │
│ │ │ Handling DAG │ │ (BigQuery) │ │
│ │ └──────┬───────┘ └──────────────┘ │
│ │ │ │
│ │ ▼ │
│ │ ┌──────────────┐ ┌──────────────┐ │
│ │ │ Automatic │───Retry──► Target │ │
│ │ │ Reprocessing │ │ Pipeline │ │
│ │ └──────────────┘ └──────────────┘ │
│ └──────────────────────────────────────────────────┘
│ │
└─────────────────────────────────────────────────────┘
Entity Dependency Checker
For systems with multiple entities (like Application1 with 3 entities), the checker waits until all are loaded.
ENTITY DEPENDENCY CHECK (Application1)
────────────────────────────
Customers arrives ──► Check: [✓] customers
(4:00 PM) [ ] accounts
[ ] decision
→ NOT READY
Accounts arrives ──► Check: [✓] customers
(4:00 PM) [✓] accounts
[ ] decision
→ NOT READY
Decision arrives ──► Check: [✓] customers
(5:00 AM next day) [✓] accounts
[✓] decision
→ ALL READY! → Trigger dbt
How It Works
from datetime import date
from gcp_pipeline_orchestration.dependency import EntityDependencyChecker
# Configure for Application1 system
checker = EntityDependencyChecker(
project_id="my-project",
system_id="Application1",
required_entities=["customers", "accounts", "decision"]
)
# Check if all entities are loaded for today
if checker.all_entities_loaded(extract_date=date.today()):
# Logic to trigger dbt
print("Triggering dbt...")
else:
# Wait - some entities not yet loaded
pass
Modules
| Module | Purpose | Key Classes |
|---|---|---|
sensors/ |
Pub/Sub sensing | BasePubSubPullSensor |
operators/ |
Custom operators | BatchDataflowOperator, StreamingDataflowOperator |
factories/ |
DAG generation | DAGFactory |
callbacks/ |
Error handlers | on_failure_callback, publish_to_dlq |
routing/ |
Pipeline routing | PipelineRouter |
dependency.py |
Entity dependency | EntityDependencyChecker |
Key Findings
1. Unified Dataflow Operators
- BaseDataflowOperator: Supports both Classic and Flex templates.
- Development Stubbing: Features a clever mechanism to allow DAG parsing and testing without a live Airflow/GCP environment (
BaseOperator if AIRFLOW_AVAILABLE else object).
2. Event-Driven Pub/Sub Sensors
- BasePubSubPullSensor: Monitors GCS notifications (e.g., waiting for
.okfiles). - Metadata Extraction: Automated extraction of file paths, entity types, and timestamps into XCom for downstream use.
3. Entity Dependency Management
- EntityDependencyChecker: Coordinates multi-entity systems (like Application1) by ensuring all required datasets (customers, accounts, decision) are present before triggering transformations.
4. Global Error Callbacks
- Standardized failure handlers that publish metadata to DLQs (Dead Letter Queues) for automated alerting and manual intervention.
Error Handling & Reprocessing
The framework implements a two-tier error handling strategy: Immediate Capture and Periodic Recovery.
1. Immediate Capture (Callbacks)
When a task fails, the on_failure_callback from the library is triggered.
- DLQ Publishing: Standardized task metadata (run_id, system_id, exception) is published to a Pub/Sub DLQ.
- Audit Logging: The error is logged to the BigQuery
error_logtable for centralized tracking.
2. Periodic Recovery (Error Handling DAG)
A dedicated Error Handling DAG (e.g., application1_error_handling_dag.py) runs every 30 minutes to manage the lifecycle of failed records.
Automated Reprocessing Flow
BigQuery Error Log Error Handling DAG Target Pipeline
────────────────── ────────────────── ───────────────
[Error Record] ───► 1. Scan for unresolved ───► 3. Transient? ───► Trigger Rerun
errors (<30m) (Backoff applied)
2. Classify (via core) ───► 4. Permanent? ───► Alert Team
(Validation vs Int) (Manual Review)
Classification Logic
The Error Handling DAG uses the ErrorClassifier from gcp-pipeline-core to determine the next step:
| Category | Strategy | Example |
|---|---|---|
| INTEGRATION | Automated Retry | Temporary connection timeout to GCS/BQ |
| RESOURCE | Exponential Backoff | Quota exceeded or Rate limiting |
| VALIDATION | Manual Review | Schema mismatch, invalid data types |
| CONFIGURATION | Manual Review | Missing Airflow variables or IAM permissions |
Manual Intervention
For non-retryable errors (e.g., VALIDATION), the Error Handling DAG:
- Quarantines the failed records/files.
- Alerts the data engineering team via Email/Slack.
- Audit Trail: Once a developer fixes the data and marks it as
RETRY_READYin theerror_log, the DAG will automatically pick it up in the next run.
Governance & Compliance
- Domain Isolation: Depends on
coreandairflow; MUST NOT importbeam. - Testing: All custom operators and sensors must be tested using the
testermocks. - Safety: Operators must support idempotency by passing
run_idto underlying Dataflow jobs.
Usage
from gcp_pipeline_orchestration.sensors import BasePubSubPullSensor
from gcp_pipeline_orchestration.factories import DAGFactory
from gcp_pipeline_orchestration.dependency import EntityDependencyChecker
from gcp_pipeline_orchestration.callbacks import on_failure_callback
Tests
PYTHONPATH=src:../gcp-pipeline-core/src python -m pytest tests/unit/ -v
# 52 passed
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file gcp_pipeline_orchestration-1.0.4.tar.gz.
File metadata
- Download URL: gcp_pipeline_orchestration-1.0.4.tar.gz
- Upload date:
- Size: 30.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
9edd28f66a6c0b2fed5900c20d633b8e00a993fa848b4e88f6d0025d6d241511
|
|
| MD5 |
9e50c987d9d4c9bc343e01661344ae04
|
|
| BLAKE2b-256 |
b7986040a88723cfb3b257f5e4b96b582051f80dfd4a6222ac2431d9dbb7895b
|
File details
Details for the file gcp_pipeline_orchestration-1.0.4-py3-none-any.whl.
File metadata
- Download URL: gcp_pipeline_orchestration-1.0.4-py3-none-any.whl
- Upload date:
- Size: 36.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
abe6e953cbf4bb7a045df739db36bcc974afb3cde43723918fed293893aa8633
|
|
| MD5 |
3678960b550f3d77e086b29b4971ae8a
|
|
| BLAKE2b-256 |
212a7524ebe222b4711f730cc5bc15736447dadd62d6fcc0aba1bc43b2a88de8
|
