Overview
The article discusses the implementation of the Unified Dynamic Framework (UDF) at Pinterest, which has significantly improved the scalability and efficiency of experiment metric computing. The UDF allows for the processing of 100X more metrics today and is designed to scale to 500X, addressing challenges such as upstream dependencies and backfilling complexities.
What You'll Learn
1
How to leverage the Unified Dynamic Framework for scalable metric computation
2
Why dynamic DAGs can improve data pipeline efficiency
3
How to automate backfilling of metrics in data pipelines
Key Questions Answered
What challenges does the Unified Dynamic Framework address in metric computation?
The Unified Dynamic Framework addresses challenges such as delays in upstream data ingestion, difficulties in backfilling skipped metrics, and scalability issues that hinder timely and reliable results. By eliminating upstream dependencies and optimizing processing, UDF enhances the efficiency of metric computation.
How does UDF improve the speed of metric delivery?
UDF improves the speed of metric delivery by allowing metrics to be processed in small, parallel batches rather than waiting for all upstream jobs to complete. This results in metrics being delivered at least 4X faster compared to previous methods.
What is the expected scalability of the UDF?
The UDF currently supports 100X more metrics and is designed to scale up to 500X in the future. This scalability accommodates growing data volumes and metric complexity with minimal maintenance effort.
Key Statistics & Figures
Scalability of metrics
100X today, designed to scale to 500X
This scalability is crucial for accommodating increasing data volumes and complexity in metrics.
Speed of metric delivery
4X faster
This speed improvement is measured against the duration between source data readiness and final metric results.
Resolution of partial data issues
90% of partial data issues resolved
This is achieved through automatic backfills, enhancing the reliability of the metrics.
Technologies & Tools
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Workflow Management
Apache Airflow
Used for creating dynamic DAGs that allow for efficient processing of metrics based on data readiness.
Database
Druid
Used for storing the results of metric computations for visualization on Helium dashboards.
Key Actionable Insights
1Implementing the Unified Dynamic Framework can drastically reduce the time required to build data pipelines from months to days.This is particularly beneficial for teams looking to enhance their experimentation capabilities without getting bogged down by infrastructure challenges.
2Utilizing dynamic DAGs allows for more efficient resource allocation in data processing.By processing metrics in parallel and adjusting batch sizes based on resource utilization, teams can ensure timely metric delivery even under variable loads.
3Automating backfills for skipped metrics can significantly improve data reliability.This feature ensures that metrics are computed and delivered promptly, maintaining the integrity of the data analysis process.
Common Pitfalls
1
Relying too heavily on upstream data jobs can lead to pipeline stalls.
Delays in any upstream job can halt the entire metric computation process. To avoid this, it's essential to implement dynamic processing that allows for parallel execution of available metrics.