b04-DataflowArchitecture.pdf
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The document discusses Google Cloud Dataflow architecture and features. It describes how Dataflow optimizes data processing pipelines, leverages services like the Shuffle Service and Streaming Engine, provides flexible resource scheduling and monitoring, includes templates for common workflows, and offers an SQL UI and Dataflow Prime serverless option.
b04-DataflowArchitecture.pdf
- 1. GCP Dataflow Architecture Svetak Sundhar Solutions Engineer, Google
- 2. Agenda 1. Overview of Dataflow Runner architecture 2. Overview of Dataflow Runner core features 3. GCP Horizontal services integrations 4. New Dataflow Runner features 2
- 3. Beam College 2021 Google Cloud Dataflow Service Google Cloud Dataflow Service Optional GCP Services Virtual Private Cloud Cloud Network Cloud IAM Stackdriver Key Management Service Compute Engine Flexible Resources Monitoring Log Collection Graph Optimization Resource Auto-scaler Intelligent Watermarking Shuffle Service Dynamic Work Rebalancer Streaming Engine Dataflow SQL S O U R C E SI N K S
- 4. Beam College 2021 Dataflow features Graph optimization ● Producer - Consumer Fusion ● Sibling Fusion ● Others...
- 5. Beam College 2021 Dataflow features Monitoring ● Dataflow job page ○ Enhanced observability features
- 6. Beam College 2021 Dataflow features Centralized Logging ● Single searchable logging via GCP logging Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1
- 7. Beam College 2021 Google Cloud Dataflow Service Google Cloud Dataflow Service Optional GCP Services Virtual Private Cloud Cloud Network Cloud IAM Stackdriver Key Management Service Compute Engine Flexible Resources Monitoring Log Collection Graph Optimization Resource Auto-scaler Intelligent Watermarking Shuffle Service Dynamic Work Rebalancer Streaming Engine Dataflow SQL S O U R C E SI N K S
- 8. Beam College 2021 Cloud Platform Regional Endpoint User pipeline code and SDK Job Manager Deploy and Schedule At a very high level: a user submits a processing pipeline to our managed service, which optimizes it and runs a pool of virtual machines (sometimes called workers) to do the work. Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1
- 9. Beam College 2021 Region endpoint ● Deploys and controls Dataflow workers and stores Job Metadata ● Region is us-central1 by default, unless explicitly set using the region parameter Zone ● Defines the locations of the Dataflow workers ● Defaults to a zone in the region selected based on available zone capacity. It can be overridden using the zone parameter. The zone does not need to be in the same region as the endpoint. Reasons you may want to do this include: ● Security and Compliance ● Data locality ● Resilience and geographic separation Caution: If you override the zone and the zone is in a different region than the regional endpoint, there may be negative impact on performance, network traffic, and network latency. Dataflow & Compute Engine Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Cloud IAM Compute Engine Cloud Network
- 10. Beam College 2021 Identity Access Management There are a minimum of 2 service accounts used by the Dataflow service ● Dataflow Service Account (service-<project-number>@dataflow-service-producer-prod.iam.gser viceaccount.com) ○ Used for worker creation, monitoring etc... ● Controller Service Account ○ <project-number>-compute@developer.gserviceaccount.com ○ Used by the workers to access resources needed by the pipeline, for example files on a Google Cloud Storage Bucket ○ Can be overridden using --serviceAccount Dataflow & Compute Engine Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Compute Engine 10GB PD 2 1 Cloud IAM Compute Engine Cloud Network
- 11. Beam College 2021 Google Cloud Dataflow Service Google Cloud Dataflow Service Optional GCP Services Virtual Private Cloud Cloud Network Cloud IAM Stackdriver Key Management Service Compute Engine Flexible Resources Monitoring Log Collection Graph Optimization Resource Auto-scaler Intelligent Watermarking Shuffle Service Dynamic Work Rebalancer Streaming Engine Dataflow SQL S O U R C E SI N K S
- 12. Beam College 2021 Dataflow features Batch Dynamic Work Redistribution ● Redistribute hot keys for more even workload distribution. ● Fully automated
- 13. Beam College 2021 Dataflow Shuffle - Batch Compute Petabit network Dataflow Shuffle Region Zone ‘a’ Zone ‘b’ Zone ‘c’ Distributed in-memory file system Distributed on-disk file system Shuffle proxy Autozone placement
- 14. Beam College 2021 Dataflow Streaming Engine Benefits Smoother autoscaling Better supportability Less worker resources User code Streaming engine Worker User code Worker User code Worker User code Worker Window state storage Streaming shuffle
- 15. Beam College 2021 Dataflow SQL UI No coding required ● Write SQL in BigQuery UI ● Use Schemas form Data Catalog ● Submit Dataflow jobs SELECT payload.userId, payload.productId FROM pubsub.topic.project.transactions WHERE payload.location.latitude < 40.72 AND payload.location.latitude > 40.699 AND payload.location.longitude < -73.969 AND payload.location.longitude > -74.747
- 16. Beam College 2021 Dataflow features Flexible Resource Scheduling ● FlexRS reduces batch processing costs by using advanced scheduling techniques, the Cloud Dataflow Shuffle service, and a combination of preemptible virtual machine (VM) instances and regular VMs. ● Jobs with FlexRS use service-based Cloud Dataflow Shuffle for joining and grouping. Standard VM Preemptible VM
- 17. Beam College 2021 Dataflow Templates No coding required ● Select one of 20+ Google-provided templates or use your own ● Popular ETL sources and sinks ● Streaming and Batch modes ● Launch from GCS or Pub/Sub browsers
- 18. Attach Graphical Processing Units (GPU) to your Dataflow workers to accelerate ML model training, batch and Streaming predictions, and general data processing What you can do with it ● Select from a range of GPU types (NVIDIA K80, P100, P4, T4, and V100s) for your job ● Accelerate ML workloads (preprocessing, feature engineering, ML inference) GPU Support
- 19. Beam College 2021 Dataflow Serverless Dataflow Prime Serverless Auto Tuning Infrastructure Serverless Smart Diagnostics Simplified Billing Unified Batch and Streaming “Streaming ML” for real time insights Open, intelligent and flexible platform Governance, Security, Lineage and Workflow Management Ingest and distribute data reliably with Serverless and OSS systems Store and analyze at scale with serverless and OSS systems
- 20. Summary 1. Architecture 2. DAG optimization 3. Shuffle Service 4. Streaming Engine 5. Monitoring / Logging 6. Flexible Resource Scheduling 7. Out of the box Templates 8. SQL UI 9. Dataflow Prime