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Recently, Pinterest disclosed its internal orchestration framework, called Hadoop Control Center (HCC), to automate the scaling and migration of its large-scale Hadoop clusters. This move addresses the operational complexity and limitations Pinterest previously faced when managing thousands of nodes across dozens of YARN clusters on AWS.

Historically, Pinterest maintained fixed-size Auto Scaling Groups (ASGs) for its Hadoop infrastructure. This configuration, while safe, meant autoscaling was effectively disabled. Manual adjustments to cluster size, especially scaling in, required extensive human intervention via Terraform. These operations involved complex sequences to drain and decommission nodes safely, while avoiding disruption to batch workloads using MapReduce, Spark, and Flink. The process was both time-consuming and error-prone, often leading to infrastructure duplication and resource waste.

The introduction of HCC transforms this manual workflow into a fully automated system that manages Hadoop cluster resizing and node migration in real-time. Operators can now request scaling actions through a unified command-line interface. Behind the scenes, HCC coordinates interactions with AWS services and Hadoop components to ensure safe decommissioning of nodes, data integrity, and service continuity.

At the heart of HCC is a manager-worker architecture distributed across Pinterest's Virtual Private Clouds (VPCs). Each VPC runs a manager node that caches cluster state and delegates actions to worker nodes. These workers interact with a custom component called the Hadoop Manager Class (HMC), which orchestrates the step-by-step decommissioning and scaling of nodes. HMC monitors key cluster metrics via JMX, updates configuration files, issues AWS API calls, and schedules internal threads to handle draining, termination, and clean-up operations.

HCC logical schema

One of HCC's key features is its ability to safely migrate nodes during in-place upgrades. Rather than deploying parallel green clusters with new configurations, Pinterest's engineers can launch a new ASG with updated instance types or AMIs and allow HCC to integrate these new nodes into the existing cluster. HCC then begins draining data and workloads from the old ASG, monitors for completion of shuffle and HDFS replication, and removes decommissioned instances in a controlled fashion. This method minimizes cost, avoids duplicative infrastructure, and removes the need to re-provision capacity or IP space for each migration.

Additionally, the system manages ASG scale-in protection at the instance level, ensuring AWS does not randomly terminate Hadoop nodes that are not ready for removal. After a successful decommission, HCC removes the node from the cluster and updates relevant Terraform variables to maintain configuration consistency and avoid drift during future infrastructure changes.

While HCC has dramatically improved operational efficiency, Pinterest is already looking to extend its capabilities. The team plans to add auto-remediation features for handling unhealthy nodes detected by AWS, enable lifecycle rotation based on OS age or AMI version, and incorporate AWS event triggers for smarter node management. These enhancements are aimed at making Pinterest’s Hadoop infrastructure more autonomous and resilient.

The move to HCC has enabled Pinterest to scale its data processing platforms on demand, reduce the risk of human error, and safely perform in-place migrations with minimal downtime or application impact. As data infrastructure becomes more dynamic and elastic in the cloud, Pinterest’s approach provides a compelling blueprint for managing stateful systems like Hadoop with modern automation principles.

Another tech giant, Uber, disclosed its phased strategy for shifting its massive Hadoop-based batch analytics stack—managing up to exabytes of data and tens of thousands of servers—to Google Cloud Platform. Initially, Uber migrated core components onto GCP-based Infrastructure-as-a-Service to replicate its on?premises environment with minimal disruption. Subsequently, the team planned gradual adoption of managed services such as Dataproc, BigQuery, and Google Cloud Storage, alongside cloud-native abstractions and reverse?compatible proxies for Spark, Hive, and Presto. Uber’s layered approach minimizes client impact while enabling a modern, scalable, and elastic platform in the cloud.

These two cases highlight a shared paradigm: large-scale Hadoop systems can be safely migrated or modernized with minimal downtime through well-designed orchestration, replication, and compatibility tooling.