Enhancing OLAP Resilience at LinkedIn

TL;DR

This work introduces Query Workload Isolation (QWI), which provides workload-level CPU and memory budgeting across Pinot's broker and server tiers via fine-grained resource accounting and sub-millisecond enforcement, delivering predictable tail latency and fairness with under 1% overhead.

Abstract

Real-time OLAP datastores are critical infrastructure for modern enterprises, powering interactive analytics on petabyte-scale datasets with subsecond latency requirements. As these systems become integral to service architectures, maintaining strict SLAs under failures, load spikes, and cluster changes is as important as raw performance. We present a set of resiliency mechanisms developed for Apache Pinot at LinkedIn, applicable to modern OLAP systems broadly. We introduce Query Workload Isolation (QWI), which provides workload-level CPU and memory budgeting across Pinot's broker and server tiers via fine-grained resource accounting and sub-millisecond enforcement, delivering predictable tail latency and fairness with under 1% overhead. We present Impact-Free Rebalancing for SLA-safe data movement during routine operations (e.g., upgrades, scale-out, and recovery), and Maintenance Zone Awareness to place replicas across fault domains and mitigate correlated failures. We also describe Adaptive Server Selection, which routes queries using real-time load and performance signals to avoid slow or failing nodes while preserving balanced utilization. Together, these mechanisms form a holistic resiliency framework deployed in production at LinkedIn, enabling stable query latency and high availability at scale.

Figures (13)

• Figure 1: (Simplified) Pinot architecture.
• Figure 2: Replica group assignment and routing
• Figure 3: Resource accounting
• Figure 4: Example workload configuration with per-node resource budgets and table-level propagation.
• Figure 5: Budget propagation: controller derives per-host limits from workload config and pushes to relevant instances.