---
title: "Apache Pinot"
description: "2 posts about Apache Pinot, summarised, each linking to the original."
---

# Apache Pinot
> 2 posts about Apache Pinot, summarised, each linking to the original.

## Articles

### [Building Zone Failure Resilience in Apache Pinot™ at Uber](https://yomu.fyi/post/building-zone-failure-resilience-in-apache-pinottm-at-uber.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jul 2, 2026
- Type: Problem & solution

Uber describes how it made Apache Pinot clusters resilient to zone failures while preserving query and ingestion availability for Tier-0 use cases. It maps Uber’s virtual resource isolation group IDs to Pinot server pools, uses pool-based instance assignment with replica-group segment assignment, and requires at least as many isolation groups as the table’s replication factor. During migration, automated pool registration, configuration backfills, granular APIs, selective rebalancing, and rollback support were used across more than 400 Tier 0-1 clusters and thousands of tables. An internal drill found that one remaining replica continued serving traffic without query degradation after a zone went down, while isolation-group-based claim and release policies enabled parallel restarts and sequential rollouts with baking time.


### [Powering Partner Gateway metrics with Apache Pinot](https://yomu.fyi/post/powering-partner-gateway-metrics-with-apache-pinot.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Alvis Chew
- Published: Sep 23, 2025
- Type: Problem & solution

Partner Gateway needed low-latency analytics for API traffic and response-time metrics across intervals from one hour to 30-day retrospectives, with a particular dataset potentially reaching approximately 6.8 billion rows in a 30-day period. Apache Pinot was selected as a distributed real-time OLAP datastore, using high-throughput ingestion and columnar, indexed, partitioned data for millisecond analytical queries. Initial aggregation queries on datasets over 150GB timed out for 20- to 30-day periods, exceeding 10 seconds and threatening a 300-millisecond SLA. The implementation routes metrics through Kafka and Apache Flink, then ingests Schema Registry-compatible messages into Pinot, while partitioning by metric name and adding aggregation-focused optimizations. For one metric, query latency fell from 5,072 to 35 milliseconds; Star-tree indexing improved speed but raised 30-day storage from 192GB to 373GB, with the additional AWS EBS cost described as minor.