StarRocks is an open‑source project under the Linux Foundation, focused on building a high‑performance, scalable analytical database that enables enterprises to create an efficient, unified lake‑house paradigm. It is widely used across many industries worldwide, helping numerous companies enhance their data analytics capabilities.
This guide explains how to design a comprehensive monitoring and alerting framework for StarRocks, covering resource usage, service availability, and business continuity with practical PromQL queries and troubleshooting steps.
JD Logistics transformed its one‑stop self‑service analytics platform, UData, by migrating from an integrated storage‑compute architecture to a storage‑compute separated design powered by StarRocks, achieving sub‑10‑second P95/P99 query latency, reducing storage costs by 90%, and cutting compute expenses around 30% while supporting massive data volumes.
NAVER migrated its massive analytics platform from ClickHouse to StarRocks, achieving dramatic improvements in multi‑table JOIN performance, real‑time aggregation speed, and horizontal scalability while simplifying data integration across heterogeneous sources on a Kubernetes‑based architecture.
This article details iQIYI's transition from Impala+Kudu and ClickHouse to StarRocks, describing the OLAP architecture, performance gains of up to 400% in advertising workloads, the technical challenges of data consistency, lake‑warehouse fusion, operational scaling, and the step‑by‑step migration process using a dual‑run platform.
The article details how Xiaohongshu's RedBI self‑service analytics platform transformed its architecture by integrating StarRocks and Iceberg, replacing ClickHouse‑based storage with Parquet, introducing DataCache, Z‑Order sorting and intelligent key selection, achieving a three‑fold P90 query speed improvement, sub‑10‑second latency, and halving storage consumption.
This article explains the principles and practical implementation of vector retrieval in StarRocks, covering approximate nearest‑neighbor algorithms, index design, query planning, performance optimizations, real‑world case studies, and future challenges, showing how query latency dropped from 15 seconds to 2 seconds while cutting costs to a third.
58.com integrated the StarRocks analytical engine into its data‑exploration platform, replacing Spark/Hive, to overcome minute‑level latency, and after a year of migration achieved over 20× query speedup, 98%+ success rate, and solved numerous Spark‑StarRocks compatibility issues while also moving the service to the cloud.
DMALL upgraded its big‑data platform by adopting StarRocks 3.x with compute‑storage separation, lakehouse external tables, and Kubernetes deployment, achieving 20% higher compute utilization, 40% lower storage cost, faster cluster provisioning, and notable improvements in development and operations efficiency.
Facing high costs and scaling limits with ClickHouse, we migrated a 4000‑core, 500TB OLAP workload to StarRocks, achieving 40% cost reduction, 50% storage savings, and up to 30× query speedups through storage‑compute separation, materialized‑view rewrites, and extensive performance tuning.
This article details Tongcheng Travel's migration from Hive/Kudu/Hudi to Paimon for lakehouse integration, highlighting a 30% resource reduction, three‑fold write speed gains, significant query acceleration via StarRocks, the end‑to‑end architecture across ODS‑DWD‑DWS‑ADS layers, and future roadmap plans.
