The article introduces Kylin 4.0's major architectural upgrades, explains why previous tuning practices no longer apply, and provides a detailed performance‑tuning guide and video covering Cube build and query optimizations, along with additional reference resources.
This article introduces Kylin, a HBase‑based multidimensional analysis platform, explains its architecture and various performance optimizations—including multi‑tenant support, dimension dictionary handling, and cube size estimation—while showcasing a real‑world deployment and case study at 58.com.
This article shares a data engineer's three‑year experience using Apache Kylin for real‑time OLAP on petabyte‑scale data, describing the business background, challenges of pre‑computation, cube modeling, dimension reduction, and various optimization techniques such as hierarchy, mandatory, and joint dimensions, as well as precise count‑distinct handling.
This article explains why raw OLAP Cubes consume exponential storage, then details six practical dimensionality‑reduction methods—Aggregation Group, Mandatory Dimension, Joint Dimension, Derived Dimension, Hierarchy Dimension, and Partial Cube—showing how each can dramatically shrink materialized tables while preserving query performance.
