This article explains a distributed‑computing approach for generating a globally unique integer index from massive string datasets, replacing single‑reducer sorting with hash‑bucket partitioning and parallel processing to cut runtime from 30 minutes to just 2 minutes.
This article explains the differences between global and local indexes in OceanBase, analyzes why queries using global unique indexes run significantly faster than those using local indexes during Oracle migration, and provides detailed SQL examples, execution plans, and performance testing results to guide database optimization.
This article explains the concepts of local and global indexes in OceanBase, demonstrates how to create tables and indexes, compares six practical scenarios—including no index, local index, partitioned queries, unique and global indexes—and provides performance metrics to guide database optimization.
The article explains the concepts, differences, and practical usage scenarios of local (partition‑aligned) and global indexes in OceanBase, providing SQL examples for queries with and without partition keys and guidance on creating unique indexes using either index type.
PolarDB‑X evolved from Alibaba’s internal sharding middleware TDDL to the DRDS cloud service and finally to a fully distributed MySQL‑compatible database, introducing a full SQL optimizer, MPP engine, global secondary indexes, strong‑consistent distributed transactions via TSO + 2PC, and full binlog compatibility.
This article explains how Apache Phoenix implements global secondary indexes using separate HBase tables, demonstrates index creation and data synchronization with example SQL, and provides design guidelines to optimize query latency and avoid full‑table scans in big‑data environments.
