StarRocks combines vectorized execution, a new cost‑based optimizer, materialized views, a real‑time storage engine, pipeline execution, and distributed joins to deliver a unified, high‑performance OLAP solution that supports both traditional and lakehouse analytics while reducing operational complexity.
The article explains how ByteHouse extends ClickHouse with a full‑featured query optimizer—including rule‑based and cost‑based techniques, analyzer modules, plan construction, and distributed optimization—to overcome ClickHouse limitations and achieve significant performance gains on complex OLAP workloads.
This article explains StarRocks' streamlined architecture, cost‑based optimizer, massively parallel processing and vectorized engine, and how they enable high‑performance queries over data stored in Hive, Iceberg, Hudi and other lake formats, backed by benchmark results and future roadmap details.
The article examines Oracle’s automatic predicate pushdown that rewrites SQL statements, illustrates its impact on execution plans with trace data and queries, and draws lessons for improving domestic database products and support in the wake of recent geopolitical shifts.
This article introduces Impala's overall architecture, storage options, key features, concurrency mechanisms, CBO‑based join optimization techniques, storage‑layer principles and data‑filtering strategies, and summarizes practical performance‑tuning experiences from Tencent's financial big‑data platform.
This article examines common performance pitfalls in Oracle hierarchical (CONNECT BY) queries, compares filtering after tree generation versus during traversal, explains why CBO estimates can be wildly inaccurate, and demonstrates how to rewrite queries with pipelined table functions and parallel hints for dramatic speed gains.
A production Oracle database suffered widespread query failures because collected system statistics gave the optimizer incorrect I/O cost values, causing it to favor index scans over full table scans; the article details the diagnosis, root cause, and a simple fix by deleting the system stats.
After migrating a core system from AIX to Linux and upgrading Oracle from 11.2.0.3 to 11.2.0.4, a critical SQL that previously ran in milliseconds began taking hundreds of seconds, prompting a detailed investigation of execution plans, CBO limitations, and three successive rewrite strategies that restored performance.
This article walks through common traps in Oracle's Cost‑Based Optimizer (CBO), explains why query transformations, FILTER operations, and inaccurate statistics can cripple performance, and provides practical solutions—including NULL‑AWARE anti‑joins, hint usage, SQLT tooling, and SQL rewrites—to dramatically speed up problematic queries.
This article examines typical problems encountered when using Cost‑Based Optimizer (CBO) in Inceptor, offers step‑by‑step troubleshooting methods, and demonstrates a real‑world case where statistical information is collected with Preanalyze, CBO is enabled, and query plans are refined for large‑scale JOIN operations.
This article explains the concept of Cost‑Based Optimization (CBO) in Inceptor, details how to collect statistics with ANALYZE or the preanalyze script, shows how to enable CBO, and presents performance gains demonstrated on TPC‑DS benchmarks.
This guide provides comprehensive Oracle database interview questions and answers covering SQL tuning techniques such as join types, execution‑plan inspection, CBO vs RBO, heavy‑SQL identification, session tracing, index structures, binding variables, plan stability, memory settings, tablespace management, backup and recovery methods including RMAN and standby, as well as system performance diagnostics.
This article examines a real‑world Oracle query where outdated or incorrect table and index statistics caused the optimizer to choose a costly index range scan instead of a partition full‑table scan, and it outlines diagnostic steps and practical solutions to restore accurate cost estimates.
This article explains how Oracle's Cost‑Based Optimizer estimates the selectivity of range predicates without histograms, demonstrates calculations with sample data, shows PL/SQL code for converting string values to internal numbers, and discusses pitfalls such as identical internal values causing cardinality errors, offering solutions like gathering histograms or using DATE columns.
