This article explains MySQL's layered architecture, how the optimizer generates multiple execution plans, evaluates their I/O and engine costs, and selects the plan with the lowest total cost, illustrated with example queries, cost tables, and JSON output.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage cost models affect performance optimization, and presents case‑study findings from Uber’s Presto production environment that highlight fragmented I/O patterns and the financial impact of storage API calls.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native environments, revealing how cloud storage cost models affect I/O optimization, and presents Uber Presto case‑study findings that highlight fragmented access patterns and financial implications of storage API calls.
This whitepaper investigates the migration of data‑intensive analytics to cloud‑native environments, using Uber’s Presto workload to expose how cloud storage cost models and fragmented I/O patterns affect performance, and proposes optimized I/O strategies to improve cost‑effectiveness and system design.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native environments, analyzes the unique cost model of cloud storage, and presents case‑study findings from Uber’s Presto production system that reveal fragmented I/O patterns and propose optimization strategies to improve cost‑performance in the cloud.
This whitepaper examines the trend of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage cost models affect I/O optimization, and using Uber’s Presto production data to show that traditional I/O strategies overlook costly storage API calls, leading to high expenses.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage’s unique cost model demands finer‑grained I/O optimization, illustrated through an empirical case study of Uber’s Presto production environment and its fragmented access patterns.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, analyzing how cloud storage cost models affect performance optimization, and presents a case study of Uber’s Presto production environment that reveals fragmented I/O patterns and the financial impact of storage API calls.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native environments, analyzes how cloud storage cost models affect performance optimization, and presents Uber Presto case‑study findings that reveal fragmented access patterns and hidden financial costs of traditional I/O strategies.
This whitepaper examines the industry shift of moving data‑intensive analytics to cloud‑native platforms, revealing how cloud storage’s unique cost model demands nuanced I/O optimization, and presents Uber Presto case‑study findings that highlight fragmented access patterns and cost‑effective design strategies for high‑performance cloud workloads.
This whitepaper examines the industry trend of migrating data‑intensive analytics workloads to cloud‑native environments, revealing how cloud storage’s unique cost model demands finer‑grained performance optimization, and presents Uber Presto case‑study findings that expose fragmented I/O patterns and associated financial impacts.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, analyzes how cloud storage cost models affect performance optimization, and presents Uber Presto case‑study findings that reveal fragmented access patterns and new I/O strategies to improve cost‑effectiveness.
This whitepaper examines the industry shift of moving data‑intensive analytics to cloud‑native platforms, analyzes how cloud storage cost models affect performance optimization, and presents Uber Presto case‑study findings that reveal fragmented access patterns and the financial impact of traditional I/O strategies in the cloud.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, analyzes the unique cost model of cloud storage, and presents case‑study findings from Uber's Presto production environment to guide efficient I/O design and cost‑effective performance optimization.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage cost models affect performance optimization and presenting case‑study‑based I/O strategies derived from Uber's Presto production environment.
This article explains MySQL's cost‑based optimizer, demonstrates how to calculate query costs for full‑table, covering, ref and range scans using actual source‑code constants, and applies the model to resolve index‑conflict cases in a store‑goods table, offering practical optimization guidelines and future tool ideas.
This article breaks down MySQL's query optimizer cost model by detailing how full‑table‑scan cost is computed, covering the underlying formulas, required statistics, memory‑vs‑disk page ratios, and configurable cost constants with concrete code examples.
This article details a joint research effort between Meituan and East China Normal University that combines cost‑based methods with AI‑driven, data‑centric models to automatically generate and evaluate missing indexes for billions of daily slow queries, improving recommendation accuracy and query performance.
The article explains how the massive compute and storage demands of today’s large language models create a “compute wall” and “storage wall,” and describes Baidu Intelligent Cloud’s four‑layer full‑stack infrastructure—combining advanced parallelism techniques, optimized GPU networking, static‑graph compilation, and cost‑model‑driven placement—to train trillion‑parameter models efficiently.
This article explains how Baidu's intelligent cloud overcomes the compute and storage walls of large‑scale model training by combining hardware design, network topology, and software optimizations such as pipeline, tensor, and expert parallelism, cost‑model‑driven placement, and future‑proof AI infrastructure evolution.
Index Condition Pushdown (ICP) in MySQL pushes filter conditions to the storage engine to reduce row lookups, but this article demonstrates through experiments that while ICP lowers runtime by decreasing back‑table accesses, the optimizer’s cost model often ignores its benefits, leading to suboptimal plan choices.
This article provides a comprehensive, step‑by‑step guide to StarRocks join optimization, covering join types, logical rewrite rules, predicate push‑down, join reorder algorithms, cost modeling, distributed join strategies, and runtime filters, while offering practical tips for achieving high‑performance query execution.
This article investigates a recurring MySQL CPU‑100% alarm caused by a slow SELECT with ORDER BY id ASC LIMIT, explains why the optimizer mistakenly chooses the primary‑key index over a suitable composite index, and presents six practical experiments—including force‑index, ORDER BY tweaks, and LIMIT adjustments—that reliably restore optimal index usage.
This article provides a comprehensive technical overview of StarRocks' query optimizer, covering its cascades/ORCA-inspired architecture, logical and physical plan transformations, cost modeling, statistics derivation, memo structure, task scheduling, and practical examples of join optimization in a distributed OLAP engine.
The article explains how Meituan uses MySQL's cost‑based optimizer to analyze slow queries, generate virtual index candidates, evaluate their costs with detailed statistics, and deploy a recommendation system that validates, tracks, and governs index suggestions to reduce CPU/IO waste and prevent database failures.
This article explains MySQL's architecture, the server and storage‑engine layers, query execution phases, status states, query cache behavior, the optimizer's parsing and planning steps, EXPLAIN output fields, the cost model, and why the optimizer selects index_author_id instead of index_title for a given query.
This article introduces the TXSQL query optimizer built on MySQL 8.0.22, detailing its cascades‑style framework, transformation rewrite rules such as outer‑join elimination and subquery flattening, join‑order heuristics, cost‑model configuration, and execution strategies, providing a comprehensive overview of its design and enhancements.
This article examines the core challenges of query optimization in relational databases—accurate statistics, massive plan spaces, and efficient plan management—and explains how OceanBase addresses them through logical/physical row concepts, real‑time statistics, distributed two‑stage planning, adaptive caching, and plan evolution mechanisms.
This article explains how MySQL 8.0 improves the optimizer by introducing configurable cost model constants and a histogram feature, showing how to query and update system tables, use ANALYZE TABLE to create and manage histograms, and explore the underlying code structures and future tuning possibilities.
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.
This article explains the inner workings of the DB2 optimizer, its four-step processing flow, cost‑based decision making, and detailed examples comparing full‑table and index scans, followed by practical tuning tips and a Q&A session for real‑world query optimization.
