This article explains the fundamentals of relational optimization, the role of the cost‑based optimizer, how CPU and I/O costs are estimated, and why accurate database statistics are essential for generating efficient SQL access paths and improving overall query performance.
At Youzan’s 2018 Database Technology Open Day in Hangzhou, 53 companies and 64 participants heard sessions on the evolving RDS architecture, MySQL index design best practices, the ZanDB operations platform, and classic MySQL performance case studies, while showcasing micro‑mall integration and outlining upcoming data‑archiving and multi‑data‑center initiatives.
This article walks through diagnosing unexpected Oracle parallel execution, resolving DBLINK‑induced plan anomalies after a database upgrade, and fixing ORA‑00604 errors caused by missing domain indexes, providing step‑by‑step queries, parameter checks, and corrective ALTER statements to restore normal performance.
This article explains the main factors that cause Redis latency—including network bottlenecks, Redis’s single‑threaded design, deployment choices, OS settings, and slow commands—and provides concrete commands and best‑practice solutions to diagnose and reduce the delay.
This guide presents New Relic’s 20 practical best‑practice recommendations—covering partitions, consumers, producers, and brokers—to help engineers design, tune, and monitor Apache Kafka deployments for reliable, high‑throughput performance.
This article details the practical performance‑tuning steps applied to a large‑scale HBase deployment handling 80 billion daily log entries, covering rowkey redesign, region redistribution, HDFS write‑timeout fixes, network‑topology adjustments, and JVM parameter tweaks that together stabilized the system and dramatically improved throughput.
This comprehensive guide introduces Redis fundamentals, explores its diverse data structures and essential commands, and delves into advanced topics such as persistence, memory management, pipelining, transactions, clustering, and Java client choices, offering practical tips for deployment, optimization, and architectural decisions.
This article presents a detailed investigation of occasional Redis connection errors in a large‑scale production environment, analyzing network packets, TCP backlog behavior, Redis internal client‑cron logic, jemalloc memory reclamation, and ultimately resolving the issue by adjusting query‑buffer handling and upgrading Redis to a newer version.
The article explains how excessive memory use in a Flutter app, caused by ImageCache and related image handling, was diagnosed with Xcode Instruments and Dart Observatory, then mitigated through refined caching policies, adaptive screenshot resolution, improved FlutterViewController lifecycle management, and targeted garbage‑collection techniques, resulting in lower memory peaks and greater stability.
This article provides a comprehensive guide to MySQL optimization, covering the underlying query process, risk-aware philosophy, practical troubleshooting steps, hardware and system tuning, essential monitoring tools, and detailed configuration adjustments to improve performance and stability in production environments.
This article provides a comprehensive guide to MySQL optimization, covering the underlying query process, philosophical considerations, practical troubleshooting steps, hardware and system tuning, essential tools, and detailed configuration adjustments to achieve stable and high‑performance database operations.
This article explains the architecture of Hadoop HDFS, identifies performance bottlenecks in page cache and metadata handling on DataNodes, and presents four practical optimization techniques—including cache‑buffer separation, barrier disabling, directory restructuring, and real‑time monitoring—demonstrating significant throughput and latency improvements in large‑scale clusters.
This article explains why Spark needs careful performance tuning, then details concrete development‑level optimizations (RDD reuse, persistence, shuffle avoidance, broadcast variables, Kryo serialization, data‑structure choices) and resource‑level settings (executor count, memory, cores, parallelism, memory fractions) with code examples and practical recommendations.
This article details the design, architecture, and implementation of ZanKV—a high‑performance, distributed key‑value store that combines RocksDB storage, etcd‑Raft consensus, and a Redis‑compatible protocol, covering data partitioning, namespace isolation, expiration strategies, cross‑datacenter deployment, and performance tuning.
This article provides a comprehensive guide to optimizing Lustre, the leading open‑source parallel file system for high‑performance computing, covering network bandwidth, stripe settings, client configuration, RAID choices, small‑file handling, and practical system commands to improve aggregate I/O performance.
This article provides a comprehensive overview of SAP's product portfolio, focusing on ERP architecture, common performance bottlenecks, and detailed recommendations for database and storage optimization to improve SAP ERP responsiveness and scalability.
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.
The article walks through two common Oracle performance problems—slow tablespace‑information queries and sluggish ASH data collection—explaining root causes, showing diagnostic steps, and providing exact SQL and PL/SQL scripts to gather statistics, clean up partitions, and restore fast query response.
This article walks through a DBA’s daily responsibilities, outlines a step‑by‑step MySQL performance‑tuning workflow, highlights key optimization areas such as application access, hardware selection, OS and instance settings, and shares practical automation practices for backup, monitoring, failover, security auditing, and log management.
This article provides a comprehensive technical overview of Sharding-Proxy, covering its transparent MySQL‑protocol design, internal architecture, PreparedStatement optimization, HikariCP tuning, result‑set streaming, memory‑strict and connection‑strict modes, and practical configuration tips for high‑performance database proxying.
This article shares practical Java backend engineering advice—including module layout, code‑level conventions, SQL execution‑plan understanding, index usage, transaction handling, connection‑pool tuning—as well as personal work‑habit reflections and career‑development suggestions for developers.
Meitu cut short‑video CDN bandwidth by over 50 % while preserving quality, reduced error rates from 1 % to 0.1 %, and achieved millisecond‑level startup for most videos by deploying a data‑driven fusion scheduler, multi‑CDN failover, FastDNS, buffer control, moov‑relocation and player‑tuning techniques.
This comprehensive guide explains Spark's ecosystem, execution principles, key features, deployment architectures, core concepts like RDD, Transformations, Actions, Jobs, Stages, Shuffle and Cache, as well as Spark Streaming mechanics and practical resource‑tuning tips for optimal big‑data processing.
This guide shows how simple MySQL configuration changes—switching to InnoDB, allocating most RAM to the buffer pool, and enabling multiple buffer pool instances—can deliver up to an 80% performance boost for typical web‑site workloads.
This article explains MySQL's logical architecture, query execution flow, and the inner workings of its optimizer, then provides practical performance‑tuning advice—including index design, query cache handling, and specific query‑type optimizations—to help developers make informed decisions for real‑world workloads.
This article presents comprehensive best‑practice guidelines for database design, indexing, SQL query tuning, Java data‑handling techniques, and hardware adjustments to dramatically improve database performance and reduce resource consumption.
The article investigates massive packet loss in Meituan‑Dianping's Redis service despite 10 Gbps NIC upgrades, traces the issue to kernel receive‑buffer drops and single‑CPU interrupt handling, and presents a step‑by‑step optimization using backlog tuning, CPU and Redis affinity, and NUMA‑aware placement to eliminate drops and improve latency.
This article shares practical experiences from a DBAplus Guangzhou tech salon, detailing three real Oracle database incident cases, the root‑cause analyses, and a three‑step framework for rapid resolution, prevention, and team management to maintain zero‑fault operations across thousands of database instances.
This article presents a comprehensive technical sharing on Golang high‑performance engineering, covering architecture design, profiling tools, concurrency optimizations, distributed rate limiting, timer mechanisms, GC tuning, and deployment strategies, supplemented with PPT links and extensive code excerpts for practical reference.
This article examines Spark's unified memory manager in version 2.2.1, detailing on‑heap and off‑heap memory regions, the four on‑heap memory pools, dynamic execution‑storage memory sharing, task memory accounting, and provides concrete calculation examples to explain UI discrepancies and runtime memory limits.
This article provides a comprehensive guide to MySQL optimization, covering the query process, risk‑aware philosophy, collaborative participation, multi‑layer optimization dimensions, essential diagnostic tools, system‑level tuning, hardware recommendations, and key database configuration tweaks to achieve stable and high‑performance database operations.
This comprehensive guide introduces Redis fundamentals, covering its core data structures and essential commands, then delves into performance optimization, high‑availability setups with replication and Sentinel, and scaling strategies using Redis Cluster, providing practical examples and best‑practice recommendations for robust in‑memory data management.
This article walks through a real‑world SQL Server performance incident, explaining how to analyze hardware, system scale, internal database factors, and software environment, and presents concrete steps—including patching, configuration tweaks, and code changes—to eliminate CMEMTHREAD waits and improve overall database throughput.
This article walks through a real‑world SQL Server performance‑tuning investigation, explaining the six‑factor analysis framework, diagnosing CMEMTHREAD waits, and presenting concrete hardware, configuration, and code‑level solutions that eliminated the issue and improved overall database throughput.
Kafka MirrorMaker provides near‑real‑time cross‑data‑center replication by consuming from a source cluster and producing to a target cluster, and this guide explains its core features, new vs. old consumer APIs, partition assignment strategies, performance tuning, network considerations, and practical command‑line examples.
The article details iQIYI’s Bubble social service cache architecture, comparing Couchbase and Redis, explaining vBucket design and management UI, and presenting three real‑world optimizations—like‑system key redesign, voting‑system aggregation, and SDK upgrade—along with migration, synchronization, and operational best‑practice recommendations.
When all other tuning options fail, this article guides Java developers through a systematic GC optimization process—covering essential concepts, a goal‑oriented workflow, and three real‑world case studies that illustrate adjusting generation sizes, employing CMS flags, and fixing PermGen to dramatically reduce pause times and latency.
This comprehensive guide uses a gaming‑level analogy to walk readers through MySQL fundamentals, architecture, storage engines, memory structures, logging, backup and recovery methods, high‑availability designs, and advanced performance tuning, providing practical commands, diagrams, and best‑practice recommendations.
This article explains how the BBR congestion control algorithm, designed for long‑fat networks, overcomes the limitations of traditional TCP to dramatically improve cloud storage download speeds, detailing its principles, implementation steps, and real‑world performance gains.
This article presents seven practical MySQL performance‑tuning techniques—including mastering EXPLAIN, building effective indexes, overriding default settings, optimizing memory usage, adopting SSD storage, scaling horizontally, and improving visibility—while explaining key configuration variables and monitoring tools to keep your database fast and reliable.
The guide explains how to optimize Elasticsearch performance by managing cluster state size through shard planning, assigning dedicated master, data, ingest, and coordinating node roles, and preventing memory swapping with bootstrap.memory_lock and proper heap and OS settings.
This article explains three practical MySQL performance‑tuning techniques—setting an appropriate max_connections value, allocating enough memory for temporary tables, and increasing the thread cache size—to help database servers survive the massive traffic surges of holiday shopping periods without downtime.
This article explains why conventional indexes waste space and perform poorly on highly skewed columns, introduces a decode‑based function index that excludes high‑frequency values, details the experimental setup with millions of rows, compares index size and query performance, and outlines the method's limitations.
An Oracle SQL query with five EXISTS permission checks runs dramatically slower when only a few permissions are configured, and the author investigates the root cause using execution plans, SQL Monitor data, and step‑by‑step testing, ultimately revealing how execution counts and row returns drive the unexpected performance degradation.
This guide walks through the design, installation, configuration, performance tuning, and daily operation of a large‑scale network packet capture and analysis platform using Moloch, Elasticsearch, and pfring on a high‑performance Linux server.
This article walks through a real‑world monitoring project that must store over 400 million rows per day in SQL Server 2012, detailing the performance bottlenecks, bulk‑copy tuning, schema redesign, partitioning strategies, index removal and creation, and query‑optimisation techniques that finally achieve sub‑second query times and meet strict latency requirements.
This article shares practical experiences and step‑by‑step guidance on tuning Redis server settings, choosing appropriate eviction policies, limiting client output buffers, disabling risky commands, handling Sentinel‑based master‑slave failover, and selecting the right persistence strategy for reliable and high‑performance deployments.
This guide explains how to tune Elasticsearch 5.x search performance by modeling documents with flattened, nested, or parent‑child structures, managing global ordinals, allocating JVM heap under 50 % of RAM, monitoring garbage collection, and choosing SSD storage and ample file‑system cache.
This article explains how MySQL processes ORDER BY, compares conventional, optimized, and priority‑queue sorting algorithms, shows how to leverage indexes, and provides practical tuning tips such as adjusting max_length_for_sort_data and sort_buffer_size to avoid costly temporary files and random I/O.
Redis is an open-source, in-memory data store offering versatile structures and commands; this guide explains its fundamentals, key data types, essential commands, practical examples, performance optimization techniques, replication, clustering, and Java client choices to help you design robust, high-performance Redis solutions.
To maximize Elasticsearch bulk-indexing speed, temporarily disable refreshes and replicas, tune merge throttling and scheduler threads, enlarge translog and index buffer thresholds, and adjust indexing and bulk thread-pool sizes, then restore defaults after the load completes.
This article explains MySQL's logical architecture, query processing steps, client/server protocol, query cache behavior, parsing, optimizer cost model, execution engine, result delivery, and provides practical performance‑tuning advice on schema design, index creation, and specific query patterns.
This article shares a comprehensive set of practical tips and solutions for common Spark SQL issues—including out‑of‑memory errors, UDF‑induced GC, thread blocking, system‑property initialization, speculation side‑effects, accumulator traps, concurrent job scheduling, and excessive logging—helping engineers improve stability and efficiency of their Spark‑based financial systems.
This guide shares a seasoned DBA's practical checklist covering environment awareness, hardware considerations, runtime monitoring, data security, routine operations, fault‑prevention plans, architecture insights, and the right mindset to help database professionals reduce errors and improve system stability.
A recent order‑fulfillment outage was traced to a MySQL query that used the primary‑key index because an ORDER BY id ASC LIMIT 350 forced a full scan, but tweaking the ORDER BY or removing it reduced execution time from 20 seconds to under one second.
This guide examines why three simple Spring Cloud microservices consume about 1.5 GB RAM, explains JVM heap and non‑heap memory structures, and provides concrete JVM option settings and container choices to dramatically reduce memory usage while maintaining functionality.
This article outlines the three‑step process of Java performance tuning—monitoring, analysis, and optimization—covers preparation, detailed CPU, memory, and I/O analysis techniques, and provides practical JVM parameter and tool recommendations for backend developers.
This article explains how upgrading to MySQL 5.7.18 caused severe performance drops on partitioned tables due to a regression that locks too many rows during single‑row updates, details the reproduction steps, shows the InnoDB lock inspection, pinpoints the faulty code path, and confirms the bug with further tests.
This article compiles a comprehensive set of MySQL interview questions covering replication mechanisms, differences between MyISAM and InnoDB, character type nuances, transaction logging, binlog formats, performance tuning, backup strategies, monitoring, consistency checks, and handling large datasets, providing concise answers for each topic.
Learn practical techniques to reduce unnecessary I/O and CPU consumption in SQL queries, including narrowing scan ranges, using partition filters, optimizing joins, leveraging hints, bind variables, and proper use of MERGE, UPDATE, EXISTS/IN, pagination, temporary tables, and other performance‑tuning strategies.
This article explains Oracle's architecture—including the SGA, Shared Pool, Data Buffer, and Log Buffer—shows how each component influences SQL parsing, bind variables, result caching, direct‑path inserts, batch commits and logging, and provides step‑by‑step experiments and practical techniques for diagnosing and tuning performance bottlenecks.
This article presents a detailed investigation of intermittent slow SQL queries observed in Meituan‑Dianping’s services, identifies the MySQL 5.6 thread‑pool as the primary cause, and demonstrates how adjusting thread‑pool parameters dramatically reduced slow‑query counts and improved overall service availability.
This comprehensive guide shares a senior DBA's hard‑learned lessons on MySQL environment awareness, security, configuration, routine operations, architecture, business understanding, and mindset, offering practical tips and tools to prevent costly mistakes and improve operational confidence.
This article walks through a real‑world MySQL performance case at Meituan‑Dianping, showing how systematic monitoring, packet capture, and step‑by‑step analysis identified the Thread‑Pool as the bottleneck and how disabling and fine‑tuning its parameters reduced slow queries from thousands to just a few dozen.
This article discusses memory optimization in Android apps by addressing excessive object allocations caused by autoboxing Long values, leading to performance issues during feed scrolling. The solution involves implementing a custom data structure to reduce memory usage and improve application responsiveness.
This article teaches how to read and manipulate Oracle execution plans using hints, SQL rewrites, design features, and statistics tricks, providing step‑by‑step examples, common pitfalls, and practical scripts to achieve predictable performance without costly statistics collection.
This comprehensive guide shares a seasoned DBA's practical advice on environment awareness, hardware considerations, security, routine operations, architecture, and mindset to help database professionals avoid common pitfalls and improve reliability in MySQL production systems.
This article explains how Oracle's parallel processing feature can accelerate data‑intensive queries by configuring session, table, or hint‑based parallelism, shows the degree‑of‑parallelism formula, and presents experimental results that compare execution times under different settings.
Drawing from a year‑and‑a‑half of ops experience, this guide outlines six practical categories—online operation standards, data handling, security, daily monitoring, performance tuning, and mindset—to help engineers avoid costly mistakes and maintain stable, secure systems.
This article explains what an Oracle SQL execution plan is, how statistics guide the optimizer, various ways to retrieve plans, the classification of plan types, and practical tips for spotting and fixing inefficient plans using real‑world examples and code snippets.
This guide walks through practical Linux hardening steps—including minimal installation, SSH port changes, sudo privilege tailoring, extensive kernel sysctl tuning, firewall adjustments, system version hiding, file protection, and package installation strategies—to boost security and performance while keeping the system lean.
This article explains what MySQL's slow query log is, when it is generated, the key configuration parameters, how to interpret its output, useful analysis utilities, and best practices for cleaning and backing up the log files.
The article shares a DBA's real‑world experiences—from a risky MySQL restart that exposed hidden issues, through practical learning advice and MySQL technical nuances, to a detailed Oracle migration case—offering actionable insights for database professionals seeking robust data management and performance optimization.
This article explains how Meituan‑Dianping leverages the open‑source tool Sysbench for repeatable database performance testing, unifies test parameters, optimizes MySQL settings such as slave_parallel_workers, extends functionality via Lua scripts, and reveals the tool’s high‑performance data‑collection techniques.
This article analyzes the limitations of the original TMP monitoring storage architecture, evaluates OpenTSDB's shortcomings at large scale, and details the design, implementation, and performance tuning of a custom HBase-based solution that achieves 3‑5× higher throughput for billions of monitoring data points per day.
This article explains Oracle database performance tuning by introducing the five key performance reports—AWR, ASH, ADDM, AWRDD, and AWRSQRPT—detailing how to obtain them, what metrics to focus on, and how to apply them in real‑world case studies for both simple and complex optimization scenarios.
BeeGFS is a GPL‑licensed parallel file system for Linux that offers scalable storage through a modular architecture of management, metadata, and object storage servers, supports a wide range of hardware and OS platforms, and provides detailed installation, configuration, and performance‑tuning guidance including the BeeOND burst‑buffer extension.
A developer added a seemingly harmless filter to an Oracle SQL statement, causing the optimizer to switch the driving table and join method, which inflated execution time from 2 seconds to 90 seconds, prompting a deep dive into execution plans, indexing, and hint techniques.
This article compiles 38 practical Oracle SQL tuning techniques—from ordering tables and rewriting WHERE clauses to using indexes, avoiding SELECT *, leveraging DECODE, replacing HAVING with WHERE, and preferring TRUNCATE over DELETE—each illustrated with clear examples to help developers write faster, more efficient queries.
When migrating applications from Oracle to MySQL, many teams encounter severe performance problems such as CPU saturation, high logical and physical reads, and slow queries, and this article outlines the root causes—including lack of parallelism, limited join algorithms, IN‑list limits, subquery inefficiencies, missing materialized views, and unsupported function indexes—and provides concrete remediation strategies for each issue.
This article examines common performance and reliability problems encountered in Redis master‑slave deployments—such as AOF and SAVE settings, key count mismatches, client output buffer limits, replication backlog size, and node liveness checks—and provides concrete configuration commands and tuning strategies to improve stability, especially in cloud environments.
This article explains how Oracle's bind variable peeking influences execution plan selection, demonstrates the performance differences between full table scans and index scans with skewed data distributions, and provides practical methods—including 10046 events, v$sql_bind_capture, and AWR views—to capture and analyze bind variable values for better SQL tuning.
This article explains what constitutes a database performance problem, outlines essential metrics such as response time, throughput, baseline and bottleneck identification, and presents practical tuning methods—including workload reduction, resource configuration, and optimization balance—to help engineers systematically improve database efficiency.
This article walks through a detailed Hadoop performance tuning case using the TPCx-HS benchmark, explaining the bottlenecks in TeraGen and TeraSort, the optimization strategies applied, hardware considerations, and the resulting improvements in CPU and network utilization.
The article explains why a seemingly harmless delete statement in an Oracle RAC+DG environment can become extremely slow, covering bind variable peek, execution‑plan changes, histogram effects, AWR/ASH analysis, rolling invalidation, and clustering factor, and shows how to diagnose and mitigate each issue.
This article explains how to identify and analyze MySQL slow queries using tools like SHOW VARIABLES, EXPLAIN, mysqldumpslow, and profiling, and provides practical recommendations for configuring server variables, optimizing indexes, and improving query performance through systematic testing and best‑practice SQL design.
This article explains essential Spark concepts, illustrates common performance bottlenecks, and provides concrete tuning strategies for memory, CPU, serialization, data locality, file I/O, and shuffle reduction, backed by real‑world examples and visual metrics.
This article explains what MariaDB Spider is, its role as a pluggable storage engine for MySQL/MariaDB sharding, demonstrates vertical and horizontal partitioning with real‑world Tencent game case, provides installation steps, configuration commands, performance considerations, tunable parameters, and load‑balancing architecture.
A colleague handed over a seemingly simple SQL Server query that ran in five seconds for early dates but never returned for later dates, and the article walks through the diagnosis, execution‑plan analysis, statistics update, and final fix that restored performance.
This article shares practical experiences and step‑by‑step guidance for migrating databases from Oracle to MySQL, covering pre‑migration preparation, target selection, data‑object migration tools such as SQL LOAD, Python scripts, Oracle GoldenGate, MySQL Migration Toolkit and Kettle, handling of views, triggers, stored procedures, data validation techniques, and key MySQL performance parameters.
The article reviews real‑world performance‑tuning practices from Netflix, RisingStack, and nearForm, explaining how they diagnose slow responses, CPU spikes, cryptographic overhead, and event‑loop blocking using techniques such as flame‑graph analysis, crypto profiling, and avoiding costly JSON.stringify operations.
This article explains Oracle's dynamic sampling feature, outlines typical scenarios such as temporary tables and complex filters, presents real CRM case studies with misestimated cardinalities, demonstrates step‑by‑step solutions using DBMS_STATS commands, and shows how 12c enhancements dramatically improve estimation accuracy for complex queries.
This article explains why frequent small commits are slower than a single bulk SQL operation, demonstrates how to rewrite PL/SQL procedures to use bulk inserts, adjust UNDO settings, and enable parallel execution, achieving up to six‑fold performance gains on large data loads.
This article outlines the five evolutionary stages of a MySQL deployment—table design, deployment, performance tuning, architecture scaling, and maintenance—detailing practical configuration tweaks, kernel and hardware optimizations, replication strategies, caching, sharding, partitioning, and essential monitoring and backup techniques.
This article shares actionable ETL tuning strategies for Inceptor, including avoiding unnecessary transaction tables, shortening transaction windows, analyzing the most impactful cases first, and iteratively refining steps until optimal performance is achieved.
This article explains SQL Server's logical and physical query execution order, offers practical techniques such as converting loops to batch operations, using temporary tables, optimizing indexes, and avoiding common pitfalls, all illustrated with code samples and performance measurements.
This article provides a step‑by‑step tutorial on preparing the environment, configuring the query analyzer, using SQL Server Profiler, and applying common commands such as clearing caches and enabling statistics to identify and resolve performance bottlenecks in SQL Server queries.
Facing a tight September release deadline, the author tackled two critical Oracle SQL performance issues by analyzing execution plans, replacing a costly view, applying driver‑table hints, removing unnecessary CASTs and a random‑number function, ultimately cutting query time from 11 seconds to under 2 seconds.
This guide walks through identifying the root cause of a SQL Server instance's 100% CPU usage using Profiler, analyzing the offending queries, and applying targeted index and query optimizations to dramatically reduce CPU consumption.
The article recounts a real‑world Oracle SQL case where a REGEXP_SUBSTR‑based IN subquery caused the optimizer to abandon an index range scan, explores why predicate pushdown fails with CONNECT BY recursion, documents attempted hints, rewrites, and the ultimate lessons for SQL performance tuning.
