The article recounts a production incident where a GC alert triggered due to excessive object creation in a scheduled ad‑transaction sync task, and explains how the problem was diagnosed, mitigated by task splitting, and finally resolved through data sharding across multiple machines.
This article lists the most frequent Java OutOfMemoryError cases—including Java heap space, GC overhead limit exceeded, PermGen/Metaspace, native thread creation failures, array size limits, and direct buffer memory—explains their root causes, and provides practical troubleshooting steps and JVM tuning parameters to resolve them.
This article presents practical MySQL performance tricks—including smarter LIMIT usage, avoiding implicit type conversion, rewriting UPDATE/DELETE with JOIN, handling mixed ordering, replacing EXISTS with JOIN, pushing down conditions, narrowing result sets early, and leveraging WITH clauses—to dramatically reduce query execution times.
The author describes a sudden 30,000‑second slowdown in a simple MySQL join query, details systematic diagnostics—including network, server load, and query testing—and ultimately discovers a missing index on the user_info table, restoring it to bring response times back to sub‑second levels.
This article explains how to assess and improve system I/O performance by defining I/O models, selecting appropriate evaluation tools for disk and network, monitoring key metrics such as IOPS, bandwidth and latency, and applying host, network, and storage‑side optimization techniques for high‑throughput and low‑latency workloads.
This article explains the G1 (Garbage‑First) garbage collector introduced in JDK7, its region‑based architecture, key differences from CMS, and the complete GC cycle—including Young GC, concurrent phases, mixed GC, and Full GC—while providing real‑world log examples and performance insights for Java developers.
This article explains how to use MySQL's EXPLAIN and EXPLAIN EXTENDED commands to analyze query execution plans, interpret each column such as id, select_type, type, and extra, and apply practical optimization techniques like indexing, avoiding full‑table scans, and forcing index usage to improve SQL performance.
This article provides a comprehensive, step‑by‑step guide to MySQL performance tuning, covering the underlying query process, optimization philosophy, practical tools, system‑level diagnostics, hardware and OS recommendations, as well as detailed configuration parameters for both the server and storage engine.
This article provides a comprehensive guide to improving Hive query performance by covering column and partition pruning, predicate pushdown, replacing ORDER BY with SORT BY, using GROUP BY instead of DISTINCT, fine‑tuning join operations, and optimizing MapReduce parameters such as mapper/reducer counts, file merging, compression, JVM reuse, parallel execution, strict mode, and storage formats.
This article presents a comprehensive guide to Java application performance optimization, covering OS‑level and JVM‑level diagnostics, profiling tools, GC analysis, JVM tuning, application‑code refactoring, and database‑layer adjustments, illustrated with real‑world case studies and code examples.
This article presents twelve practical SQL optimization techniques—from using proper comparison operators and LIMIT clauses to indexing strategies and query consistency—to dramatically improve database query speed and overall application performance.
This article examines frequent MySQL performance problems such as inefficient LIMIT pagination, implicit type conversion, sub‑query updates, mixed ordering, EXISTS usage, condition push‑down limits, early result set reduction, and intermediate result set optimization, providing rewritten SQL examples that dramatically improve execution speed.
Meltwater details how it processes millions of daily media posts using a custom‑tuned Elasticsearch 1.7.6 cluster of over 400 nodes on AWS, covering data volume, query complexity, node configuration, indexing strategy, performance optimizations, and lessons learned for large‑scale search deployments.
This article compiles practical ELK knowledge, covering character‑set conversion, removing unwanted log lines, Grok pattern handling for multi‑line logs, multiline plugin usage in Filebeat and Logstash, date filtering, log type classification, performance optimization, Redis buffering, and Elasticsearch node tuning.
This article provides a comprehensive overview of the Java Virtual Machine, detailing its main components, runtime data areas, class loading mechanism, garbage collection algorithms and collectors, heap vs stack differences, object reclamation criteria, and common JVM tuning tools and parameters.
This guide outlines practical Linux operations standards, covering safe testing, backup habits, multi‑user coordination, security hardening, continuous monitoring, performance tuning methods, and the right mindset to avoid costly mistakes in production environments.
This article explains MySQL's logical architecture, query processing steps, and the inner workings of its optimizer, cache, and execution engine, then offers practical performance‑tuning advice on schema design, index creation, and specific query patterns to help developers make informed optimization decisions.
This guide details comprehensive Nginx performance optimization, covering worker process sizing, CPU affinity, file descriptor limits, event model settings, HTTP directives, fastcgi parameters, gzip compression, expires caching, anti‑hotlinking, and essential Linux kernel tweaks for high‑traffic servers.
The NetEase News team improved video playback by analyzing the production and playback pipelines, prioritizing reading and buffering, and applying client‑side techniques such as preloading, fine‑grained caching and adaptive buffer control, selecting ExoPlayer for Android, optimizing file layout and CDN strategies, and using data‑driven measurements, ultimately reaching a 94% instant‑play rate with minimal stutter and failure.
When a critical Oracle 10g database on AIX spikes to 100% CPU, this detailed case study walks through emergency actions, AWR analysis, hidden bottlenecks, and practical tuning steps that reduced CPU usage by half and restored system stability.
YouZan’s experience with Flume shows how the at‑least‑once delivery model, combined with FileChannel storage and custom extensions such as an NsqSource, hourly‑based HdfsEventSink, metric reporting server, and timestamp interceptor, can reliably move MySQL binlog data to HDFS, while tuning transaction batch size and channel capacity boosts throughput and stability, paving the way for a unified management platform.
A SAP month‑end CO module query suffered severe slowdown despite standard index hints, prompting a deep dive into execution plans, statistics, and a little‑known num_index_keys hint that dramatically improves IN‑list iterator access and reduces filter overhead.
This article explains the essential concepts, philosophy, participants, dimensions, tools, and step‑by‑step procedures for safely and effectively optimizing MySQL databases, covering hardware, system, application, and MySQL‑level tuning together with practical command‑line examples.
This article explains MySQL's logical architecture, query processing steps, and the inner workings of the optimizer, then provides practical performance‑tuning advice covering data types, index design, cache usage, and specific query patterns such as COUNT, JOIN, LIMIT pagination and UNION.
An in‑depth guide to MySQL optimization covering the query execution process, risk‑aware philosophy, hardware and system tuning, essential diagnostic tools, practical SQL and index adjustments, and comprehensive configuration settings to boost performance while maintaining stability and security.
This article explains how Spark's memory management models and configuration parameters can be tuned to handle massive billing data efficiently, covering StaticMemoryManager vs UnifiedMemoryManager, storage and shuffle memory fractions, common OOM and file‑not‑found issues, and practical performance‑optimisation tips.
This article outlines the philosophy, risks, participants, and step‑by‑step strategies for MySQL optimization, covering query flow, hardware and system tuning, configuration parameters, monitoring tools, and SQL‑level improvements to achieve stable, high‑performance database operations.
This article explains the fundamentals of relational optimization, the role of the optimizer, how CPU and I/O costs are estimated, and why accurate database statistics are essential for effective SQL performance tuning.
Meltwater’s media‑monitoring platform runs a custom Elasticsearch 1.7.6 cluster of over 400 nodes on AWS, handling 200 TB of primary data and 3 million daily documents while serving thousands of complex queries per minute, achieved through careful shard design, master‑node configuration, extensive performance tuning, and automated provisioning.
This comprehensive guide explains the purpose, lifecycle, trade‑offs, common issues, and performance‑tuning strategies of Java's Concurrent Mark‑Sweep (CMS) garbage collector, helping developers understand when it runs and how to configure it for low‑latency applications.
To alleviate GPU scarcity, iQIYI’s cloud platform migrated deep‑learning inference to CPUs and applied system‑level (MKL‑DNN, OpenVINO), application‑level, and algorithm‑level optimizations—tuning threads, batch size, NUMA, pruning and quantization—delivering 1‑9× speedups across thousands of cores while preserving latency and accuracy.
Meituan’s DSP system boosted high‑QPS ad serving performance by layering an LRU cache in front of Redis, then adding time‑based eviction, sharding the cache into HashLruCache instances to cut lock contention, and employing a zero‑copy, reference‑counted design, ultimately cutting average latency to about 20 % of the original and similarly reducing 99.9th‑percentile delays.
This article explains the origins, design goals, and detailed mechanisms of Java's G1 garbage collector—including regions, CSet, RSet, SATB, the full collection cycle, common pitfalls, and practical tuning recommendations—to help developers achieve predictable pause times and high throughput.
This comprehensive guide explains Redis fundamentals, its rich data structures and common commands, then dives into performance optimization, replication, and clustering, offering practical examples and best‑practice recommendations for building reliable, high‑performance Redis deployments.
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.
