This article explains why deep pagination in Elasticsearch leads to high CPU usage, introduces core concepts such as indices, shards, nodes, and documents, and details the distributed CRUD operations, query and fetch phases, and segment management that affect search performance.
iQIYI Douya’s emoji search evolved from ElasticSearch to a pure Lucene implementation and added semantic vector retrieval, enabling fast, scalable, and more accurate text‑based search of AI‑generated images for small‑to‑medium businesses by combining custom tokenization, dense embeddings, and hybrid ranking.
After setting up an ES cluster, this guide shows how to use cURL to inspect node and master status, query cluster health, perform CRUD operations, manage indices, execute bulk actions, and run multi‑search queries, complete with example commands for both Kibana and Bash.
This tutorial walks backend engineers through real‑world scenarios of log collection, parsing, and analysis using the ELK stack—Logstash, Elasticsearch, and Kibana—showing configuration examples, Grok patterns, RESTful API queries, aggregations, and visualizations to boost operational efficiency.
The article explains the concept of hot and cold data, why separating them reduces cost, and presents heterogeneous and homogeneous architectural solutions—including Elasticsearch, HBase, AWS S3, and cloud‑based UltraWarm—illustrated with network‑behavior and e‑commerce order system case studies.
The article explains how to design a time‑based Elasticsearch cluster using a hot‑warm architecture with dedicated master, hot, and warm nodes, shows how to configure node attributes, allocate indices via settings or Curator, and discusses best‑practice compression and rollover strategies for large‑scale log data.
Learn how to configure Kibana index patterns, set up Elasticsearch Index Lifecycle Management policies across hot, warm, cold, and delete phases, create index templates, and monitor lifecycle status to optimize performance and storage in your ELK stack.
This article provides a comprehensive, ordered procedure for upgrading a Zabbix 4.0 monitoring system to version 5.0 using blue‑green deployment, backing up configurations, updating repositories, applying MySQL patches, and then migrating the associated Elasticsearch cluster from 6.1 to 7.x, including all necessary command‑line steps, configuration edits, and post‑upgrade validation.
This article provides a comprehensive overview of Elasticsearch architecture and presents practical index and search optimization techniques, configuration recommendations, stress‑testing methods, and monitoring tools to improve cluster performance and reliability.
This article walks through a practical, step‑by‑step migration of an Elasticsearch cluster from a public‑cloud environment to a self‑hosted data‑center, covering strategy, configuration changes, node role separation, manual data transfer, and post‑migration re‑enabling of automatic balancing to ensure a smooth, low‑impact transition.
This article reviews how leading Chinese internet companies such as JD.com, Ctrip, Qunar, 58.com, and Didi have adopted Elasticsearch for large‑scale order search, log analysis, real‑time monitoring, and security, describing the evolution of cluster architectures, shard strategies, multi‑cluster pipelines, and performance optimizations.
This article provides a detailed examination of Elasticsearch's distributed consistency mechanisms, covering the shard write path, the PacificA replication algorithm, the role of SequenceNumber and Checkpoint, and a comparison of ES's implementation with the original algorithm, based on version 6.2.
The article explains how to build a unified user‑behavior data collection platform that standardizes event definitions, front‑end reporting, and back‑end storage using Kafka pipelines and Elasticsearch, enabling comprehensive analysis of user interactions across Android, iOS, and web clients.
This article details the design and implementation of a unified log platform that handles peak write rates of up to one million events per second, balances performance, stability, and cost, and leverages Filebeat, Kafka, Flink, and Elasticsearch across multi‑cloud environments.
This article explains how Elasticsearch uses Analyzer components—character filters, tokenizers, and token filters—to perform text analysis, reviews the built‑in analyzers such as standard, simple, stop, whitespace, keyword, pattern, language, ICU and IK, and provides practical _analyze API examples with code snippets and result screenshots.
This article explains what a search engine is, describes its core components—indexing and search modules—detailing the workflow from content acquisition to result rendering, and provides an in‑depth overview of Elasticsearch, including its architecture, clusters, shards, replicas, mappings, and basic configuration.
This guide explains how to install the Elasticsearch Python client, build a wrapper class for index management and CRUD operations, import data from MongoDB, use a Celery‑based crawler to harvest Baidu Baike content, and expose search functionality through Flask or other Python web frameworks.
This guide explains how to gather Nginx access and error logs using Filebeat, Logstash, and rsyslog, and forward them to an Elasticsearch cluster for analysis, covering three practical pipelines with configuration examples and deployment steps.
This article explains why Elasticsearch cross-index queries are essential, outlines their technical principles, showcases classic use cases such as business analytics, big‑data pipelines and log management, and provides practical methods, code examples, and performance considerations for effective implementation.
This article examines Elasticsearch query performance by explaining Lucene's underlying data structures, describing how composite queries are merged, and presenting benchmark numbers for various query types such as term, range, and combined queries, highlighting optimization techniques and practical conclusions.
This article summarizes the design, architecture, and key modules of a company-wide data lake platform—named “Tianchi”—built on HBase, Kafka, and Elasticsearch, detailing data ingestion, strategy output, metadata management, indexing, monitoring, and offline analysis, and shares lessons learned and future plans.
This article explains the differences between Master‑Slave and P2P ring architectures, introduces Elasticsearch’s core concepts, internal components, master election, shard routing, indexing and search processes, and discusses how the system avoids split‑brain scenarios and ensures high availability.
This article walks through using Elasticsearch SQL to query data, covering installation, loading sample datasets, describing index schemas, executing simple and complex SQL queries with functions, converting SQL to Elasticsearch DSL, reindexing, alias management, and performance considerations, all illustrated with code snippets.
This article provides a comprehensive overview of Elasticsearch's internal architecture, explaining how data flows from memory buffers to Lucene segments, the role of refresh and translog for durability, segment merging strategies, shard routing, replica consistency, allocation controls, hot‑cold data separation, and cluster discovery settings.
This article details Tencent's large‑scale Elasticsearch deployment, covering its massive usage scenarios, the availability, performance, cost and scalability challenges faced, and the comprehensive kernel‑level optimizations—including memory‑based throttling, storage‑model merging, off‑heap caching, rollup and metadata improvements—that enable PB‑level clusters with high reliability and low expense.
This article details how JD.com’s order center evolved its Elasticsearch deployment from a single, default‑configured cluster to a real‑time, dual‑cluster architecture with replica tuning, master‑slave failover, version upgrades, and optimized data synchronization to handle billions of documents and hundreds of millions of daily queries.
The article surveys how leading Chinese tech firms such as JD Daojia, Ctrip, Qunar, 58.com, and Didi have adopted Elasticsearch for large‑scale search, real‑time analytics, and security, detailing their evolving cluster architectures, shard strategies, data volumes, and supporting services.
This article provides a comprehensive comparison of Elasticsearch with its major competing technologies—including Lucene, Solr, relational databases, OpenTSDB, HBase, MongoDB, ClickHouse, and Druid—highlighting each product’s strengths, weaknesses, and suitable application scenarios, and concluding that Elasticsearch generally outperforms alternatives in search and many data use cases.
This article offers a detailed, experience‑driven comparison of Elasticsearch against its main rivals—Lucene, Solr, relational databases, OpenTSDB, HBase, MongoDB, ClickHouse, and Druid—highlighting where Elasticsearch excels, where it falls short, and practical guidance for choosing the right data solution.
This article describes the background, requirement‑driven prototype design, Elasticsearch‑based query‑DSL selection, and extensive performance tuning—including hardware configuration, indexing parameters, JVM and garbage‑collector adjustments—that enabled real‑time ingestion of hundreds of thousands of records and sub‑second search responses for a city‑wide data platform.
This guide walks you through installing Elastic Workplace Search and Elastic Enterprise Search on a CentOS 8 VM using Vagrant, configuring data sources such as GitHub and Atlassian products, setting up user groups and weights, and verifying search results, all within a day.
This guide walks you through configuring Kibana visualizations, customizing Nginx and Apache logs in JSON, setting up Logstash pipelines, integrating Redis as a buffering queue, and deploying a production-ready ELK stack for efficient log management and analysis.
This article provides a detailed comparison of Solr 7.3 and Elasticsearch 7.4, covering their shared Lucene foundation, distributed architecture, feature sets, configuration approaches, storage options, shard management, and ecosystem differences to help readers choose the right search platform for their needs.
This comprehensive tutorial walks you through installing Java, Elasticsearch, Logstash, Kibana, and related tools on two CentOS 7 servers, configuring cluster settings, verifying health, and visualizing logs with Kibana, complete with command‑line examples and troubleshooting tips.
This article explains the motivations for moving a high‑volume operation‑log system from MongoDB to Elasticsearch, outlines the existing architecture, details capacity planning, index design, and a step‑by‑step migration process using Kafka, DataX, and Spring Boot, and shares the performance gains and lessons learned.
This comprehensive guide introduces Elasticsearch fundamentals, its features and use cases, then walks through integrating it with SpringBoot, configuring Maven dependencies, performing index and document operations, and demonstrates a variety of query types and aggregations using both RESTful APIs and Java code examples.
Learn step‑by‑step how to provision an Elasticsearch server with Vagrant, install Elastic App Search, configure credentials, index a large video‑games dataset via API, customize schema, create synonyms and boosts, and deploy a React‑based search UI to Nginx, all with detailed commands and code snippets.
This guide shows how to set up Elastic Stack (Elasticsearch, Logstash, Kibana) on a Vagrant CentOS‑8 VM, import COVID‑19 data from DXY and WHO sources, customize mappings, enrich fields with painless scripts, and create interactive visualizations and a dashboard to monitor the pandemic in real time.
Over seven months, Didi’s Elasticsearch team upgraded more than 30 clusters, 2,000 nodes and 4 PB of data from version 2.3.3 to 6.6.1, overcoming protocol and mapping incompatibilities with a multi‑version Arius Gateway, custom Java SDK, ECM and AMS, while saving 1 PB of storage, decommissioning 400 machines, boosting query speed by 40 %, write throughput by 30 % and cutting CPU use 10 % for an estimated 80 w/month cost reduction.
This article surveys how major Chinese tech firms such as JD.com, Ctrip, Qunar, 58.com and Didi design, scale, and operate massive Elasticsearch clusters for search, real‑time analytics, and security, detailing architecture choices, shard strategies, data pipelines and performance optimizations.
This step‑by‑step guide shows how to provision a single‑node Elastic Stack with TLS‑enabled authentication, configure Kibana and Filebeat modules, and verify distributed log collection in a Vagrant‑based CentOS 8 environment.
This article introduces Elasticsearch by explaining its core concepts such as indices, types, documents, mappings, and Query DSL, demonstrates common use cases, and provides step‑by‑step instructions for creating, updating, viewing, and deleting indices and documents using RESTful APIs, curl commands, and Docker‑compose deployment.
Facing limited storage of Zabbix historical data in Elasticsearch, the article outlines a comprehensive strategy—expanding nodes, adding SSDs, redesigning index mapping, using hot‑cold node tiers, employing Curator for automated shrink, segment merging, and lifecycle management—to retain up to a year of monitoring data efficiently.
Leveraging Tencent Cloud Elasticsearch, the nationwide COVID‑19 health code platform handled over 1.6 billion scans for more than 900 million users, achieving millisecond‑level search, seamless horizontal scaling, multi‑zone high availability, and robust security, while simplifying development through RESTful APIs and rich UI tools.
This article curates the ten most starred Java‑related GitHub repositories, summarizing their key features—from interview prep and design patterns to SpringBoot, Elasticsearch, and advanced concurrency—providing developers with essential open‑source resources and highlighting the impact of projects like Dubbo.
This article outlines a step‑by‑step procedure for safely taking an Elasticsearch node offline—checking master‑eligible settings, adjusting minimum_master_nodes, excluding the node from routing, waiting for shard relocation, stopping the service, and restoring the cluster routing—ensuring no data loss or service interruption.
The article describes how Dada rebuilt its log processing pipeline using Kubernetes mixed deployment, Filebeat for automated collection, Storm for efficient parsing, and Elasticsearch cold/hot nodes to handle over 130 billion daily log entries and 300TB storage.
This article explains how a large e‑commerce platform replaced a MySQL‑centric intermediate table with a binlog‑driven pipeline that streams changes through Kafka into Elasticsearch, ensuring ordered, complete, and low‑latency data synchronization while addressing schema evolution and operational monitoring.
This article details how JD.com’s order center migrated its Elasticsearch cluster through multiple architectural stages—initial deployment, isolation, replica tuning, master‑slave adjustments, and real‑time dual‑cluster backup—while addressing data synchronization, scaling, and performance pitfalls to achieve high availability and query stability.
This article examines how Elasticsearch implements write operations on top of Lucene, detailing the challenges of Lucene's write path and describing Elasticsearch's distributed design, near‑real‑time refresh, translog reliability, shard replication, partial updates, and the complete write workflow from coordinating node to primary and replica shards.
This guide walks through setting up ELK to collect, parse, and visualize Nginx access logs, covering Logstash configuration, Grok patterns, GeoIP enrichment, Elasticsearch indexing, and Nginx proxy with basic authentication, enabling real-time log analysis and dashboard creation.
Tencent leverages Elasticsearch at massive scale across log analytics, search services, and time‑series monitoring, addressing challenges of high availability, low cost, and high performance through kernel optimizations, resource‑aware throttling, cold‑data merging, rollup, caching, and open‑source contributions.
Tencent shares how it leverages Elasticsearch at trillion‑scale across logging, search, and time‑series workloads, detailing the challenges of high availability, low cost, and high performance, and describing concrete kernel‑level optimizations, resource‑limiting strategies, storage tiering, rollup, cache, and merge techniques that enable robust, efficient operation.
This article provides a comprehensive guide on designing Elasticsearch indices for massive data volumes, covering shard and replica sizing, mapping strategies, rollover templates, curator cleanup, tokenization choices, query type selection, and multi‑table association techniques to achieve efficient, reliable search at PB‑scale.
This article details Ctrip's migration of large‑scale log processing from Elasticsearch to ClickHouse, explaining why ClickHouse was chosen, the high‑availability deployment architecture, data ingestion strategies, dashboard integration, performance gains, operational practices, and overall cost and reliability improvements.
This article details how Tencent leverages Elasticsearch for log analysis, search services, and time‑series data, outlines the specific challenges faced in high‑availability and cost‑efficiency, and presents the comprehensive optimization techniques and future open‑source contributions that improve performance, scalability, and reliability.
This guide introduces Elasticsearch fundamentals—including its core concepts, main features, and essential terminology—followed by step‑by‑step Docker setup and practical API commands for creating, indexing, searching, and managing documents in a single‑node cluster.
This article presents a comprehensive real‑time and offline integrated solution for a channel analysis system, detailing challenges, architecture, implementation using Flink, Spark Streaming, Kafka, Elasticsearch, and HIVE, and demonstrating minute‑level latency and high accuracy through performance evaluations.
This article compares Elasticsearch and Solr, examining their history, community, licensing, core technologies, APIs, scalability, vendor support, ecosystem, performance, management tools, and visualization options to help organizations decide which open‑source search engine best fits their big‑data and search requirements.
This article introduces Elasticsearch as a distributed search and analytics engine, outlines its common application scenarios, provides step‑by‑step installation commands, explains core concepts such as documents and indices, and demonstrates basic indexing, retrieval, bulk processing, and aggregation operations.
This article explains the architecture of Elasticsearch and Lucene, outlines common performance bottlenecks, and provides concrete indexing and query optimization techniques—including shard routing, refresh intervals, doc values, and hardware considerations—to achieve sub‑second query responses on billions of records.
Elasticsearch is a distributed, open‑source search and analytics engine built on Apache Lucene, offering a simple REST API, scalability, and a rich ecosystem—including Kibana, Beats, and Logstash—that enables storage, querying, and aggregation of text, numeric, and geospatial data for diverse use cases.
This guide explains how to enable and configure the free core security features of Elastic Stack 6.8/7.1, including TLS encryption, user and role management, Kibana Spaces for multi‑tenant protection, and step‑by‑step setup of certificates, keystore passwords, and Logstash‑to‑Elasticsearch authentication across multiple nodes.
This article explains the architecture of Elasticsearch and Lucene, outlines common performance bottlenecks, and provides concrete indexing and search optimization techniques—including bulk writes, shard routing, doc values tuning, and pagination strategies—to achieve sub‑second query responses on billions of records.
The article explains three approaches to ensuring data consistency in big‑data systems—basic multi‑node replication, HDFS pipeline replication, and Elasticsearch primary‑replica replication—detailing their workflows, advantages, and drawbacks.
A massive Elasticsearch data breach revealed over 2.7 billion email addresses, 1 billion passwords, and hundreds of thousands of personal documents, highlighting how misconfigured cloud storage on AWS S3 can lead to large‑scale exposure of sensitive information and underscore the need for robust cloud security practices.
A recent ElasticSearch breach exposed 2.7 billion email addresses, one‑billion plain‑text passwords and hundreds of thousands of birth‑certificate copies, highlighting persistent security gaps in cloud‑based search services despite growing corporate safeguards.
An in‑depth guide walks through Elasticsearch’s underlying Lucene architecture, explains shard routing and DocValues, then presents concrete index‑ and search‑performance tweaks—bulk writes, refresh intervals, memory allocation, SSD usage, field mapping, pagination strategies—and shows benchmark results that reduce query latency to seconds for billions of records.
The article explains how Elasticsearch’s use of Lucene’s flush mechanism, concurrent shard writes, and IndexWriter buffering lead to an excess of small segments, outlines the flush conditions, and offers guidance on managing write concurrency for better performance.
This step‑by‑step guide shows how to install JDK, set up Elasticsearch via Homebrew, verify its operation, optionally adjust the cluster name, install Kibana, and access the Kibana web UI for managing Elasticsearch data.
This article presents a comprehensive guide to optimizing Elasticsearch for massive data volumes, covering Lucene fundamentals, index and shard design, practical performance‑tuning techniques, and real‑world testing results that enable cross‑month queries and sub‑second response times.
The Elastic Shenzhen Meetup on November 16, 2019 gathered over 200 tech enthusiasts to hear in‑depth talks on Elasticsearch deployments, K8S integration, DB‑to‑ES synchronization, and performance optimizations from experts at ByteDance, Tencent, Alibaba Cloud, and Gitee, followed by interactive Q&A and a lively prize draw.
This article describes the challenges of handling billions of travel records in Qunar's MySQL databases, compares open‑source data sync solutions like Databus and Canal, outlines the legacy system’s issues, and presents a refactored architecture that introduces Otter, ES gateway, and improved aggregation to achieve low‑latency, reliable, and scalable data synchronization.
In an interview, Gitee’s chief architect Chen Xin explains why Elasticsearch was chosen for code search, outlines how combining search with NLP can both aid semantic understanding and enrich repository queries, and shares his views on the platform’s fast‑evolving ecosystem and upcoming community meetup.
At the Shenzhen Elastic Community meetup, Alibaba search expert Ouyang Chucai detailed how Alibaba Cloud Elasticsearch tackles stability challenges through multi‑AZ deployment, high‑IOPS storage, offline indexing and health diagnostics, and shared high‑concurrency design guidelines such as SSD usage, JVM limits, node sizing, capacity planning, and future ecosystem expansion.
At a Shenzhen Elastic Community meetup, ByteDance senior engineer Huang Yangfeng detailed how his team built an Elasticsearch service platform on Kubernetes—addressing isolation and performance‑analysis challenges, outlining a roadmap for cross‑region recovery, monitoring, and automation, and sharing insights on ecosystem growth and community needs.
This article details how JD Daojia's order center migrated from MySQL to Elasticsearch, iteratively refined its ES cluster architecture across five stages, tackled scalability and reliability challenges, and implemented robust data synchronization and optimization techniques to support billions of documents and hundreds of millions of daily queries.
In an interview, Tencent Cloud senior engineer Chen Xi explains how Tencent optimizes Elasticsearch for massive log, monitoring, and document search workloads by prioritizing stability through kernel tweaks, boosting performance with scenario‑specific settings, cutting costs via redundant storage trimming and off‑heap indexing, and leveraging rich data‑pipeline components and robust distributed cluster management to lower operational overhead while anticipating future ecosystem growth and community collaboration.
This article details how JD Daojia's order center migrated its Elasticsearch cluster through multiple architectural stages—from an initial loosely configured setup to a real‑time dual‑cluster solution—addressing scalability, high availability, data synchronization, and performance optimization for billions of documents and hundreds of millions of daily queries.
This guide explains Elastic APM’s architecture—including agents, server, Elasticsearch, and Kibana—provides step‑by‑step instructions for deploying the APM server with Docker, configuring it, installing agents for various languages, and visualizing performance data in Kibana, enabling developers to monitor and troubleshoot application latency and errors.
This article explains how to build a real-time search engine indexing pipeline using Flink, covering background, batch versus incremental indexing strategies, a hybrid architecture that merges both approaches, and a concrete cloud‑based implementation involving MySQL binlog, Logtail, SLS, and Elasticsearch.
This article explains how to design and implement a real‑time data verification framework using Flink to generate wide tables, storing detailed records in Elasticsearch or HDFS with Hive for cross‑checking against offline data, ensuring trustworthy metrics for dashboards and stakeholders.
Learn how to integrate Spring Cloud Sleuth for request tracing, configure Zipkin to collect and visualize logs, and persist trace data to Elasticsearch, with detailed code snippets and step‑by‑step instructions for setting up services, running Zipkin, and analyzing trace information.
This article explains Elasticsearch X‑Pack's machine‑learning capabilities, covering supervised and unsupervised learning concepts, data preparation, task creation types, architecture components, data flow, result indices, and provides code examples for configuring and running ML jobs.
This article describes how 58 Group's DB department evaluated and adopted Elastic Stack to build a scalable, reliable centralized log processing system, detailing its components, four architectural patterns, and a MySQL audit log example for unified log collection, storage, analysis, and alerting.
This article provides a comprehensive guide to Elasticsearch’s architecture, node roles, data organization, and the most important performance metrics—including search, indexing, memory, JVM garbage collection, host‑level system metrics, cluster health, and resource saturation—offering practical advice on monitoring and tuning the cluster for reliability and efficiency.
This article details how JD.com’s order center migrated its Elasticsearch cluster from a basic, mixed‑node setup to a real‑time, dual‑cluster architecture with increased replicas, physical isolation, version upgrades, and a robust data‑sync strategy to handle billions of documents and hundreds of millions of daily queries.
This guide details the prerequisites, RPM-based installation, configuration files, and startup procedures for Elasticsearch, Logstash, and Kibana on a CentOS 7 virtual machine, including verification steps to confirm each component is running correctly.
iQIYI upgraded its big‑data stack by adopting Druid as the core engine for free‑time queries and ElasticSearch for pre‑computed fixed‑time queries, overcoming early API, security and scaling challenges through monthly segment granularity, parallel sub‑queries, Redis caching and failover, cutting typical query latency from over two seconds to about 150 ms and reaching 99.9 % service success.
Tencent engineers improved Elasticsearch’s high‑concurrency write path, reducing the time to load ten million records from eighteen to fifteen minutes—a 20 % speed boost—earning thanks from Elastic’s CEO and showcasing the company’s broader open‑source contributions and strategic cloud‑search partnership.
The article details Youku’s new media‑asset platform, which replaces a fragmented MySQL‑based system with a domain‑driven entity model stored in Ali‑HBase, leverages Elasticsearch for flexible front‑and back‑end search, and adds an aggregation layer that unifies diverse data sources and reusable computation tasks, delivering high‑availability, low‑latency service for billions of daily API calls.
JD Daojia’s order center faced massive query loads, prompting a shift from MySQL to Elasticsearch and a multi‑stage evolution of its ES cluster—from an initial loosely configured setup, through isolation, replica tuning, master‑slave adjustments, to a real‑time dual‑cluster architecture—enhancing stability, throughput, and scalability.
To meet growing e‑commerce search demands, the team replaced a MySQL‑based intermediate table with a real‑time binlog‑driven pipeline that streams changes through Kafka into Elasticsearch, detailing design choices, ordering and completeness guarantees, custom modules, and monitoring for sub‑second sync latency.
The cloud+ community and Kuaishou hosted a big‑data technology salon where experts detailed the evolution, architecture, and practical deployments of Spark‑based cloud data warehouses, ElasticSearch, Yarn, and Flink, highlighting trends, optimization techniques, and future directions for enterprise data analytics.
This guide explains how to improve Elasticsearch indexing speed, search speed, and overall cluster performance by adjusting bulk request sizes, refresh intervals, replica settings, hardware resources, mapping choices, caching strategies, and query preferences, while also offering general best‑practice recommendations.
When retrieving large result sets from Elasticsearch, the traditional from+size method hits a 10,000 record limit and can destabilize the cluster, so Elasticsearch offers three deep‑pagination techniques—scroll, sliced scroll, and search_after—to efficiently fetch massive data batches.
This article explains Elasticsearch's cluster architecture, including nodes, indices, shards, replicas, deployment models, and data layer storage, and compares two types of distributed data system designs—local file‑system based and shared‑storage based—highlighting their advantages and trade‑offs.
This article details JD.com’s order center journey from a simple Elasticsearch deployment to a highly available, dual‑cluster architecture, covering isolation, replica tuning, hot‑cold data separation, version upgrades, and practical lessons on pagination, field data, and doc values.
This article explains the principles of full‑text search, contrasts structured and unstructured data retrieval methods, introduces Lucene, Solr, and Elasticsearch, and provides a detailed comparison of their features, community support, maturity, and documentation to help developers choose the right search engine for their projects.
This article provides a comprehensive overview of Elasticsearch, covering its role in handling structured and unstructured data, core concepts such as Lucene, inverted indexes, clusters, shards, replicas, mapping, indexing processes, storage mechanisms, and practical performance tuning tips for deployment.
