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Elasticsearch

1257 articles · Page 11 of 13
Architect
Architect
May 15, 2020 · Databases

Understanding Elasticsearch Architecture: Segments, Translog, Refresh, Shard Allocation and Cluster Operations

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.

Cluster ManagementElasticsearchIndexing
0 likes · 23 min read
Understanding Elasticsearch Architecture: Segments, Translog, Refresh, Shard Allocation and Cluster Operations
Tencent Tech
Tencent Tech
May 11, 2020 · Big Data

How Tencent Scaled Elasticsearch to Thousands of Nodes: Core Kernel Optimizations Revealed

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.

Big DataElasticsearchdistributed systems
0 likes · 27 min read
How Tencent Scaled Elasticsearch to Thousands of Nodes: Core Kernel Optimizations Revealed
ITPUB
ITPUB
May 11, 2020 · Operations

Scaling JD.com Order Search: Real‑Time Dual‑Cluster Elasticsearch Architecture

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.

Cluster ArchitectureData synchronizationElasticsearch
0 likes · 13 min read
Scaling JD.com Order Search: Real‑Time Dual‑Cluster Elasticsearch Architecture
Java Captain
Java Captain
May 8, 2020 · Big Data

Elasticsearch Adoption and Architecture Cases in Major Chinese Companies

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.

Big DataElasticsearcharchitecture
0 likes · 11 min read
Elasticsearch Adoption and Architecture Cases in Major Chinese Companies
Big Data Technology Architecture
Big Data Technology Architecture
May 8, 2020 · Big Data

Comparative Analysis of Elasticsearch and Its Competing Products

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.

Data PlatformsElasticsearchproduct comparison
0 likes · 14 min read
Comparative Analysis of Elasticsearch and Its Competing Products
dbaplus Community
dbaplus Community
May 5, 2020 · Databases

Why Elasticsearch Beats Its Competitors: A Deep Technical Comparison

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.

ComparisonElasticsearchLucene
0 likes · 15 min read
Why Elasticsearch Beats Its Competitors: A Deep Technical Comparison
Zhengtong Technical Team
Zhengtong Technical Team
Apr 30, 2020 · Big Data

Design and Performance Optimization of an Intelligent Search System for City Operations Big Data Center

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.

Big DataElasticsearchIntelligent Search
0 likes · 12 min read
Design and Performance Optimization of an Intelligent Search System for City Operations Big Data Center
DevOps Coach
DevOps Coach
Apr 26, 2020 · Operations

Deploy Elastic Workplace Search with Vagrant: Step‑by‑Step Guide

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.

DevOpsElasticsearchVagrant
0 likes · 14 min read
Deploy Elastic Workplace Search with Vagrant: Step‑by‑Step Guide
Big Data Technology Architecture
Big Data Technology Architecture
Apr 20, 2020 · Big Data

Comparing Solr and Elasticsearch: Similarities, Differences, and Practical Insights

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.

ComparisonDistributed ArchitectureElasticsearch
0 likes · 16 min read
Comparing Solr and Elasticsearch: Similarities, Differences, and Practical Insights
dbaplus Community
dbaplus Community
Apr 12, 2020 · Databases

Why and How to Migrate from MongoDB to Elasticsearch: A Practical Guide

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.

Data MigrationDataXDatabase Architecture
0 likes · 14 min read
Why and How to Migrate from MongoDB to Elasticsearch: A Practical Guide
Programmer DD
Programmer DD
Apr 12, 2020 · Big Data

Master Elasticsearch: From Basics to SpringBoot Integration and Advanced Queries

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.

Big DataElasticsearchFull-text Search
0 likes · 46 min read
Master Elasticsearch: From Basics to SpringBoot Integration and Advanced Queries
DevOps Coach
DevOps Coach
Apr 11, 2020 · Backend Development

How to Quickly Set Up Elastic App Search with Vagrant and Deploy a Custom Search UI

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.

App SearchElasticsearchReAct
0 likes · 12 min read
How to Quickly Set Up Elastic App Search with Vagrant and Deploy a Custom Search UI
DevOps Coach
DevOps Coach
Apr 9, 2020 · Big Data

Build a Real‑Time COVID‑19 Dashboard with Elastic Stack and Kibana

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.

COVID-19Elastic StackElasticsearch
0 likes · 18 min read
Build a Real‑Time COVID‑19 Dashboard with Elastic Stack and Kibana
Didi Tech
Didi Tech
Mar 31, 2020 · Big Data

Elasticsearch Version Upgrade: Architecture, Challenges, and Performance Optimization at Didi

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.

Elasticsearcharchitectureperformance optimization
0 likes · 18 min read
Elasticsearch Version Upgrade: Architecture, Challenges, and Performance Optimization at Didi
Programmer DD
Programmer DD
Mar 27, 2020 · Big Data

How Leading Chinese Companies Scale Elasticsearch for Billions of Queries

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.

Big DataElasticsearchSearch Architecture
0 likes · 12 min read
How Leading Chinese Companies Scale Elasticsearch for Billions of Queries
360 Quality & Efficiency
360 Quality & Efficiency
Mar 24, 2020 · Databases

Introduction to Elasticsearch: Core Concepts, Use Cases, and Practical Operations

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.

CRUDDockerElasticsearch
0 likes · 5 min read
Introduction to Elasticsearch: Core Concepts, Use Cases, and Practical Operations
Ops Development Stories
Ops Development Stories
Mar 13, 2020 · Operations

How to Extend Zabbix Monitoring Data Retention in Elasticsearch for a Year

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.

CuratorElasticsearchHot/Cold Nodes
0 likes · 6 min read
How to Extend Zabbix Monitoring Data Retention in Elasticsearch for a Year
Tencent Tech
Tencent Tech
Mar 11, 2020 · Big Data

Scaling the Health Code: Tencent Cloud Elasticsearch at Billion-User Scale

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.

Big DataElasticsearchHealth Code
0 likes · 12 min read
Scaling the Health Code: Tencent Cloud Elasticsearch at Billion-User Scale
Programmer DD
Programmer DD
Mar 10, 2020 · Backend Development

Top 10 Must‑Star Java GitHub Projects Every Backend Engineer Should Know

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.

ElasticsearchGitHubJava
0 likes · 7 min read
Top 10 Must‑Star Java GitHub Projects Every Backend Engineer Should Know
Big Data Technology Architecture
Big Data Technology Architecture
Mar 7, 2020 · Operations

How to Perform a Graceful Shutdown of an Elasticsearch Node

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.

Cluster ManagementDevOpsElasticsearch
0 likes · 6 min read
How to Perform a Graceful Shutdown of an Elasticsearch Node
Mafengwo Technology
Mafengwo Technology
Feb 28, 2020 · Backend Development

How We Achieve Real‑Time MySQL‑to‑Elasticsearch Sync with Binlog and Kafka

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.

Data synchronizationElasticsearchGo
0 likes · 11 min read
How We Achieve Real‑Time MySQL‑to‑Elasticsearch Sync with Binlog and Kafka
Big Data Technology Architecture
Big Data Technology Architecture
Feb 24, 2020 · Operations

Evolution and Optimization of JD.com Order Center Elasticsearch Cluster Architecture

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.

Cluster ArchitectureData synchronizationElasticsearch
0 likes · 13 min read
Evolution and Optimization of JD.com Order Center Elasticsearch Cluster Architecture
Big Data Technology Architecture
Big Data Technology Architecture
Feb 21, 2020 · Databases

Analysis of Elasticsearch Write Operations and Underlying Mechanisms

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.

ElasticsearchLuceneShard
0 likes · 14 min read
Analysis of Elasticsearch Write Operations and Underlying Mechanisms
Efficient Ops
Efficient Ops
Feb 10, 2020 · Big Data

How Tencent Scales Elasticsearch for Massive Log, Search, and Time‑Series Workloads

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.

ElasticsearchLog AnalyticsSearch Optimization
0 likes · 20 min read
How Tencent Scales Elasticsearch for Massive Log, Search, and Time‑Series Workloads
dbaplus Community
dbaplus Community
Feb 5, 2020 · Databases

Tencent’s Secrets to Scaling Elasticsearch for Trillion‑Level Data

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.

ElasticsearchLarge‑Scale SearchTime‑Series Data
0 likes · 18 min read
Tencent’s Secrets to Scaling Elasticsearch for Trillion‑Level Data
Big Data Technology Architecture
Big Data Technology Architecture
Feb 5, 2020 · Big Data

Elasticsearch Index Design: Scaling to PB/TP Levels and Best Practices

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.

CuratorElasticsearchRollover
0 likes · 24 min read
Elasticsearch Index Design: Scaling to PB/TP Levels and Best Practices
Ctrip Technology
Ctrip Technology
Jan 22, 2020 · Databases

Migrating Log Processing from Elasticsearch to ClickHouse: Architecture, Deployment, Optimization, and Benefits

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.

ClickHouseDatabase MigrationElasticsearch
0 likes · 12 min read
Migrating Log Processing from Elasticsearch to ClickHouse: Architecture, Deployment, Optimization, and Benefits
Big Data Technology & Architecture
Big Data Technology & Architecture
Jan 19, 2020 · Big Data

Tencent's Elasticsearch Practices: Application Scenarios, Challenges, Optimizations, and Future Directions

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.

Big DataElasticsearchTencent
0 likes · 16 min read
Tencent's Elasticsearch Practices: Application Scenarios, Challenges, Optimizations, and Future Directions
Architects Research Society
Architects Research Society
Jan 16, 2020 · Big Data

Elasticsearch vs Solr: Choosing the Right Open‑Source Search Engine

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.

Big DataElasticsearchSolr
0 likes · 12 min read
Elasticsearch vs Solr: Choosing the Right Open‑Source Search Engine
DevOps Cloud Academy
DevOps Cloud Academy
Jan 2, 2020 · Big Data

Introduction, Use Cases, Installation, and Basic Operations of Elasticsearch

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.

ElasticsearchLog Analyticsbulk API
0 likes · 4 min read
Introduction, Use Cases, Installation, and Basic Operations of Elasticsearch
Architect's Tech Stack
Architect's Tech Stack
Dec 25, 2019 · Backend Development

Elasticsearch Optimization Practices for Large-Scale Data Platforms

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.

ElasticsearchIndexingLucene
0 likes · 12 min read
Elasticsearch Optimization Practices for Large-Scale Data Platforms
Ops Development Stories
Ops Development Stories
Dec 22, 2019 · Information Security

Secure Your ELK Stack Using Free X‑Pack: TLS, Auth, and RBAC

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.

ELKElasticsearchKibana
0 likes · 13 min read
Secure Your ELK Stack Using Free X‑Pack: TLS, Auth, and RBAC
macrozheng
macrozheng
Dec 20, 2019 · Big Data

How to Supercharge Elasticsearch for Billion‑Row Queries: Practical Optimization Guide

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.

Big DataElasticsearchLucene
0 likes · 14 min read
How to Supercharge Elasticsearch for Billion‑Row Queries: Practical Optimization Guide
21CTO
21CTO
Dec 13, 2019 · Information Security

What Happens When an Elasticsearch Cluster Exposes 2.7 Billion Emails?

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.

AWS S3Cloud securityElasticsearch
0 likes · 6 min read
What Happens When an Elasticsearch Cluster Exposes 2.7 Billion Emails?
ITPUB
ITPUB
Dec 13, 2019 · Information Security

Why ElasticSearch Data Breaches Keep Happening: 2.7 B Emails Exposed

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.

Elasticsearchcloud storagedata breach
0 likes · 4 min read
Why ElasticSearch Data Breaches Keep Happening: 2.7 B Emails Exposed
dbaplus Community
dbaplus Community
Dec 10, 2019 · Backend Development

How to Optimize Elasticsearch for Billions of Records: Practical Tuning Guide

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.

Big DataElasticsearchLucene
0 likes · 13 min read
How to Optimize Elasticsearch for Billions of Records: Practical Tuning Guide
DevOps Coach
DevOps Coach
Nov 26, 2019 · Backend Development

Why Elasticsearch Creates Too Many Segments and How Lucene Flush Works

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.

ElasticsearchIndexWriterLucene
0 likes · 10 min read
Why Elasticsearch Creates Too Many Segments and How Lucene Flush Works
Architecture Digest
Architecture Digest
Nov 22, 2019 · Big Data

Elasticsearch Optimization Practices for Large‑Scale Data Platforms

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.

Big DataElasticsearchLucene
0 likes · 14 min read
Elasticsearch Optimization Practices for Large‑Scale Data Platforms
vivo Internet Technology
vivo Internet Technology
Nov 19, 2019 · Industry Insights

Inside Elastic Shenzhen Meetup: Real‑World ES Practices from ByteDance, Tencent, Alibaba & More

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.

ElasticsearchKubernetesMeetup
0 likes · 7 min read
Inside Elastic Shenzhen Meetup: Real‑World ES Practices from ByteDance, Tencent, Alibaba & More
Qunar Tech Salon
Qunar Tech Salon
Nov 18, 2019 · Databases

Data Synchronization Architecture and Refactoring for Large-Scale Travel Data at Qunar

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.

Data synchronizationETLElasticsearch
0 likes · 19 min read
Data Synchronization Architecture and Refactoring for Large-Scale Travel Data at Qunar
vivo Internet Technology
vivo Internet Technology
Nov 12, 2019 · Artificial Intelligence

Elasticsearch Retrieval Optimization in Gitee: Interview with Chen Xin

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.

Code searchElasticsearchGitee
0 likes · 4 min read
Elasticsearch Retrieval Optimization in Gitee: Interview with Chen Xin
vivo Internet Technology
vivo Internet Technology
Nov 12, 2019 · Big Data

Alibaba Cloud Elasticsearch Optimization and Application Practices

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.

AICloud ComputingElasticsearch
0 likes · 6 min read
Alibaba Cloud Elasticsearch Optimization and Application Practices
vivo Internet Technology
vivo Internet Technology
Nov 12, 2019 · Cloud Native

Practices of Building an Elasticsearch Service Platform on Kubernetes at ByteDance

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.

CloudNativeElasticsearchKubernetes
0 likes · 4 min read
Practices of Building an Elasticsearch Service Platform on Kubernetes at ByteDance
Java Backend Technology
Java Backend Technology
Nov 12, 2019 · Backend Development

How JD.com Scaled Its Order System with Elasticsearch: A Journey Through Cluster Evolution

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.

Cluster ArchitectureData synchronizationElasticsearch
0 likes · 13 min read
How JD.com Scaled Its Order System with Elasticsearch: A Journey Through Cluster Evolution
vivo Internet Technology
vivo Internet Technology
Nov 6, 2019 · Big Data

Elasticsearch Optimization Practices at Tencent: An Interview with Tencent Cloud Engineer Chen Xi

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.

ElasticsearchPerformance TuningTencent Cloud
0 likes · 4 min read
Elasticsearch Optimization Practices at Tencent: An Interview with Tencent Cloud Engineer Chen Xi
Big Data Technology & Architecture
Big Data Technology & Architecture
Nov 2, 2019 · Big Data

Evolution of Elasticsearch Cluster Architecture for JD Daojia Order Center

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.

Big DataCluster ArchitectureData synchronization
0 likes · 12 min read
Evolution of Elasticsearch Cluster Architecture for JD Daojia Order Center
Programmer DD
Programmer DD
Oct 28, 2019 · Operations

How to Deploy and Use Elastic APM for Full-Stack Performance Monitoring

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.

DockerElasticsearchJava
0 likes · 7 min read
How to Deploy and Use Elastic APM for Full-Stack Performance Monitoring
Big Data Technology & Architecture
Big Data Technology & Architecture
Oct 22, 2019 · Big Data

Real-Time Data Verification: Building a Log Comparison Solution with Flink, Elasticsearch, and Hive

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.

Big DataData verificationElasticsearch
0 likes · 7 min read
Real-Time Data Verification: Building a Log Comparison Solution with Flink, Elasticsearch, and Hive
Tencent Database Technology
Tencent Database Technology
Sep 26, 2019 · Artificial Intelligence

Understanding X‑Pack Machine Learning in Elasticsearch: Features, Architecture, and Implementation

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.

ElasticsearchX-Packanomaly detection
0 likes · 16 min read
Understanding X‑Pack Machine Learning in Elasticsearch: Features, Architecture, and Implementation
Big Data Technology Architecture
Big Data Technology Architecture
Sep 26, 2019 · Databases

Elasticsearch Core Overview and Key Performance Metrics

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.

Cluster ManagementElasticsearchIndexing
0 likes · 27 min read
Elasticsearch Core Overview and Key Performance Metrics
Big Data Technology Architecture
Big Data Technology Architecture
Sep 16, 2019 · Operations

Evolution of the Elasticsearch Cluster Architecture in JD.com Order System

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.

Cluster ArchitectureData synchronizationElasticsearch
0 likes · 13 min read
Evolution of the Elasticsearch Cluster Architecture in JD.com Order System
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 12, 2019 · Big Data

iQIYI's Big Data Architecture Evolution and Adoption of Druid

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.

Bitmap IndexElasticsearchdata architecture
0 likes · 12 min read
iQIYI's Big Data Architecture Evolution and Adoption of Druid
Tencent Cloud Developer
Tencent Cloud Developer
Sep 9, 2019 · Databases

Tencent Optimizes Elasticsearch High-Concurrency Write Performance, Cutting 10M Data Load Time by 20%

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.

Big DataElasticsearchTencent Cloud
0 likes · 6 min read
Tencent Optimizes Elasticsearch High-Concurrency Write Performance, Cutting 10M Data Load Time by 20%
Youku Technology
Youku Technology
Sep 4, 2019 · Backend Development

Technical Deep Dive of Youku Media Asset Platform: Storage, Search, and Data Aggregation

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.

Elasticsearchdata aggregationmedia asset
0 likes · 9 min read
Technical Deep Dive of Youku Media Asset Platform: Storage, Search, and Data Aggregation
Java Backend Technology
Java Backend Technology
Sep 4, 2019 · Operations

Scaling JD Daojia Order Search with Elasticsearch: Cluster Evolution Journey

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.

Cluster ArchitectureData synchronizationElasticsearch
0 likes · 13 min read
Scaling JD Daojia Order Search with Elasticsearch: Cluster Evolution Journey
dbaplus Community
dbaplus Community
Sep 3, 2019 · Backend Development

How We Built Real-Time MySQL-to-Elasticsearch Sync with Binlog and Kafka

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.

Data synchronizationElasticsearchKafka
0 likes · 13 min read
How We Built Real-Time MySQL-to-Elasticsearch Sync with Binlog and Kafka
Tencent Cloud Developer
Tencent Cloud Developer
Aug 30, 2019 · Big Data

How Tencent Cloud Leverages Spark, ElasticSearch, and Flink for PB‑Scale Data Warehousing

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.

Big DataCloud ComputingElasticsearch
0 likes · 22 min read
How Tencent Cloud Leverages Spark, ElasticSearch, and Flink for PB‑Scale Data Warehousing
Big Data Technology Architecture
Big Data Technology Architecture
Aug 29, 2019 · Databases

Elasticsearch Indexing and Search Performance Tuning Guide

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.

Elasticsearchhardware optimizationsearch performance
0 likes · 18 min read
Elasticsearch Indexing and Search Performance Tuning Guide
Architecture Digest
Architecture Digest
Aug 19, 2019 · Big Data

Elasticsearch Cluster Architecture and Distributed Data System Design

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.

Cluster ArchitectureData StorageElasticsearch
0 likes · 13 min read
Elasticsearch Cluster Architecture and Distributed Data System Design
ITPUB
ITPUB
Aug 12, 2019 · Operations

How JD.com Scaled Its Order Search with a Real‑Time Dual Elasticsearch Cluster

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.

Cluster ArchitectureData synchronizationElasticsearch
0 likes · 13 min read
How JD.com Scaled Its Order Search with a Real‑Time Dual Elasticsearch Cluster
Big Data Technology Architecture
Big Data Technology Architecture
Aug 12, 2019 · Fundamentals

Understanding Full‑Text Search and Comparing Solr, Lucene, and Elasticsearch

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.

ElasticsearchFull-text SearchLucene
0 likes · 15 min read
Understanding Full‑Text Search and Comparing Solr, Lucene, and Elasticsearch
Big Data Technology Architecture
Big Data Technology Architecture
Aug 9, 2019 · Databases

Understanding Elasticsearch: Architecture, Core Concepts, and Performance Optimization

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.

ElasticsearchPerformance TuningReplication
0 likes · 35 min read
Understanding Elasticsearch: Architecture, Core Concepts, and Performance Optimization
Big Data Technology & Architecture
Big Data Technology & Architecture
Jul 28, 2019 · Operations

Comprehensive Guide to Building an ELK Log Management Platform with Kafka and Filebeat

This article provides a detailed tutorial on designing, deploying, and operating an ELK log management platform—including Elasticsearch, Logstash, Kibana, Kafka, and Filebeat—covering architecture options, configuration files, command‑line operations, cluster setup, and best‑practice recommendations for scalable, real‑time log collection and analysis.

ELKElasticsearchFilebeat
0 likes · 22 min read
Comprehensive Guide to Building an ELK Log Management Platform with Kafka and Filebeat
21CTO
21CTO
Jul 11, 2019 · Big Data

Boost Elasticsearch Queries on Billions of Docs: Filesystem Cache & Smart Design

Elasticsearch performance at billions‑scale can be dramatically improved by leveraging the OS filesystem cache, limiting indexed fields, separating hot and cold data, pre‑warming caches, and using scroll or search_after for pagination, while avoiding costly joins and ensuring the dataset fits in memory.

ElasticsearchFilesystem CachePagination
0 likes · 12 min read
Boost Elasticsearch Queries on Billions of Docs: Filesystem Cache & Smart Design
Mafengwo Technology
Mafengwo Technology
Jul 11, 2019 · Backend Development

How We Achieved Near‑Real‑Time MySQL‑to‑Elasticsearch Sync Using Binlog and Kafka

This article explains why traditional MySQL queries no longer meet the growing e‑commerce data needs, describes the limitations of a MySQL‑to‑Elasticsearch intermediate table, and details a binlog‑driven, Kafka‑based pipeline with custom modules, upsert handling, filtering, and monitoring to ensure fast, reliable data synchronization.

ElasticsearchKafkaMySQL
0 likes · 11 min read
How We Achieved Near‑Real‑Time MySQL‑to‑Elasticsearch Sync Using Binlog and Kafka
Big Data Technology Architecture
Big Data Technology Architecture
Jul 9, 2019 · Operations

Elasticsearch Node Shutdown Process and Risks During Rolling Upgrade

During a rolling upgrade of an Elasticsearch cluster, stopping nodes—especially the master—can block write requests, cause client connection failures, trigger master re‑election, and lead to temporary data duplication, making it essential to understand the shutdown sequence and its impact on read/write operations.

Cluster OperationsElasticsearchNode Shutdown
0 likes · 5 min read
Elasticsearch Node Shutdown Process and Risks During Rolling Upgrade
ITPUB
ITPUB
Jul 6, 2019 · Backend Development

How Elasticsearch Revolutionized Search and Logging: The ELK Stack Story

This article narrates the origin and evolution of Elasticsearch, from its Lucene roots through Compass to the modern ELK Stack, illustrating how it simplifies full‑text search, log analysis, and real‑time monitoring for developers and operations teams.

BeatsELKElasticsearch
0 likes · 13 min read
How Elasticsearch Revolutionized Search and Logging: The ELK Stack Story
Big Data Technology Architecture
Big Data Technology Architecture
Jul 3, 2019 · Backend Development

Step-by-Step Guide to Installing Elasticsearch 7.x (Single‑Node) and Elasticsearch‑head

This article provides a comprehensive tutorial for installing Elasticsearch 7.x in single‑node mode, configuring its key settings, deploying the Elasticsearch‑head web UI via Tomcat, and includes reference configuration files for Elasticsearch 6.x, complete with command‑line examples and code snippets.

ConfigurationElasticsearchElasticsearch-head
0 likes · 8 min read
Step-by-Step Guide to Installing Elasticsearch 7.x (Single‑Node) and Elasticsearch‑head
Programmer DD
Programmer DD
Jun 28, 2019 · Backend Development

Master Elasticsearch with Jest: A Hands‑On Java Client Guide

This tutorial walks you through using Jest, a fluent Java HTTP client for Elasticsearch, covering Maven setup, creating a reusable client, performing CRUD operations, bulk and asynchronous requests, and showcases code examples that illustrate how to manage indices and documents efficiently.

ClientElasticsearchJava
0 likes · 12 min read
Master Elasticsearch with Jest: A Hands‑On Java Client Guide
JavaEdge
JavaEdge
Jun 26, 2019 · Backend Development

How Does Elasticsearch Write and Query Data? A Deep Dive into ES Internals

This article explains the complete workflow of Elasticsearch write, read, search, delete, and update operations, covering coordinating nodes, shard routing, buffer refresh, translog, segment files, commit/flush processes, and the underlying inverted index mechanism.

ElasticsearchSearch Architecturenear real-time
0 likes · 10 min read
How Does Elasticsearch Write and Query Data? A Deep Dive into ES Internals
Architecture Digest
Architecture Digest
Jun 25, 2019 · Operations

Design and Implementation of a Unified Monitoring and Alert System for MaFengWo Large Transportation Business

This article describes the motivation, architecture, key components, rule engine, alert actions, and practical lessons learned while building a unified monitoring and alarm system for MaFengWo's large‑scale transportation platform, highlighting data collection, Elasticsearch storage, scheduling, and future enhancements.

ElasticsearchJavaMonitoring
0 likes · 13 min read
Design and Implementation of a Unified Monitoring and Alert System for MaFengWo Large Transportation Business
Architect's Tech Stack
Architect's Tech Stack
Jun 23, 2019 · Big Data

Elasticsearch Interview Questions: Architecture, Indexing, Optimization, and Operations

This article compiles common Elasticsearch interview questions and detailed answers covering cluster architecture, inverted index fundamentals, index design, write/query optimizations, master election, document indexing flow, search process, Linux tuning, and Lucene internals, providing practical guidance for candidates.

ClusterElasticsearchIndexing
0 likes · 10 min read
Elasticsearch Interview Questions: Architecture, Indexing, Optimization, and Operations
macrozheng
macrozheng
Jun 14, 2019 · Operations

Step‑by‑Step: Deploy the Mall Application on Linux Using Docker

This guide walks you through installing Docker on CentOS 7.6 and deploying a complete mall stack—including MySQL, Redis, Nginx, RabbitMQ, Elasticsearch, MongoDB, and a SpringBoot service—inside Docker containers, configuring volumes, ports, firewalls, and testing the APIs.

DockerElasticsearchLinux
0 likes · 11 min read
Step‑by‑Step: Deploy the Mall Application on Linux Using Docker
21CTO
21CTO
Jun 10, 2019 · Databases

Master Elasticsearch in Python: From Installation to Advanced Queries

This tutorial introduces Elasticsearch, explains its architecture and use cases, walks through installation, index creation, mapping, CRUD operations, and demonstrates how to integrate and query Elasticsearch from Python using both the REST API and the official client library.

ElasticsearchNoSQLPython
0 likes · 13 min read
Master Elasticsearch in Python: From Installation to Advanced Queries
dbaplus Community
dbaplus Community
Jun 2, 2019 · Databases

Why Sharding (Database Partitioning) Beats Partitioning and NoSQL for Massive Data

The article explains why sharding (splitting databases and tables) is the preferred solution for handling massive user, order, and transaction data in high‑traffic internet applications, comparing it with partitioning and NoSQL/NewSQL alternatives, and detailing practical middleware choices, sharding column selection, and integration with Elasticsearch and HBase.

ElasticsearchHBaseMySQL
0 likes · 14 min read
Why Sharding (Database Partitioning) Beats Partitioning and NoSQL for Massive Data
Efficient Ops
Efficient Ops
May 30, 2019 · Operations

How to Supercharge Elasticsearch for Massive Log Analytics: Real-World Optimizations

This article examines the unique characteristics of log data, outlines the challenges of using Elasticsearch at scale, and presents practical optimization techniques—including ingestion, mapping, time‑range search, metadata loading, and a custom C++ engine—to dramatically improve performance, stability, and cost efficiency.

ElasticsearchLog Analyticsbackend
0 likes · 11 min read
How to Supercharge Elasticsearch for Massive Log Analytics: Real-World Optimizations
Architecture Digest
Architecture Digest
May 28, 2019 · Backend Development

Improving Elasticsearch Query Performance for Billion‑Scale Datasets

To boost Elasticsearch query speed on billions of records, allocate sufficient filesystem cache memory, store only searchable fields, separate hot and cold data, warm up cache, avoid complex joins, and replace deep pagination with Scroll API or search_after for millisecond‑level responses.

ElasticsearchFilesystem CachePagination
0 likes · 10 min read
Improving Elasticsearch Query Performance for Billion‑Scale Datasets