This article examines how pagination queries on massive MySQL tables become dramatically slower as the offset grows, defines what constitutes a slow SQL, and presents three practical optimization techniques—including returning only primary keys, range filtering, and using Elasticsearch—to dramatically improve query performance.
This tutorial shows how to use Elasticsearch's built‑in SQL support to run familiar MySQL‑style queries—creating an index, inserting documents, and performing SELECT, WHERE, ORDER BY, GROUP BY, LIMIT, aggregation, date and string functions—making ES data retrieval as easy as relational databases.
This guide explains Elasticsearch's mapping concepts, compares dynamic and static mappings, and demonstrates how to choose between Array, Flattened, and Nested field types with concrete code examples, performance trade‑offs, and practical recommendations for real‑world business systems.
The author builds a fully AI‑driven pipeline that scrapes JD.com comments about an Elasticsearch book, processes the data through cleaning and preprocessing, indexes it into Elasticsearch, and creates a series of Kibana visualizations, while reflecting on model selection and practical challenges.
The article presents a decoupled business operation log management architecture that uses Alibaba’s Canal to capture MySQL binlog changes, streams them through Kafka, and stores structured before‑and‑after records in Elasticsearch with nested mappings, enabling multi‑table correlation via transaction IDs, visual querying, and reliable rollback without modifying application code.
The article compares RedisSearch and Elasticsearch across definition, storage model, performance, query capabilities, scalability, ease of integration, and operational considerations, helping readers decide which solution best matches requirements such as real‑time response, data volume, query complexity, and existing tech stacks.
This guide presents a comprehensive, step‑by‑step deep‑inspection plan for the EFLK stack, covering Elasticsearch health checks, node performance metrics, shard and index monitoring, Logstash and Kibana validation, DSL query examples, and automated Python‑based metric collection with cron scheduling.
This guide walks you through setting up an ELK stack to collect, centralize, and visualize logs from Dockerized applications, covering rsyslog configuration, container deployment for Elasticsearch, Logstash, Kibana, and Nginx, and demonstrates verification via Kibana queries.
This article explains how to use Filebeat as a lightweight log shipper together with Graylog for centralized log aggregation, storage, and analysis, covering installation, configuration files, Docker deployment, Spring Boot integration, and query techniques for efficient operations monitoring.
This article provides a detailed overview of log analysis, explains its importance, introduces major collection tools such as Splunk and ELK, and walks through step‑by‑step Docker‑based installation and configuration of Elasticsearch, Logstash, Kibana, and a Spring Boot application for centralized logging.
This article walks through the design and implementation of a decoupled, high‑performance business operation log solution that captures MySQL binlog changes via Canal, streams them through Kafka, and stores and queries them in Elasticsearch, addressing challenges such as batch operations, multi‑table transactions, and non‑business data filtering.
This comprehensive guide details a step‑by‑step deep‑inspection and monitoring strategy for an Elasticsearch‑Filebeat‑Logstash‑Kibana (EFLK) stack, covering cluster health, node and shard metrics, index status, query profiling, Filebeat, Logstash and Kibana validation, DSL query examples, and a Python script for automated metric collection.
This article compares four pagination techniques—MySQL LIMIT OFFSET, LIMIT with primary‑key filtering, HasMore scrolling, and Elasticsearch pagination—explaining their implementations, advantages, drawbacks such as deep‑page performance issues, and recommending appropriate scenarios for each method.
This article presents a comprehensive step‑by‑step guide for deeply inspecting the health, performance, and configuration of Elasticsearch, Filebeat, Logstash, and Kibana, including API calls, key metrics, DSL queries, Python automation scripts, and cron scheduling to ensure stable EFLK clusters.
This article explains how to use Filebeat as a lightweight log shipper together with Graylog, Elasticsearch, and MongoDB to collect, process, and visualize logs from multiple environments, including detailed configuration examples, deployment scripts, and integration with Docker and Spring Boot.
This article compares Elasticsearch and ClickHouse in terms of write throughput, query speed, and server cost, provides a cost analysis, and offers step‑by‑step deployment instructions for Zookeeper, Kafka, FileBeat, and ClickHouse, including troubleshooting tips and configuration examples.
This article walks through the new features of the Elasticsearch 8.x Java API client, shows how to set up the development environment, initialize the client with HTTPS or API‑key authentication, and provides complete, ready‑to‑run code examples for index creation, document indexing, searching, aggregations, and scripted sorting.
This article explains Elasticsearch's distributed architecture, shard and replica model, the detailed write path—including buffer, transaction log, refresh and flush operations—and the read path with coordinating nodes, Lucene query execution, aggregation, caching, and performance optimizations.
At the 2024 Cloud Xi Conference, Alibaba Cloud expert Jia Xinyu detailed how Elasticsearch Serverless addresses core log‑analysis pain points with out‑of‑the‑box, high‑performance, pay‑as‑you‑go capabilities, delivering significant cost savings and eliminating operational overhead.
This tutorial walks you through configuring Elasticsearch’s Open Inference API to connect with Alibaba Cloud AI Search, covering setup of text generation, rerank, sparse and dense vector services, and demonstrates end‑to‑end requests with code examples for building RAG and semantic search applications.
This guide walks through five real‑world Elasticsearch use cases—including full‑text product search with highlighting, centralized log collection and analysis, personalized video recommendation, price‑range aggregation for e‑commerce, and geo‑location restaurant search—detailing index design, query syntax, Docker setup, and front‑end integration.
This guide explains why Elasticsearch throws cluster_block_exception, walks through diagnosing disk space, cluster health, node failures, read‑only settings, and index misconfigurations, and provides step‑by‑step commands and preventive measures to keep your cluster stable.
This article compares API composition and CQRS patterns for handling distributed queries in evolving monolithic systems, illustrating their workflows with e‑commerce and online‑education examples, discussing performance trade‑offs, implementation details using Canal and ElasticSearch, and offering practical guidance on when to adopt each approach.
This article explains Elasticsearch aggregation queries, covering metric, bucket, and pipeline aggregations, the role of doc_values and fielddata, multi‑field mapping, practical query examples such as terms, date_histogram, range, nested and pipeline aggregations, as well as sorting techniques and performance optimization tips.
This article introduces RediSearch, compares its memory usage and query performance with Elasticsearch on modest hardware, shows installation steps, demonstrates core index commands and Java integration, and concludes with practical advice on using RediSearch as a lightweight full‑text search engine.
This article recounts the evolution of Ctrip's vacation product log platform—from a single‑table DB solution to a platformized ES + HBase architecture—detailing the challenges of massive data volume, the design of RowKey, write and query flows, and the subsequent business and supplier empowerment.
This article describes the development trajectory of Ctrip's travel product log system, detailing its three major phases—from a single‑table DB approach to a platform‑based solution and finally an empowered version—while discussing technical challenges, design decisions, and the implementation of HBase, Elasticsearch, and related components to handle billions of log entries efficiently.
The article analyzes Elasticsearch’s shift from Apache 2.0 to a dual ELv2 + SSPL/AGPL licensing model, explains the competitive pressure from AWS, and offers guidance on choosing appropriate open‑source licenses for different types of foundational software.
By refactoring the station‑profile index to eliminate over‑indexed and mis‑typed fields—cutting complex types from 282 to 74 and keywords from 67 to 59—the team lowered CPU peaks from 60 % to 50 %, reduced average CPU to 20 %, cut query latency to 150 ms, accelerated Flink sync to 10 minutes, and decommissioned two nodes, achieving substantial performance gains and cost savings.
The article walks through Elasticsearch’s document retrieval workflow, covering single‑document GET, multi‑document mget, the two‑stage query_then_fetch mechanism, shard routing, load balancing, query and fetch phases, collectors, rescore, and pagination optimizations.
This article walks through Elasticsearch’s document indexing pipeline—from single‑document and bulk request routing, through the write‑refresh‑flush‑merge cycle, to the roles of Lucene, the translog, primary and replica shards, and how reliability, consistency, atomicity, isolation, real‑time visibility and performance are achieved.
This article explains Elasticsearch’s overall structure, the underlying Lucene index layout, the indexing and search processing flow, and how built‑in analyzers work, illustrating each concept with diagrams, token examples, and query scenarios.
The article details Didi's comprehensive use of Elasticsearch across all online retrieval scenarios, covering its physical‑machine architecture, gateway and control layers, data synchronization methods, cross‑datacenter replication, JDK17 + ZGC performance upgrades, cost‑saving ZSTD compression, multi‑tenant isolation, custom security, and ongoing stability practices leading to a planned upgrade to Elasticsearch 8.13.
This article details Alibaba Cloud Elasticsearch's AI search advancements, covering embedding vectors, HNSW-based approximate nearest neighbor search, hardware-accelerated vector engines, sparse vectors, hybrid retrieval, the Inference API, and RAG implementations that together boost performance, efficiency, and relevance for modern AI-driven search applications.
This article explains how Elasticsearch’s advanced search capabilities—including vector and semantic search, hardware acceleration, hybrid retrieval, model re‑ranking, multi‑vector support, and integrated security—enable robust RAG implementations and outlines future directions such as a new compute engine, stronger vector engines, and cloud‑native serverless deployment.
Elastic's CTO Shay Banon explains the shift from Apache 2.0 to AGPL for Elasticsearch and Kibana, detailing how licensing changes aim to curb unauthorized cloud services, respond to AWS's OpenSearch fork, and reshape the open‑source search market.
A large table with tens of millions of rows caused 20‑second query times, but by decoupling reads through RabbitMQ and writing data asynchronously to Elasticsearch, query latency dropped to 500 ms, with Java code examples for both producer and consumer, sync handling, and migration tips.
This article walks through Elasticsearch’s licensing history, version selection, installation, cluster health monitoring, shard routing, storage mechanisms, refresh and translog processes, segment merging, and practical performance optimizations such as disk choices, index settings, and JVM tuning.
This article examines various methods for synchronizing MySQL data with Elasticsearch, including synchronous and asynchronous dual‑write, Logstash pipelines, binlog real‑time replication, Canal, and Alibaba Cloud DTS, comparing their architectures, advantages, disadvantages, and suitable application scenarios.
This article traces Elasticsearch's transformation from a pure text search engine to a versatile hybrid platform that integrates structured, geospatial, aggregation, and vector search capabilities, highlighting its AI-driven innovations, performance optimizations, and growing adoption across enterprises and academia.
This article examines the replication and partitioning designs of Redis, Kafka, and Elasticsearch, drawing on concepts from Designing Data‑Intensive Applications to illustrate core distributed‑system principles, common challenges, and practical configuration options.
This article details the three‑stage evolution of Ctrip's vacation product log system—from a simple single‑table DB approach, through a platform‑based ES + HBase solution, to a scalable V3.0 architecture that improves storage, search, and business empowerment while handling billions of log entries.
This article provides a comprehensive overview of Elasticsearch, covering its distributed storage architecture, core data model concepts, analysis and query capabilities, practical next‑token pagination techniques, join strategies, and various data synchronization methods for integrating Elasticsearch with other systems.
This article details how to construct a log‑monitoring platform using the ELK stack, Kafka buffering, and a Python scheduler to collect, process, and alert on error logs, offering practical configuration tips and performance optimizations for production environments.
This article explains how to keep MySQL and Elasticsearch data in sync using synchronous calls, asynchronous notifications, or binlog listeners, and dives deep into Elasticsearch cluster design, node roles, distributed storage, query phases, split‑brain handling, and fault‑tolerance mechanisms.
This guide explains how to identify and remove outdated Elasticsearch indices by listing shards, filtering target indices, deleting them with a shell loop, and automating the process with a scheduled cron job, helping keep ELK performance optimal.
This article explains various methods for synchronizing MySQL data to Elasticsearch, including synchronous and asynchronous double‑write, Logstash pipelines, binlog‑based real‑time sync, Canal, and Alibaba Cloud DTS, comparing their advantages, disadvantages, and suitable scenarios.
This guide walks you through using Alibaba Cloud Data Transmission Service (DTS) to continuously synchronize production data from an RDS MySQL instance to an Elasticsearch cluster, covering prerequisites, task creation, verification, Kibana queries, and incremental sync testing.
This guide shows how to use Elasticsearch's match, match_phrase, multi_match, and query_string queries—including field boosting, phrase slop, and type options—through clear PHP client examples and sample responses for precise full‑text search control.
This article explains why MySQL‑Elasticsearch synchronization is needed, compares six practical approaches—including sync write, async write, Logstash, binlog, Canal, and Alibaba Cloud DTS—detailing their implementation methods, advantages, disadvantages, and suitable use cases for large‑scale data environments.
To prevent IO saturation during Elasticsearch ILM hot‑to‑cold migrations, the article recommends batching migrations, fine‑tuning max_bytes_per_sec, upgrading cold‑node storage and adding hot nodes, and automating monitoring with scripts that pause or resume ILM based on real‑time utilization thresholds.
This article compares Meilisearch and Elasticsearch, highlighting their core differences, ease of use, performance, relevance, configuration requirements, and suitable use‑cases, helping developers decide which search engine best fits their application needs.
This guide explains why e‑commerce platforms need a high‑performance search engine, introduces Elasticsearch fundamentals, compares it with traditional engines, and provides step‑by‑step PHP code for installing, configuring, indexing, and querying data, complete with practical examples and best‑practice tips.
This guide compares four MySQL‑to‑Elasticsearch synchronization methods—synchronous dual‑write, asynchronous MQ‑based dual‑write, timer‑driven SQL extraction, and real‑time Binlog replication—and reviews popular CDC tools such as Canal, Alibaba Cloud DTS, Databus, and others to help you choose the right solution.
This article describes how to combine Elasticsearch’s keyword matching with Milvus’s vector‑based semantic search to build a scalable document search service, covering data preprocessing, architecture, query handling, custom scoring, DSL configuration, and result merging.
This article provides a step‑by‑step guide on using Elasticsearch’s Painless scripting language to create, index, and manipulate array‑type fields, covering basic operations like length and element access, as well as advanced aggregations, filtering, and weighted calculations, while highlighting performance considerations.
Explore nine major database technologies—from traditional relational MySQL to NoSQL Redis, columnar HBase and ClickHouse, document-oriented MongoDB, search engine Elasticsearch, graph Neo4j, time‑series Prometheus, and vector Milvus—plus practical best‑practice guides, real‑world polyglot persistence scenarios, and recommended resources for mastering modern data storage.
This article provides an in‑depth overview of nine major database categories—including relational, key‑value, columnar, document, graph, time‑series, and vector databases—detailing their strengths, weaknesses, best practices, and typical application scenarios, and explains how polyglot persistence combines multiple databases for optimal performance and scalability.
This article details the practical journey of building and scaling an Elasticsearch‑based search system that supports tens of millions to billions of items, covering architecture design, capacity planning, multi‑data‑center deployment, data synchronization via RocketMQ and Flink, and multi‑layer reconciliation to ensure consistency and high QPS.
This article presents the design and implementation of a high‑availability, real‑time logistics supply‑chain monitoring dashboard, covering its data processing pipeline with Flink, storage choices between Elasticsearch and ClickHouse, multi‑layer architecture, metric consistency, stability mechanisms, extensibility configurations, and monitoring practices.
This guide walks you through installing Java, Elasticsearch, Kibana, Logstash, and Filebeat on Ubuntu 18.04, configuring a dedicated data disk, adjusting service settings, and testing log collection with a complete script for a compact ELK demo environment.
This article explains how to overcome performance bottlenecks in high‑concurrency Elasticsearch prefix searches by applying an edge‑ngram tokenizer, detailing the problem, configuration steps, analysis results, and query recommendations for faster, more accurate search experiences.
This article details Didi’s use of Elasticsearch 7.6, covering its architecture on physical machines, gateway and control layers, user console features, deployment models, data synchronization strategies, engine iterations such as fine‑grained tiered protection, multi‑active replication, JDK 17/ZGC performance upgrades, cost reductions, multi‑tenant isolation, security enhancements, and future migration to ES 8.13.
This comprehensive tutorial walks developers through preparing the operating system, disabling firewalls and SELinux, setting up users and JVM options, configuring single‑node and multi‑node Elasticsearch clusters, integrating Kibana, and deploying everything via Docker on CentOS with detailed code examples.
This article explains how Elasticsearch transforms simple keyword matching into a high‑performance, scalable search engine by using inverted indexes, term dictionaries, posting lists, term indexes, stored fields, doc values, segments, and distributed node architectures to achieve fast, reliable full‑text search on massive data sets.
This article describes how a unified membership system serving billions of users across multiple platforms achieves high performance and high availability through dual‑center Elasticsearch clusters, traffic isolation, deep ES optimizations, Redis caching, dual‑center MySQL partitioning, seamless migration, and fine‑grained flow‑control and degradation strategies.
This article shares a practical case study on how a coupon‑distribution system evolved from a single‑table design to a sharded, multi‑database architecture with query optimization, ElasticSearch integration, and read/write splitting to handle growing data volume, complex queries, and increasing request traffic.
This article reviews four common MySQL‑to‑Elasticsearch synchronization methods—synchronous dual‑write, asynchronous dual‑write via MQ, timer‑based SQL extraction, and real‑time Binlog replication—evaluates their pros and cons, and compares popular migration tools such as Canal, Alibaba DTS, Databus and others.
This article details how a large‑scale Elasticsearch deployment was transformed from physical servers to a Kubernetes‑based, containerized architecture, addressing cost, scalability, API compatibility, security, observability, and multi‑cloud migration using the 极限网关 API gateway.
This guide walks through installing NFS on all nodes, creating a dynamic storage class with Helm, setting up Elastic Cloud on Kubernetes (ECK) operator, deploying Elasticsearch and Kibana clusters, configuring Filebeat for log collection, and tuning system parameters for a production‑ready Kubernetes environment.
This article compares four MySQL‑to‑Elasticsearch synchronization methods—synchronous dual‑write, asynchronous dual‑write, SQL extraction, and Binlog‑based real‑time sync—evaluates their pros and cons, and reviews popular migration tools such as Canal, Alibaba DTS, Databus, Flink, CloudCanal, Maxwell, and DRDS.
This article details how upgrading from Elasticsearch 5.x/2.x to 8.x dramatically improves search, aggregation, and write performance while reducing storage costs, introduces vector KNN, synthetic _source, TSDS, searchable snapshots, security enhancements, and provides migration examples and code snippets for enterprise search platforms.
This article explains the concept of query separation, outlines its suitable scenarios, compares implementation methods such as synchronous, asynchronous, and binlog approaches, discusses storage system choices like MongoDB, HBase, and Elasticsearch, and addresses consistency and operational challenges when decoupling read workloads from write workloads.
This article details Bilibili’s journey of containerizing Elasticsearch and ClickHouse on Kubernetes, covering the challenges of stateful services, architectural decisions, custom operators, storage and network solutions, deployment steps, observability enhancements, and the resulting cost, quality, and efficiency gains.
This comprehensive guide introduces Elasticsearch’s core concepts, installation, REST API operations, and practical examples of indexing, updating, deleting, bulk processing, searching, filtering, and aggregations, helping readers build scalable, near‑real‑time search and analytics solutions for diverse use cases.
This article introduces Elasticsearch, an open‑source distributed search and analytics engine, explaining its architecture, core concepts such as clusters, nodes, shards, replicas, indices, inverted indexes, documents and fields, and how these components enable fast, scalable searching and data analysis.
Learn how to install, configure, and connect Kibana with Elasticsearch, explore data using Discover, craft powerful queries with Lucene syntax, create visualizations and dashboards, and monitor your logs—all illustrated with step‑by‑step screenshots and practical tips for effective data analysis.
This article explains query separation as a strategy to accelerate slow large‑table queries by duplicating data to a dedicated query store, outlines when to adopt it, compares synchronous, asynchronous, and binlog approaches, discusses storage choices such as MongoDB, HBase, Elasticsearch, and addresses consistency and MQ challenges.
Didi’s Elasticsearch platform, built on ES 7.6 and deployed on physical machines with containerized gateway and control layers, provides a multi‑tenant, high‑performance search service—featuring a user console, operational controls, ZGC‑based latency reductions, cost‑saving compression, custom security, real‑time cross‑datacenter replication, and a roadmap toward ES 8.13.
This article examines how vector search using Milvus handles sentence and paragraph queries compared to traditional Elasticsearch, demonstrating the advantages of embedding‑based semantic matching for document search scenarios through practical experiments and visual results.
During flash‑sale events like JD or Taobao, a tiny fraction of products generate the majority of traffic, causing server overload; this guide explains how to identify and collect hotspot product data in real time using an OpenResty‑Lua front‑end, Kafka for messaging, and Elasticsearch for storage and analysis.
This article details Didi's comprehensive big data cost governance framework, covering its data architecture, asset management scoring, Hadoop and Elasticsearch cost optimization methods, and practical insights on organizational processes and incentives for effective cost control.
This article describes the challenges of synchronizing billions of order records to Elasticsearch and presents the design, architecture, and key technical details of a generic data‑sync service (ECP) that supports multiple data sources, dynamic rate limiting, retry strategies, SPI‑based extensibility, environment isolation, health‑check, fault recovery, smooth migration, and elegant logging.
This article walks through upgrading the open‑source Mall e‑commerce system to Spring Boot 3 and JDK 17, covering dependency version updates, migration from SpringFox to SpringDoc, new Spring Data Elasticsearch usage, revised Spring Security configuration, Docker deployment tips, and essential code changes.
This guide shows how to deploy a lightweight Elasticsearch instance alongside a proxy and Kibana with Docker‑Compose, configure SLS credentials, create index patterns, and use Kibana’s query and visualization features to explore logs stored in Alibaba Cloud Log Service.
When faced with a business requirement to filter up to 100 000 records from a pool of tens of millions and then sort and de‑duplicate them, this article explores four technical solutions—multithreaded ClickHouse pagination, Elasticsearch scroll‑scan, a combined Elasticsearch‑HBase approach, and RediSearch with RedisJSON—detailing their design, implementation, performance testing, and trade‑offs.
This article explains the challenges of synchronizing billions of order records to Elasticsearch, outlines the design of the ECP service that automates heterogeneous data integration, and details its implementation including multi‑source reading, SQL parsing, dynamic rate limiting, retry mechanisms, SPI‑based extensibility, environment isolation, health‑check, smooth migration, and logging.
Facing the complexities of running stateful services like Elasticsearch and ClickHouse in production, Bilibili’s infrastructure team detailed their migration to Kubernetes, describing the architectural design, custom operators, storage provisioning with LVM, network configuration, high‑availability strategies, observability, and the resulting cost, quality, and efficiency gains.
This guide explains how to monitor Kubernetes events, differentiate normal and warning events, and use tools like kube-eventer and kube-event-exporter to collect, alert on, and analyze cluster events through webhook, Kafka, Logstash, and Elasticsearch, enabling comprehensive observability and troubleshooting.
This article provides a comprehensive, hands‑on guide to installing and configuring the ELK logging stack—including Elasticsearch, Logstash, Kibana, and Filebeat—using Docker on an Ubuntu VM, covering architecture, command‑line setup, configuration files, troubleshooting tips, and future extension options.
This article examines a MySQL slow‑query incident caused by the optimizer using the primary clustered index instead of an appropriate secondary index, explains the underlying index structures, and presents solutions such as FORCE INDEX and migrating complex queries to Elasticsearch for long‑term performance improvement.
This article details Didi's comprehensive big‑data asset governance platform, covering its architectural layers, Hadoop and Elasticsearch governance practices, health‑score models, lifecycle recommendations, and future plans for automated and intelligent data governance to reduce cost and manual effort.
This article explains how traditional keyword search with Elasticsearch often yields inaccurate or incomplete results for document retrieval, introduces vectorization and semantic search using NLP embeddings, and demonstrates a practical workflow that combines these techniques with the Milvus vector database to achieve more accurate and efficient document search.
JD’s Mini‑Program Data Center integrates data collection, storage, and real‑time analysis using Flink, ClickHouse, and Elasticsearch to provide comprehensive monitoring, user behavior insights, and scalable analytics for mini‑programs across JD’s ecosystem, enabling precise operations and future AI‑driven enhancements.
The author recounts taking the Elastic Certified Engineer exam in March 2024, outlines the official syllabus, shares ten hands‑on exam tasks with code snippets, and provides practical tips and personal reflections on preparation and execution.
This tutorial explains how to let users upload PDF, Word, or TXT files, install the Elasticsearch Ingest Attachment Processor Plugin, create an ingest pipeline and index mapping, convert files to Base64, and perform fuzzy searches with highlighted results using Spring Boot and Java code examples.
This article walks through the end‑to‑end design and implementation of a searchable system that scales from millions to hundreds of millions of products, covering business background, data‑center architecture, ES cluster sizing, multi‑room data sync, RocketMQ‑Flink pipelines, and multi‑layer reconciliation to achieve high QPS and strong consistency.
This article explains how JD ElasticSearch has integrated the high‑performance ZSTD compression algorithm, details the motivations behind its adoption, presents benchmark results comparing it with LZ4 and best_compression, and provides step‑by‑step instructions and code snippets for configuring and using the new jd_zstd codec in Elasticsearch.
Bilibili migrated its Elasticsearch and ClickHouse clusters to Kubernetes using custom operators, CRDs, LVM‑based local storage, MacVLAN networking, and pod anti‑affinity, achieving higher resource utilization, isolation, and automation that cut server count, reduced latency spikes, and lowered operational costs dramatically.
This guide walks through creating a Spring Boot backend that accepts PDF, Word, and TXT uploads, extracts their text using Elasticsearch's ingest‑attachment plugin, stores metadata in MySQL, and provides fuzzy search and highlighted results via Elasticsearch queries.
