This article summarizes Bao Yongjun’s presentation on JD.com’s end‑to‑end big data platform, covering its strategic value, industry trends, architectural design, development phases from scale‑out to intelligent real‑time processing, and future directions for a cloud‑native, AI‑driven data ecosystem.
This article examines Apache Pulsar, an open‑source messaging platform created by Yahoo, compares it with Kafka by outlining Kafka’s common pain points, highlights Pulsar’s multi‑tenant architecture, layered storage, built‑in functions, and security features, and discusses the trade‑offs of each solution.
The article introduces the JUST engine, a spatio‑temporal data platform that extends GeoMesa with three new indexes (Z2T, XZ2T, time_range), defines nine common and three specialized data models, provides default indexing strategies, and offers detailed SQL usage guidelines for efficient querying of massive urban datasets.
This article explains what data skew is, why it occurs in large‑scale Hadoop and Spark jobs, how to recognize its symptoms such as stuck reducers or OOM executors, and presents practical strategies—including business‑level adjustments, code refactoring, and platform‑specific tuning—to mitigate the problem.
Fluid 0.4 introduces a DataLoad custom resource for declarative data pre‑warming, enhances support for massive small‑file datasets, adds HDFS‑compatible access for Spark and other big‑data frameworks, and enables mixed‑deployment of multiple datasets on a single node, all backed by significant performance gains.
This article details the three‑stage evolution of 58.com’s commercial data warehouse, describing its massive scale, four‑layer architecture, technical challenges, migrations from MapReduce to Hive and Flink, real‑time streaming upgrades, and the resulting improvements in stability, accuracy, and timeliness.
This article walks through the evolution of big‑data systems to modern stream processing, explains core Flink concepts such as state, checkpoints, event‑time and windowing, and details Kuaishou’s real‑time UV calculation and fast‑failover techniques for high‑availability streaming jobs.
The article summarizes Beike's one‑stop big data development platform, describing its data business background, the evolution from a simple Hadoop‑Kafka‑Hive stack to a metadata‑driven, asset‑oriented platform, and outlines current capabilities in data management, integration, scheduling, quality, openness, and future plans.
Apache Spark Core is a high‑performance, fault‑tolerant engine that abstracts distributed computation through SparkContext, DAG and Task schedulers, supports in‑memory and disk storage, runs on various cluster managers (YARN, Kubernetes, etc.), and unifies batch, streaming, ML and graph processing via its rich ecosystem.
This article explores the challenges big data brings to traditional data technologies and reviews various OLAP solutions—including MPP, batch processing, pre‑computation, and Hadoop‑based engines—while detailing Youku’s specific business scenarios and how different OLAP engines are selected to meet performance, scalability, and real‑time analysis requirements.
This article demonstrates how to scrape the Hurun Rich List from 2015 to 2020 using Python, clean the data, and create interactive visualizations of the top 20 wealth holders, wealth trends over six years, and industry shifts with Pyecharts.
Alibaba’s Double 11 2023 showcased a suite of cutting‑edge technologies—including the GRTN real‑time transmission network, edge‑AI voice interaction, massive digital infrastructure, AI‑driven smart sample rooms, and 3D virtual home‑decoration live streams—that together delivered sub‑second latency, 30% cost reduction, and unprecedented merchant scalability.
This article explains how to build a real‑time click‑stream data warehouse using Flink for stream processing and ClickHouse for near‑real‑time OLAP, covering click‑stream characteristics, dimensional modeling, layered warehouse design, async dimension joins, sink implementation, and data rebalancing strategies.
This article details the high‑complexity logistics scenario of Cainiao's international import business, explains the evolution from offline to real‑time data warehouses (versions 1.0 and 2.0), describes the layered architecture, enumerates technical challenges such as multi‑source joins, state explosion, out‑of‑order processing, and presents concrete solutions using Flink features, logical middle‑layers, union‑all joins, deduplication, timer services, and batch‑stream hybrid processing.
This guide walks through downloading Apache Flume, setting up a master‑slave cluster, and configuring NetCat, Exec, and Avro sources with corresponding sinks and memory channels, including verification commands to ensure the agents run correctly.
The article presents a comprehensive big‑data solution that stores massive GPS trajectory records in HBase, processes them with Spark to identify individuals who visited a pandemic source region, and visualizes their spatio‑temporal distribution in target cities to support precise epidemic control measures.
This article explains how to identify and resolve performance bottlenecks in Apache Flume by configuring Taildir sources, optimizing channel capacities, tuning Kafka sinks, adjusting JVM options, and using simple monitoring scripts, enabling a single Flume‑NG agent to sustain over 50,000 RPS in production.
This article explains the concept of sparse indexes in database systems, compares them with dense indexes, and demonstrates their use in Kafka log files and ClickHouse MergeTree tables, highlighting implementation details, lookup procedures, and configuration parameters.
This recommendation highlights two iTech Forum pieces—one detailing iQiyi’s effect advertising exploration and implementation, and another documenting the company’s Druid practice and technical evolution—providing readers with in‑depth case studies, performance insights, and practical guidance for similar large‑scale data and advertising systems.
This article introduces HBase’s column‑oriented architecture, explains the roles of Master, RegionServer, and Zookeeper, and provides detailed environment preparation and installation commands for setting up an HBase cluster on Hadoop.
This article reviews the challenges of scheduling heterogeneous workloads—micro‑services, big‑data, AI, and HPC—on a unified cloud‑native platform, compares existing schedulers like Mesos and YARN, examines Kubernetes ecosystem extensions such as Volcano and YuniKorn, and details the design and components of the Transwarp Scheduler built on Kubernetes Scheduling Framework v2.
Ctrip’s hotel data team tackled a 3 TB daily data load by building a ClickHouse cluster on VMware, creating custom sync and execution tools, applying query optimizations, and handling merge and memory errors, ultimately achieving over 400% performance gains across multiple reporting themes.
The article presents a novel spatio‑temporal trajectory risk metric and an efficient query framework, including a space‑first index (SFT) and distributed storage, to identify high‑risk contacts of COVID‑19 patients from massive movement data, with real‑world deployment results in several Chinese cities.
The talk outlines why enterprise data visualization is essential for extracting value from massive, multi‑dimensional data, describes design and development challenges, presents AntV's comprehensive frontend visualization solutions, and predicts future trends such as intelligent, democratized, and decision‑integrated visual analytics.
This article explains how to design a scalable log collection system for big‑data platforms by combining Flume for data ingestion, Kafka for buffering and high‑throughput transport, and downstream processing components, providing configuration examples and best‑practice recommendations.
This article demonstrates how to use standard shell utilities such as grep, cut, sort, uniq, awk, and join to quickly extract insights—like top companies, common surnames, popular given names, age distribution, and hometown rankings—from a JSON dataset of Beijing points‑based residency applicants.
This article explains Hive performance tuning techniques, including enabling parallel execution, configuring strict mode to prevent risky queries, reusing JVMs to reduce overhead, and using speculative execution to mitigate slow tasks, with configuration examples and practical considerations.
This article describes how to build a Java‑based crawler to collect millions of Zhihu user profiles, handle anti‑crawling measures with rotating user‑agents and a proxy pool, deduplicate data using a Bloom filter, import the results into ElasticSearch, and analyze the dataset with Kibana and ECharts visualizations.
This article explains how Hive determines the number of map and reduce tasks based on input file size and block configuration, discusses when to increase or decrease map counts, and provides practical commands for adjusting reducer settings to optimize large‑scale data processing.
This article explains how Tencent's self‑developed KonaJDK underpins cloud Java services, enhances micro‑service monitoring, adds national cryptography support, optimizes large‑heap tools like jmap, and delivers performance gains for big‑data workloads, while contributing key features back to the OpenJDK community.
The article outlines Java’s history, its widespread adoption by top companies, key features such as simplicity, portability and security, and its extensive use in big‑data frameworks, IoT, Android, finance, web development, scientific tools, and cloud services, arguing why it will stay popular.
This article reviews the background, requirements, and architectural designs of major open‑source log collection systems—including Facebook’s Scribe, Apache’s Chukwa, LinkedIn’s Kafka, Cloudera’s Flume—and evaluates mature monitoring tools such as ELK, highlighting their features, use cases, advantages, and drawbacks for large‑scale log processing.
This article details ZTO Express's journey of adopting Apache Kylin for OLAP, comparing it with Presto, describing platform architecture, performance gains, integration with scheduling and monitoring systems, and the practical optimizations and future plans that enabled sub‑second query responses on massive daily data volumes.
From the early days of static web pages to today's data‑driven, AI‑enhanced interfaces, this article explores how the big‑data boom and artificial‑intelligence advances since 2010 have transformed front‑end technologies, driving innovations in data visualization, web‑based software, and diverse user interactions.
Zheng Zhisheng from Bilibili presents the Saber real-time computing platform, detailing its pain points, evolution, Apache Flink‑based architecture, SQL‑centric BSQL programming, DAG drag‑and‑drop design, AI use cases, and future development plans to improve scalability, operability, and AI integration.
The article explains how to sort a massive 4.6 GB file containing 500 million random integers by first attempting in‑memory quicksort and merge sort, then using a bitmap approach, and finally applying an external sort that splits the data into manageable chunks and merges them efficiently.
This article introduces Apache Hudi, explaining its core concepts, design principles, table architecture, write and compaction mechanisms, and the three query modes that enable efficient batch and incremental processing on modern data lakes.
Delta Lake adds ACID transaction support, schema enforcement, data versioning, and unified batch‑and‑stream processing to Apache Spark‑based data lakes, addressing reliability, quality, performance, and update challenges of traditional data lake architectures.
By parallelizing Spark’s driver‑side commit, trash, and move phases—previously single‑threaded operations that caused costly copy‑on‑rename when writing massive files to object storage—the Tencent Cloud EMR case achieved over a tenfold (1,100 %) speedup, making object storage a viable alternative to HDFS.
This article details NetEase Cloud Music's evolution of a real‑time data warehouse built on Flink 1.9 and Calcite, covering platform scale, architectural design, metadata management, SDK simplifications, monitoring improvements, and concrete use cases such as AB‑testing, live reporting, and feature serving.
This article describes the design and implementation of an intelligent traffic distribution system for a reading platform, detailing its background, overall architecture, sub-modules such as the small‑traffic experiment platform, near‑line computation, retrieval strategies, pacing algorithms, and how it balances user personalization with content ecosystem growth.
This article traces the 20‑year evolution of big‑data technologies, compares data lakes and data warehouses, explains their complementary strengths, and presents Alibaba Cloud’s lakehouse solution that unifies storage and compute to deliver flexible, performant, and cost‑effective analytics for enterprises.
This article describes Meituan's massive OLAP workloads, the specific challenges of data scale, complex schemas, and precise counting, explains how Apache Kylin was integrated using wide tables and bitmap deduplication, compares its performance and features with Presto, Druid and other engines, and outlines future improvements.
This article explains how VIPKID leverages real‑time streaming with Flink to build a unified data platform, automatically tag and process help requests during 1‑v‑1 live classes, and achieve significant reductions in manual monitoring while improving course quality and user experience.
This article explains the open‑source Stateful Functions framework, its API and Flink‑based runtime, and how it simplifies building distributed stateful applications by combining serverless concepts with robust state management for event‑driven architectures.
This article introduces Meituan Waimai's approach to constructing a universal real‑time data warehouse, covering streaming technology choices, Lambda/Kappa architectures, layered design, platformization, SLA management, and a practical Lambda‑style use case for real‑time analytics.
Alibaba Cloud’s Log Service (SLS) has evolved into a unified observability middle‑platform that handles tens of petabytes daily, offering integrated storage, processing, and AI‑driven analysis for logs, metrics, and traces, while addressing challenges of data ingestion, performance, and scalability across diverse Ops scenarios.
Didi’s three‑year journey with Presto transformed it into the company’s primary ad‑hoc and Hive‑SQL acceleration engine, serving over 6 000 users, processing 2‑3 PB of HDFS data daily, and achieving major gains in stability, performance, cost, and usability through extensive architectural tweaks, resource isolation, connector extensions, and monitoring enhancements.
This article presents a novel method for real-time reachable area analysis that leverages recent trajectory data, introduces a Skip Graph Index for efficient query processing, predicts optimal trajectory‑splicing parameters with machine learning, and demonstrates its effectiveness through extensive experiments on multiple real‑world datasets.
At Didi, Presto has been the default ad‑hoc and Hive‑SQL engine for over three years, serving 6,000 users, processing 2‑3 PB daily and 30‑35 trillion rows, with mixed and dedicated clusters, migration to PrestoSQL 340, extensive Hive compatibility, label‑based isolation, a native Druid connector, usability and stability enhancements, and JVM‑level performance optimizations, while planning further resource‑saving upgrades.
From the early days of static web pages to today’s data‑driven, AI‑enhanced interfaces, this article explores how the rise of big data platforms like Alibaba Cloud’s Feitian has transformed front‑end development through advanced visualization, software‑Web convergence, and diverse new interactions.
This article outlines the Yanxuan data warehouse’s layered architecture, the offline and real‑time development platforms, the comprehensive standards for metric definition, model design, and SQL development, and proposes a six‑dimensional evaluation system covering data norms, security, quality, stability, continuous improvement, and development efficiency.
This article details Didi's end‑to‑end real‑time data warehouse design for the carpool business, covering its objectives, architecture layers from ODS to application, naming conventions, StreamSQL development, operational tooling, challenges faced, and future batch‑stream integration plans.
Qualitis is a big‑data‑platform‑based data‑quality‑management service that defines, detects, and reports data‑set quality issues, featuring idempotent backend services, load‑balanced high‑availability, Zookeeper‑coordinated process synchronization, thread‑pool throttling, and clearly separated internal and external APIs.
DataOps, an Agile‑derived methodology that extends DevOps principles to data analytics, emphasizes automation, collaboration, and continuous delivery to accelerate and improve data processing, quality, and business insight, while outlining its benefits, relationship to Agile/DevOps, and practical steps for adoption.
The 2020 Tencent Advertising Algorithm Live Week presented expert insights on federated learning, machine learning, big data, and deep‑learning applications in advertising, offering a comprehensive Q&A that explains how massive data fuels AI breakthroughs and reshapes business problem solving.
This article reviews the core algorithm modules of recommendation systems from an architectural perspective, discussing offline, near‑line, and online layers, the trade‑offs between personalization, timeliness, and resource consumption, system boundaries, external dependencies, and the practical design of each layer.
This article demonstrates how to develop a real-time TopN ranking feature in Apache Flink, covering stream setup, word count aggregation, global and grouped TopN calculations, and nested TopN strategies to mitigate hotspot issues, complete with Java code examples.
The article details Meituan Waimai's offline data warehouse evolution from its initial V1.0 design through V2.0 improvements to the V3.0 modeling‑tool driven architecture, covering the four‑layer framework, Spark‑based ETL, data governance processes, resource optimization, security measures, and future development plans.
This article presents three HiveSQL optimization case studies—restructuring a large‑scale query with partitioned tables, breaking a complex window‑function query into smaller subsets with joins, and refactoring excessive PERCENTILE_APPROX calls—demonstrating how each change reduces execution time from hours to minutes and improves overall performance.
During the Mid‑Autumn holiday, Carrefour’s Nanjing Bridge store showcased how AI‑driven big‑data analytics, self‑checkout innovations, and smart supply‑chain upgrades are reshaping retail operations, from receipt printing to pre‑packaged produce and rapid home‑delivery services.
The article details how JD.com's order center migrated its massive order query workload from MySQL to Elasticsearch, iteratively improving cluster isolation, node deployment, replica tuning, master‑slave redundancy, version upgrades, and data synchronization while addressing performance pitfalls such as deep pagination and FieldData usage.
Leveraging large-scale courier trajectory data and spatiotemporal analytics, the DTInf framework infers parcel delivery times by detecting stay points, correcting delivery locations, and matching delivery events using a trained MLP model, achieving a mean absolute error of 401 seconds and outperforming baselines by over 30%.
Beike’s OLAP platform has progressed from a basic Hive‑MySQL batch pipeline to a Kylin‑based single‑engine solution, and now to a flexible multi‑engine architecture that uses a query‑engine layer to route metrics across Kylin, Druid, ClickHouse and Doris, dramatically cutting cube‑build times, supporting real‑time ingestion, and paving the way for further engine consolidation and automated performance routing.
This article explains how Kafka consumer groups assign partitions, the four situations that trigger a rebalance, the impact of consumer poll timeouts, and practical ways to tune max.poll.interval.ms and max.poll.records to avoid rebalance‑related errors.
This article explains when Elasticsearch triggers a master election, describes each election stage—including active master and candidate selection, Bully algorithm comparison, and master node responsibilities—while providing code excerpts that illustrate the underlying implementation details.
This article explains how Kafka consumer groups accelerate message consumption by distributing partitions across multiple consumers, details the three key characteristics of consumer groups, and provides in‑depth guidance on partition assignment strategies and offset management with practical Java code examples.
This article details Suning’s comprehensive digital transformation of Carrefour China, outlining three implementation phases, the integration of AI, big data and cloud technologies, and the resulting operational efficiencies, sales growth, and enhanced omnichannel retail experience.
Youku’s digital content‑supply‑chain system transforms long‑video production by introducing a three‑stage framework—structured evaluation of talent and scripts, information‑driven production management, and a unified demand‑aligned content strategy—that curtails delays, mitigates risk, and saves over 100 million RMB while scaling to billions of data records daily.
This article explains how to monitor Kafka consumer groups using the built‑in kafka‑consumer‑groups tool and the Kafka Manager UI, providing commands, field explanations, and setup steps to ensure real‑time data availability for downstream services such as MongoDB or Elasticsearch.
The article explains how big‑data algorithms enable online price discrimination—often called “kill‑familiar” pricing—illustrates real‑world e‑commerce examples, outlines the recently enacted Chinese online tourism regulation prohibiting such practices, and discusses broader data‑privacy and security concerns.
Discover five open‑source stock‑trading utilities—funds, ZVT, QUANTAXIS, StockAnalysisSystem, and match‑trade—each offering real‑time data, backtesting, multi‑asset support, and high‑performance matching to help programmers build powerful quantitative finance applications.
The article explains how a flexible, high‑performance user‑tagging system is built on a batch‑stream integrated architecture using big‑data technologies such as Impala, HDFS, and Flink to support both offline and real‑time label generation for precise marketing, product improvement, and operational analytics.
This article reviews how major Chinese companies such as JD.com, Ctrip, Quark, 58.com, and Didi have adopted Elasticsearch for large‑scale search, log analysis, and real‑time analytics, detailing their cluster architectures, scaling strategies, and operational practices.
This article explains how Flink's Complex Event Processing (CEP) library implements pattern matching using a nondeterministic finite automaton with matching caches (NFAb), covering its theoretical foundation, construction, state transition semantics, event selection strategies, shared versioned match buffers, and computation state details.
This article analyzes a 60‑second MySQL query that retrieves article comments with multiple filters, explains why the optimizer chooses a small table as the driver, and presents a step‑by‑step optimization—including avoiding semi‑joins, improving index usage, refining range conditions, and moving GROUP BY into a subquery—that reduces execution time to 1.3 seconds, achieving a 60‑fold speedup.
This article examines essential considerations for selecting and designing a data warehouse—including data volume, scalability, on‑premises versus cloud options, pricing models, and ETL/ELT approaches—to help organizations choose the most suitable solution for their needs.
This article explains why ETL is a critical step in building data warehouses, introduces eight core ETL algorithms—including full delete/insert, upsert, append, and various link‑table models—describes their ideal use cases, and provides ready‑to‑run SQL code examples for each.
This article explains why data lake acceleration is essential, outlines the three key architectural decisions for big‑data architects, and details Alibaba Cloud's JindoFS solutions—including basic adaptation, cache acceleration, and deep‑customization modes—to boost performance and reliability of lake storage and compute.
This article explains ClickHouse's strengths as a high‑performance MPP database, details hardware selection, read/write separation, shard expansion steps, batch‑size tuning, and presents a three‑layer monitoring model, while also describing its practical application in Tencent's game analytics platform.
This article explains the internal workings of Elasticsearch, covering its cloud‑based cluster architecture, Lucene‑based indexing structures such as segments, shards, inverted indexes, stored fields and doc values, as well as search processing, caching, merging, routing, and scaling strategies.
This article details how JD.com's order center migrated its Elasticsearch cluster from a simple, default‑configured setup to a highly available, multi‑replica, dual‑cluster architecture with version upgrades, data synchronization strategies, and performance optimizations to support billions of documents and hundreds of millions of daily queries.
The article describes the design, architecture, and operational details of a unified logging framework at Ctrip's payment center, covering log production via a Log4j2 extension, Kafka‑Camus collection, Hive/ORC storage, MapReduce parsing optimizations, and governance strategies for massive daily TB‑scale data.
This technical presentation explains how Youku tackles the massive, real‑time update problem of video‑content graphs by adopting a graph‑database architecture, sub‑graph partitioning, schema‑driven logical views, and Flink‑based pipelines to achieve second‑level updates for billions of entities and attributes.
This article examines performance problems with a 20‑million‑row MySQL user‑log table on Alibaba Cloud RDS, outlines three solution paths—optimizing the existing database, migrating to a MySQL‑compatible high‑performance service, and adopting a big‑data engine—and provides detailed guidance on schema design, indexing, partitioning, and practical SQL tweaks.
This presentation details NetEase's massive multi‑domain data ecosystem, the design of its user‑profile center—including basic, behavior, preference, and predictive tags—ID‑mapping techniques, quality assurance processes, and several real‑time and offline use cases such as marketing, recommendation, growth operations, advertising, and fraud detection.
JD’s Algorithmic Intelligence team built a ClickHouse‑based real‑time analytics engine that ingests Kafka and offline data, uses MergeTree tables with strategic partitioning and sorting, and employs batch writes, materialized views, and monitoring to achieve second‑level queries over billions of rows.
This article explains how the Alibaba DChain Data Converger (ADC) automatically creates wide‑table SQL for real‑time cross‑database analytics by using a pipeline architecture, priority‑queue‑driven task scheduling, and specific design patterns to handle metadata, joins, and resource management.
This article presents Alibaba's search‑recommendation real‑time data warehouse, describing its business background, typical use cases, key requirements, the evolution from architecture 1.0 to 2.0 with Flink and Hologres, best‑practice patterns such as row/column storage, stream‑batch integration, high‑concurrency updates, and future directions like real‑time joins and persistent dimension storage.
This article consolidates practical Elasticsearch tuning techniques—including configuration file adjustments, system‑level optimizations, and usage‑level settings—to improve cluster performance, stability, and resource efficiency for production environments.
Apache Hudi is an open‑source data‑lake framework that leverages Spark to ingest, manage, and incrementally query large analytical datasets on HDFS‑compatible storage, offering features such as timeline management, copy‑on‑write and merge‑on‑read tables, and support for snapshot, incremental, and read‑optimized queries across engines like Hive, Spark SQL and Presto.
This guide explains how to enable LZO compression in Hadoop by installing the twitter‑provided hadoop‑lzo library, updating core‑site.xml, synchronizing files across nodes, creating LZO indexes, and running a WordCount MapReduce job with LZO‑compressed output.
This article introduces NetEase's real-time computing platform Sloth, detailing its architecture, component layers, integrated IDE, operational tooling, unified metadata management, challenges such as Kudu write amplification, and proposes a tiered real‑time data‑warehouse model with a vision for storage‑compute separation and unified batch‑stream APIs.
Within three weeks, the team built an interest‑based live‑stream recommendation platform for Xianyu that combined operational insights, BI analysis, and offline algorithms to generate user‑anchor interest tags, sync them to an online graph, and dramatically boost top‑room UV and click‑through rates.
This article details the step‑by‑step evolution of the JD Daojia order‑center Elasticsearch cluster—from an initial loosely configured deployment to a real‑time dual‑cluster architecture with replica tuning, master‑slave adjustments, data‑sync strategies, and lessons learned about pagination, fielddata, and doc values—highlighting how each phase improved query throughput, stability, and scalability for billions of documents.
This article provides a comprehensive guide on integrating Hive with Spark SQL, covering Hive‑on‑Spark and Spark‑on‑Hive setups, spark‑shell and spark‑sql usage, HiveServer2 with Beeline, Scala scripts for reading and writing Hive tables, and partition handling for aggregated results.
This article explains the fundamentals of Kylin Cube construction—including dimensions, measures, Cuboid generation, layer-by-layer and in‑memory building algorithms, storage mechanisms, and various optimization strategies such as derived dimensions, aggregation groups, row‑key design, and concurrency granularity—providing a comprehensive guide for big‑data OLAP practitioners.
This article explains how large-scale recommendation systems rely on efficient feature engineering, describes the three-layer architecture (offline, stream, online), and details how Spark SQL and the LLVM‑optimized FESQL engine improve performance and ensure offline‑online feature consistency.
This article provides a comprehensive overview of Apache HBase’s architecture, detailing its core components such as RegionServer, HMaster, ZooKeeper, WAL, MemStore, and HFiles, and explains key processes including read/write paths, compaction, region splitting, load balancing, and recovery mechanisms.
Elasticsearch, built on Apache Lucene, provides a distributed, near‑real‑time search platform that scales to billions of documents across thousands of nodes, supporting use cases such as log analytics, time‑series monitoring, and product search, while Tencent’s CES adds advanced availability, performance, and cost‑optimizing features.
