With the surge in real‑time data from sensors and devices, choosing the right streaming engine is critical; this article compares Apache Spark and Apache Flink—examining their architectures, micro‑batch vs continuous processing, strengths, limitations, and use‑case suitability for Kafka‑driven pipelines.
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.
On Linux, this guide shows how to locate and terminate a Java background process using ps and kill, then explains why force‑killing can leave resources open, and provides a complete Java SignalHandler implementation to receive termination signals, safely close files, database connections, and Kafka consumers.
This article explains why maintaining message order is critical, describes common scenarios that cause ordering problems in RabbitMQ and Kafka, and presents practical strategies such as queue partitioning, single‑consumer designs, and internal memory queues to guarantee ordered consumption.
This article explains the core principles of message queues, outlines common data‑loss scenarios for RabbitMQ and Kafka, and provides practical techniques—such as transactions, confirm mode, persistence settings, and replication configurations—to ensure reliable, loss‑free messaging.
The article explains why message queues are employed for decoupling, asynchronous processing, and load‑shedding, outlines their advantages and disadvantages, and compares popular MQ products such as Kafka, ActiveMQ, RabbitMQ, and RocketMQ to guide technology selection.
This article explains why Apache Kafka has become a cornerstone of modern big‑data pipelines by detailing its high‑throughput, fault‑tolerant publish‑subscribe architecture, real‑time processing capabilities, extensive language support, scalability mechanisms, and the wide range of use cases adopted by leading enterprises.
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.
This article presents New Relic's 20 best‑practice recommendations for operating Apache Kafka at high throughput, covering partitions, consumers, producers, and brokers, and explains key concepts, configuration tuning, monitoring, and architectural considerations to ensure reliable, scalable streaming pipelines.
This article explains how Apache Flink manages state, uses checkpointing for fault-tolerant recovery, and achieves exactly-once semantics when consuming Kafka streams by persisting offsets, describing the checkpoint mechanism, recovery process, and practical considerations for production deployments.
This article explains the financial‑industry driven requirements for real‑time data sync, describes the TDSQL‑MULTISRCSYNC architecture—including producer, store, and consumer components—and details core designs such as row‑hash concurrency, idempotent binlog handling, and a lock‑based ordering mechanism that ensure high throughput and consistency.
Learn how to set up agent‑less log collection for Nginx using Rsyslog, forward logs via the omkafka module to a Kafka cluster, and process them with Logstash into Elasticsearch for visualization in Kibana, including installation, configuration, and testing steps.
This article explains the concept of high availability for message queues, analyzes interview expectations, and details the HA mechanisms of RabbitMQ (including single, normal cluster, and mirrored modes) and Kafka (partition replication and leader election), highlighting their advantages, drawbacks, and practical considerations.
EPX is a high‑availability, high‑performance data pipeline that captures MySQL binlog changes in real time, parses and filters them, and streams unified JSON events to Kafka for downstream services, while providing monitoring, alerting, backup, and migration capabilities across many business units.
Kafka achieves its ultra‑high throughput and low latency by writing data to the OS page cache, performing sequential disk writes, and employing zero‑copy techniques that eliminate unnecessary data copies during consumption, enabling tens of thousands to millions of messages per second.
This article explains Kafka's mechanisms for ensuring data reliability through partition replicas, producer acknowledgments, and leader election, and describes how the high‑water‑mark and ISR concepts maintain strong data consistency across the cluster.
This article explains the motivations behind using message‑queue middleware, outlines its benefits such as decoupling, asynchronous processing and peak‑shaving, describes point‑to‑point and publish‑subscribe communication models, and provides a detailed overview of Kafka’s architecture, terminology, data flow, storage strategy, and consumer group mechanics.
This article provides a comprehensive 17‑point comparison of five popular message‑queue systems—Kafka, RabbitMQ, ZeroMQ, RocketMQ, and ActiveMQ—covering documentation, language support, protocols, storage, transactions, load balancing, clustering, management UI, availability, duplication guarantees, throughput, subscription models, ordering, acknowledgements, replay, retry mechanisms, and concurrency characteristics.
The article explains Kafka's consumer‑group rebalance and Pulsar's unified queue/stream subscription models, compares their partition assignment strategies, and demonstrates both with Docker‑based Pulsar setups, Java consumer code, and practical failover and exclusive scenarios.
This article explains how Zhihu designed a scalable long‑connection gateway that decouples business logic via a publish‑subscribe model, implements ACL‑based authorization, ensures message reliability with acknowledgments and sliding windows, and leverages OpenResty, Redis, and Kafka for load‑balanced, fault‑tolerant backend services.
This article introduces Kafka, explains its core concepts and design principles, outlines typical use cases, and discusses common operational problems and troubleshooting tips for this high‑throughput distributed messaging system.
This article explains Kafka's Exactly-Once Semantics (EOS), the role of idempotence, and how transactional support works, covering EOS semantics, producer id and sequence numbers, configuration properties, and providing Java code examples for initializing, beginning, committing, and aborting transactions.
This article examines the need for real‑time computation, explains streaming versus real‑time concepts, and compares Apache Spark and Apache Flink—covering their architectures, micro‑batch and continuous processing, advantages, limitations, windowing, event‑time handling, and watermarks—to guide engine selection for Kafka‑driven workloads.
This article explains how the team designed and implemented a unified monitoring and alert platform for a multi‑service transportation business, covering architecture, data collection, storage, rule engine, alert delivery, troubleshooting aids, encountered pitfalls, and future enhancements.
This article provides a comprehensive overview of Apache Kafka's architecture, covering topics such as producer and consumer workflows, partition and replica management, leader election, offset handling, message delivery semantics, transaction support, and file organization, illustrating how Kafka achieves high performance and scalability.
In a DTCC 2019 keynote, Zhao Qun, director of big‑data platform at Percent Point, outlines the challenges of trillion‑scale real‑time analytics and presents a transparent, fine‑grained architecture built on Kafka, Spark Streaming, ClickHouse, HBase, Ceph and Elasticsearch, detailing design principles, component sizing, multi‑center deployment, performance testing and operational safeguards.
This article provides a comprehensive technical guide to Kafka, covering its distributed architecture, producer and consumer workflows, partition and leader management, message delivery semantics, exactly‑once guarantees, transaction handling, file organization, and key configuration parameters.
This article provides a detailed side‑by‑side analysis of five popular message‑queue systems—Kafka, RabbitMQ, ZeroMQ, RocketMQ, and ActiveMQ—covering documentation, programming languages, protocols, storage, transactions, load balancing, clustering, management interfaces, availability, duplication handling, throughput, subscription models, ordering, acknowledgements, backtracking, retry mechanisms, and concurrency characteristics.
This article explains how Apache Kafka implements transactional messaging, covering the atomic write guarantees across partitions, the role of TransactionCoordinator, transaction state management, producer and consumer handling, code examples, and the end‑to‑end workflow for exactly‑once semantics.
This article summarizes practical Kafka best‑practice guidelines covering hardware sizing, OS and JVM tuning, disk layout choices, replica and controller settings, broker and topic evaluation, as well as producer and consumer configuration, monitoring metrics, and strategies to prevent data loss.
This article explains Kafka message structure and offset retrieval, details Hadoop's map and reduce shuffle processes, outlines Spark's deployment modes, describes HDFS read/write mechanisms, compares reduceByKey and groupByKey performance, and discusses Spark streaming integration with Kafka and data loss prevention.
This article explains how Apache Kafka implements producer idempotence by introducing Producer IDs (PID) and sequence numbers, describes the request‑response flow for PID allocation, details server‑side PID management, shows the exact‑once guarantee mechanism, and answers common configuration questions with code examples.
To improve the performance of writing streaming data to Kafka, the article demonstrates how to replace per-partition KafkaProducer creation with a lazily-initialized, broadcasted producer in Scala, reducing overhead and achieving dozens‑fold speed gains.
Apollo is a configurable, extensible content‑processing platform that models each step as a node defined in a configuration file, supports multiple implementations for A/B testing, decouples producers and consumers via Kafka, ensures fault‑tolerant retries and replay, captures fine‑grained metrics through Canal‑to‑TiDB pipelines, and cuts new‑type development effort to roughly ten percent of the original cost while delivering high‑quality data to downstream teams.
This article presents Gong Yunfei's 2019 talk from the Beijing Apache Flink x Apache Kafka conference, detailing how ByteDance leverages Kafka for its large‑scale streaming and data processing needs.
This article explains how Kafka attains high throughput by using sequential disk writes, memory‑mapped files, sendfile zero‑copy, and batch compression, detailing both write and read path optimizations and their impact on performance.
This article provides a comprehensive overview of Kafka's architecture, covering topics, partitions, producer and consumer workflows, replication and leader election, offset management, consumer group coordination, rebalance processes, delivery semantics (at‑most‑once, at‑least‑once, exactly‑once), transactional messaging, and underlying file and configuration details.
The article explains Kafka’s master‑write master‑read architecture, why it does not implement a master‑write slave‑read model, the trade‑offs of consistency and latency, and how Kafka achieves load balancing through its leader‑replica design and operational safeguards.
This article details Youzan's evolution from a simple Flume‑based log collector to a multi‑tenant, Kafka‑buffered, Spark‑processed, HBase‑backed logging architecture that now handles hundreds of billions of log entries per day, highlighting challenges, design decisions, and future improvements.
This article introduces Apache Kafka as a distributed streaming platform, explaining its three core capabilities, key concepts such as producers, topics, brokers, partitions and consumers, and detailing how messages are delivered, stored in partitions, and consumed by consumer groups.
This article answers common Kafka questions, explaining why Kafka cannot operate without Zookeeper, describing its two retention strategies based on time and size, detailing how simultaneous time‑ and size‑based cleanup works, listing performance bottlenecks, and offering practical guidelines for sizing and configuring Kafka clusters.
This guide walks through setting up a comprehensive security log collection pipeline—covering WAF, firewall, and Nginx logs—using ELK, Logstash, Kafka, and Flume, and then configuring real‑time alerts with Sentinl or ElastAlert integrated with DingTalk and email notifications.
This article explains how disk I/O performance is shaped by page cache behavior, sequential versus random access, elevator scheduling, and storage data structures such as B+Tree and LSM trees, showing why operating systems and databases must design for sequential reads and writes to achieve optimal throughput.
The article details how Didi built and iterated its real‑time data platform, describing the shift from MySQL‑based batch processing to a Kafka‑Samza‑Druid architecture with Spark Streaming and Flink, the challenges addressed, and the current capabilities and operational metrics.
The article describes the architecture, core functions, and workflow of a MySQL slow‑query analysis system built for the CloudDB platform, covering both daily report generation and real‑time slow‑SQL monitoring using tools such as pt‑query‑digest, ELK, and Kafka.
This article examines the design, deployment, and optimization of massive log systems, comparing architectures, discussing real‑time versus near‑real‑time requirements, and presenting practical improvements such as memory, CPU, network tuning, data partitioning, storage reduction, and component upgrades using ELK, Kafka, Fluentd, and HBase.
This article examines the role of message‑queue middleware in high‑concurrency IM systems, compares popular open‑source options such as ActiveMQ, RabbitMQ, Kafka, RocketMQ and ZeroMQ, and provides a detailed multi‑dimensional framework—including functionality, performance, reliability, operational management, and ecosystem factors—to help engineers select the most suitable queue for their specific business needs.
This article provides a comprehensive technical overview of Kafka, covering its core concepts, producer and consumer models, architecture, configuration parameters, replication mechanisms, performance optimizations, operational monitoring, tooling scripts, and related product implementations for real-time data processing.
This article explains Meituan's architecture for accurately and efficiently moving MySQL data into a Hive data warehouse by capturing binlog streams with Canal, transporting them via Kafka, and restoring them offline with Camus and a merge process that handles inserts, updates, and deletes.
The article presents a CDC solution that combines Alibaba’s Canal for real‑time MySQL binlog capture into Kafka with LinkedIn’s Camus for hourly Kafka‑to‑Hive loading, then merges snapshots and incremental binlog data to accurately and efficiently rebuild ODS tables, supporting sharding and delete events.
UCloud’s physical network orchestrator automates large‑scale data‑center switch configuration, reducing IDC network build cycles from 2‑3 days to 2‑3 hours, boosting success rates to 99%, while handling 3000+ switches, 200 Gbps access throughput, and supporting hybrid‑cloud real‑time connectivity through a scenario‑driven, Kafka‑backed architecture.
This article explains why duplicate message consumption occurs when using Spring Cloud Stream with RabbitMQ or Kafka, introduces the concept of consumer groups, and provides a step‑by‑step Java example showing how to configure and use consumer groups to ensure each message is processed by only one instance.
This guide presents New Relic’s 20 practical best‑practice recommendations—covering partitions, consumers, producers, and brokers—to help engineers design, tune, and monitor Apache Kafka deployments for reliable, high‑throughput performance.
Based on interviews with 31 IT leaders from 28 organizations, this article reveals the most popular programming languages, frameworks, and platforms—such as Python, Scala, Spark, Kafka, TensorFlow, and Tableau—currently driving big‑data extraction, analysis, and reporting, and highlights emerging trends and tool preferences.
KafkaBridge is an open‑source, multi‑language SDK built on librdkafka that offers a minimal, easy‑to‑use interface for producing and consuming messages in Apache Kafka, with optimizations for PHP‑FPM, extensive language support, and detailed performance benchmarks.
This article describes how Ctrip's flight ticket team built a massive log‑tracking platform using Elasticsearch, Kafka, and Spark, evaluated storage options such as Cassandra and HBase, introduced secondary indexing and hot‑cold data separation, and continuously evolved the architecture to balance resource usage and query performance.
The article details the evolution from an offline to a real‑time HBase data warehouse, covering business scenarios, the use of Maxwell for MySQL‑to‑Kafka ingestion, Phoenix for SQL access, CDH cluster tuning, monitoring, and several production case studies.
KafkaBridge is a lightweight, multi‑language SDK that wraps Kafka read/write operations, offering a minimal, reliable API for C/C++, PHP, Python and Go, with php‑fpm long‑connection optimizations, and includes compilation, usage, and performance testing details.
KafkaBridge is a lightweight, multi-language SDK that simplifies Kafka read/write operations, offering unified interfaces, long‑connection reuse for PHP‑FPM, and reliable message delivery, with detailed compilation steps, usage examples, and performance benchmarks across C++, Python, PHP, and Go.
This article explains how shifting from synchronous command‑driven interactions to asynchronous event‑driven flows reduces coupling, improves scalability, and enables flexible querying in distributed systems, while also discussing hybrid patterns, the single‑writer principle, and practical advantages illustrated with Kafka‑based examples.
This guide walks through collecting security device and Nginx logs using ELK 5.5.2, Logstash grok patterns, Kafka and Flume pipelines on CentOS 7, and configuring Sentinl or ElastAlert for DingTalk and email alerts, complete with code snippets and deployment commands.
This article examines the high‑real‑time requirements of incremental data ingestion for search engines, compares three update schemes, and details how adopting a Kafka subscription‑based message‑queue approach dramatically improves latency and flexibility for the Nuomi search framework.
This article describes the evolution, architectural design, and key implementation details of an order dispatch system for instant‑delivery services, covering problem analysis, delay‑task mechanisms such as database polling, DelayQueue and TimingWheel, and the final solution that combines Redis with a timing‑wheel scheduler and asynchronous processing.
This article describes the shortcomings of a legacy log‑collection architecture, proposes a streamlined real‑time design that centralises configuration in Etcd, uses a single Go‑based logagent to tail files, applies per‑service rate limiting, and forwards logs to Kafka for downstream processing.
This article explains the architecture of a Real‑Time Data Platform (RTDP), details the technical selection of core components such as DBus, Kafka, Wormhole, Moonbox and Davinci, and discusses data management, security, operations, and four deployment modes—synchronization, flow, rotation and intelligent—illustrating how each fits different business scenarios.
This article provides a comprehensive overview of Apache Kafka, comparing it with ActiveMQ, explaining its distributed architecture, topics, partitions, consumption models, high‑availability mechanisms, exactly‑once semantics, and includes detailed Java producer and consumer code examples for practical implementation.
This article evaluates the server‑side performance of three popular message middleware platforms—Kafka, RabbitMQ, and RocketMQ—by measuring throughput and latency when sending small 124‑byte messages, revealing that Kafka leads, followed by RocketMQ and then RabbitMQ.
Meituan‑Dianping’s User Action System unifies disparate user‑behavior events with a 5W1H format, ingests them via a proprietary MAPI channel into Kafka, processes them in real‑time using Storm and a Lambda batch‑speed architecture, and delivers millisecond‑level responses for billions of daily events while offering flexible, modular query and storage options.
This article reviews the essential concepts of message‑queue middleware, covering why it is used, its disadvantages, how to choose among popular MQs, and practical techniques for ensuring high availability, avoiding duplicate consumption, reliable transmission, and ordered processing.
This article explains Kafka's role as a distributed message queue, covering its architecture, replication mechanisms, producer‑consumer workflow, message integrity guarantees, schema management, and performance tuning for high‑throughput, low‑latency backend systems.
This article examines the challenges of handling massive log data in high‑traffic e‑commerce platforms and presents a comprehensive architecture, optimization strategies, and practical implementations—including Rsyslog, Kafka, Fluentd, and the ELK stack—to improve scalability, performance, and reliability of log management systems.
This article compares the architectures of Kafka and RocketMQ, examines their availability and reliability mechanisms, evaluates their strengths and weaknesses, and proposes hybrid designs and simplified MQ architectures for building highly available and reliable messaging systems.
This article presents a practical, China‑focused microservice reference stack built on Spring Cloud, detailing core support components such as Zuul, Eureka, Apollo, and Spring Boot, as well as monitoring tools like Kafka, ELK, CAT, KairosDB, ZMon, and Hystrix, and explains when and how to apply each in production environments.
After a recent feature release caused a sharp decline in a key data metric, the team followed a systematic, fourteen‑step troubleshooting process—including verification, code review, DBA involvement, local debugging, environment comparison, logging, packet capture, service restarts, request mode changes, load testing, and partition resizing—to identify and resolve a Kafka‑related throughput bottleneck.
This article walks through building a Java‑based flash‑sale (秒杀) service, diagnosing overselling issues, and progressively enhancing it with optimistic locking, distributed rate limiting, Redis caching, and asynchronous Kafka processing to achieve higher throughput and data consistency under heavy concurrency.
This article explains how to design a distributed backend that ingests millions of clicks per second, stores data with Kafka and HDFS, computes top‑N articles using sliding windows and periodic aggregation, handles node failures, and mitigates click fraud, all while balancing accuracy and resource usage.
Tencent Cloud’s senior engineer Yang Yuan explains how their managed Kafka service tackles version diversity, resource allocation, dynamic scaling, broker addition/removal, and partition migration using versioned clusters, bin‑packing algorithms, penalty weighting, and predictive scheduling to sustain trillions of messages and billions of messages per minute.
This article explains the evolution of Kafka message formats from version 0.7.x through 0.8.x (including 0.9.x) to 0.10.x, detailing each field, compression handling, and the design motivations behind the changes.
This article explains why Kafka has become essential for real‑time data streaming in the big‑data era, detailing its performance advantages, core use cases, major adopters, multilingual support, and how its scalable storage and retention mechanisms empower modern data pipelines.
Ctrip’s real‑time user behavior service, a core foundation for recommendations, ads, and user profiling, was redesigned with a Java‑based stack (Kafka, Storm, Redis, MySQL) to achieve millisecond‑level latency, high availability, and ten‑fold scalability across processing and output flows.
This case study details how Beijing 恒远华信息技术有限公司 built a dynamic face‑capture and real‑time recognition solution on Huawei FusionInsight HD, leveraging deep‑learning algorithms, distributed storage, and stream processing to handle hundreds of millions of faces with high speed, efficiency, and security.
The article examines the reliability challenges of microservice architectures that rely heavily on synchronous RPC calls, and proposes a comprehensive solution that converts failing RPCs to asynchronous message‑queue workflows, introduces a write‑ahead‑queue for transactional consistency between databases and queues, and outlines offset management to ensure end‑to‑end fault tolerance.
Milano, a distributed log collection and analysis platform built on the ELK stack, leverages Filebeat, Kafka, Logstash, Elasticsearch, and Kibana to provide high‑throughput, low‑latency, secure, and visual log management for massive clusters, addressing the challenges of traditional manual log inspection.
The article details Snowball's challenges with a saturated CDH Hadoop cluster, outlines the limitations of the original Kafka‑based log pipeline, and explains how a comprehensive redesign using FlumeNG, Spillable Memory Channels, and custom HDFS sinks resolves latency, data loss, and high‑load issues while supporting future growth.
This article introduces message queue middleware, explains its role in distributed systems for decoupling, asynchronous processing, traffic shaping, and log handling, and reviews typical use cases, architectural patterns, JMS models, and popular products such as ActiveMQ, RabbitMQ, ZeroMQ, and Kafka.
This article shares a seasoned developer’s perspective on selecting a message‑queue middleware, outlining typical adoption stages, three key evaluation criteria—coder expertise, current and future requirements, and community/ecosystem health—and offering candid advice on avoiding common pitfalls.
This article details Lianjia's journey of designing and implementing a low‑latency, stable real‑time computing platform using Spark Streaming on YARN, covering technical selection, architecture components, version compatibility challenges, exactly‑once semantics, graceful shutdown, Kafka tuning, and future enhancements.
This article introduces vehicle networking concepts, explains Huawei's OceanConnect solution, and details how data open and data collection are implemented using platforms like DAP, Kafka, and Hadoop to provide reliable, real‑time vehicle information for various applications.
Learn how to construct a scalable data processing platform using the SMACK stack—Spark, Mesos, Akka, Cassandra, and Kafka—covering storage design, processing workflows, resource management, deployment options, and fault‑tolerant task execution for both batch and streaming workloads.
iQIYI’s big‑data collection system has progressed from simple HTTP log uploads to a Flume‑Kafka pipeline and finally to a custom Venus‑Agent architecture with centralized configuration, persistent offsets, dual‑Kafka streams and Flink processing, now handling tens of millions of queries per second and over three hundred billion records daily to power its AI‑driven services.
This article describes the background, design goals, architecture, implementation details, and lessons learned from building a low‑overhead, low‑intrusion distributed tracing system using Kafka, Elasticsearch, and OpenTracing to monitor microservice interactions and support performance analysis and DevOps decision‑making.
Kafka MirrorMaker provides near‑real‑time cross‑data‑center replication by consuming from a source cluster and producing to a target cluster, and this guide explains its core features, new vs. old consumer APIs, partition assignment strategies, performance tuning, network considerations, and practical command‑line examples.
To cope with Meituan Hotel’s exploding micro‑service complexity, the infrastructure team built the Satellite System—combining MTrace and a selective, zero‑intrusion Log4j2‑based logging pipeline that streams enriched logs through Kafka, Storm, Redis and Elasticsearch, delivering second‑level trace‑log queries and six‑month retention, dramatically speeding up debugging.
This article explains Kafka’s core concepts—including producers, consumers, topics, partitions, brokers, and consumer groups—describes its distributed architecture with leader‑follower replication, and provides three hands‑on kafka‑python experiments that demonstrate basic messaging, fault‑tolerant consumer groups, and offset management for reliable consumption.
This article explains Kafka's internal message storage mechanism, detailing how topics are divided into partitions, how partitions are segmented into LogSegments with data and index files, and how sparse indexing enables efficient offset lookups.
This article explains the design and implementation of a unified seller‑operation logging platform that uses Kafka for ingestion, Storm for real‑time processing, Elasticsearch for hot‑data search, and HBase for cold‑data storage, detailing the challenges faced and the optimizations applied.
This article describes the design and implementation of a seller log system, detailing the use of Kafka for high‑throughput messaging, Storm for real‑time stream processing, Elasticsearch for hot‑data search, and HBase for cold‑data storage, along with challenges faced and optimization solutions.
This article delves into Kafka’s network layer implementation, explaining the Selector class’s role in registering socket channels, handling connection events, and orchestrating reads and writes via KafkaChannel and TransportLayer, while illustrating packet structures and providing code snippets for key functions like register, connect, poll, and send.
This article describes the background, requirements, architecture choices, performance testing, and lessons learned from building a large‑scale, distributed log collection and analysis platform at Hujiang using Elasticsearch, Logstash, Kibana, Filebeat, and Kafka to handle billions of log entries daily.
This article details JD's end‑to‑end seller log system architecture, explaining why Kafka, Storm, Elasticsearch and HBase were chosen, the challenges faced during scaling, and the practical solutions implemented to achieve a unified, high‑throughput logging platform for merchants and operations.
