A recent breach of the AI companion apps Chattee and GiMe Chat exposed over 40 million private conversations, hundreds of thousands of media files, and transaction records due to publicly accessible Kafka brokers lacking any authentication, putting user privacy and security at serious risk.
This article walks through the design and implementation of a simplified TikTok‑style recommendation system using Spring Cloud microservices, covering business requirements, service decomposition, Eureka registration, Feign clients, Redis and Kafka integration, circuit‑breaker fallback, and end‑to‑end testing.
When a flash‑sale causes a 10 million‑message backlog and consumers only process 200 messages per second, this guide shows a five‑step, 5‑hour strategy—horizontal scaling, message downgrade, flow control, temporary dump, and parallel blasting—to restore throughput and prevent system collapse.
This comprehensive guide presents 100 advanced Linux interview questions tailored for big data operations, covering command-line mastery, system performance tuning, cluster configuration for Hadoop, Kafka, Spark, and Flink, complex troubleshooting scenarios, automation with Ansible and Prometheus, security hardening, and containerized deployments on Kubernetes.
This case study details how Qunar Travel's engineering team analyzed Kafka production bottlenecks during peak traffic, added targeted monitoring, tuned thread and batch parameters, and validated the changes through gray‑scale tests, ultimately saving about 2000 CPU cores across three clusters while reducing request volume and improving network and disk utilization.
This article deeply explores Kafka consumer group rebalance mechanisms, identifies performance pitfalls of frequent rebalances, and provides a comprehensive set of configuration tweaks, assignment strategies, batch processing techniques, and monitoring practices to achieve a more stable and high‑throughput Kafka consumer system.
This comprehensive guide presents 100 Linux interview questions tailored for big data operations, covering basic commands, permissions, process management, networking, log processing, shell scripting, and cluster-specific operations with detailed answers and practical explanations.
This article breaks down the essential requirements and architecture of a high‑performance like system, covering fast response, data consistency, scalability under traffic spikes, and cost‑efficient resource use, while detailing the technical stack, caching strategies, async persistence, and practical optimizations.
This article explains the core Kafka techniques—sequential disk writes, Linux page cache usage, producer/broker batching with compression, and zero‑copy data transfer via sendfile—that together enable massive concurrency and near‑memory write performance in large‑scale streaming architectures.
This guide explains how to configure Kafka producers with acks=all, enable idempotence, set appropriate retry and replication factors, and adjust broker flush intervals to achieve exactly‑once semantics and prevent message loss even during network glitches or broker failures.
This article explains Kafka's storage architecture, identifies three major message‑loss scenarios across production, storage, and consumption, and provides practical end‑to‑end configurations, detection methods, and business‑level patterns to achieve near‑zero message loss in high‑concurrency distributed systems.
Zepto transformed its brand‑analytics platform from a Postgres MVP into a production‑grade, sub‑second real‑time analytics solution by adopting StarRocks, redesigning its data pipeline with Databricks, Kafka, and Flink, and choosing a storage‑compute architecture that supports massive joins and rapid insights.
This article dissects Kafka’s storage architecture, identifies loss points in production, storage, and consumption phases, and presents interview‑ready strategies—including acks settings, flush tuning, consumer batch commits, detection via sequence numbers, and transactional messaging—to guarantee virtually 100 % message durability.
This guide walks you through installing a high‑availability Kafka cluster, covering both bare‑metal/virtual‑machine setups and Kubernetes‑based deployments with Helm, including environment preparation, ZooKeeper configuration, broker settings, Helm chart customization, KRaft mode, and production‑grade tuning recommendations.
This article explores the fundamentals of message ordering in distributed message queues, explains why ordering is determined by broker arrival, compares global and partial ordering, and presents practical solutions—from single-partition designs to multi-partition hashing, handling data skew, and safe expansion—plus interview tips.
This guide explains comprehensive Kafka message loss prevention techniques, covering producer configurations like acks=all, retries, idempotence, broker settings such as replication factor and min.insync.replicas, consumer handling with manual offset commits, and full‑stack compensation and retry mechanisms with code examples.
This article explains why message ordering is critical in scenarios such as financial transactions, e‑commerce order updates, audit logs, and IoT devices, then details Kafka’s core components—topics, partitions, offsets, producers, brokers, and consumer groups—and describes how single‑partition consumption and consistent key hashing ensure ordered processing while balancing throughput.
The article explains how Kafka attains high‑throughput performance by using sequential disk writes, leveraging the OS page cache, employing producer and consumer batching with configurable parameters, and utilizing zero‑copy sendfile to minimize CPU and memory overhead, enabling stable million‑message per second rates.
This article examines the core evaluation criteria for enterprise message queues and provides a detailed comparison of Kafka, RabbitMQ, and Pulsar—including performance, reliability, scalability, operational complexity, ecosystem maturity, and business fit—offering actionable guidance for selecting the optimal solution in cloud‑native environments.
This comprehensive guide walks you through the challenges of large‑scale log collection, real‑time processing, storage optimization, and visualization, detailing practical configurations for Flume, Logstash, Elasticsearch, Kibana, Filebeat, Kafka, Kubernetes, and future AIOps integrations to build a reliable, cost‑effective distributed logging system.
This article explains how to move data from an HBase cluster back into Kafka by extracting RowKeys with a MapReduce job, handling massive volumes and lack of partitioning, and reliably writing the records to Kafka while tracking successes and failures.
This article explores ten practical use‑cases for message queues—covering system decoupling, asynchronous processing, traffic shaping, data synchronization, log collection, broadcast updates, ordered and delayed messages, retry mechanisms, and transactional messaging—illustrated with Java code examples and architectural diagrams.
This article explains what Kafka message backlog is, why it harms performance and stability, and provides practical producer‑side throttling and consumer‑side scaling techniques, illustrated with diagrams, to help engineers eliminate backlog and keep their Kafka clusters healthy.
The article dissects the interview question about ES latency in a MySQL‑Canal‑to‑Elasticsearch pipeline, explains the root causes across four system layers, and presents a comprehensive four‑layer optimization, end‑to‑end observability, routing‑based degradation, and a Java‑based LatencyProbe component to measure and control delay.
This case study details how a high‑traffic Kafka logging cluster was optimized by analyzing low compression ratios, tuning Filebeat parameters, adjusting memory queues and round‑robin settings, and validating the changes through gray‑scale tests, resulting in up to 35% higher throughput and significant resource savings.
This article provides a concise overview of Kafka cluster fundamentals, describing how multiple brokers form a distributed system, the role of topics and partitions for scalability, and illustrating data distribution and replication with a three‑broker example.
Learn how to transform PHP logs into real‑time, structured events by implementing a log event pipeline that includes JSON logging, lightweight collectors like Filebeat, streaming platforms such as Kafka or Flink, enrichment, and visualization with Grafana, enabling instant monitoring, alerting, and data‑driven decisions.
This article explains the essential components of a Kafka cluster—including producers, consumers, brokers, Zookeeper, and the KRaft controller—detailing how topics, partitions, and replication enable high‑throughput, scalable, and fault‑tolerant distributed messaging.
This article explains Kafka’s core architecture—producer, broker storage, and consumer groups—detailing how messages are partitioned, buffered, replicated, and consumed to achieve high throughput, low latency, and scalable real‑time data processing.
This article walks through a five‑round Java interview simulation where the candidate designs, optimizes, and secures a massive push‑notification system for Sohu, covering Spring Boot high‑concurrency architecture, sharding and cache‑penetration mitigation, Kafka reliability, Kubernetes deployment with gray releases, multi‑region high‑availability, and AI‑driven smart‑push strategies, complete with concrete code snippets and real‑world performance numbers.
The article walks through a simulated five‑round Java interview where the candidate designs a high‑concurrency invoice‑issuing service, covering gateway architecture, async Kafka processing, cache strategies, JVM thread‑pool and memory tuning, database‑cache coordination, microservice messaging, system design, performance optimization and AI integration, all illustrated with concrete numbers and code snippets.
This article explains how to design and implement a generic asynchronous processing SDK for Java backend services, covering its purpose, advantages, underlying principles, component choices, database schema, configuration, usage patterns, and best‑practice notes, with code examples and diagrams.
This case study details how Hypergryph leveraged Alibaba Cloud EMR Serverless StarRocks, Flink, and Kafka to replace a ClickHouse data warehouse with a high‑performance, elastic, and easy‑to‑operate real‑time analytics platform that dramatically improved query speed, stability, operational efficiency, and cost for their gaming business.
This article explains Kafka's storage architecture, including its partition‑based log files, sequential append writes, log segment management, index files, and configurable time‑ and size‑based retention policies that together enable ultra‑high write throughput while controlling disk usage.
This article provides a comprehensive guide to building enterprise‑grade, highly available Kafka clusters, covering architecture design, hardware planning, production‑level broker configurations, ISR management, monitoring, fault‑tolerance procedures, rolling upgrades, capacity planning, and automation scripts for seamless operations.
Facing daily data growth from millions to 150 billion records, Joydata‑U transformed its analytics platform through three architectural stages—Hadoop, Hadoop + Trino, and finally StarRocks—introducing resource isolation, Flat JSON acceleration, and Bitmap indexing to cut query latency by up to seven times and achieve sub‑2‑minute data freshness across BI, ad‑tech, game analytics, and CRM workloads.
This article shares 360 Group's systematic Kafka capacity‑planning methodology, covering hardware performance analysis, disk I/O benchmarking, cluster configuration, load‑testing procedures, observed write‑read dynamics, and practical recommendations for reliable Kafka deployments.
This guide walks you through the core Kafka commands for starting and stopping the service, creating, listing, describing, and deleting topics, as well as producing and consuming messages, while explaining key parameters such as Zookeeper, partitions, and replication factors.
When Kafka backs up and Logstash fails to write to Elasticsearch with a 403 cluster_block_exception, the index is locked by a write block, which can be resolved by clearing the index.blocks.write setting via the Elasticsearch API or Kibana dev tools.
This guide explains how to efficiently produce messages with TDMQ CKafka, covering topic creation, partition sizing, retry policies, asynchronous sending, key/value handling, batch tuning, sticky partitioning, idempotence, ACK settings, code examples, and strategies to avoid data skew and ensure high‑throughput, low‑latency messaging.
This guide explains why Kafka consumer throughput matters, then details practical techniques—including multi‑threaded consumption, scaling with consumer groups, client parameter tuning, and batch processing—to dramatically increase throughput while maintaining reliability in high‑concurrency, large‑scale data pipelines.
The author compares two architectures for collecting ElasticSearch wildcard index data when DataX lacks support: a Logstash-Kafka-MaxCompute pipeline with existing tools but longer chain, and a simpler Flink ETL approach writing directly to MaxCompute, concluding Flink is preferable due to lower cost and operational complexity.
This article explains how Apache Kafka attains million‑level write throughput by sequentially appending messages to log files, leveraging operating‑system page cache and asynchronous disk flushing, and details the step‑by‑step data flow from producer to broker to storage.
This article recounts a series of Kafka message backlog incidents in a restaurant ordering system and explains how targeted optimizations—batch database queries, index tuning, data archiving, and thread‑pool scaling—eliminated the delays and restored reliable kitchen display performance.
This article explains how to use Spring's TransactionSynchronizationManager to detect active transactions and reliably send Kafka messages either immediately or after transaction commit, illustrated with a payment‑system use case and complete code examples.
This article explains how Kafka achieves extremely high throughput by using a Reactor‑based non‑blocking I/O model, zero‑copy data transfer, sequential disk writes, memory‑mapped files, sparse indexing, partition load‑balancing, compression, batch processing, and a lock‑free offset design.
Kafka ensures that a producer’s repeated message sends, caused by network glitches or broker failures, result in only one persisted record per partition by using a unique Producer ID, monotonically increasing sequence numbers, and broker-side tracking of the latest committed sequence for each PID‑partition pair.
This article explains why Kafka’s at‑least‑once delivery can cause duplicate message processing, outlines the business risks of such duplicates, and presents four practical solutions—including idempotent design, manual offset commits, exactly‑once semantics, and dead‑letter queues—to ensure reliable consumption.
This guide demonstrates using StarRocks Routine Load to ingest JSON and CSV data from Kafka, covering environment setup, table creation, load job configuration with critical parameters like jsonpaths, troubleshooting null-field errors, task management commands, and performance optimization tips for real-time analytics.
This article explains how Kafka sustains massive traffic by writing logs sequentially to disk, leveraging the operating system’s page cache for fast in‑memory writes, employing zero‑copy techniques like sendfile to avoid user‑space copying, and batching messages to reduce network overhead, thereby delivering high‑throughput, low‑latency streaming.
This article explains Kafka’s high‑performance distributed streaming architecture, key components such as topics, partitions, producers, consumers, brokers, offsets, and ZooKeeper, and provides step‑by‑step workflows for producers and consumers along with performance‑testing tips and Maven setup.
This guide walks you through installing Logstash and the logstash-output-oss plugin, then shows a complete logstash.conf that reads from Kafka, filters and enriches events, and outputs them to Alibaba Cloud OSS with compression, rotation, and recovery settings.
This article explains how to improve front‑end data transfer using the browser's navigator.sendBeacon API and how to streamline back‑end processing with Go, Kafka, and ClickHouse to achieve higher performance and stability in feature‑heavy web applications.
This guide presents a step‑by‑step Kafka learning roadmap covering core concepts, architecture, configuration, monitoring tools, practical project ideas, advanced components like Streams and KSQL, plus code samples and resource recommendations to help beginners become proficient in real‑time data streaming.
This article introduces a non‑intrusive asynchronous processing SDK that leverages Spring transaction events, Kafka queues, and MySQL tables to ensure reliable execution, eventual consistency, and automatic retry without impacting the main business flow.
This article provides a comprehensive overview of Kafka, covering its high‑throughput distributed messaging model, key components such as producers, consumers, brokers, topics, partitions, and metadata management, as well as the end‑to‑end workflow from production to consumption.
When Kafka consumers can't keep up with producers, messages pile up, storage pressure rises, latency spikes, and downstream services suffer, but increasing consumer threads, batching, and asynchronous processing can alleviate these issues and restore system stability.
Facing petabyte‑scale log traffic, the Qunar team identified low compression rates in their Kafka‑Filebeat pipeline as the main bottleneck and, through systematic tuning of batch size, memory queues, and round‑robin settings, achieved a 35% reduction in traffic and a 30‑42% drop in request volume while raising per‑minute throughput by 35%.
This article introduces a generic asynchronous processing SDK for Java, explaining its purpose, advantages, underlying principles, component choices such as Kafka, XXL‑Job, MySQL, and Spring AOP, and provides detailed configuration, database schema, usage examples, and best‑practice guidelines to achieve reliable, non‑blocking execution and eventual consistency in backend systems.
This article walks through the common frustrations of IDE and environment setup for Spring Boot projects, explains version compatibility between Spring Cloud, Spring Boot, and Kafka, demonstrates how Maven and auto‑configuration simplify dependencies, and provides ready‑to‑use code snippets and useful tooling recommendations for backend developers.
This article examines the challenges of high‑concurrency write scenarios in ad‑billing systems, explains why row‑level locking becomes a bottleneck, and presents a step‑by‑step solution using Kafka for asynchronous peak shaving, parallel consumer processing, database sharding, hotspot dispersion, and batch deduction to achieve reliable real‑time balance updates.
This article explains the three message delivery semantics, the common causes of duplicate messages in queue systems, and presents concrete producer‑side, broker‑side, and consumer‑side deduplication techniques for Kafka and Pulsar, including code samples and best‑practice recommendations.
This article shares practical Kafka production insights, covering architecture overview, producer throughput tuning, message loss prevention, broker and consumer configurations, duplicate consumption avoidance, backlog mitigation, ordering guarantees, and the mechanics of consumer group rebalancing, helping engineers build stable, high‑performance streaming pipelines.
This article explains how Kafka achieves high concurrency through its distributed broker cluster, partitioned topics, sequential log writes, message compression, asynchronous producer mechanisms, and OS page‑cache techniques, illustrating the combined architectural and performance optimizations that enable massive throughput.
The article analyzes why Kafka delivers about 50% higher throughput than RocketMQ, examining zero‑copy mechanisms (mmap vs sendfile), the resulting system‑call and data‑copy differences, and how RocketMQ’s richer feature set trades off raw performance for functionality.
This article explains Kafka's event streaming concept, detailing events and streams, core components such as producers, topics, partitions, consumers, persistence, and typical real‑time data pipeline, event‑driven architecture, stream processing, and log aggregation use cases, highlighting its role as a foundational big‑data infrastructure.
This tutorial walks through downloading, extracting, configuring, starting, creating topics, producing and consuming messages, and finally stopping Apache Kafka 3.8.1 on a Linux system, including all necessary command‑line instructions.
This article examines the typical reasons Kafka messages are lost across producers, brokers, and consumers, and provides detailed configuration recommendations and best‑practice solutions to significantly reduce the risk of data loss in distributed streaming systems.
This article explains the fundamentals, architectures, key concepts, advantages, and disadvantages of Kafka, RabbitMQ, RocketMQ, and ActiveMQ, and provides practical guidance on selecting the most suitable message queue for various application scenarios such as high‑throughput logging, financial transactions, or small‑scale services.
This article details practical techniques for boosting Kafka producer throughput, covering batch sending optimization, linger.ms tuning, asynchronous send handling, compression options, and concurrent sending strategies, while providing code examples and performance considerations for high‑throughput streaming applications.
This article explains why Kafka is widely adopted by top companies, outlines its high throughput, scalability, and durability, and describes key real‑time data pipeline, stream processing, and big‑data integration scenarios, concluding that mastering Kafka is essential for modern backend and data engineering roles.
This article explains the CQRS architectural pattern, its benefits and pitfalls, and provides a step‑by‑step guide to implementing CQRS with Spring Boot, Axon, and Kafka, including command and query handling, event sourcing, and practical considerations for microservice architectures.
This article explains Kafka's core concepts—including topics, partitions, log segmentation, offset management, and acknowledgment levels—while also providing a step‑by‑step guide to deploying Zookeeper, Kafka, Filebeat, and ELK, troubleshooting common issues, and visualizing logs in Kibana.
This article reviews six mainstream solutions for keeping MySQL and Elasticsearch in sync—including synchronous double‑write, asynchronous MQ‑based double‑write, Logstash polling, Canal binlog listening, DataX batch migration, and Flink stream processing—detailing their scenarios, advantages, drawbacks, and practical code examples to guide optimal technical selection.
Airbnb's media team engineered a scalable postcard generation pipeline that matches destinations, handles localized text layout, and delivers high‑performance images across iOS, Android, Web, and external messaging platforms using flexible templates, async processing, and pre‑generation strategies.
Kafka 4.0 replaces ZooKeeper with the native KRaft (Kafka Raft) consensus layer, simplifying deployment, boosting scalability, cutting latency, and introducing a new incremental consumer rebalance protocol and share‑group model that together reshape how distributed streaming applications are built and operated.
This technical case study details how a high‑traffic Kafka logging cluster was optimized by adjusting Filebeat and Kafka parameters, increasing compression batch size, and tuning Kubernetes settings, resulting in significant reductions in request volume, network traffic, CPU usage, and overall resource consumption.
This article explains how to import 1 billion 1 KB log records stored in HDFS or S3 into MySQL by analyzing single‑table limits, using batch inserts, choosing storage engines, sharding, optimizing file‑reading methods, and coordinating distributed tasks with Redis, Redisson, and Zookeeper to ensure ordered, reliable, and high‑throughput data loading.
Grab replaced its fragmented Grafana‑Superset stack with a StarRocks‑backed Iris platform, achieving over ten‑fold query speedups, 40% lower resource usage, and a unified real‑time and historical data store for Spark observability across its Southeast Asian super‑app ecosystem.
Kafka’s internal mechanisms for leader election, partition assignment, and file storage are explained, covering how the Controller uses ZooKeeper, the ISR-based leader selection process, partition distribution strategies, segment file structures, and the evolution of offset management from Zookeeper to the __consumer_offsets topic.
This article compares six mainstream MySQL‑to‑Elasticsearch synchronization methods—synchronous double‑write, asynchronous MQ, Logstash polling, Canal binlog listening, DataX batch sync, and Flink streaming—detailing scenarios, code samples, advantages, drawbacks, and practical selection guidance for developers.
This guide reviews common backend interview topics, explaining TCP TIME_WAIT behavior, multi‑port listening, full page load steps, Redis data types, Kafka consumer sizing and at‑most‑once semantics, the CAP theorem, Singleton usage, C++ std::map complexity, and an O(n) doubly‑linked list reversal algorithm.
Kafka attains million‑level message throughput by writing logs sequentially to disk, leveraging OS page cache for batch flushing, sending messages in large batches, and employing zero‑copy techniques that move data directly from disk to network sockets, dramatically reducing I/O overhead and CPU usage.
This article shares hands‑on production experience with Kafka, covering how to tune producer throughput, prevent message loss, control duplicate consumption, handle backlog, maintain ordering, understand rebalance, and explains why Kafka delivers such high performance.
This article explains how Ctrip’s hybrid‑cloud FinOps billing system uses a custom Kafka Gatekeeper to detect, locate, and automatically remediate data‑quality issues across dozens of self‑built PaaS services, improving coverage, timeliness, and responsibility attribution while supporting high‑availability deployments.
This guide walks through initializing a Spring Boot project, handling IDE setup challenges, managing version compatibility between Spring Cloud, Spring Boot, and Kafka, and provides practical code examples for global exception handling, logging, CORS configuration, Swagger integration, and useful development tools.
This article visually breaks down Kafka’s fundamental concepts—including producers, consumers, topics, consumer groups, partitions, and cluster architecture—explaining how messages flow, are stored, and achieve load balancing and ordering within a distributed streaming system.
This article explains common causes of duplicate message consumption in high‑traffic systems and presents a three‑layer defense—producer idempotence, broker de‑duplication, and consumer idempotent design—plus monitoring and reconciliation strategies to achieve reliable exactly‑once processing.
This article explains how RocketMQ, a Chinese‑origin message queue, simplifies Kafka’s architecture while adding powerful features such as tag‑based filtering, transactional messaging, delayed and dead‑letter queues, and a unified commit‑log storage model, making delayed processing and high‑throughput scenarios easier to implement.
This article introduces a reusable asynchronous processing SDK built on Spring, Kafka, and MySQL that leverages @AsyncExec annotations, transactional event listeners, and configurable thread pools to ensure reliable, non‑blocking execution, data consistency, and fault‑tolerant handling of business logic in backend applications.
Kafka 4.0 introduces KRaft, a ZooKeeper‑free metadata layer built on the Raft consensus algorithm, detailing role transitions, leader election, log replication, controller and broker responsibilities, and fault‑tolerance mechanisms, enabling a more scalable and self‑managed architecture for large‑scale distributed streaming.
This article compares Kafka, RocketMQ, and RabbitMQ, detailing their architectures, performance characteristics, strengths, and ideal use‑cases to help engineers select the most suitable message‑queue solution for high‑throughput, fault‑tolerant, and real‑time processing scenarios.
This article compiles a comprehensive collection of over two hundred interview questions covering Linux system administration, Nginx web server, MySQL databases, Redis caching, Kafka messaging, and core network security topics, providing a one‑stop resource for candidates preparing for major tech company interviews.
Didi replaced its legacy agent with Alibaba’s open‑source iLogTail, re‑architected it to use a shared thread‑pool and SIMD‑accelerated parsing, rewrote critical plugins in C++ and added robust Kafka retry logic, achieving over twice the throughput while cutting CPU usage by more than half and maintaining near‑zero latency at massive scale.
This guide walks you through deploying the Kubernetes Event Exporter, packaging its Helm chart and Docker image, configuring Kafka TLS secrets, setting up Logstash to ingest events and forward them to Elasticsearch, creating an index template, and verifying the end‑to‑end pipeline.
This article compares Kafka and RabbitMQ across design philosophy, performance, data model, delivery semantics, ecosystem, and typical use cases, helping readers choose the right messaging middleware for high‑throughput or reliable delivery scenarios.
iQIYI transformed its real‑time data warehouse by replacing a costly Kafka‑based Lambda stack with a unified stream‑batch Iceberg lake, cutting storage expenses by 90%, halving compute costs, extending data retention, and delivering minute‑level freshness for 90% of use cases while preserving second‑level processing where needed.
Apache Kafka 4.0 eliminates Zookeeper with the KRaft consensus protocol, upgrades Java requirements to 17, introduces a next‑generation consumer rebalance protocol, adds Share Group queue semantics, and bundles numerous performance, security, and API improvements for modern streaming workloads.
This article explores why message queues can lose consistency, presents concrete solutions such as transactional two‑phase commits, persistence settings, replica configurations, unique IDs, idempotent designs, and dead‑letter queues, and shares ten practical lessons drawn from real‑world incidents.
Kafka 4.0 marks a milestone release that replaces ZooKeeper with the KRaft consensus engine, improves scalability and performance, introduces a server‑side consumer‑group protocol, adds shared‑group queue capabilities, and updates Java requirements and documentation, delivering a more robust and flexible streaming platform.
